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TTASTJ01 Bloom Cast: Efficient

and Effective Full-Text

Retrieval in

Unstructured P2P

Networks

Efficient and effective full-text retrieval in

unstructured peer-to-peer networks remains

a challenge in the research community.

First, it is difficult, if not impossible, for

unstructured P2P systems to effectively

locate items with guaranteed recall. Second,

existing schemes to improve search success

rate often rely on replicating a large number

of item replicas across the wide area

network, incurring a large amount of

communication and storage costs. In this

paper, we propose BloomCast, an efficient

and effective full-text retrieval scheme, in

unstructured P2P networks. By leveraging a

hybrid P2P protocol, BloomCast replicates

the items uniformly at random across the

P2P networks, achieving a guaranteed recall

at a communication cost of O(√N), where N

is the size of the network. Furthermore, by

casting Bloom Filters instead of the raw

documents across the network, BloomCast

significantly reduces the communication and

storage costs for replication. We

demonstrate the power of BloomCast design

through both mathematical proof and

comprehensive simulations based on the

query logs from a major commercial search

engine and NIST TREC WT10G data

collection. Results show that BloomCast

achieves an average query recall of 91

percent, which outperforms the existing WP

algorithm by 18 percent, while BloomCast

greatly reduces the search latency for query

processing by 57 percent.

IEEE 2012

TTAECJ02 Cooperative Density

Estimation in Random

Wireless Ad Hoc

Networks

Density estimation is crucial for wireless ad

hoc networks for adequate capacity

planning. Protocols have to adapt their

operation to the density since the

throughput in an ad hoc network approaches

asymptotically to zero as the density

increases. A wireless node can estimate the

global density by using local information

such as the received power from neighbors.

In this paper, we propose a cross layer

protocol to compute the density estimate.

IEEE 2012

The accuracy of the estimate can be

enhanced and its variance can be reduced

through cooperation among the nodes.

Nodes share the received power

measurements with each other. Based on

the collected observations, the maximum

likelihood estimate is computed. It is shown

that cooperative density estimation has

better accuracy with less variance than the

individual estimation. When nodes share

received power measurements from further

away neighbors, the variance of the

estimate is further reduced.

TTAECJ03 FireCol A Collaborative

Protection Network for

the Detection of

Flooding DDoS Attacks

Distributed denial-of-service (DDoS) attacks

remain a major security problem, the

mitigation of which is very hard especially

when it comes to highly distributed botnet-

based attacks. The early discovery of these

attacks, although challenging, is necessary

to protect end-users as well as the

expensive network infrastructure resources.

In this paper, we address the problem of

DDoS attacks and present the theoretical

foundation, architecture, and algorithms of

FireCol. The core of FireCol is composed of

intrusion prevention systems (IPSs) located

at the Internet service providers (ISPs)

level. The IPSs form virtual protection rings

around the hosts to defend and collaborate

by exchanging selected traffic information.

The evaluation of FireCol using extensive

simulations and a real dataset is presented,

showing FireCol effectiveness and low

overhead, as well as its support for

incremental deployment in real networks.

IEEE 2012

TTAECJ04 Game-Theoretic Pricing

for Video Streaming in

Mobile Networks

Mobile phones are among the most popular

consumer devices, and the recent

developments of 3G networks and smart

phones enable users to watch video

programs by subscribing data plans from

service providers. Due to the ubiquity of

mobile phones and phone-to-phone

communication technologies, data-plan

subscribers can redistribute the video

content to nonsubscribers. Such a

redistribution mechanism is a potential

competitor for the mobile service provider

and is very difficult to trace given users' high

mobility. The service provider has to set a

reasonable price for the data plan to prevent

such unauthorized redistribution behavior to

IEEE 2012

protect or maximize his/her own profit. In

this paper, we analyze the optimal price

setting for the service provider by

investigating the equilibrium between the

subscribers and the secondary buyers in the

content-redistribution network. We model

the behavior between the subscribers and

the secondary buyers as a non-cooperative

game and find the optimal price and

quantity for both groups of users. Based on

the behavior of users in the redistribution

network, we investigate the evolutionarily

stable ratio of mobile users who decide to

subscribe to the data plan. Such an analysis

can help the service provider preserve

his/her profit under the threat of the

redistribution networks and can improve the

quality of service for end users.

TTAECJ5,TT

AECD5

Throughput and Energy

Efficiency in Wireless

Ad Hoc Networks With

Gaussian Channels

This paper studies the bottleneck link

capacity under the Gaussian channel model

in strongly connected random wireless ad

hoc networks, with n nodes independently

and uniformly distributed in a unit square.

We assume that each node is equipped with

two transceivers (one for transmission and

one for reception) and allow all nodes to

transmit simultaneously. We draw lower and

upper bounds, in terms of bottleneck link

capacity, for homogeneous networks (all

nodes have the same transmission power

level) and propose an energy-efficient power

assignment algorithm (CBPA) for

heterogeneous networks (nodes may have

different power levels), with a provable

bottleneck link capacity guarantee of

Ω(Blog(1+1/√nlog2n)), where B is the

channel bandwidth. In addition, we develop

a distributed implementation of CBPA

with O(n2) message complexity and provide

extensive simulation results.

IEEE 2012

TTAECJ06 Packet-Hiding Methods

for Preventing

Selective Jamming

Attacks

The open nature of the wireless medium

leaves it vulnerable to intentional

interference attacks, typically referred to as

jamming. This intentional interference with

wireless transmissions can be used as a

launch-pad for mounting Denial-of-Service

attacks on wireless networks. Typically,

jamming has been addressed under an

external threat model. However, adversaries

with internal knowledge of protocol

specifications and network secrets can

IEEE 2012

launch low-effort jamming attacks that are

difficult to detect and counter. In this work,

we address the problem of selective

jamming attacks in wireless networks. In

these attacks, the adversary is active only

for a short period of time, selectively

targeting messages of high importance. We

illustrate the advantages of selective

jamming in terms of network performance

degradation and adversary effort by

presenting two case studies; a selective

attack on TCP and one on routing. We show

that selective jamming attacks can be

launched by performing real-time packet

classification at the physical layer. To

mitigate these attacks, we develop three

schemes that prevent real-time packet

classification by combining cryptographic

primitives with physical-layer attributes. We

analyze the security of our methods and

evaluate their computational and

communication overhead.

TTAECJ07 Optimizing Cloud

Resources for

Delivering IPTV

Services through

Virtualization

Virtualized cloud-based services can take

advantage of statistical multiplexing across

applications to yield significant cost savings.

However, achieving similar savings with

real-time services can be a challenge. In this

paper, we seek to lower a provider's costs

for real-time IPTV services through a

virtualized IPTV architecture and through

intelligent time-shifting of selected services.

Using Live TV and Video-on-Demand (VoD)

as examples, we show that we can take

advantage of the different deadlines

associated with each service to effectively

multiplex these services. We provide a

generalized framework for computing the

amount of resources needed to support

multiple services, without missing the

deadline for any service. We construct the

problem as an optimization formulation that

uses a generic cost function. We consider

multiple forms for the cost function (e.g.,

maximum, convex and concave functions)

reflecting the cost of providing the service.

The solution to this formulation gives the

number of servers needed at different time

instants to support these services. We

implement a simple mechanism for time-

shifting scheduled jobs in a simulator and

study the reduction in server load using real

traces from an operational IPTV network.

IEEE 2012

Our results show that we are able to reduce

the load by ~ 24% (compared to a possible

~ 31%). We also show that there are

interesting open problems in designing

mechanisms that allow time-shifting of load

in such environments. TTAECJ08 Maximal Scheduling in

Wireless Ad Hoc

Networks With

Hypergraph

Interference Models

This paper proposes a hyper graph

interference model for the scheduling

problem in wireless ad hoc networks. The

proposed hyper graph model can take the

sum interference into account and,

therefore, is more accurate as compared

with the traditional binary graph model.

Further, different from the global signal-to-

interference-plus-noise ratio (SINR) model,

the hyper graph model preserves a localized

graph-theoretic structure and, therefore,

allows the existing graph-based efficient

scheduling algorithms to be extended to the

cumulative interference case. Finally, by

adjusting certain parameters, the hyper

graph can achieve a systematic tradeoff

between the interference approximation

accuracy and the user node coordination

complexity during scheduling. As an

application of the hyper graph model, we

consider the performance of a simple

distributed scheduling algorithm, i.e.,

maximal scheduling, in wireless networks.

We propose a lower bound stability region

for any maximal scheduler and show that it

achieves a fixed fraction of the optimal

stability region, which depends on the

interference degree of the underlying hyper

graph. We also demonstrate the interference

approximation accuracy of hyper graphs in

random networks and show that hyper

graphs with small hyper edge sizes can

model the interference quite accurately.

Finally, the analytical performance is verified

by simulation results.

IEEE 2012

TTAECD09 Load Balancing

Multipath Switching

System with Flow

Multipath Switching systems (MPS) are

intensely used in state-of-the-art core

routers to provide terabit or even petabit

switching capacity. One of the most

intractable issues in designing MPS is how to

load balance traffic across its multiple paths

while not disturbing the intra flow packet

orders. Previous packet-based solutions

either suffer from delay penalties or lead to

O(N2 ) hardware complexity, hence do not

IEEE 2012

scale. Flow-based hashing algorithms also

perform badly due to the heavy-tailed flow-

size distribution. In this paper, we develop a

novel scheme, namely, Flow Slice (FS) that

cuts off each flow into flow slices at every

intra flow interval larger than a slicing

threshold and balances the load on a finer

granularity. Based on the studies of tens of

real Internet traces, we show that setting a

slicing threshold of 1-4 ms, the FS scheme

achieves comparative load-balancing

performance to the optimal one. It also

limits the probability of out-of-order packets

to a negligible level (10-6

) on three popular

MPSes at the cost of little hardware

complexity and an internal speedup up to

two. These results are proven by theoretical

analyses and also validated through trace-

driven prototype simulations.

TTAECD10 Distributed Throughput

Maximization in

Wireless Networks via

Random Power

Allocation

We develop a distributed throughput-optimal

power allocation algorithm in wireless

networks. The study of this problem has

been limited due to the non-convexity of the

underlying optimization problems that

prohibits an efficient solution even in a

centralized setting. By generalizing the

randomization framework originally

proposed for input queued switches to SINR

rate-based interference model, we

characterize the throughput-optimality

conditions that enable efficient and

distributed implementation. Using gossiping

algorithm, we develop a distributed power

allocation algorithm that satisfies the

optimality conditions, thereby achieving

(nearly) 100 percent throughputs. We

illustrate the performance of our power

allocation solution through numerical

simulation. solution (an NP-hard problem).

IEEE 2012

TTAECD11 Automatic

Reconfiguration for

Large-Scale Reliable

Storage Systems

Byzantine-fault-tolerant replication enhances

the availability and reliability of Internet

services that store critical state and preserve

it despite attacks or software errors.

However, existing Byzantine-fault-tolerant

storage systems either assume a static set

of replicas, or have limitations in how they

handle reconfigurations (e.g., in terms of the

scalability of the solutions or the consistency

levels they provide). This can be problematic

in long-lived, large-scale systems where

IEEE 2012

system membership is likely to change

during the system lifetime. In this paper, we

present a complete solution for dynamically

changing system membership in a large-

scale Byzantine-fault-tolerant system. We

present a service that tracks system

membership and periodically notifies other

system nodes of membership changes. The

membership service runs mostly

automatically, to avoid human configuration

errors; is itself Byzantine-fault-tolerant and

reconfigurable; and provides applications

with a sequence of consistent views of the

system membership. We demonstrate the

utility of this membership service by using it

in a novel distributed hash table called dBQS

that provides atomic semantics even across

changes in replica sets. dBQS is interesting

in its own right because its storage

algorithms extend existing Byzantine

quorum protocols to handle changes in the

replica set, and because it differs from

previous DHTs by providing Byzantine fault

tolerance and offering strong semantics. We

implemented the membership service and

dBQS. Our results show that the approach

works well, in practice: the membership

service is able to manage a large system

and the cost to change the system

membership is low.

TTAECD12 Connectivity of Multiple

Cooperative Cognitive

Radio Ad Hoc Networks

In cognitive radio networks, the signal

reception quality of a secondary user

degrades due to the interference from

multiple heterogeneous primary networks,

and also the transmission activity of a

secondary user is constrained by its

interference to the primary networks. It is

difficult to ensure the connectivity of the

secondary network. However, since there

may exist multiple heterogeneous secondary

networks with different radio access

technologies, such secondary networks may

be treated as one secondary network via

proper cooperation, to improve connectivity.

In this paper, we investigate the

connectivity of such a cooperative secondary

network from a percolation-based

perspective, in which each secondary

network's user may have other secondary

networks' users acting as relays. The

connectivity of this cooperative secondary

network is characterized in terms of

IEEE 2012

percolation threshold, from which the benefit

of cooperation is justified. For example,

while a non-cooperative secondary network

does not percolate, percolation may occur in

the cooperative secondary network; or when

a non-cooperative secondary network

percolates, less power would be required to

sustain the same level of connectivity in the

cooperative secondary network.

DOMAIN –WIRELESS COMMUNICATION/WIRELESS NETWORK

TTAECJ13 An Adaptive

Opportunistic Routing

Scheme for Wireless

Ad-hoc Networks

A distributed adaptive opportunistic routing

scheme for multi-hop wireless ad hoc

networks is proposed. The proposed scheme

utilizes a reinforcement learning framework

to opportunistically route the packets even

in the absence of reliable knowledge about

channel statistics and network model. This

scheme is shown to be optimal with respect

to an expected average per-packet reward

criterion. The proposed routing scheme

jointly addresses the issues of learning and

routing in an opportunistic context, where

the network structure is characterized by the

transmission success probabilities. In

particular, this learning framework leads to a

stochastic routing scheme that optimally

“explores” and “exploits” the opportunities in

the network.

IEEE 2012

TTAECJ14 AMPLE An Adaptive

Traffic Engineering

System Based on

Virtual Routing

Topologies

Handling traffic dynamics in order to avoid

network congestion and subsequent service

disruptions is one of the key tasks

performed by contemporary network

management systems. Given the simple but

rigid routing and forwarding functionalities in

IP base environments, efficient resource

management and control solutions against

dynamic traffic conditions is still yet to be

obtained. In this article, we introduce AMPLE

- an efficient traffic engineering and

management system that performs adaptive

traffic control by using multiple virtualized

routing topologies. The proposed system

consists of two complementary components:

offline link weight optimization that takes as

input the physical network topology and

tries to produce maximum routing path

diversity across multiple virtual routing

IEEE 2012

topologies for long term operation through

the optimized setting of link weights. Based

on these diverse paths, adaptive traffic

control performs intelligent traffic splitting

across individual routing topologies in

reaction to the monitored network dynamics

at short timescale. According to our

evaluation with real network topologies and

traffic traces, the proposed system is able to

cope almost optimally with unpredicted

traffic dynamics and, as such, it constitutes

a new proposal for achieving better quality

of service and overall network performance

in IP networks.

DOMAIN : NETWORK SECURITY

CODE

PROJECT TITLE

DESCRIPTION

REFERENCE

TTAECJ15 Distributed Private Key

Generation for Identity

Based Cryptosystems in

Ad Hoc Networks

Identity Based Cryptography (IBC) has

the advantage that no public key

certification is needed when used in a

mobile ad hoc network (MANET). This is

especially useful when bi-directional

channels do not exist in a MANET.

However, IBC normally needs a

centralized server for issuing private

keys for different identities. We give a

protocol distributing this task among all

users, thus eliminating the need of a

centralized server in IBC for use in

MANETs.

IEEE 2012

TTAECJ16 Joint Relay and Jammer

Selection for Secure Two-

Way Relay Networks

In this paper, we investigate joint relay

and jammer selection in two-way

cooperative networks, consisting of two

sources, a number of intermediate

nodes, and one eavesdropper, with the

constraints of physical-layer security.

Specifically, the proposed algorithms

select two or three intermediate nodes to

enhance security against the malicious

eavesdropper. The first selected node

operates in the conventional relay mode

and assists the sources to deliver their

data to the corresponding destinations

using an amplify-and-forward protocol.

The second and third nodes are used in

IEEE 2012

different communication phases as

jammers in order to create intentional

interference upon the malicious

eavesdropper. First, we find that in a

topology where the intermediate nodes

are randomly and sparsely distributed,

the proposed schemes with cooperative

jamming outperform the conventional

non-jamming schemes within a certain

transmitted power regime. We also find

that, in the scenario where the

intermediate nodes gather as a close

cluster, the jamming schemes may be

less effective than their non-jamming

counterparts. Therefore, we introduce a

hybrid scheme to switch between

jamming and non-jamming modes.

Simulation results validate our

theoretical analysis and show that the

hybrid switching scheme further

improves the secrecy rate.

TTAECJ17 A Secure Single Sign-On

Mechanism for Distributed

Computer Networks

User identification is an important access

control mechanism for client-server

networking architectures. The concept of

single sign-on can allow legal users to

use the unitary token to access different

service providers in distributed computer

networks. Recently, some user

identification schemes have been

proposed for distributed computer

networks. Unfortunately, most existing

schemes cannot preserve user

anonymity when possible attacks occur.

Also, the additional time-synchronized

mechanisms they use may cause

extensive overhead costs. To overcome

these drawbacks, we propose a secure

single sign-on mechanism that is

efficient, secure, and suitable for mobile

devices in distributed computer

networks.

IEEE 2012

TTAECD18 A Novel Data Embedding

Method Using Adaptive

Pixel Pair Matching

This paper proposes a new data-hiding

method based on pixel pair matching

(PPM). The basic idea of PPM is to use

the values of pixel pair as a reference

coordinate, and search a coordinate in

the neighborhood set of this pixel pair

according to a given message digit. The

pixel pair is then replaced by the

searched coordinate to conceal the digit.

Exploiting modification direction (EMD)

IEEE 2012

and diamond encoding (DE) are two

data-hiding methods proposed recently

based on PPM. The maximum capacity of

EMD is 1.161 bpp and DE extends the

payload of EMD by embedding digits in a

larger notational system. The proposed

method offers lower distortion than DE

by providing more compact

neighborhood sets and allowing

embedded digits in any notational

system. Compared with the optimal pixel

adjustment process (OPAP) method, the

proposed method always has lower

distortion for various payloads.

Experimental results reveal that the

proposed method not only provides

better performance than those of OPAP

and DE, but also is secure under the

detection of some well-known steg-

analysis techniques.

TTAECD19 Characterizing the

Efficacy of the NRL

Network Pump in

Mitigating Covert Timing

Channels

The Naval Research Laboratory (NRL)

Network Pump, or Pump, is a standard

for mitigating covert channels that arise

in a multilevel secure (MLS) system

when a high user (HU) sends

acknowledgements to a low user (LU).

The issue here is that HU can encode

information in the "timings" of the

acknowledgements. The Pump aims at

mitigating the covert timing channel by

introducing buffering between HU and

LU, as well as adding noise to the

acknowledgment timings. We model the

working of the Pump in certain

situations, as a communication system

with feedback and use then this

perspective to derive an upper bound on

the capacity of the covert channel

between HU and LU in the Pump. This

upper bound is presented in terms of a

directed information flow over the

dynamics of the system. We also present

an achievable scheme that can transmit

information over this channel. When the

support of the noise added by Pump to

acknowledgment timings is finite, the

achievable rate is nonzero, i.e., infinite

number of bits can be reliably

communicated. If the support of the

noise is infinite, the achievable rate is

zero and hence a finite number of bits

can be communicated.

IEEE 2012

TTAECD20 Design and

Implementation of TARF A

Trust-Aware Routing

Framework for WSNs

The multi-hop routing in wireless sensor

networks (WSNs) offers little protection

against identity deception through

replaying routing information. An

adversary can exploit this defect to

launch various harmful or even

devastating attacks against

the routing protocols, including sinkhole

attacks, wormhole attacks, and Sybil

attacks. The situation is further

aggravated by mobile and harsh network

conditions. Traditional cryptographic

techniques or efforts at developing trust-

aware routing protocols do not

effectively address this severe problem.

To secure the WSNs against adversaries

misdirecting the multi-hop routing, we

have designed and implementedTARF, a

robust trust-

aware routing framework for dynamic

WSNs. Without tight time

synchronization or known geographic

information, TARF provides

trustworthy and energy-efficient route.

Most importantly, TARF proves effective

against those harmful attacks developed

out of identity deception; the

resilience of TARF is verified through

extensive evaluation with both

simulation and empirical experiments on

large-scale WSNs under various

scenarios including mobile and RF-

shielding network conditions. Further, we

have implemented a low-overhead

TARF module in TinyOS; as

demonstrated, this implementation can

be incorporated into

existing routing protocols with the least

effort. Based on TARF, we also

demonstrated a proof-of-concept mobile

target detection application that

functions well against an ant detection

mechanism.

IEEE 2012

TTAECD21 Risk-Aware Mitigation for

MANET Routing Attacks

Mobile Ad hoc Networks (MANET) have

been highly vulnerable to attacks due to

the dynamic nature of its network

infrastructure. Among

these attacks, routing attacks have

received considerable attention since it

could cause the most devastating

damage to MANET. Even though there

exist several intrusions response

IEEE 2012

techniques to mitigate such

critical attacks, existing solutions

typically attempt to isolate malicious

nodes based on binary or naive fuzzy

response decisions. However, binary

responses may result in the unexpected

network partition, causing additional

damages to the network infrastructure,

and naive fuzzy responses could lead to

uncertainty in

countering routing attacks in MANET. In

this paper, we propose a risk-

aware response mechanism to

systematically cope with the

identified routing attacks. Our risk-

aware approach is based on an extended

Dempster-Shafer mathematical theory of

evidence introducing a notion of

importance factors. In addition, our

experiments demonstrate the

effectiveness of our approach with the

consideration of several performance

metrics.

DOMAIN :CLOUD COMPUTING

TTASTD22,T

TASTJ22,TT

ASTA22

Payments for Outsourced

Computations

With the recent advent of cloud

computing, the concept of outsourcing

computations, initiated by volunteer

computing efforts, is being revamped.

While the two paradigms differ in several

dimensions, they also share challenges,

stemming from the lack of trust between

outsourcers and workers. In this work,

we propose a unifying trust framework,

where correct participation is financially

rewarded: neither participant is trusted,

yet outsourced computations are

efficiently verified and validly

remunerated. We propose three solutions

for this problem, relying on an offline

bank to generate and redeem payments;

the bank is oblivious to interactions

between outsourcers and workers. We

propose several attacks that can be

launched against our framework and

study the effectiveness of our solutions.

We implemented our most secure

solution and our experiments show that

it is efficient: the bank can perform

hundreds of payment transactions per

second and the overheads imposed on

IEEE 2012

outsourcers and workers are negligible.

TTASTJ23 In Cloud, Can Scientific

Communities Benefit from

the Economies of Scale?

The basic idea behind cloud computing is

that resource providers offer elastic

resources to end users. In this paper, we

intend to answer one key question to the

success of cloud computing: in cloud,

can small-to-medium scale scientific

communities benefit from the economies

of scale? Our research contributions are

threefold: first, we propose an innovative

public cloud usage model for small-to-

medium scale scientific communities to

utilize elastic resources on a public cloud

site while maintaining their flexible

system controls, i.e., create, activate,

suspend, resume, deactivate, and

destroy their high-level management

entities-service management layers

without knowing the details of

management. Second, we design and

implement an innovative system-

Dawning Cloud, at the core of which are

lightweight service management layers

running on top of a common

management service framework. The

common management service framework

of Dawning Cloud not only facilitates

building lightweight service management

layers for heterogeneous workloads, but

also makes their management tasks

simple. Third, we evaluate the systems

comprehensively using both emulation

and real experiments. We found that for

four traces of two typical scientific

workloads: High-Throughput Computing

(HTC) and Many-Task Computing (MTC),

Dawning Cloud saves the resource

consumption maximally by 59.5 and 72.6

percent for HTC and MTC service

providers, respectively, and saves the

total resource consumption maximally by

54 percent for the resource provider with

respect to the previous two public cloud

solutions. To this end, we conclude that

small-to-medium scale scientific

communities indeed can benefit from the

economies of scale of public clouds with

the support of the enabling system.

IEEE 2012

TTASTJ24 Secure Erasure Code-

Based Cloud Storage

System with Secure Data

A cloud storage system, consisting of a

collection of storage servers, provides

long-term storage services over the

IEEE 2012

Forwarding

Internet. Storing data in a third party's

cloud system causes serious concern

over data confidentiality. General

encryption schemes protect data

confidentiality, but also limit the

functionality of the storage system

because a few operations are supported

over encrypted data. Constructing a

secure storage system that supports

multiple functions is challenging when

the storage system is distributed and has

no central authority. We propose a

threshold proxy re-encryption scheme

and integrate it with a decentralized

erasure code such that a secure

distributed storage system is formulated.

The distributed storage system not only

supports secure and robust data storage

and retrieval, but also lets a user forward

his data in the storage servers to

another user without retrieving the data

back. The main technical contribution is

that the proxy re-encryption scheme

supports encoding operations over

encrypted messages as well as

forwarding operations over encoded and

encrypted messages. Our method fully

integrates encrypting, encoding, and

forwarding. We analyze and suggest

suitable parameters for the number of

copies of a message dispatched to

storage servers and the number of

storage servers queried by a key server.

These parameters allow more flexible

adjustment between the number of

storage servers and robustness.

DOMAIN :MOBILE COMPUTING

TTAECJ25 Improving QoS in High-

Speed Mobility Using

Bandwidth Maps

It is widely evidenced that location has a

significant influence on the actual

bandwidth that can be expected from

Wireless Wide Area Networks (WWANs),

e.g., 3G. Because a fast-moving vehicle

continuously changes its location,

vehicular mobile computing is confronted

with the possibility of significant

variations in available network

bandwidth. While it is difficult for

providers to eliminate bandwidth

disparity over a large service area, it

may be possible to map network

IEEE 2012

bandwidth to the road network through

repeated measurements. In this paper,

we report results of an extensive

measurement campaign to demonstrate

the viability of such bandwidth maps. We

show how bandwidth maps can be

interfaced with adaptive multimedia

servers and the emerging vehicular

communication systems that use on-

board mobile routers to deliver Internet

services to the passengers. Using

simulation experiments driven by our

measurement data, we quantify the

improvement in Quality of Service (QoS)

that can be achieved by taking

advantage of the geographical

knowledge of bandwidth provided by the

bandwidth maps. We find that our

approach reduces the frequency of

disruptions in perceived QoS for both

audio and video applications in high-

speed vehicular mobility by several

orders of magnitude.

TTAECJ26 Energy-Efficient

Strategies for Cooperative

Multichannel MAC

Protocols

Distributed Information Sharing (DISH)

is a new cooperative approach to

designing multichannel MAC protocols. It

aids nodes in their decision making

processes by compensating for their

missing information via information

sharing through neighboring nodes. This

approach was recently shown to

significantly boost the throughput of

multichannel MAC protocols. However, a

critical issue for ad hoc communication

devices, viz. energy efficiency, has yet to

be addressed. In this paper, we address

this issue by developing simple solutions

that reduce the energy consumption

without compromising the throughput

performance and meanwhile maximize

cost efficiency. We propose two energy-

efficient strategies: in-situ energy

conscious DISH, which uses existing

nodes only, and altruistic DISH, which

requires additional nodes called altruists.

We compare five protocols with respect

to these strategies and identify altruistic

DISH to be the right choice in general: it

1) conserves 40-80 percent of energy, 2)

maintains the throughput advantage,

and 3) more than doubles the cost

efficiency compared to protocols without

IEEE 2012

this strategy. On the other hand, our

study also shows that in-situ energy

conscious DISH is suitable only in certain

limited scenarios.

TTAECJ27 FESCIM: Fair, Efficient,

and Secure Cooperation

Incentive Mechanism for

Multi-hop Cellular

Networks

In multi-hop cellular networks, the

mobile nodes usually relay others'

packets for enhancing the network

performance and deployment. However,

selfish nodes usually do not cooperate

but make use of the cooperative nodes

to relay their packets, which has a

negative effect on the network fairness

and performance. In this paper, we

propose a fair and efficient incentive

mechanism to stimulate the node

cooperation. Our mechanism applies a

fair charging policy by charging the

source and destination nodes when both

of them benefit from the communication.

To implement this charging policy

efficiently, hashing operations are used

in the ACK packets to reduce the number

of public-key-cryptography operations.

Moreover, reducing the overhead of the

payment checks is essential for the

efficient implementation of the incentive

mechanism due to the large number of

payment transactions. Instead of

generating a check per message, a

small-size check can be generated per

route, and a check submission scheme is

proposed to reduce the number of

submitted checks and protect against

collusion attacks. Extensive analysis and

simulations demonstrate that our

mechanism can secure the payment and

significantly reduce the checks'

overhead, and the fair charging policy

can be implemented almost

computationally free by using hashing

operations.

IEEE 2012

TTAECJ28 Topology Control in

Mobile Ad Hoc Networks

with Cooperative

Communications

Cooperative communication has received

tremendous interest for wireless

networks. Most existing works on

cooperative communications are focused

on link-level physical layer issues.

Consequently, the impacts of cooperative

communications on network-level upper

layer issues, such as topology control,

routing and network capacity, are largely

ignored. In this article, we propose a

IEEE 2012

Capacity-Optimized Cooperative (COCO)

topology control scheme to improve the

network capacity in MANETs by jointly

considering both upper layer network

capacity and physical layer cooperative

communications. Through simulations,

we show that physical layer cooperative

communications have significant impacts

on the network capacity, and the

proposed topology control scheme can

substantially improve the network

capacity in MANETs with cooperative

communications.

TTAECD29 Cooperative download in

vehicular environments

We consider a complex (i.e., nonlinear)

road scenario where users aboard

vehicles equipped with communication

interfaces are interested in downloading

large files from road-side Access Points

(APs). We investigate the possibility of

exploiting opportunistic encounters

among mobile nodes so to augment the

transfer rate experienced by vehicular

downloaders. To that end, we devise

solutions for the selection of carriers and

data chunks at the APs, and evaluate

them in real-world road topologies,

under different AP deployment

strategies. Through extensive

simulations, we show that carry &

forward transfers can significantly

increase the download rate of vehicular

users in urban/suburban environments,

and that such a result holds throughout

diverse mobility scenarios, AP

placements and network loads.

IEEE 2012

TTAECD30 Network Assisted Mobile

Computing with Optimal

Uplink Query Processing

Many mobile applications retrieve

content from remote servers via user

generated queries. Processing these

queries is often needed before the

desired content can be identified.

Processing the request on the mobile

devices can quickly sap the limited

battery resources. Conversely,

processing user-queries at remote

servers can have slow response times

due communication latency incurred

during transmission of the potentially

large query. We evaluate a network-

assisted mobile computing scenario

where mid-network nodes with "leasing"

capabilities are deployed by a service

provider. Leasing computation power can

IEEE 2012

reduce battery usage on the mobile

devices and improve response times.

However, borrowing processing power

from mid-network nodes comes at a

leasing cost which must be accounted for

when making the decision of where

processing should occur. We study the

tradeoff between battery usage,

processing and transmission latency, and

mid-network leasing. We use the

dynamic programming framework to

solve for the optimal processing policies

that suggest the amount of processing to

be done at each mid-network node in

order to minimize the processing and

communication latency and processing

costs. Through numerical studies, we

examine the properties of the optimal

processing policy and the core tradeoffs

in such systems.

TTASTJ31 Smooth Trade-Offs

between Throughput and

Delay in Mobile Ad Hoc

Networks

Throughput capacity in mobile ad hoc

networks has been studied extensively

under many different mobility models.

However, most previous research

assumes global mobility, and the results

show that a constant per-node

throughput can be achieved at the cost

of very high delay. Thus, we are having a

very big gap here, i.e., either low

throughput or low delay in static

networks or high throughput and high

delay in mobile networks. In this paper,

employing a practical restricted random

mobility model, we try to fill this gap.

Specifically, we assume that a network of

unit area with n nodes is evenly divided

into cells with an area of n -2α, each of

which is further evenly divided into

squares with an area of n-2β

(0≤ α ≤ β

≤1/2). All nodes can only move inside

the cell which they are initially

distributed in, and at the beginning of

each time slot, every node moves from

its current square to a uniformly chosen

point in a uniformly chosen adjacent

square. By proposing a new multihop

relay scheme, we present smooth trade-

offs between throughput and delay by

controlling nodes' mobility. We also

consider a network of area nγ (0 ≤ γ ≤

1) and find that network size does not

IEEE 2012

affect the results obtained before.

TTASTJ32 Stateless Multicast

Protocol for Ad Hoc

Networks

Multicast routing protocols typically rely

on the a priori creation of a multicast

tree (or mesh), which requires the

individual nodes to maintain state

information. In dynamic networks with

bursty traffic, where long periods of

silence are expected between the bursts

of data, this multicast state maintenance

adds a large amount of communication,

processing, and memory overhead for no

benefit to the application. Thus, we have

developed a stateless receiver-based

multicast (RBMulticast) protocol that

simply uses a list of the multicast

members' (e.g., sinks') addresses,

embedded in packet headers, to enable

receivers to decide the best way to

forward the multicast traffic. This

protocol, called Receiver-Based Multicast,

exploits the knowledge of the geographic

locations of the nodes to remove the

need for costly state maintenance (e.g.,

tree/mesh/neighbor table maintenance),

making it ideally suited for multicasting

in dynamic networks. RBMulticast was

implemented in the OPNET simulator and

tested using a sensor network

implementation. Both simulation and

experimental results confirm that

RBMulticast provides high success rates

and low delay without the burden of

state maintenance.

IEEE 2012

TTASTJ33 Handling Selfishness in

Replica Allocation over a

Mobile Ad Hoc Network

In a mobile ad hoc network, the mobility

and resource constraints of mobile nodes

may lead to network partitioning or

performance degradation. Several data

replication techniques have been

proposed to minimize performance

degradation. Most of them assume that

all mobile nodes collaborate fully in

terms of sharing their memory space. In

reality, however, some nodes may

selfishly decide only to cooperate

partially, or not at all, with other nodes.

These selfish nodes could then reduce

the overall data accessibility in the

network. In this paper, we examine the

impact of selfish nodes in a mobile ad

IEEE 2012

hoc network from the perspective of

replica allocation. We term this selfish

replica allocation. In particular, we

develop a selfish node detection

algorithm that considers partial

selfishness and novel replica allocation

techniques to properly cope with selfish

replica allocation. The conducted

simulations demonstrate the proposed

approach outperforms traditional

cooperative replica allocation techniques

in terms of data accessibility,

communication cost, and average query

delay.

TTASTNS34 Secure High-Throughput

Multicast Routing in

Wireless Mesh Networks

Recent work in multicast routing for

wireless mesh networks has focused on

metrics that estimate link quality to

maximize throughput. Nodes must

collaborate in order to compute the path

metric and forward data. The assumption

that all nodes are honest and behave

correctly during metric computation,

propagation, and aggregation, as well as

during data forwarding, leads to

unexpected consequences in adversarial

networks where compromised nodes act

maliciously. In this work, we identify

novel attacks against high-throughput

multicast protocols in wireless mesh

networks. The attacks exploit the local

estimation and global aggregation of the

metric to allow attackers to attract a

large amount of traffic. We show that

these attacks are very effective against

multicast protocols based on high-

throughput metrics. We conclude that

aggressive path selection is a double-

edged sword: While it maximizes

throughput, it also increases attack

effectiveness in the absence of defense

mechanisms. Our approach to defend

against the identified attacks combines

measurement-based detection and

accusation-based reaction techniques.

The solution accommodates transient

network variations and is resilient

against attempts to exploit the defense

mechanism itself. A detailed security

analysis of our defense scheme

establishes bounds on the impact of

attacks. We demonstrate both the

attacks and our defense using ODMRP, a

IEEE 2012

representative multicast protocol for

wireless mesh networks, and SPP, an

adaptation of the well-known ETX unicast

metric to the multicast setting.

DOMAIN :ANDROID

TTASTA35,T

TASTJ35

Ubisoap: A Service-

Oriented Middleware for

Ubiquitous Networking

The computing and networking capacities

of today's wireless portable devices allow

for ubiquitous services, which are

seamlessly networked. Indeed, wireless

handheld devices now embed the

necessary resources to act as both

service clients and providers. However,

the ubiquitous networking of services

remains challenged by the inherent

mobility and resource constraints of the

devices, which make services a priori

highly volatile. This paper discusses the

design, implementation, and

experimentation of the ubiSOAP service-

oriented middleware, which leverages

wireless networking capacities to

effectively enable the ubiquitous

networking of services. ubiSOAP

specifically defines a layered

communication middleware that

underlies standard SOAP-based

middleware, hence supporting legacy

Web Services while exploiting nowadays

ubiquitous connectivity.

IEEE 2012

TTASTA36,T

TASTJ36

Ensuring Distributed

Accountability for Data

Sharing in the Cloud

Cloud computing enables highly scalable

services to be easily consumed over the

Internet on an as-needed basis. A major

feature of the cloud services is that

users' data are usually processed

remotely in unknown machines that

users do not own or operate. While

enjoying the convenience brought by this

new emerging technology, users' fears of

losing control of their own data

(particularly, financial and health data)

can become a significant barrier to the

wide adoption of cloud services. To

address this problem, in this paper, we

propose a novel highly decentralized

information accountability framework to

keep track of the actual usage of the

users' data in the cloud. In particular, we

propose an object-centered approach

that enables enclosing our logging

mechanism together with users' data and

IEEE 2012

policies. We leverage the JAR

programmable capabilities to both create

a dynamic and traveling object, and to

ensure that any access to users' data will

trigger authentication and automated

logging local to the JARs. To strengthen

user's control, we also provide

distributed auditing mechanisms. We

provide extensive experimental studies

that demonstrate the efficiency and

effectiveness of the proposed

approaches.

TTASTA37,T

TASTJ37

Who, When, Where:

Timeslot Assignment to

Mobile Client

We consider variations of a problem in

which data must be delivered to mobile

clients en route, as they travel toward

their destinations. The data can only be

delivered to the mobile clients as they

pass within range of wireless base

stations. Example scenarios include the

delivery of building maps to firefighters

responding to multiple alarms. We cast

this scenario as a parallel-machine

scheduling problem with the little-studied

property that jobs may have different

release times and deadlines when

assigned to different machines. We

present new algorithms and also adapt

existing algorithms, for both online and

offline settings. We evaluate these

algorithms on a variety of problem

instance types, using both synthetic and

real-world data, including several

geographical scenarios, and show that

our algorithms produce schedules

achieving near-optimal throughput.

IEEE 2012

TTASTA38 Characterizing the

Security Implications of

Third-Party Emergency

Alert Systems over

Cellular Text Messaging

Services

Cellular text messaging services are

increasingly being relied upon to

disseminate critical information during

emergencies. Accordingly, a wide

range of organizations including colleges

and universities now partner with third-

party providers that promise to improve

physical security by rapidly delivering

such messages. Unfortunately, these

products do not work as advertised due

to limitations of cellular infrastructure

and therefore provide a false

sense of security to their users. In this

paper, we perform the first extensive

investigation and

characterization of the limitations of an E

IEEE 2012

mergency Alert System (EAS)

using text messages as

a security incident response mechanism.

We show emergency alert systems built

on text messaging not only can

meet the 10 minute delivery requirement

mandated by the WARN Act, but also

potentially cause other voice and SMS

traffic to be blocked at rates

upward of 80 percent. We then show

that our results are representative of

reality by comparing them to a

number of documented but not

previously understood failures. Finally,

we analyze a

targeted messaging mechanism as a

means of efficiently using currently

deployed infrastructure and third-

party EAS. In so doing, we demonstrate

that this increasingly

deployed security infrastructure does not

achieve its stated requirements for large

populations.

TTASTA39 Design and

Implementation of

Improved Authentication

System for Android

Smartphone Users

The devices most often used for IT

services are changing from PCs and

laptops to smart phones and tablets.

These devices need to be small for

increased portability. These technologies

are convenient, but as the devices start

to contain increasing amounts of

important personal information, better

security is required. Security systems are

rapidly being developed, as well as

solutions such as remote control

systems. However, even with these

solutions, major problems could still

result after a mobile device is lost. In

this thesis, we present our upgraded

Lock Screen system, which is able to

support authentication for the user's

convenience and provide a good security

system for smart phones. We also

suggest an upgraded authentication

system for Android smart phones.

IEEE 2012

TTASTA40 Android Application for

Spiral Analysis in

Parkinson’s Disease

The paper presents an application for

spiral analysis in Parkinson's Disease

(PD). PD is one of the most common

degenerative disorders of the central

nervous system that affects elderly. Four

cardinal symptoms of the disease are

IEEE 2012

tremor, rigidity, slowness of movement,

and postural instability. The current

diagnosis is based on clinical observation

which relies on skills and experiences of

a trained specialist. Thus, an additional

method is desirable to help in the

diagnosis process and possibly improve

the detection of early PD as well as the

measurement of disease severity. Many

studies have reported that the spiral

analysis may be useful in the diagnosis

of motor dysfunction in PD patient. We

therefore implement a mobile, safe, easy

to use, inexpensive, and online

application for detection of movement

disorders with a comprehensive test

analysis according to the indices from

Archimedean and octagon spirals tracing

tasks. We introduce the octagon tracing

task along with the conventional

Archimedean spiral task because a shape

tracing task with clear sequential

components may increase a likelihood of

detecting tremors and other cardinal

features of PD. A widely used Android

mobile operating system, the fastest

markets share growth among

smartphone platforms, is chosen as our

development platform. We also show

that the preliminary results of selected

indices in the application could

potentially be used to distinguish

between PD patient and healthy control.

TTASTA41 Android Suburban Railway

Ticketing with GPS as

Ticket Checker

One of the biggest challenges in the

current ticketing facility is “QUEUE” in

buying our suburban railway tickets. In

this fast growing world of technology we

still stand in the queue or buy with

oyster & octopus cards for our suburban

tickets, which is more frustrating at

times to stand in the queue or if we

forget our cards. This paper Android

Suburban Railway (ASR) ticketing is

mainly to buy the suburban tickets which

is the most challenging when compared

to booking the long journey tickets

through `M-ticket' which fails with

suburban(local travel) tickets. Our ASR

ticket can be bought with just a smart

phone application, where you can carry

your suburban railway tickets in your

smart phone as a QR (Quick Response)

IEEE 2012

code. It uses the smart phones “GPS”

facility to validate and delete your ticket

automatically after a specific interval of

time once the user reaches the

destination. User's ticket information is

stored in a CLOUD database for security

purpose which is missing in the present

suburban system. Also the ticket checker

is provided with a checker application to

search for the user's ticket with the

ticket number in the cloud database for

checking purposes.

TTASTA42 On the Use of Mobile

Phones and Biometrics for

Accessing Restricted Web

Services

In this study, an application that allows a

mobile phone to be used as a biometric-

capture device is shown. The main

contribution of our proposal is that this

capture, and later recognition, can be

performed during a standard web

session, using the same architecture that

is used in a personal computer (PC), thus

allowing a multiplatform (PC, personal

digital assistant (PDA), mobile phone,

etc.) biometric web access. The review,

which is from both an academic and

commercial point of view, of the

biometry and mobile device state of the

art shows that in other related works,

the biometric capture and recognition is

either performed locally in the mobile or

remotely but using special

communication protocols and/or

connection ports with the server. The

second main contribution of this study is

an in-depth analysis of the present

mobile web-browser limitations; thus, it

is concluded that, in general, it is

impossible to use the same technologies

that can be used to capture biometrics in

PC platforms (i.e., Applet Java, ActiveX

Control, JavaScript, or Flash); therefore,

new solutions, as shown here, are

needed.

IEEE 2012

TTASTA43 Android-Based Mobile

Payment Service

Protected by 3-Factor

Authentication and Virtual

Private Ad Hoc

Networking

This work develops a pair of mobile

payment devices, a counter reader and a

paying client, on Android-based smart

phone platforms for emerging mobile

payment or electronic wallet services.

These two devices featuring 3-factor

authentication and virtual private Ad Hoc

networking can make an easier and

much secure transaction than traditional

credit cards or electronic payment cards.

IEEE 2012

3-factor authentication feature combines

PIN code authentication, USIM card

authentication, and facial biometric

authentication. Especially, this work

proposes and implements a simple but

practical method, Fast Semi-3D Face

Vertical Pose Recovery, to cope with the

vertical pose variation issue bothering

face recognition systems so far.

Experimental results show the proposed

method can significantly raise the

recognition accuracy and enlarge the

operating angle range of face recognition

system under various vertical pose

conditions. Besides, virtual private Ad

Hoc networking feature based on

OpenSSL and i-Jetty open-source

libraries is also integrated seamlessly.

TTASTA44 Research and Design of

Chatting Room System

based on Android

Bluetooth

Bluetooth provides a low-power and low-

cost wireless connection among mobile

devices and their accessories, which is

an open standard for implementing a

short-range wireless communication.

Bluetooth is integrated into Android

which is a mainstream smart phone

platform as a mean of mobile

communication. Android has attracted a

large number of developers because of

its character of open sourcing and

powerful application AP I. This article

takes designing a Bluetooth chat room

for example to research Bluetooth and its

architecture of android platform and

introduce the process of realizing the

Bluetooth communication in detail. Then

we design and implement a chat room

based on Bluetooth by using APIs of

Android platform. At last, a further

prospect of the function of this system

was made.

IEEE 2012

TTASTA45 Android application for

sending SMS messages

with speech recognition

interface

Voice SMS is an application developed in

this work that allows a user to record

and convert spoken messages into SMS

text message. User can send messages

to the entered phone number or the

number of contact from the phonebook.

Speech recognition is done via the

Internet, connecting to Google's server.

The application is adapted to input

messages in English. Used tools are

Android SDK and the installation is done

IEEE 2012

on mobile phone with Android operating

system. In this article we will give basic

features of the speech recognition and

used algorithm. Speech recognition for

Voice SMS uses a technique based on

hidden Markov models (HMM - Hidden

Markov Model). It is currently the most

successful and most flexible approach to

speech recognition.

TTASTA46 Android Mobile

Augmented Reality

Application Based on

Different Learning

Theories for Primary

School Children

Due to advancements in the mobile

technology and the presence of strong

mobile platforms, it is now possible to

use the revolutionizing augmented

reality technology in mobiles. This

research work is based on the

understanding of different types of

learning theories, concept of mobile

learning and mobile augmented reality

and discusses how applications using

these advanced technologies can shape

today's education systems.

IEEE 2012

TTASTA47 Android Botnets on the

Rise: Trends and

Characteristics

Smart phones are the latest technology

trend of the 21st century. Today's social

expectation of always staying connected

and the need for an increase in

productivity are the reasons for the

increase in smart phone usage. One of

the leaders of the smart phone evolution

is Google's Android Operating System

(OS). The openness of the design and

the ease of customizing are the aspects

that are placing Android ahead of the

other smart phone OSs. Such popularity

has not only led to an increase in

Android usage but also to the rise of

Android malware. Although such

malware is not having a significant

impact on the popularity of Android

smart phones, it is however creating new

possibilities for threats. One such threat

is the impact of botnets on Android

smart phones. Recently, malware has

surfaced that revealed specific

characteristics relating to traditional

botnet activities. Malware such as

Geinimi, Pjapps, DroidDream, and

RootSmart all display traditional botnet

functionalities. These malicious

applications show that Android botnets is

a reality. From a security perspective it is

important to understand the underlying

IEEE 2012

structure of an Android botnet. This

paper evaluates Android malware with

the purpose of identifying specific trends

and characteristics relating to botnet

behavior. The botnet trends and

characteristics are detected by a

comprehensive literature study of well-

known Android malware applications.

The identified characteristics are then

further explored in terms of the Android

Botnet Development Model and the

Android Botnet Discovery Process. The

common identified trends and

characteristics aid the understanding of

Android botnet activities as well as the

possible discovery of an Android bot.

DOMAIN : WEBMINING

CODE

PROJECT TITLE

DESCRIPTION

REFERENCE

TTAECJ48 Learn to Personalized

Image Search from the

Photo Sharing Websites

Increasingly developed social sharing

websites like Flickr and Youtube allow

users to create, share, annotate, and

comment media. The large-scale user-

generated metadata not only facilitate

users in sharing and organizing

multimedia content, but provide useful

information to improve media retrieval

and management. Personalized search

serves as one of such examples where

the web search experience is improved

by generating the returned list according

to the modified user search intents. In

this paper, we exploit the social

annotations and propose a novel

framework simultaneously considering

the user and query relevance to learn to

personalized image search. The basic

premise is to embed the user preference

and query-related search intent into

user-specific topic spaces. Since the

users' original annotation is too sparse

for topic modeling, we need to enrich

users' annotation pool before user-

specific topic spaces construction. The

IEEE 2012

proposed framework contains two

components: (1) a ranking-based multi-

correlation tensor factorization model is

proposed to perform annotation

prediction, which is considered as users'

potential annotations for the images; (2)

we introduce user-specific topic modeling

to map the query relevance and user

preference into the same user-specific

topic space. For performance evaluation,

two resources involved with users' social

activities are employed. Experiments on

a large-scale Flickr dataset demonstrate

the effectiveness of the proposed

method.

DOMAIN :DATA MINING

TTASTD49 A Genetic Programming

Approach to Record De-

duplication

Several systems that rely on consistent

data to offer high-quality services, such

as digital libraries and e-commerce

brokers, may be affected by the

existence of duplicates, quasi replicas, or

near-duplicate entries in their

repositories. Because of that, there have

been significant investments from private

and government organizations for

developing methods for removing

replicas from its data repositories. This is

due to the fact that clean and replica-

free repositories not only allow the

retrieval of higher quality information but

also lead to more concise data and to

potential savings in computational time

and resources to process this data. In

this paper, we propose a genetic

programming approach to record de-

duplication that combines several

different pieces of evidence extracted

from the data content to find a de-

duplication function that is able to

identify whether two entries in a

repository are replicas or not. As shown

by our experiments, our approach

outperforms an existing state-of-the-art

method found in the literature.

Moreover, the suggested functions are

computationally less demanding since

they use fewer evidences. In addition,

our genetic programming approach is

capable of automatically adapting these

IEEE 2012

functions to a given fixed replica

identification boundary, freeing the user

from the burden of having to choose and

tune this parameter.

TTASTD50 Discover Dependencies

from Data—A Review

Functional and inclusion dependency

discovery is important to knowledge

discovery, database semantics analysis,

database design, and data quality

assessment. Motivated by the

importance of dependency discovery,

this paper reviews the methods for

functional dependency, conditional

functional dependency, approximate

functional dependency, and inclusion

dependency discovery in relational

databases and a method for discovering

XML functional dependencies.

IEEE 2012

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DESCRIPTION

REFERENCE

TTASTD

51

Tree-Based Mining for Discovering Patterns of

Human Interaction in Meetings

Discovering semantic knowledge is

significant for understanding and

interpreting how people

interact in a meetingdiscussion. In this

paper, we propose a mining method to

extract

frequent patterns of human interaction base

don the captured content of face-to-

face meetings. Human interactions, such as

proposing an idea, giving comments, and

expressing a positive opinion, indicate user

intention toward a topic or role in a

discussion.Human interaction flow in a

discussion session is represented as

a tree. Tree-

based interaction mining algorithms are

designed to analyze the structures of the

trees and to

extract interaction flow patterns. The

experimental results show that we can

successfully extract several

interesting patterns that are useful for the

interpretationof human behavior in meeting

discussions, such as determining

frequent interactions,

typical interaction flows, and relationships

between different types of interactions.

IEEE 2012

TTASTD

52

Automatic Discovery of

Personal Name Aliases from the Web

An individual is typically referred by

numerous name aliases on the web.

Accurate identification of aliases of a given

person name is useful in various web related

tasks such as information retrieval,

sentiment analysis,

personal name disambiguation, and relation

extraction. We propose a method to

extract aliases of a given

personal name from the web. Given

a personal name, the proposed method first

extracts a set of candidate aliases. Second,

we rank the extracted candidates according

to the likelihood of a candidate being a

correct alias of the given name. We propose

a novel, automatically extracted lexical

pattern-based approach to efficiently extract

a large

set of candidate aliases from snippets

retrieved from a web search engine. We

define numerous ranking scores to evaluate

candidate aliases using three approaches:

lexical pattern frequency, word co-

occurrences in an anchor text graph, and

page counts on the web. To construct a

robust alias detection system, we

integrate the different ranking scores into a

single ranking function using ranking

support vector machines. We

evaluate the proposed method on three data

sets: an English personal names data set, an

English place names data set, and a

Japanese personal names data

set. The proposed method outperforms

numerous baselines and previously

proposed name alias extraction methods,

achieving a statistically significant mean

reciprocal rank (MRR) of 0.67. Experiments

carried out using location names and

Japanese personal namessuggest the possibi

lity of extending the proposed method to

extract aliases for different

types of named entities, and for different

languages. Moreover, the aliases extracted

using the proposed method are successfully

utilized in an information retrieval task and

improve recall by 20 percent in a relation-

detection task.

IEEE 2012

TTASTD

53

Horizontal Aggregations in SQL to Prepare Data Sets

for Data Mining Analysis

Preparing a data set for analysis is generally

the most time consuming

task in a data mining project, requiring

many complex SQL queries, joining tables,

IEEE 2012

and aggregating columns.

Existing SQL aggregations have

limitationsto prepare data sets because they

return one column per aggregated

group. In general, a significant manual effort

is required to build data sets, where

a horizontal layout is required. We propose

simple, yet powerful,

methods to generate SQL code to return

aggregated columns in a horizontal tabular

layout, returning a set of numbers instead of

one number per row. This new class of

functions is

called horizontal aggregations. Horizontalagg

regations build data sets with

a horizontal denormalized layout (e.g.,

point-dimension, observation-variable,

instance-feature), which is the standard

layout required by

most data mining algorithms. We propose

three fundamental

methods to evaluate horizontal aggregations

: CASE: Exploiting the programming CASE

construct; SPJ: Based on standard relational

algebra operators (SPJ queries); PIVOT:

Using the PIVOT operator, which is offered

by some DBMSs. Experiments with large

tables compare the proposed query

evaluation methods. Our CASE method has

similar speed to the PIVOT operator and it is

much faster than the SPJ

method. In general, the CASE and PIVOT

methods exhibit linear scalability, whereas

the SPJ method does not

TTASTD

54

Outsourced Similarity Search on Metric Data

Assets

This paper considers a cloud computing

setting in which similarity querying

of metric data is outsourced to a service

provider. The data is to be revealed only to

trusted users, not to the service provider or

anyone else. Users query the server for the

most similar data objects to a query

example. Outsourcing offers the data owner

scalability and a low-initial investment. The

need for privacy may be due to

the data being sensitive (e.g., in medicine),

valuable (e.g., in astronomy), or otherwise

confidential. Given this setting, the paper

presents techniques that transform

the data prior to supplying it to the service

provider for similarity queries on the

transformed data. Our techniques provide

interesting trade-offs between query cost

IEEE 2012

and accuracy. They are then further

extended to offer an intuitive privacy

guarantee. Empirical studies with

real data demonstrate that the techniques

are capable of offering privacy while

enabling efficient and accurate processing

of similarity queries.

TTASTD

55

On the Spectral Characterization and Scalable Mining of Network

Communities

Network communities refer to

groups of vertices within which their

connecting links are dense but between

which they are sparse.

A network community mining problem (or

NCMP for short) is concerned

with the problem offinding all

such communities from a given network. A

wide variety of applications can be

formulated as NCMPs, ranging from

social and/or biological network analysis to

web mining and searching. So far, many

algorithms addressing NCMPs have been

developed and most of them fall

into the categories of either optimization

based or heuristic methods. Distinct

from the existing studies, the work

presented in this paper

explores the notion ofnetwork communities

and their properties

based on the dynamics of a stochastic model

naturally introduced. Inthe paper, a

relationship

between the hierarchical community structur

e of a network and the local mixing

properties of such a stochastic model has

been established with the large-deviation

theory. Topological information regarding

to the community structures hidden

in networks can be inferred from

their spectralsignatures.

Based on the above-mentioned relationship,

this work proposes a general framework for

characterizing,

analyzing, and mining network communities.

Utilizing the two basic

properties of metastability, i.e., being locally

uniform and temporarily fixed, an efficient

implementation of the framework,

called the LM algorithm, has been developed

that can scalably mine communities hidden

in large-

scale networks. Theeffectiveness and efficien

cy of the LM algorithm have been

theoretically analyzed as well as

IEEE 2012

experimentally validated.

TTASTD

56

Mining Web Graphs for Recommendations

As the exponential explosion of various

contents generated on

the Web, Recommendation techniques have

become increasingly indispensable.

Innumerable different kinds

of recommendations are made on

the Web every day, including movies, music,

images, books recommendations, query

suggestions, tags recommendations, etc. No

matter what types of data sources are

used for the recommendations, essentially

these data sources can be modeled in the

form of various types of graphs. In this

paper, aiming at providing a general

framework on mining

Web graphs for recommendations, (1) we

first propose a novel diffusion method which

propagates similarities between different

nodes and generates recommendations; (2)

then we illustrate how to generalize different

recommendation problems into

our graph diffusion framework. The

proposed framework can be utilized in many

recommendation tasks on the World

Wide Web, including query suggestions,

tag recommendations, expert finding,

image recommendations, image

annotations, etc. The experimental analysis

on large data sets shows the promising

future of our work

IEEE 2012

TTAECJ

57

Ranking Model Adaptation for Domain-Specific Search

With the explosive emergence of

vertical search domains, applying the broad-

based ranking model directly to different

domains is no longer desirable due

to domain differences, while building a

unique ranking model for each domain is

both laborious for labeling data and time

consuming for training models. In this

paper, we address these difficulties by

proposing a regularization-based algorithm

called ranking adaptation SVM (RA-SVM),

through which we can adapt an

existing ranking model to a new domain, so

that the amount of labeled data and the

training cost is reduced while the

performance is still guaranteed. Our

algorithm only requires the prediction from

IEEE 2012

the existing ranking models, rather than

their internal representations or the data

from auxiliary domains. In addition, we

assume that documents similar in

the domain-specific feature space should

have consistent rankings, and add some

constraints to control the margin and slack

variables of RA-SVM adaptively.

Finally, ranking adaptability measurement is

proposed to quantitatively estimate if an

existing ranking model can be adapted to a

new domain. Experiments performed over

Lector and two large scale data sets crawled

from a commercial search engine

demonstrate the applicability’s of the

proposed ranking adaptation algorithms and

the ranking adaptability measurement.

TTAEC

D58

Segmentation and

Sampling of Moving Object Trajectories Based on

Representativeness

Moving Object Databases (MOD), although

ubiquitous, still call for methods that will be

able to understand, search,

analyze, and browse their spatiotemporal

content. In this paper, we propose a method

for trajectorysegmentation and sampling bas

ed on the representativeness of the

(sub)trajectories in the MOD. In order to find

the most representative sub trajectories, the

following methodology is proposed. First, a

novel global voting algorithm is

performed, based on local

density and trajectory similarity information.

This method is applied for each

segment of the trajectory, forming a

local trajectory descriptor that represents

line segment representativeness. The

sequence of this descriptor over

a trajectory gives the voting

signal of the trajectory, where high values

correspond to the most representative parts.

Then, a novel segmentation algorithm is

appliedon this signal that automatically

estimates the number of partitions and the

partition borders, identifying homogenous

partitions concerning

their representativeness. Finally,

a sampling method over the resulting

segments yields the most representative

subtrajectories in the MOD. Our

experimental results in synthetic andreal

MOD verify the effectiveness of the proposed

scheme, also in comparison with

other sampling techniques..

IEEE 2012

TTASTJ

59

Effective Pattern Discovery

for Text Mining

Many data mining techniques have been

proposed for mining useful patterns in text d

ocuments. However, how to effectively use

and update discovered patterns is still an

open research issue, especially in the

domain of textmining. Since most

existing text mining methods adopted term-

based approaches, they all suffer from the

problems of polysemy and synonymy. Over

the years, people have often held the

hypothesis that pattern (or phrase)-based

approaches should perform better than the

term-based ones, but many experiments do

not support this hypothesis. This paper

presents an innovative

and effective pattern discovery technique

which includes the processes

of pattern deploying and pattern evolving, to

improve the effectiveness of using and

updating discovered patterns for finding

relevant and interesting information.

Substantial experiments on RCV1 data

collection and TREC topics demonstrate that

the proposed solution achieves encouraging

performance.

IEEE 2012

TTASTJ

60

Incremental Information Extraction Using Relational Databases

Information extraction systems are traditionally implemented as a

pipeline of special-purpose processing modules targeting

the extraction of a particular kind of information. A major drawback of

such an approach is that whenever a new extraction goal emerges or a

module is improved, extraction has to be reapplied from scratch to the

entire text corpus even though only a small part of the corpus might be

affected. In this paper, we describe a novel approach

for information extraction in

which extraction needs are expressed in the form

of databasequeries, which are evaluated and optimized

by database systems. Using database queries

for informationextraction enables

IEEE 2012

generic extraction and minimizes

reprocessing of data by performing incremental extraction to

identify which part of the data is affected by the change of

components or goals. Furthermore, our approach provides automated

query generation components so that casual users do not have to learn the

query language in order to perform extraction. To demonstrate

the feasibility of

our incremental extraction approach, we performed experiments to

highlight two important aspects of an information extraction system:

efficiency and quality of extraction results. Our

experiments show that in the event of deployment of a new module, our

incremental extraction approach reduces the processing time by

89.64 percent as compared to a traditional pipeline approach. By

applying our methods to a corpus of 17 million biomedical abstracts, our

experiments show that the query

performance is efficient for real-time applications. Our experiments also

revealed that our approach achieves high

quality extraction results.

TTASTJ

61

Learning Bregman Distance Functions for

Semi-Supervised Clustering

Learning distance functions with side

information plays a key role in many

data mining applications.

Conventionaldistance metric learning

approaches often assume that the

target distance function is

represented in some form of

Mahalanobis distance. These

approaches usually work well when

data are in low dimensionality, but

often become computationally

IEEE 2012

expensive or even infeasible when

handling high-dimensional data. In

this paper, we propose a novel

scheme

of learning nonlinear distance functio

ns with side information. It aims

to learn a Bregmandistance function

using a nonparametric approach that

is similar to Support Vector

Machines. We emphasize that the

proposed scheme is more general

than the conventional

approach for distance metric learning

, and is able to handle high-

dimensional data efficiently. We

verify the efficacy of the

proposed distance learning method

with extensive experiments on semi-

supervised clustering. The

comparison with state-of-the-art

approaches forlearning distance functi

ons with side information reveals clear

advantages of the proposed technique.

TTASTJ

62

Resilient Identity Crime Detection

Identity crime is well known, prevalent, and

costly; and credit application fraud is a

specific case of identitycrime. The existing

nondata mining detection system of

business rules and scorecards, and known

fraud matching have limitations. To address

these limitations and

combat identity crime in real time, this

paper proposes a new

multilayered detection system

complemented with two additional layers:

communal detection(CD) and

spike detection (SD). CD finds real social

relationships to reduce the suspicion score,

and is tamper resistant to synthetic social

relationships. It is the whitelist-oriented

approach on a fixed set of attributes. SD

finds spikes in duplicates to increase the

suspicion score, and is probe-resistant for

attributes. It is the attribute-oriented

IEEE 2012

approach on a variable-size set of attributes.

Together, CD and SD can detect more types

of attacks, better account for changing legal

behavior, and remove the redundant

attributes. Experiments were carried out on

CD and SD with several million real credit

applications. Results on the data support the

hypothesis that successful credit application

fraud patterns are sudden and exhibit sharp

spikes in duplicates. Although this research

is specific to credit application

fraud detection, the concept of resilience,

together with adaptivity and quality data

discussed in the paper, are general to the

design, implementation, and evaluation of

all detectionsystems.

TTASTJ

63

TSCAN: A Content

Anatomy Approach to

Temporal Topic

Summarization

A topic is defined as a seminal event or

activity along with all directly related events

and activities. It is represented

by a chronological sequence of documents

published by different authors on the

Internet. In this study, we define a task

called topic anatomy, which summarizes and

associates the core parts

of a topictemporally so that readers can

understand the content easily. The

proposed topic anatomy model,

called TSCAN, derives the major themes

of a topic from the eigenvectors

of a temporal block association matrix.

Then, the significant events of the themes

and their summaries are extracted by

examining the constitution of the

eigenvectors. Finally, the extracted events

are associated through

their temporal closeness and context

similarity to form an evolution graph of

the topic. Experiments based on the official

TDT4 corpus demonstrate that the

generated temporal summaries present the

storylines of topics in a comprehensible

form. Moreover, in terms

of content coverage, coherence, and

consistency, the summaries are

superior to those derived by

existingsummarization methods based on

human-composed reference summaries.

IEEE 2012

TTASTJ

64

Privacy Preserving Decision Tree

Learning Using Unrealized Data

Sets

Privacy preservation is important for

machine learning and data mining, but

measures designed to protect private

information often result in a trade-off:

reduced utility of the training samples. This

paper introduces

a privacypreserving approach that can be

applied to decision tree learning, without

concomitant loss of accuracy. It describes an

approach to the preservation of

the privacy of collected data samples in

cases where information from the sample

database has been partially lost. This

approach converts the original

sample data sets into a group of

unreal data sets, from which the original

samples cannot be reconstructed without the

entire group of unreal data sets. Meanwhile,

an accurate decision tree can be built

directly from those unreal data sets. This

novel approach can be applied directly to

the data storage as soon as the first sample

is collected. The approach is compatible with

other privacy preserving approaches, such

as cryptography, for extra protection.

IEEE 2012

TTASTJ

65

Mining O Mining Online

Reviews for Predicting Sales

Performance: A Case Study in the

Movie Domain

Posting reviews online has become an

increasingly popular way for people to

express opinions and sentiments

toward the products bought or services

received. Analyzing the large volume

of online reviews available would produce

useful actionable knowledge that could be of

economic values to vendors and other

interested parties. In this paper, we

conduct a case study in the movie domain,

and tackle the problem of mining reviews for

predicting product sales performance. Our

analysis shows that both the sentiments

expressed in the reviews and the quality

of the reviews have a significant impact

on the future sales performance of

products in question. Forthe sentiment

factor, we propose Sentiment PLSA (S-

PLSA), in which a review is considered

as a document generated by a number of

hidden sentiment factors, in order to

capture the complex nature of sentiments.

Training an S-PLSA model enables us to

obtain a succinct summary of the sentiment

information embedded in there views. Based

on S-PLSFA, we propose ARSA, an

Autoregressive Sentiment-Aware

IEEE 2012

model for sales prediction. We then seek to

further improve the accuracy of prediction

by considering the quality factor,

with a focus on predicting the quality

of a review in the absence of user-supplied

indicators, and present ARSQA, an

Autoregressive Sentiment and Quality Aware

model, to utilize sentiments and

quality for predicting product sales

performance. Extensive experiments

conducted on a large movie data set

confirm the effectiveness of the proposed

approach.

TTASTJ

66

Ranking Model Adaptation for

Domain-Specific Search

With the explosive emergence of

vertical search domains, applying the broad-

based ranking model directly to different

domains is no longer desirable due

to domain differences, while building a

unique ranking model for each domain is

both laborious for labeling data and time

consuming for training models. In this

paper, we address these difficulties by

proposing a regularization-based algorithm

called ranking adaptation SVM (RA-SVM),

through which we can adapt an

existing ranking model to a new domain, so

that the amount of labeled data and the

training cost is reduced while the

performance is still guaranteed. Our

algorithm only requires the prediction from

the existing ranking models, rather than

their internal representations or the data

from auxiliary domains. In addition, we

assume that documents similar in

the domain-specific feature space should

have consistent rankings, and add some

constraints to control the margin and slack

variables of RA-SVM adaptively.

Finally, ranking adaptability measurement is

proposed to quantitatively estimate if an

existing ranking model can be adapted to a

new domain. Experiments performed over

Letor and two large scale data sets crawled

from a commercial search engine

demonstrate the applicability’s of the

proposed ranking adaptation algorithms and

the ranking adaptability measurement.

IEEE 2012

DOMAIN :IMAGE PROCESSING

CODE

PROJECT TITLE

DESCRIPTION

REFERENCE

TTAECD67 A Fusion Approach for Efficient Human

Skin Detection

A reliable human skin detection method

that is adaptable to

different human skin colors and

illumination conditions is

essential for better human skin segment

ation. Even though different human skin-

color detection solutions have been

successfully applied, they are prone to

false skin detection and are not able to

cope with the variety

of human skin colors across different

ethnic. Moreover, existing methods

require high computational cost. In this

paper, we propose a

novel human skin detection approach tha

t combines a smoothed 2-D histogram

and Gaussian

model, for automatic human skin detecti

on in color image(s). In our approach, an

eye detector is used to refine

the skin model for a specific person. The

proposed approach reduces

computational costs as no training is

required, and it improves the accuracy

of skin detection despite wide variation

in ethnicity and illumination. To the best

of our knowledge, this is the first method

to employ fusion strategy for this

purpose. Qualitative and quantitative

results on three standard public datasets

and a comparison with state-of-the-art

methods have shown the effectiveness

and robustness of the

proposed approach.

IEEE 2012

TTAECD68 PDE-Based Enhancement

of Color Images in RGB

Space

A novel method

for color image enhancement is proposed

as an extension of the scalar-diffusion-

shock-filter coupling model, where noisy

and blurred images are de noised and

sharpened. The proposed model

is based on using the single

vectors of the gradient magnitude and

the second derivatives as a manner to

relate different

color components of the image. This

model can be viewed as a

generalization of the Bettahar-Stambouli

filter to multivalve images. The proposed

IEEE 2012

algorithm is more efficient than the

mentioned filter and some previous

works at color images de noising and

deblurring without creating false colors.

TTASTD69 Improving Various Reversible Data

Hiding Schemes Via Optimal Codes for

Binary Covers

In reversible data hiding (RDH), the

original cover can be lossless restored

after the embedded information is

extracted. Walker and Willets established

a rate-distortion model for RDH, in which

they proved out the rate-distortion

bound and proposed a

recursive code construction. In our

previous paper, we improved the

recursive construction to approach the

rate-distortion bound. In this paper, we

generalize the method in our previous

paper using a decompression algorithm

as

the coding scheme for embedding data a

nd prove that the generalized codes can

reach the rate-distortion bound as long

as the compression algorithm reaches

entropy. By the proposed binary codes,

we improve three RDH schemes that

use binary feature sequence as covers,

i.e., an RSscheme for spatial images,

one scheme for JPEG images, and a

pattern

substitution scheme for binary images.

The experimental results show that the

novel codes can significantly reduce the

embedding distortion. Furthermore, by

modifying the histogram shift (HS)

manner, we also apply

this coding method to one scheme that

uses HS, showing that the

proposed codes can be also exploited

to improve integer-operation-based

schemes.

IEEE 2012

TTASTD70 Impact of the Lips for Biometrics

In this

paper, the impact of the lips for identity

recognition is investigated. In fact, it is a

challenging issue for identity recognition

solely by the lips. In the first

stage of the proposed system, a fast box

filtering is proposed to generate a noise-

free source with high processing

efficiency. Afterward, five various mouth

corners are detected

through the proposed system, in which it

is also able to resist shadow, beard, and

IEEE 2012

rotation problems. For the feature

extraction, two geometric ratios and ten

parabolic-related parameters are

adopted for further recognition

through the support vector machine.

Experimental results demonstrate that,

when the number of subjects is fewer or

equal to 29, the correct accept rate

(CAR) is greater than 98%, and the false

accept rate (FAR) is smaller than 0.066%

. Moreover, the processing

speed of the overall system achieves

34.43 frames per second, which

meets the real-time requirement.

Thus, the proposed system can be

effective

candidate forfacial biometrics application

s when other facial organs are covered or

when it is applied for an access control

system.

TTASTJ71 Image Editing With

Spectrogram Transfer

This paper presents a unified model

for image editing in terms of Sparse

Matrix-Vector (SpMV) multiplication. In

our framework, we cast image editing as

a linear energy minimization problem

and ad dress it by solving a sparse linear

system, which is able to yield a globally

optimal solution. First, three

classical image editing operations,

including linear filtering, resizing and

selecting, are reformulated in the SpMV

multiplication form. The SpMV form helps

us set up a straightforward mechanism

to flexibly and naturally combine

various image features (low-level visual

features or geometrical features) and

constraints together into an integrated

energy minimization function under the

L2 norm. Then, we apply our model to

implement the tasks of pan-

sharpening, image cloning, image

mixed editing and texture transfer, which

are now popularly used in the field of

digital art. Comparative experiments are

reported to validate the effectiveness

and efficiency of our model.

IEEE 2012

DOMAIN :PARALLEL & DISTRIBUTED COMPUTING

TTAECD72 An Efficient Adaptive A deadlock-

free minimal routing algorithm call

IEEE 2012

Deadlock-Free Routing

Algorithm for Torus

Networks

ed clue is first proposed for VCT

(virtual cut-through)-switched tori. Only two virtual

channels are required. One channel is applied in

the deadlock-free routing algorithm for the

mesh sub network based on a known base routing scheme, such

as, negative-first or dimension-order routing. The other channel

is similar to an adaptive channel.

This combination presents a novel fully adaptive minimal routing

scheme because the first channel does not

supply routing paths for every source-destination pair. Other two

algorithms named flow controlled clue and wormhole clue are

proposed. Flow controlled clue is proposed for VCT-switched tore,

which is fully adaptive minimal deadlock-

free with no virtual channel. Each input port requires at least two

buffers, each of which is able to

keep a packet. A simple but well-designed flow control function is

used in the proposed flow controlled

clue routing algorithm to avoid deadlocks. Wormhole clue is

proposed for wormhole-switched tori. It is

partially adaptive because we add some constraints to

the adaptive channels for deadlock avoidance. It is shown

that clue and flow controlled clue work better than the bubble flow

control scheme under several

popular traffic patterns in 3-dimensional (3D) torus. In a

wormhole-switched tori, the

advantage of wormhole clue over Duato's protocol is also very

apparent.

TTAECD73 Scalable and Secure

Sharing of Personal

Health Records in

Cloud Computing Using

Attribute-Based

Encryption

Personal health record (PHR) is an

emerging patient-centric

model of health information exchange,

which is often outsourced to be stored at

a third party, such as cloud providers.

However, there have been wide privacy

concerns as personal health information

could be exposed to those third party

servers and to unauthorized parties. To

assure the patients' control over access

to their own PHRs, it is a promising

method to encrypt the PHRs before

outsourcing. Yet, issues such as

risks of privacy exposure,

scalability in key management, flexible

access, and efficient user revocation,

have remained the most important

challenges toward achieving fine-

grained, cryptographically enforced data

access control. In this paper, we propose

a novel patient-centric framework and a

suite of mechanisms for data access

control to PHRs stored in semitrusted

servers. To achieve fine-

grained and scalable data access control

for PHRs, we leverage attribute-

based encryption (ABE) techniques to

encrypt each patient's PHR file. Different

from previous works in secure data

outsourcing, we focus on the multiple

data owner scenario, and divide the

users in the PHR system into multiple

security domains that greatly reduces

the key management complexity for

owners and users. A high

degree of patient privacy is guaranteed

simultaneously by exploiting

multiauthority ABE. Our scheme also

enables dynamic modification ofaccess

policies or file attributes, supports

efficient on-demand

user/attribute revocation and break-glass

access under emergency scenarios.

Extensive analytical and experimental

results are presented which show the

security, scalability, and efficiency of our

proposed scheme

IEEE 2012

TTAECD74 SPOC A Secure and

Privacy preserving

Opportunistic Computing

Framework for Mobile

Healthcare Emergency

With the pervasiveness of smart phones and the advance of

wireless body sensor networks

(BSNs), mobile Healthcare (m-Healthcare), which extends the

operation of Healthcare provider into a pervasive environment for

better health monitoring, has attracted considerable interest

recently. However, the flourish of m-Healthcare still faces many

challenges including information security and privacy preservation.

In this paper, we propose a secure and privacy-preserving

opportunistic computing framework, called SPOC, for m-

Healthcare emergency. With

SPOC, smart phone resources including computing power and

energy can be opportunistically gathered to process the

computing-intensive personal health information (PHI) during

m-Healthcare emergency with minimal privacy disclosure. In

specific, to leverage the PHI privacy disclosure and the high

reliability of PHI process and transmission in m-Healthcare

emergency, we introduce an efficient user-centric privacy

access control in SPOC

framework, which is based on an attribute-based access control and

a new privacy-preserving scalar product computation (PPSPC)

technique, and allows a medical user to decide who can participate

in the opportunistic computing to assist in processing his

overwhelming PHI data. Detailed security analysis shows that the

IEEE 2012

proposed SPOC framework can

efficiently achieve user-centric privacy access control in m-

Healthcare emergency. In addition, performance evaluations

via extensive simulations demonstrate the SPOC's

effectiveness in term of providing high reliable PHI process and

transmission while minimizing the privacy disclosure during m-

Healthcare emergency.

TTAECD75 The Three-Tier Security

Scheme in Wireless

Sensor Networks with

Mobile Sinks

Mobile sinks (MSs) are

vital in many wireless sensor network (WSN) applications for

efficient data accumulation, localized sensor reprogramming,

and for distinguishing and revoking compromised sensors.

However, in sensor networks that make use of the existing key

redistribution schemes for pair wise key establishment and

authentication between sensor nodes

and mobile sinks, the employment of mobile sinks for data collection

elevates a

new security challenge: in the basic probabilistic and q-composite

key redistribution schemes, an attacker can easily obtain a large

number of keys by capturing a small fraction of nodes, and

hence, can gain control of the network by deploying a

replicated mobile sink preloaded with some compromised keys.

This article describes a three-tier general framework that

permits the use of any pair wise key redistribution scheme as its

basic component. The new

IEEE 2012

framework requires two separate

key pools, one for the mobile sink to access the

network, and one for pair wise key establishment

between the sensors. To further reduce the damages caused by

stationary access node replication attacks, we have

strengthened the authentication mechanism between the

sensor and the stationary

TTASTJ76 User-Level

Implementations of Read-

Copy Update

Read-copy update (RCU) is a synchronization technique that

often replaces reader-writer locking because RCU'sread-side

primitives are both wait-free and an order of magnitude faster than

uncondensed locking. Although RCUupdates are relatively heavy

weight, the importance of read-

side performance is increasing as computing systems become more

responsive to changes in their environments. RCU is heavily

used in several kernel-level environments. Unfortunately,

kernel-level implementations use facilities that are often unavailable

to user applications. The few prior user-

level RCU implementations either provided inefficient read-side

primitives or restricted the application architecture. This

paper fills this gap by describing

efficient and flexible RCUimplementations based on

primitives commonly available to user-level applications. Finally,

this paper compares these RCU implementations with each

other and with standard locking, which enables choosing the best

IEEE 2012

mechanism for a given workload.

This work opens the door to widespread user-application

use of RCU.

TTASTJ77 Aho-Corasick String

Matching on Shared and

Distributed-Memory

Parallel Architectures

String matching requires a combination

of (sometimes all) the following

characteristics: high and/or predictable

performance, support for large data

sets and flexibility of

integration and customization. This paper

compares several software-based

implementations of the Aho-

Corasick algorithm for high-performance

systems. We focus on the matching of

unknown inputs streamed from a single

source, typical of security

applications and difficult to manage since

the input cannot be preprocessed to

obtain locality. We consider shared-

memory architectures (Niagara 2, x86

multiprocessors, and Cray

XMT) and distributed-

memory architectures with homogeneous

(InfiniBand cluster of x86 multicourse) or

heterogeneous processing elements

(InfiniBand cluster of x86 multicourse

with NVIDIA Tesla C1060 GPUs). We

describe how each solution achieves the

objectives of supporting large

dictionaries, sustaining high

performance, and enabling

customization and flexibility using

various data sets.

IEEE 2012

TTASTJ78 Semantic-Aware Metadata

Organization Paradigm in

Next-Generation File

Systems

Existing data

storage systems based on the hierarchical directory-

tree organization do not meet the scalability and functionality

requirements for exponentially growing data sets and

increasingly

complex metadata queries inlarge-scale, Exabyte-

level file systems with billions of files. This paper proposes a

novel decentralized semantic-aware metadata organization,

IEEE 2012

called Smart Store, which exploits

semantics of files' metadata to judiciously aggregate

correlated files into semantic-aware groups by using

information retrieval tools. The key idea of Smart Store is to limit

the search scope of a complex metadata query to a

single or a minimal number of semantically correlated groups

and avoid or alleviate brute-force

search in the entire system. The decentralized design of Smart

Store can improve system scalability and

reduce query latency for complex queries

TTASTJ79 Enabling Secure and

Efficient Ranked Keyword

Search over Outsourced

Cloud Data

Cloud computing economically enables the

paradigm

of data service outsourcing. However, to protect data privacy,

sensitive cloud data have to be encrypted before outsourced to

the commercial public cloud, which makes

effective data utilization service a very challenging task. Although

traditional searchable encryption techniques allow users to

securely search over encrypted data through keywords, they

support only Boolean search and are not yet sufficient

to meet the

effective data utilization need that is inherently demanded by large

number of users and huge amount of data files in cloud. In

this paper, we define and solve the problem

of secureranked keyword search over encrypted cloud data. Ranked

IEEE 2012

search greatly enhances system

usability by enabling search result relevance ranking instead of

sending undifferentiated results, and further ensures the

file retrieval accuracy. Specifically, we explore the statistical measure

approach, i.e., relevance score, from information retrieval to build

a secure searchable index, and develop a one-to-many

order-preserving mapping

technique to properly protect those sensitive score information.

The resulting design is able to facilitate efficient server-side

ranking without losing keyword privacy. Thorough

analysis shows that our proposed solution enjoys “as-strong-as-

possible” security guarantee compared to previous searchable

encryption schemes, while correctly realizing the goal

of ranked keyword search. Extensive experimental results

demonstrate the efficiency of the

proposed solution

TTASTD80 Bloom Cast: Efficient and

Effective Full-Text

Retrieval in Unstructured

P2P Networks

Efficient and effective full-

text retrieval in unstructured peer-to-peer networks remains a

challenge in the research community. First, it is difficult, if

not impossible, for unstructured P2P systems to

effectively locate items with

guaranteed recall. Second, existing schemes to improve

search success rate often rely on replicating a large number of item

replicas across the wide area network, incurring a large

amount of communication and storage costs. In this paper, we

IEEE 2012

propose BloomCast,

an efficient and effective full-text retrieval scheme, inunstructu

red P2P networks. By leveraging a hybrid P2P protocol, BloomCast

replicates the items uniformly at random across the P2P networks,

achieving a guaranteed recall at a communication cost of O(√N),

where N is the size of the network. Furthermore,

by casting Bloom Filters instead of

the raw documents across the network, BloomCast

significantly reduces the communication and storage costs

for replication. We demonstrate the power of BloomCast design

through both mathematical proof and comprehensive

simulations based on the query logs from a major commercial

search engine and NIST TREC WT10G data collection. Results

show that BloomCast achieves an average query recall of 91

percent, which outperforms the

existing WP algorithm by 18 percent, while BloomCast greatly

reduces the search latency for query processing by 57 percent.

.

TTASTD81 A Systematic Approach

toward Automated

Performance Analysis and

Tuning

High productivity is critical in

harnessing the power of high-performance computing systems

to solve science andengineering

problems. It is a challenge to bridge the gap between the

hardware complexity and the software limitations. Despite

significant progress in

IEEE 2012

programming language,

compiler, and performance tools, tuning an application remains

largely a manual task, and is done mostly by experts. In this paper,

we propose a systematicapproach toward auto

mated performance analysis and tuning that we expect to improve

the productivity ofperformance debugging

significantly. Our approach seeks

to build a framework that facilitates the combination of

expert knowledge, compiler techniques, and performance rese

arch for performance diagnosis and sol

ution discovery. With our framework, once a

diagnosis and tuning strategy has been developed, it can be stored

in an open and extensible database and thus be reused in

the future. We demonstrate the effectiveness of

ourapproach through

the automated performance analysis and tuning of two scientific

applications. We show that the tuning process is

highly automated, and the performance improvement is significant.

TTASTJ82 ST-CDP: Snapshots in

TRAP for Continuous

Data Protection

Continuous Data Protection (CDP)

has become increasingly important as digitization

continues. This paper presents a new architecture and an

implementation of CDP in Linux kernel. The new architecture

takes advantages of both traditional snapshot technology

and recent Timely Recovery to

IEEE 2012

Any Point-in-time (TRAP)

architecture [CHECK END OF SENTENCE]. The idea is to

periodically insert snapshots within the parity

logs of changed data blocks in order to

ensure fast and reliable data recovery in case of

failures. A mathematical model is developed as a guide to designers

to determine when and how to

insert snapshots to optimize performance interms of space

usage and recovery time. Based on the mathematical model, we

have designed and implemented a CDP module in the Linux

system. Our implementation is at block level as a device driver that

is capable of recovering data to any point-in-time in case of

various failures. Extensive experiments have been carried

out to show that the implementation is fairly robust

and numerical results

demonstrate that the implementation is efficient.

DOMAIN :DEPENDABLE AND SECURE COMPUTING

TTASTJ83 Detecting and Resolving

Firewall Policy Anomalies

The advent of emerging computing technologies such as

service-oriented architecture and cloud computing

has enabled us to perform business services more

efficiently and effectively.

However, we still suffer from unintended security leakages by

unauthorized actions in business services. Firewalls are the most

widely deployed security mechanism to ensure the security

IEEE 2012

of private networks in most

businesses and institutions. The effectiveness of security

protection provided by a firewall mainly depends on the

quality of policy configured in the firewall. Unfortunately,

designing and managing firewall policies are often error prone due

to the complex nature of firewall configurations as well as the lack

of systematic analysis

mechanisms and tools. In this paper, we represent an

innovative policy anomaly management framework for firewalls,

adopting a rule-based segmentation technique to

identify policy anomalies and derive effective anomaly resolutions.

In particular, we articulate a grid-based representation technique,

providing an intuitive cognitive sense about policy anomaly. We

also discuss a proof-of-concept implementation of a visualization-

based firewall policy analysis tool

called Firewall Anomaly Management Environment

(FAME). In addition, we demonstrate how efficiently our

approach can discover and resolve anomalies in firewall polici

es through rigorous experiments.

TTASTJ84 Double Guard: Detecting

Intrusions in Multitier

Web Applications

Internet services and applications have become an

inextricable part of daily life, enabling communication and the

management of personal information from anywhere. To

accommodate this increase in application and data

complexity, web services have

IEEE 2012

moved to a multitier design

wherein the web server runs the application front-end logic and

data are outsourced to a database or file server. In this paper, we

present Double Guard, an IDS system that models the network

behavior of user sessions across both the front-end web server and

the back-end database. By monitoring both web and

subsequent database requests, we

are able to ferret out attacks that independent IDS would not be

able to identify. Furthermore, we quantify the limitations of

any multitierIDS in terms of training sessions and functionality

coverage. We implemented Double Guard using an Apache

web server with MySQL and lightweight virtualization. We then

collected and processed real-world traffic over a 15-day period of

system deployment in both dynamic and

static web applications. Finally,

using DoubleGuard, we were able to expose a wide range of attacks

with 100 percent accuracy while maintaining 0 percent false

positives for static web services and 0.6 percent false positives for

dynamic web services.

TTASTD85 Automatic

Reconfiguration for

Large-Scale Reliable

Storage Systems

Byzantine-fault-tolerant replication enhances the

availability and reliability of Internet services that store critical

state and preserve it despite attacks or software errors.

However, existing Byzantine-fault-tolerant storage systems either

assume a static set of replicas, or

IEEE 2012

have limitations in how they

handle reconfigurations (e.g., in terms of the scalability of the

solutions or the consistency levels they provide). This can be

problematic in long-lived, large-scale systems where system mem

bership is likely to change during the system lifetime. In this paper,

we present a complete solution for dynamically

changing system membership in

a large-scale Byzantine-fault-tolerant system. We present a

service that tracks system membership and

periodically notifies other system nodes of membership changes.

The membership service runs mostly automatically, to avoid

human configuration errors; is itself Byzantine-fault-tolerant and

reconfigurable; and provides applications with a sequence of

consistent views of the system membership. We

demonstrate the utility of this

membership service by using it in a novel distributed hash table

called dBQS that provides atomic semantics even across changes in

replica sets. dBQS is interesting in its own right because

its storage algorithms extend existing Byzantine quorum

protocols to handle changes in the replica set, and because it differs

from previous DHTs by providing Byzantine fault tolerance and

offering strong semantics. We implemented the membership

service and dBQS. Our results

show that the approach works well, in practice: the membership

service is able to manage

a large system and the cost to change the system membership is

low.

DOMAIN :SERVICS COMPUTING

TTASTD86 Dynamic Authentication

for Cross-Realm SOA-

Based Business Processes

Modern distributed applications

are embedding an increasing degree of dynamism,

from dynamic supply-chain management, enterprise

federations, and virtual collaborations

to dynamic resource acquisitions and service interactions across

organizations. Such dynamism leads to new challenges in

security and dependability. Collaborating services in a system

with a Service-Oriented Architecture (SOA) may belong to

different security realms but often

need to be engaged dynamically at runtime. If their security

realms do not have a directcross-realm authentication relationship,

it is technically difficult to enable any secure collaboration between

the services. A potential solution to this would be to locate

intermediate realms at runtime, which serve as an

authentication path between the two separate realms. However,

the process of generating an authentication path for two

distributed services can be highly

complicated. It could involve a large number of extra operations

for credential conversion and require a long chain of invocations

to intermediate services. In this paper, we address this problem

by designing and implementing a

IEEE 2012

new cross-

realm authentication protocol for dynamic service

interactions, based on the notion of service-oriented

multiparty business sessions. Our protocol requires neither

credential conversion nor establishment of

any authentication path between the participating services in

a business session. The

correctness of the protocol is formally analyzed and proven,

and an empirical study is performed using two production-

quality Grid systems, Globes 4 and CROWN. The experimental

results indicate that the proposed protocol and its implementation

have a sound level of scalability and impose only a limited degree

of performance overhead, which is for example comparable with

those security-related overheads in Globes 4.

TTASTD87 A Proxy-Based

Architecture for Dynamic

Discovery and Invocation

of Web Services from

Mobile Devices

Mobile devices are getting more

pervasive, and it is becoming

increasingly necessary to

integrate web servicesinto

applications that run on

these devices. We introduce a novel

approach for dynamically

invoking web servicemethods from m

obile devices with minimal user

intervention that only involves

entering a search

phrase andvalues for the method

parameters.

The architecture overcomes technical

challenges that involve consuming

IEEE 2012

discovered services dynamically by

introducing a man-in-the-middle

(MIM) server that

provides a web servicewhose

responsibility is to discover

needed services and build the client-

side proxies at runtime.

The architecturemoves to the MIM

server energy-consuming tasks that

would otherwise run on

the mobile device. Such tasks involve

communication with servers over the

Internet, XML-

parsing of files, and on-the-fly

compilation of source code. We

perform extensive evaluations of the

system performance to measure

scalability as it relates to the

capacity of the MIM server in

handling mobile client

requests, and device battery power

savings resulting fromdelegating

the service discovery tasks to the

server.

DOMAIN:SOFTWARE ENGINEERING

TTASTJ88 Automatically

Generating Test Cases for

Specification Mining

Dynamic specification mining obse

rves program executions to infer models of normal program

behavior. What makes us believe that we have seen sufficiently

many executions ? The TAUTOKO (“Tautoko” is the Maori word

for “enhance, enrich.”) type state miner generates test cases that

cover previously unobserved

behavior, systematically extending the execution space,

and enriching the specification. To our knowledge, this is the first

combination of

IEEE 2012

systematic test case generation

and type state mining-a combination with clear benefits:

On a sample of 800 defects seeded into six Java subjects, a

static type state verifier fed with enriched models would report

significantly more true positives and significantly fewer false

positives than the initial models.

TTASTD89 Automatic Detection of

Unsafe Dynamic

Component Loadings

Dynamic loading of software components (e.g., libraries or modules)

is a widely used mechanism for an

improved system modularity and flexibility.

Correct component resolution is critical for reliable and secure

software execution. However, programming mistakes may lead

to unintended or even malicious components being

resolved and loaded. In particular, dynamic loading can be

hijacked by placing an arbitrary file with the specified name in a

directory searched before resolving the target component.

Although this issue has been

known for quite some time, it was not considered serious because

exploiting it requires access to the local file system on the vulnerable

host. Recently, such vulnerabilities have started to

receive considerable attention as their remote exploitation became

realistic. It is now important to detect and fix these

vulnerabilities. In this paper, we present the first automated

technique to detect vulnerable and unsafe dynamic component lo

adings. Our analysis has two

phases: 1) apply dynamic binary

instrumentation to collect runtime information

on component loading (online phase), and 2) analyze the

collected information to detect vulnerable component loadings (of

fline phase). For evaluation, we implemented our technique to

detect vulnerable and unsafe component loadings in

popular software on Microsoft

Windows and Linux. Our evaluation results show

that unsafe component loading is prevalent in software on both OS

platforms, and it is more severe on Microsoft Windows. In

particular, our tool detected more than

4,000 unsafe component loadings in our evaluation, and some can

lead to remote code execution on Microsoft Windows.

DOMAIN:PERVASIVE COMPUTING

TTASTJ90 Advertising on Public

Display Networks

For advertising-

based public display networks to

become truly pervasive, they must

provide a tangible social benefit and

be engaging without being obtrusive,

blending advertisements with

informative content.

IEEE 2012

DOMAIN: IT IN BIOMEDICINE / FRENSICS AND SECURITY

TTASTJ91 SparkMed: A Framework

for Dynamic Integration of

Multimedia Medical Data

Into Distributed m-Health

Systems

With the advent of 4G and other long-term evolution (LTE) wireless

networks, the traditional

boundaries of patient record propagation are diminishing as

networking technologies extend the reach of hospital

IEEE 2012

infrastructure and provide on-

demand mobile access to medical multimedia data.

However, due to legacy and proprietary software, storage and

decommissioning costs, and the price of centralization and

redevelopment, it remains complex, expensive, and often

unfeasible for hospitals to deploy their infrastructure for online and

mobile use. This paper proposes

the Sparked data integration framework for mobile healthcare (m-

Health), which significantly benefits from the enhanced

network capabilities of LTE wireless technologies, by

enabling a wide range of heterogeneous medical s

oftware and database systems (such as the

picture archiving and communication systems, hospital

information system, and reporting systems) to be

dynamically

integrated into acloud-like peer-to-peer multimedia data store.

Our framework allows medical data applications to share data with

mobile hosts over a wireless network (such as Wi-Fi and 3G),

by binding to existing software systems and deploying

them as m-Health applications. SparkMed int

egrates techniques from multimedia streaming, rich

Internet applications (RIA), and remote procedure call

(RPC) frameworks to

construct a Self-managing, Pervasive Automated

network for Medical Enterprise Da

ta (Sparked). Further, it is resilient to failure, and able to use

mobile and handheld devices to maintain its network, even in the

absence of dedicated server devices. We have

developed a prototype of the SparkMed framework for evaluation

on a radiological workflow simulation, which

uses SparkMed to

deploy a radiological image viewer as an m-Health application for tele

medical use by radiologists and stakeholders. We have evaluated

our prototype using ten devices over Wi-Fi and 3G, verifying that

our framework meets its two main objectives: 1) interactive-

delivery of medical multimedia data to mobile devices; and 2)

attaching to non-networked medical software

processes without significantly impacting their performance.

Consistent response

times of under 500 ms and graphical frame rates of over 5

frames per second were observed under intended usage conditions.

Further, overhead measurements displayed linear scalability and low

resource requirements.

TTASTJ92 Data Fusion and Cost

Minimization for

Intrusion Detection

Statistical pattern recognition techniques have recently been

shown to provide a finer balance between misdetections and false

alarms than the more conventional intrusion detection a

pproaches, namely misuse detection and anomaly detection.

A variety of classical machine

IEEE 2011 IEEE

2012

learning and pattern recognition

algorithms has been applied to intrusion detection with varying

levels of success. We make two observations about intrusion

detection. One is that intrusion detection is

significantly more effective by using multiple sources of

information in an intelligent way, which is precisely what human

experts rely on. Second, different

errors in intrusion detection have different costs associated with

them-a simplified example being that a false alarm may be more

expensive than a misdetection and, hence, the true

objective function to be minimized is the cost of errors and not the

error rate itself. We present a pattern recognition approach that

addresses both of these issues. It utilizes an ensemble of a

classifiers approach to intelligently combine information from multiple

sources and is explicitly tuned

toward minimizing the cost of the errors as opposed to the error

rate itself. The information fusion approach learning alone is

shown to achieve state-of-the-art performances better than those

reported in the literature so far, and the cost minimization stra

tegy dCMS further reduces the cost with a significant margin.

DOMAIN: GRID COMPUTING

TTASTJ93 Leveraging a Compound

Graph-Based DHT for

Multi-Attribute Range

Queries with

Performance Analysis

Resource discovery is critical to the usability and accessibility of

grid computing systems. Distributed Hash Table (DHT) has

been applied to grid systems

IEEE 2012

as a distributed

mechanism for providing scalable range-query and multi-

attribute resource discovery. Multi-DHT-

based approaches depend on multiple DHT networks with each

network responsible for a single attribute.

Single-DHT-based approaches keep the resource information of

all attributes in a single node.

Both classes of approaches lead to high overhead. In this paper, we

propose a Low-Overhead Range-query Multi-

attribute (LORM) DHT-based resource discovery

approach. Unlike other DHT-based approaches, LORM relies

on a single compound graph-based DHT network and

distributes resource information among nodes in balance by taking

advantage of the compound graph structure.

Moreover, it has high capability to

handle the large-scale and dynamic characteristics of

resources in grids. Experimental results demonstrate the efficiency

of LORM in comparison with other resource discovery approaches.

LORM dramatically reduces maintenance and resource

discovery overhead. In addition, it yields significant improvements in

resource location efficiency. We also analyze the performance of

the LORM approach rigorously by comparing it with othermulti-DHT-

based and single-DHT-

based approaches with respect to their overhead and efficiency. The

analytical results are

consistent with experimental results, and prove the superiority

of the LORM approach in theory.

TTASTJ94 Locality-Preserving

Clustering and Discovery

of Resources in Wide-Area

Distributed

Computational Grids

In large-

scale computational Grids, discovery of heterogeneous resources as

a working group is crucial to achieving scalable performance.

This paper presents a resource management scheme

including a hierarchical cycloid overlay

architecture, resource clustering and discovery algorithms for wide-

area distributed Grid systems. We establish

program/data locality by clustering resources based on their

physical proximity and functional

matching with user applications. We further develop dynamism-

resilient resource management algorithm, cluster-token

forwarding algorithm, and deadline-

driven resource management algorithms. The advantage of the

proposed scheme lies in low overhead, fast and dynamism-

resilient multiresource discovery. The paper presents the scheme,

new performance metrics, and experimental

simulation results. This scheme

compares favorably with other resource discovery methods

in static and dynamic Grid applications. In particular, it supports

efficient resource clustering, reduces communications

cost, and enhances resource discovery success rate in promoting

IEEE 2012

large-

scale distributed supercomputing applications.

TTASTJ95 Online System for Grid

Resource Monitoring and

Machine Learning-Based

Prediction

Resource allocation and job

scheduling are the core functions

of grid computing. These functions

are based on adequate information of

available resources. Timely

acquiring resource status information

is of great importance in ensuring

overall performance

of grid computing. This work aims at

building a distributed system for grid

resource monitoring and prediction.

In this paper, we present the

design and evaluation of

a system architecturefor grid resourc

e monitoring and prediction. We

discuss the key

issues for system implementation,

includingmachine learning-

based methodologies for modeling an

d optimization

of resource prediction models.

Evaluations are performed on a

prototype system. Our experimental

results indicate that the

efficiency and accuracy of

oursystem meet the demand

of online system for grid resource mo

nitoring and prediction.

IEEE 2012

TTASTJ96 Real-Time Head

and Hand Tracking Based

on 2.5D Data

A novel real-time algorithm

for head and hand tracking is proposed in this paper. This

approach is based on 2.5Ddata from a

range camera, which is exploited to resolve

IEEE 2012

ambiguities and overlaps.

Experimental results show high robustness against partial

occlusions and fast movements. The estimated positions are fairly

stable, allowing the extraction of accurate trajectories which may

be used for gesture classification purposes

DOMAIN: MATLAB

TTASTM97 Semantic Image Retrieval

in Magnetic Resonance

Brain Volumes

Practitioners in the area of neurology

often need to retrieve

multimodal magnetic resonance (MR)

images of thebrain to study disease

progression and to correlate

observations across multiple

subjects. In this paper, a novel

technique for retrieving 2-D

MR images (slices) in 3-

D brain volumes is proposed. Given a

2-D MR query slice, the technique

identifies the 3-D volume among

multiple subjects in the database,

associates the query slice with a

specific region of the brain, and

retrieves the matching slice within

this region in the identified volumes.

The proposed technique is capable of

retrieving an image in multimodal

and noisy scenarios. In this study,

support vector machines (SVM) are

used for identifying 3-D

MR volume and for

performing semantic classification of

the human brain into

various semantic regions. In order to

achieve

reliable image retrieval performance i

n the presence of misalignments,

an image registration-

based retrieval framework is

IEEE 2012

developed. The

proposed retrievaltechnique is tested

on various modalities. The test

results reveal superior robustness

performance with respect to

accuracy, speed, and multimodality.

PROJECT

ID:

TTASTM98

(2005)

Drowsiness Detection

based on Eye Movement,

Yawn Detection and

Head Rotation

IEEE 2012

TTASTM99

(2010)

Automatic Detection of

Geospatial objects using

Taxonomic Semantics

In this letter, we propose a novel

method to solve the problem of detecting geospatial o

bjects present in high-resolution remote sensing images

automatically. Each image is represented as a segmentation

tree by applying a multi scale segmentation algorithm at first,

and all of the tree nodes are

described as coherent groups instead of binary classified values.

The trees are matched to select the maximally matched sub trees,

denoted as common subcategories. Then, we organize

these subcategories to learn the embedded taxonomic semantics o

f objects categories, which allow categories to be defined

recursively, and express both explicit and implicit spatial

configuration of categories. Detection, recognition, and

segmentation of the geospatial ob

IEEE 2012

jects in a new image can be

simultaneously conducted by using the

learned taxonomic semantics. This procedure also provides a

meaningful explanation for image understanding. Experiments for

complex and compound objects demonstrate

the precision, robustness, and effectiveness of the proposed

method.

TTASTM10

0

Improved Iris recognition

through fusion of

hamming distance &

fragile bit distance

The most common iris biometric algorithm represents the

texture of an iris using a binary iris code. Not all bits in

an iris code are equally consistent. A bit is

deemed fragile if its value

changes across iris codes created from different images of the

same iris. Previous research has shown

that iris recognition performance can be improved by masking

these fragile bits. Rather than ignoring fragile bits completely,

we consider what beneficial information can be obtained from

the fragile bits. We find that the locations of fragile bits tend to be

consistent across different iris codes of the same

eye. We present a metric, called

the fragile bit distance, which quantitatively measures the

coincidence of the fragile bit patterns in two iris codes. We find that

score fusion of fragile bit distance and Hamming distance w

orks better

TTASTM10

1

Iris Recognition Using

Possibility Fuzzy

Matching on Local

Features

In this paper, we propose a novel

possibilistic fuzzy matching strategy with invariant properties,

which can provide a robust and effective matching scheme for two

sets of iris feature points. In addition, the nonlinear

normalization model is adopted to provide more accurate position

before matching. Moreover, an effective irissegmentation method

is proposed to refine the detected

inner and outer boundaries to smooth curves.

Forfeature extraction, the Gabor filters are adopted to detect

the local feature points from the segmented iris image in the

Cartesian coordinate system and to generate a rotation-invariant

descriptor for each detected point. After that, the

proposed matching algorithm is used to compute a similarity

score for two sets of feature points from a pair

of iris images. The experimental

results show that the performance of our system is better than those

of the systems based on the local features and is

comparable to those of the typical systems.

IEEE 2012

TTASTM10

2

A change Information

Based Fast Algorithm

for Video Object

Detection and Tracking

In this paper, we

present a novel algorithm for moving object detection and tracking.

The proposed algorithm includes two schemes: one for spatio-

temporal spatial segmentation and the

other for temporal segmentation. A combination of

IEEE 2012

these schemes is used to identify

moving objects and to track them. A compound Markov random field

(MRF) model is used as the prior image attribute model, which

takes care of the spatial distribution of color, temporal

color coherence and edge map in the temporal frames to

obtain a spatio-temporal spatial segmentation. In this scheme,

segmentation is considered

as a pixel labeling problem and is solved using the

maximum posteriori probability (MAP) estimation technique. The

MRF-MAP framework is computation intensive due to

random initialization. To reduce this burden, we

propose change information based heuristic initialization technique.

The scheme requires an initially segmented frame. For initial

frame segmentation, compound MRF model is used to model

attributes and MAP estimate is

obtained by a hybrid algorithm [combinatio

n of both simulated annealing (SA) and iterative conditional

mode (ICM)] that converges fast. For temporal

segmentation, instead of using a gray level

difference based change detection mask (CDM), we

propose a CDM based on label difference of two frames. The

proposed scheme resulted in less effect of silhouette.

Further, a combination of both

spatial and temporal segmentation process is used to

detect the moving objects. Results

of the proposed spatial segmentation approach are

compared with those of JSEG method, and edgeless and edge

based approaches of segmentation. It is noticed that

the proposed approach provides a better spatial

segmentation