Java Titles abstracts 2013-2014

IEEE- Project Title 2013

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33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551

Mobile: 9025439777/ www.sybiantechnologies.com / [email protected]

JAVA

BIO MEDICAL / MEDICAL IMAGING

1. Simultaneously Identifying All True Vessels From Segmented Retinal

Images

ABSTRACT:

Measurements of retinal blood vessel morphology have been shown to be

related to the risk of cardiovascular diseases. The wrong identification of vessels

may result in a large variation of these measurements, leading to a wrong clinical

diagnosis. In this paper, we address the problem of automatically identifying true

vessels as a postprocessing step to vascular structure segmentation. We model the

segmented vascular structure as a vessel segment graph and formulate the problem

of identifying vessels as one of finding the optimal forest in the graph given a set

of constraints. We design a method to solve this optimization problem and evaluate

it on a large real-world dataset of 2446 retinal images. Experiment results are

analyzed with respect to actual measurements of vessel morphology. The results

show that the proposed approach is able to achieve 98.9% pixel precision and

98.7% recall of the true vessels for clean segmented retinal images, and remains

robust even when the segmented image is noisy.


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2. A modified fuzzy c-means algorithm for bias field estimation and

segmentation of MRI data

ABSTRACT:

We present a novel algorithm for fuzzy segmentation of magnetic resonance

imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic.

MRI intensity inhomogeneities can be attributed to imperfections in the radio-

frequency coils or to problems associated with the acquisition sequences. The

result is a slowly varying shading artifact over the image that can produce errors

with conventional intensity-based classification. Our algorithm is formulated by

modifying the objective function of the standard fuzzy c-means (FCM) algorithm

to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel)

to be influenced by the labels in its immediate neighborhood. The neighborhood

effect acts as a regularizer and biases the solution toward piecewise-homogeneous

labelings. Such a regularization is useful in segmenting scans corrupted by salt and

pepper noise. Experimental results on both synthetic images and MR data are given

to demonstrate the effectiveness and efficiency of the proposed algorithm.

3. Feature-Based Image Patch Approximation for Lung Tissue

Classification

ABSTRACT:

In this paper, we propose a new classification method for five categories of

lung tissues in high-resolution computed tomography (HRCT) images, with

feature-based image patch approximation. We design two new feature descriptors


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for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary

patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented

gradients (MCHOG) gradient descriptor. Together with intensity features, each

image patch is then labeled based on its feature approximation from reference

image patches. And a new patch-adaptive sparse approximation (PASA) method is

designed with the following main components: minimum discrepancy criteria for

sparse-based classification, patch-specific adaptation for discriminative

approximation, and feature-space weighting for distance computation. The patch-

wise labelings are then accumulated as probabilistic estimations for region-level

classification. The proposed method is evaluated on a publicly available ILD

database, showing encouraging performance improvements over the state-of-the-

arts.

4. Automatic Segmentation of the Pulmonary Lobes From Chest CT Scans

Based on Fissures, Vessels, and Bronchi

ABSTRACT:

Segmentation of the pulmonary lobes is relevant in clinical practice and

particularly challenging for cases with severe diseases or incomplete fissures. In

this work, an automated segmentation approach is presented that performs a

marker-based watershed transformation on computed tomography (CT) scans to

subdivide the lungs into lobes. A cost image for the watershed transformation is

computed by combining information from fissures, bronchi, and pulmonary

vessels. The lobar markers are calculated by an analysis of the automatically

labeled bronchial tree. By integration of information from several anatomical


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structures the segmentation is made robust against incomplete fissures. For

evaluation the method was compared to a recently published method on 20 CT

scans with no or mild disease. The average distances to the reference segmentation

were 0.69, 0.67, and 1.21 mm for the left major, right major, and right minor

fissure, respectively. In addition the results were submitted to LOLA11, an

international lung lobe segmentation challenge with publically available data

including cases with severe diseases. The average distances to the reference for the

55 CT scans provided by LOLA11 were 0.98, 3.97, and 3.09 mm for the left major,

right major, and right minor fissure. Moreover, an analysis of the relation between

segmentation quality and fissure completeness showed that the method is robust

against incomplete fissures.


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CLOUD COMPUTING

1. Efficient Resource Mapping Framework over Networked Clouds via

Iterated Local Search-Based Request Partitioning

ABSTRACT:

The cloud represents a computing paradigm where shared configurable

resources are provided as a service over the Internet. Adding intra- or intercloud

communication resources to the resource mix leads to a networked cloud

computing environment. Following the cloud infrastructure as a Service paradigm

and in order to create a flexible management framework, it is of paramount

importance to address efficiently the resource mapping problem within this

context. To deal with the inherent complexity and scalability issue of the resource

mapping problem across different administrative domains, in this paper a

hierarchical framework is described. First, a novel request partitioning approach

based on Iterated Local Search is introduced that facilitates the cost-efficient and

online splitting of user requests among eligible cloud service providers (CPs)

within a networked cloud environment. Following and capitalizing on the outcome

of the request partitioning phase, the embedding phase-where the actual mapping

of requested virtual to physical resources is performed can be realized through the

use of a distributed intracloud resource mapping approach that allows for efficient

and balanced allocation of cloud resources. Finally, a thorough evaluation of the

proposed overall framework on a simulated networked cloud environment is

provided and critically compared against an exact request partitioning solution as

well as another common intradomain virtual resource embedding solution.


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2. Hessian Regularized Support Vector Machines for Mobile Image

Annotation on the Cloud

ABSTRACT:

With the rapid development of the cloud computing and mobile service,

users expect a better experience through multimedia computing, such as automatic

or semi-automatic personal image and video organization and intelligent user

interface. These functions heavily depend on the success of image understanding,

and thus large-scale image annotation has received intensive attention in recent

years. The collaboration between mobile and cloud opens a new avenue for image

annotation, because the heavy computation can be transferred to the cloud for

immediately responding user actions. In this paper, we present a scheme for image

annotation on the cloud, which transmits mobile images compressed by Hamming

compressed sensing to the cloud and conducts semantic annotation through a novel

Hessian regularized support vector machine on the cloud. We carefully explained

the rationality of Hessian regularization for encoding the local geometry of the

compact support of the marginal distribution and proved that Hessian regularized

support vector machine in the reproducing kernel Hilbert space is equivalent to

conduct Hessian regularized support vector machine in the space spanned by the

principal components of the kernel principal component analysis. We conducted

experiments on the PASCAL VOC'07 dataset and demonstrated the effectiveness

of Hessian regularized support vector machine for large-scale image annotation.


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3. An Adaptive Cloud Downloading Service

ABSTRACT:

Video content downloading using the P2P approach is scalable, but does not

always give good performance. Recently, subscription-based premium services

have emerged, referred to as cloud downloading. In this service, the cloud storage

and server caches user-interested content and updates the cache based on user

downloading requests. If a requested video is not in the cache, the request is held in

a waiting state until the cache is updated. We call this design server mode. An

alternative design is to let the cloud server serve all downloading requests as soon

as they arrive, behaving as a helper peer. We call this design helper mode. Our

model and analysis show that both these designs are useful for certain operating

regimes. The helper mode is good at handling a high request rate, while the server

mode is good at scaling with video population size. We design an adaptive

algorithm (AMS) to select the service mode automatically. Intuitively, AMS

switches service mode from server mode to helper mode when too many peers

request blocked movies, and vice versa. The ability of AMS to achieve good

performance in different operating regimes is validated by simulation .

4. A Highly Practical Approach toward Achieving Minimum Data Sets

Storage Cost in the Cloud

ABSTRACT:

Massive computation power and storage capacity of cloud computing

systems allow scientists to deploy computation and data intensive applications


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without infrastructure investment, where large application data sets can be stored

in the cloud. Based on the pay-as-you-go model, storage strategies and

benchmarking approaches have been developed for cost-effectively storing large

volume of generated application data sets in the cloud. However, they are either

insufficiently cost-effective for the storage or impractical to be used at runtime. In

this paper, toward achieving the minimum cost benchmark, we propose a novel

highly cost-effective and practical storage strategy that can automatically decide

whether a generated data set should be stored or not at runtime in the cloud. The

main focus of this strategy is the local-optimization for the tradeoff between

computation and storage, while secondarily also taking users' (optional)

preferences on storage into consideration. Both theoretical analysis and simulations

conducted on general (random) data sets as well as specific real world applications

with Amazon's cost model show that the cost-effectiveness of our strategy is close

to or even the same as the minimum cost benchmark, and the efficiency is very

high for practical runtime utilization in the cloud.

5. AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and

Efficient Social Video Sharing in the Clouds

ABSTRACT:

While demands on video traffic over mobile networks have been souring,

the wireless link capacity cannot keep up with the traffic demand. The gap between

the traffic demand and the link capacity, along with time-varying link conditions,

results in poor service quality of video streaming over mobile networks such as

long buffering time and intermittent disruptions. Leveraging the cloud computing


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technology, we propose a new mobile video streaming framework, dubbed AMES-

Cloud, which has two main parts: adaptive mobile video streaming (AMoV) and

efficient social video sharing (ESoV). AMoV and ESoV construct a private agent

to provide video streaming services efficiently for each mobile user. For a given

user, AMoV lets her private agent adaptively adjust her streaming flow with a

scalable video coding technique based on the feedback of link quality. Likewise,

ESoV monitors the social network interactions among mobile users, and their

private agents try to prefetch video content in advance. We implement a prototype

of the AMES-Cloud framework to demonstrate its performance. It is shown that

the private agents in the clouds can effectively provide the adaptive streaming, and

perform video sharing (i.e., prefetching) based on the social network analysis.

6. Efficient Resource Provisioning and Rate Selection for Stream Mining

in a Community Cloud

ABSTRACT:

Real-time stream mining such as surveillance and personal health

monitoring, which involves sophisticated mathematical operations, is computation-

intensive and prohibitive for mobile devices due to the hardware/computation

constraints. To satisfy the growing demand for stream mining in mobile networks,

we propose to employ a cloud-based stream mining system in which the mobile

devices send via wireless links unclassified media streams to the cloud for

classification. We aim at minimizing the classification-energy cost, defined as an

affine combination of classification cost and energy consumption at the cloud,

subject to an average stream mining delay constraint (which is important in real-


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time applications). To address the challenge of time-varying wireless channel

conditions without a priori information about the channel statistics, we develop an

online algorithm in which the cloud operator can dynamically adjust its resource

provisioning on the fly and the mobile devices can adapt their transmission rates to

the instantaneous channel conditions. It is proved that, at the expense of increasing

the average stream mining delay, the online algorithm achieves a classification-

energy cost that can be pushed arbitrarily close to the minimum cost achieved by

the optimal offline algorithm. Extensive simulations are conducted to validate the

analysis.

7. ptimal Multiserver Configuration for Profit Maximization in Cloud

Computing

ABSTRACT:

As cloud computing becomes more and more popular, understanding the

economics of cloud computing becomes critically important. To maximize the

profit, a service provider should understand both service charges and business

costs, and how they are determined by the characteristics of the applications and

the configuration of a multiserver system. The problem of optimal multiserver

configuration for profit maximization in a cloud computing environment is studied.

Our pricing model takes such factors into considerations as the amount of a

service, the workload of an application environment, the configuration of a

multiserver system, the service-level agreement, the satisfaction of a consumer, the

quality of a service, the penalty of a low-quality service, the cost of renting, the

cost of energy consumption, and a service provider's margin and profit. Our


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approach is to treat a multiserver system as an M/M/m queuing model, such that

our optimization problem can be formulated and solved analytically. Two server

speed and power consumption models are considered, namely, the idle-speed

model and the constant-speed model. The probability density function of the

waiting time of a newly arrived service request is derived. The expected service

charge to a service request is calculated. The expected net business gain in one unit

of time is obtained. Numerical calculations of the optimal server size and the

optimal server speed are demonstrated.

8. CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring

ABSTRACT:

Cloud-assisted mobile health (mHealth) monitoring, which applies the

prevailing mobile communications and cloud computing technologies to provide

feedback decision support, has been considered as a revolutionary approach to

improving the quality of healthcare service while lowering the healthcare cost.

Unfortunately, it also poses a serious risk on both clients' privacy and intellectual

property of monitoring service providers, which could deter the wide adoption of

mHealth technology. This paper is to address this important problem and design a

cloud-assisted privacy preserving mobile health monitoring system to protect the

privacy of the involved parties and their data. Moreover, the outsourcing

decryption technique and a newly proposed key private proxy reencryption are

adapted to shift the computational complexity of the involved parties to the cloud

without compromising clients' privacy and service providers' intellectual property.


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Finally, our security and performance analysis demonstrates the effectiveness of

our proposed design.

9. Cloud-Based Image Coding for Mobile Devices—Toward Thousands to

One Compression

ABSTRACT:

Current image coding schemes make it hard to utilize external images for

compression even if highly correlated images can be found in the cloud. To solve

this problem, we propose a method of cloud-based image coding that is different

from current image coding even on the ground. It no longer compresses images

pixel by pixel and instead tries to describe images and reconstruct them from a

large-scale image database via the descriptions. First, we describe an input image

based on its down-sampled version and local feature descriptors. The descriptors

are used to retrieve highly correlated images in the cloud and identify

corresponding patches. The down-sampled image serves as a target to stitch

retrieved image patches together. Second, the down-sampled image is compressed

using current image coding. The feature vectors of local descriptors are predicted

by the corresponding vectors extracted in the decoded down-sampled image. The

predicted residual vectors are compressed by transform, quantization, and entropy

coding. The experimental results show that the visual quality of reconstructed

images is significantly better than that of intra-frame coding in HEVC and JPEG at

thousands to one compression .


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10. Optimizing Cloud Resources for Delivering IPTV Services Through

Virtualization

ABSTRACT:

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. Our results

show that we are able to reduce the load by ~24%(compared to a possible ~31.3%

as predicted by the optimization framework).


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11. A Network and Device Aware QoS Approach for Cloud-Based Mobile

Streaming

ABSTRACT:

Cloud multimedia services provide an efficient, flexible, and scalable data

processing method and offer a solution for the user demands of high quality and

diversified multimedia. As intelligent mobile phones and wireless networks

become more and more popular, network services for users are no longer limited to

the home. Multimedia information can be obtained easily using mobile devices,

allowing users to enjoy ubiquitous network services. Considering the limited

bandwidth available for mobile streaming and different device requirements, this

study presented a network and device-aware Quality of Service (QoS) approach

that provides multimedia data suitable for a terminal unit environment via

interactive mobile streaming services, further considering the overall network

environment and adjusting the interactive transmission frequency and the dynamic

multimedia transcoding, to avoid the waste of bandwidth and terminal power.

Finally, this study realized a prototype of this architecture to validate the feasibility

of the proposed method. According to the experiment, this method could provide

efficient self-adaptive multimedia streaming services for varying bandwidth

environments.


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12. Anchor: A Versatile and Efficient Framework for Resource

Management in the Cloud

ABSTRACT:

We present Anchor, a general resource management architecture that uses

the stable matching framework to decouple policies from mechanisms when

mapping virtual machines to physical servers. In Anchor, clients and operators are

able to express a variety of distinct resource management policies as they deem fit,

and these policies are captured as preferences in the stable matching framework.

The highlight of Anchor is a new many-to-one stable matching theory that

efficiently matches VMs with heterogeneous resource needs to servers, using both

offline and online algorithms. Our theoretical analyses show the convergence and

optimality of the algorithm. Our experiments with a prototype implementation on a

20-node server cluster, as well as large-scale simulations based on real-world

workload traces, demonstrate that the architecture is able to realize a diverse set of

policy objectives with good performance and practicality.

13. Load Rebalancing for Distributed File Systems in Clouds

ABSTRACT:

Distributed file systems are key building blocks for cloud computing

applications based on the MapReduce programming paradigm. In such file

systems, nodes simultaneously serve computing and storage functions; a file is

partitioned into a number of chunks allocated in distinct nodes so that MapReduce

tasks can be performed in parallel over the nodes. However, in a cloud computing


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environment, failure is the norm, and nodes may be upgraded, replaced, and added

in the system. Files can also be dynamically created, deleted, and appended. This

results in load imbalance in a distributed file system; that is, the file chunks are not

distributed as uniformly as possible among the nodes. Emerging distributed file

systems in production systems strongly depend on a central node for chunk

reallocation. This dependence is clearly inadequate in a large-scale, failure-prone

environment because the central load balancer is put under considerable workload

that is linearly scaled with the system size, and may thus become the performance

bottleneck and the single point of failure. In this paper, a fully distributed load

rebalancing algorithm is presented to cope with the load imbalance problem. Our

algorithm is compared against a centralized approach in a production system and a

competing distributed solution presented in the literature. The simulation results

indicate that our proposal is comparable with the existing centralized approach and

considerably outperforms the prior distributed algorithm in terms of load

imbalance factor, movement cost, and algorithmic overhead. The performance of

our proposal implemented in the Hadoop distributed file system is further

investigated in a cluster environment.

14. Towards Trustworthy Resource Scheduling in Clouds

ABSTRACT:

Managing the allocation of cloud virtual machines at physical resources is a

key requirement for the success of clouds. Current implementations of cloud

schedulers do not consider the entire cloud infrastructure neither do they consider

the overall user and infrastructure properties. This results in major security,


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privacy, and resilience concerns. In this paper, we propose a novel cloud scheduler

which considers both user requirements and infrastructure properties. We focus on

assuring users that their virtual resources are hosted using physical resources that

match their requirements without getting users involved with understanding the

details of the cloud infrastructure. As a proof-of-concept, we present our prototype

which is built on OpenStack. The provided prototype implements the proposed

cloud scheduler. It also provides an implementation of our previous work on cloud

trust management which provides the scheduler with input about the trust status of

the cloud infrastructure.


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DATA MINING

1. Anonymization of Centralized and Distributed Social Networks by

Sequential Clustering

ABSTRACT:

We study the problem of privacy-preservation in social networks. We

consider the distributed setting in which the network data is split between several

data holders. The goal is to arrive at an anonymized view of the unified network

without revealing to any of the data holders information about links between nodes

that are controlled by other data holders. To that end, we start with the centralized

setting and offer two variants of an anonymization algorithm which is based on

sequential clustering (Sq). Our algorithms significantly outperform the SaNGreeA

algorithm due to Campan and Truta which is the leading algorithm for achieving

anonymity in networks by means of clustering. We then devise secure distributed

versions of our algorithms. To the best of our knowledge, this is the first study of

privacy preservation in distributed social networks. We conclude by outlining

future research proposals in that direction.

2. A Proxy-Based Approach to Continuous Location-Based Spatial

Queries in Mobile Environments

ABSTRACT:

Caching valid regions of spatial queries at mobile clients is effective in

reducing the number of queries submitted by mobile clients and query load on the


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server. However, mobile clients suffer from longer waiting time for the server to

compute valid regions. We propose in this paper a proxy-based approach to

continuous nearest-neighbor (NN) and window queries. The proxy creates

estimated valid regions (EVRs) for mobile clients by exploiting spatial and

temporal locality of spatial queries. For NN queries, we devise two new algorithms

to accelerate EVR growth, leading the proxy to build effective EVRs even when

the cache size is small. On the other hand, we propose to represent the EVRs of

window queries in the form of vectors, called estimated window vectors (EWVs),

to achieve larger estimated valid regions. This novel representation and the

associated creation algorithm result in more effective EVRs of window queries. In

addition, due to the distinct characteristics, we use separate index structures,

namely EVR-tree and grid index, for NN queries and window queries,

respectively. To further increase efficiency, we develop algorithms to exploit the

results of NN queries to aid grid index growth, benefiting EWV creation of

window queries. Similarly, the grid index is utilized to support NN query

answering and EVR updating. We conduct several experiments for performance

evaluation. The experimental results show that the proposed approach significantly

outperforms the existing proxy-based approaches.

3. Efficient Algorithms for Mining High Utility Itemsets from

Transactional Databases

ABSTRACT:

Mining high utility itemsets from a transactional database refers to the

discovery of itemsets with high utility like profits. Although a number of relevant


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algorithms have been proposed in recent years, they incur the problem of

producing a large number of candidate itemsets for high utility itemsets. Such a

large number of candidate itemsets degrades the mining performance in terms of

execution time and space requirement. The situation may become worse when the

database contains lots of long transactions or long high utility itemsets. In this

paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and

UP-Growth+, for mining high utility itemsets with a set of effective strategies for

pruning candidate itemsets. The information of high utility itemsets is maintained

in a tree-based data structure named utility pattern tree (UP-Tree) such that

candidate itemsets can be generated efficiently with only two scans of database.

The performance of UP-Growth and UP-Growth+ is compared with the state-of-

the-art algorithms on many types of both real and synthetic data sets. Experimental

results show that the proposed algorithms, especially UP-Growth+, not only reduce

the number of candidates effectively but also outperform other algorithms

substantially in terms of runtime, especially when databases contain lots of long

transactions.

4. Multiparty Access Control for Online Social Networks: Model and

Mechanisms

ABSTRACT:

Online social networks (OSNs) have experienced tremendous growth in

recent years and become a de facto portal for hundreds of millions of Internet

users. These OSNs offer attractive means for digital social interactions and

information sharing, but also raise a number of security and privacy issues. While


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OSNs allow users to restrict access to shared data, they currently do not provide

any mechanism to enforce privacy concerns over data associated with multiple

users. To this end, we propose an approach to enable the protection of shared data

associated with multiple users in OSNs. We formulate an access control model to

capture the essence of multiparty authorization requirements, along with a

multiparty policy specification scheme and a policy enforcement mechanism.

Besides, we present a logical representation of our access control model that allows

us to leverage the features of existing logic solvers to perform various analysis

tasks on our model. We also discuss a proof-of-concept prototype of our approach

as part of an application in Facebook and provide usability study and system

evaluation of our method.

5. Discovering Characterizations of the Behavior of Anomalous

Subpopulations

ABSTRACT:

We consider the problem of discovering attributes, or properties, accounting

for the a priori stated abnormality of a group of anomalous individuals (the

outliers) with respect to an overall given population (the inliers). To this aim, we

introduce the notion of exceptional property and define the concept of

exceptionality score, which measures the significance of a property. In particular,

in order to single out exceptional properties, we resort to a form of minimum

distance estimation for evaluating the badness of fit of the values assumed by the

outliers compared to the probability distribution associated with the values

assumed by the inliers. Suitable exceptionality scores are introduced for both


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numeric and categorical attributes. These scores are, both from the analytical and

the empirical point of view, designed to be effective for small samples, as it is the

case for outliers. We present an algorithm, called EXPREX, for efficiently

discovering exceptional properties. The algorithm is able to reduce the needed

computational effort by not exploring many irrelevant numerical intervals and by

exploiting suitable pruning rules. The experimental results confirm that our

technique is able to provide knowledge characterizing outliers in a natural manner.

6. On Identifying Critical Nuggets of Information during Classification

Tasks

ABSTRACT:

In large databases, there may exist critical nuggets-small collections of

records or instances that contain domain-specific important information. This

information can be used for future decision making such as labeling of critical,

unlabeled data records and improving classification results by reducing false

positive and false negative errors. This work introduces the idea of critical nuggets,

proposes an innovative domain-independent method to measure criticality,

suggests a heuristic to reduce the search space for finding critical nuggets, and

isolates and validates critical nuggets from some real-world data sets. It seems that

only a few subsets may qualify to be critical nuggets, underlying the importance of

finding them. The proposed methodology can detect them. This work also

identifies certain properties of critical nuggets and provides experimental

validation of the properties. Experimental results also helped validate that critical

nuggets can assist in improving classification accuracies in real-world data sets.


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7. A Predictive-Reactive Method for Improving the Robustness of Real-

Time Data Services

ABSTRACT:

Supporting timely data services using fresh data in data-intensive real-time

applications, such as e-commerce and transportation management is desirable but

challenging, since the workload may vary dynamically. To control the data service

delay to be below the specified threshold, we develop a predictive as well as

reactive method for database admission control. The predictive method derives the

workload bound for admission control in a predictive manner, making no statistical

or queuing-theoretic assumptions about workloads. Also, our reactive scheme

based on formal feedback control theory continuously adjusts the database load

bound to support the delay threshold. By adapting the load bound in a proactive

fashion, we attempt to avoid severe overload conditions and excessive delays

before they occur. Also, the feedback control scheme enhances the timeliness by

compensating for potential prediction errors due to dynamic workloads. Hence, the

predictive and reactive methods complement each other, enhancing the robustness

of real-time data services as a whole. We implement the integrated approach and

several baselines in an open-source database. Compared to the tested open-loop,

feedback-only, and statistical prediction + feedback baselines representing the state

of the art, our integrated method significantly improves the average/transient delay

and real-time data service throughput.


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8. Tweet Analysis for Real-Time Event Detection and Earthquake

Reporting System Development

ABTSRACT:

Twitter has received much attention recently. An important characteristic of

Twitter is its real-time nature. We investigate the real-time interaction of events

such as earthquakes in Twitter and propose an algorithm to monitor tweets and to

detect a target event. To detect a target event, we devise a classifier of tweets based

on features such as the keywords in a tweet, the number of words, and their

context. Subsequently, we produce a probabilistic spatiotemporal model for the

target event that can find the center of the event location. We regard each Twitter

user as a sensor and apply particle filtering, which are widely used for location

estimation. The particle filter works better than other comparable methods for

estimating the locations of target events. As an application, we develop an

earthquake reporting system for use in Japan. Because of the numerous

earthquakes and the large number of Twitter users throughout the country, we can

detect an earthquake with high probability (93 percent of earthquakes of Japan

Meteorological Agency (JMA) seismic intensity scale 3 or more are detected)

merely by monitoring tweets. Our system detects earthquakes promptly and

notification is delivered much faster than JMA broadcast announcements.


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IMAGE PROCESSING

1. Discrete Wavelet Transform and Data Expansion Reduction in

Homomorphic Encrypted Domain

ABSTRACT:

Signal processing in the encrypted domain is a new technology with the goal

of protecting valuable signals from insecure signal processing. In this paper, we

propose a method for implementing discrete wavelet transform (DWT) and

multiresolution analysis (MRA) in homomorphic encrypted domain. We first

suggest a framework for performing DWT and inverse DWT (IDWT) in the

encrypted domain, then conduct an analysis of data expansion and quantization

errors under the framework. To solve the problem of data expansion, which may be

very important in practical applications, we present a method for reducing data

expansion in the case that both DWT and IDWT are performed. With the proposed

method, multilevel DWT/IDWT can be performed with less data expansion in

homomorphic encrypted domain. We propose a new signal processing procedure,

where the multiplicative inverse method is employed as the last step to limit the

data expansion. Taking a 2-D Haar wavelet transform as an example, we conduct a

few experiments to demonstrate the advantages of our method in secure image

processing. We also provide computational complexity analyses and comparisons.

To the best of our knowledge, there has been no report on the implementation of

DWT and MRA in the encrypted domain.


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2. Scanned Document Compression Using Block-Based Hybrid Video

Codec

ABSTRACT:

This paper proposes a hybrid pattern matching/transform-based compression

method for scanned documents. The idea is to use regular video interframe

prediction as a pattern matching algorithm that can be applied to document coding.

We show that this interpretation may generate residual data that can be efficiently

compressed by a transform-based encoder. The efficiency of this approach is

demonstrated using H.264/advanced video coding (AVC) as a high-quality single

and multipage document compressor. The proposed method, called advanced

document coding (ADC), uses segments of the originally independent scanned

pages of a document to create a video sequence, which is then encoded through

regular H.264/AVC. The encoding performance is unrivaled. Results show that

ADC outperforms AVC-I (H.264/AVC operating in pure intramode) and

JPEG2000 by up to 2.7 and 6.2 dB, respectively. Superior subjective quality is also

achieved.

3. Perceptual Quality-Regulable Video Coding System With Region-Based

Rate Control Scheme

ABSTRACT:

In this paper, we discuss a region-based perceptual quality-regulable H.264

video encoder system that we developed. The ability to adjust the quality of

specific regions of a source video to a predefined level of quality is an essential


27



technique for region-based video applications. We use the structural similarity

index as the quality metric for distortion-quantization modeling and develop a bit

allocation and rate control scheme for enhancing regional perceptual quality.

Exploiting the relationship between the reconstructed macroblock and the best

predicted macroblock from mode decision, a novel quantization parameter

prediction method is built and used to achieve the target video quality of the

processed macroblock. Experimental results show that the system model has only

0.013 quality error in average. Moreover, the proposed region-based rate control

system can encode video well under a bitrate constraint with a 0.1% bitrate error in

average. For the situation of the low bitrate constraint, the proposed system can

encode video with a 0.5% bit error rate in average and enhance the quality of the

target regions.

4. General Framework to Histogram-Shifting-Based Reversible Data

Hiding

ABSTRACT:

Histogram shifting (HS) is a useful technique of reversible data hiding

(RDH). With HS-based RDH, high capacity and low distortion can be achieved

efficiently. In this paper, we revisit the HS technique and present a general

framework to construct HS-based RDH. By the proposed framework, one can get a

RDH algorithm by simply designing the so-called shifting and embedding

functions. Moreover, by taking specific shifting and embedding functions, we

show that several RDH algorithms reported in the literature are special cases of this

general construction. In addition, two novel and efficient RDH algorithms are also


28



introduced to further demonstrate the universality and applicability of our

framework. It is expected that more efficient RDH algorithms can be devised

according to the proposed framework by carefully designing the shifting and

embedding functions.

5. Exploring Visual and Motion Saliency for Automatic Video Object

Extraction

ABSTRACT:

This paper presents a saliency-based video object extraction (VOE)

framework. The proposed framework aims to automatically extract foreground

objects of interest without any user interaction or the use of any training data (i.e.,

not limited to any particular type of object). To separate foreground and

background regions within and across video frames, the proposed method utilizes

visual and motion saliency information extracted from the input video. A

conditional random field is applied to effectively combine the saliency induced

features, which allows us to deal with unknown pose and scale variations of the

foreground object (and its articulated parts). Based on the ability to preserve both

spatial continuity and temporal consistency in the proposed VOE framework,

experiments on a variety of videos verify that our method is able to produce

quantitatively and qualitatively satisfactory VOE results.


29



MOBILE COMPUTING

1. Content Sharing over Smartphone-Based Delay-Tolerant Networks

ABSTRACT:

With the growing number of smartphone users, peer-to-peer ad hoc content

sharing is expected to occur more often. Thus, new content sharing mechanisms

should be developed as traditional data delivery schemes are not efficient for

content sharing due to the sporadic connectivity between smartphones. To

accomplish data delivery in such challenging environments, researchers have

proposed the use of store-carry-forward protocols, in which a node stores a

message and carries it until a forwarding opportunity arises through an encounter

with other nodes. Most previous works in this field have focused on the prediction

of whether two nodes would encounter each other, without considering the place

and time of the encounter. In this paper, we propose discover-predict-deliver as an

efficient content sharing scheme for delay-tolerant smartphone networks. In our

proposed scheme, contents are shared using the mobility information of

individuals. Specifically, our approach employs a mobility learning algorithm to

identify places indoors and outdoors. A hidden Markov model is used to predict an

individual's future mobility information. Evaluation based on real traces indicates

that with the proposed approach, 87 percent of contents can be correctly discovered

and delivered within 2 hours when the content is available only in 30 percent of

nodes in the network. We implement a sample application on commercial

smartphones, and we validate its efficiency to analyze the practical feasibility of


30



the content sharing application. Our system approximately results in a 2 percent

CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at

most.

2. Distributed Cooperative Caching in Social Wireless Networks

ABSTRACT:

This paper introduces cooperative caching policies for minimizing electronic

content provisioning cost in Social Wireless Networks (SWNET). SWNETs are

formed by mobile devices, such as data enabled phones, electronic book readers

etc., sharing common interests in electronic content, and physically gathering

together in public places. Electronic object caching in such SWNETs are shown to

be able to reduce the content provisioning cost which depends heavily on the

service and pricing dependences among various stakeholders including content

providers (CP), network service providers, and End Consumers (EC). Drawing

motivation from Amazon's Kindle electronic book delivery business, this paper

develops practical network, service, and pricing models which are then used for

creating two object caching strategies for minimizing content provisioning costs in

networks with homogenous and heterogeneous object demands. The paper

constructs analytical and simulation models for analyzing the proposed caching

strategies in the presence of selfish users that deviate from network-wide cost-

optimal policies. It also reports results from an Android phone-based prototype

SWNET, validating the presented analytical and simulation results.


31



3. Predicting Human Movement Based on Telecom's Handoff in Mobile

Networks

ABSTRACT:

Investigating human movement behavior is important for studying issues

such as prediction of vehicle traffic and spread of contagious diseases. Since

mobile telecom network can efficiently monitor the movement of mobile users, the

telecom's mobility management is an ideal mechanism for studying human

movement issues. The problem can be abstracted as follows: What is the

probability that a person at location A will move to location B after T hours. The

answer cannot be directly obtained because commercial telecom networks do not

exactly trace the movement history of every mobile user. In this paper, we show

how to use the standard outputs (handover rates, call arrival rates, call holding

time, and call traffic) measured in a mobile telecom network to derive the answer

for this problem.

4. Spatial Distribution and Channel Quality Adaptive Protocol for

Multihop Wireless Broadcast Routing in VANET

ABSTRACT:

Multihop wireless broadcast is an important component in vehicular

networks. Many applications are built on broadcast communications, so efficient

routing methods are critical for their success. Here, we develop the Distribution-

Adaptive Distance with Channel Quality (DADCQ) protocol to address this need

and show that it performs well compared to several existing multihop broadcast


32



proposals. The DADCQ protocol utilizes the distance method to select forwarding

nodes. The performance of this method depends heavily on the value of the

decision threshold, but it is difficult to choose a value that results in good

performance across all scenarios. Node density, spatial distribution pattern, and

wireless channel quality all affect the optimal value. Broadcast protocols tailored

to vehicular networking must be adaptive to variation in these factors. In this work,

we address this design challenge by creating a decision threshold function that is

simultaneously adaptive to the number of neighbors, the node clustering factor, and

the Rician fading parameter. To calculate the clustering factor, we propose using

the quadrat method of spatial analysis. The resulting DADCQ protocol is then

verified with JiST/SWANS and shown to achieve high reachability and low

bandwidth consumption in urban and highway scenarios with varying node density

and fading intensity.

5. A Robust Indoor Pedestrian Tracking System with Sparse

Infrastructure Support

ABSTRACT:

Existing approaches to indoor tracking have various limitations. Location-

fingerprinting approaches are labor intensive and vulnerable to environmental

changes. Trilateration approaches require at least three line-of-sight beacons for

coverage at any point in the service area, which results in heavy infrastructure cost.

Dead reckoning (DR) approaches rely on knowledge of the initial location and

suffer from tracking error accumulation. Despite this, we adopt DR for location

tracking because of the recent emergence of affordable hand-held devices equipped


33



with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian

tracking system that comprises of a DR subsystem implemented on a mobile phone

and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion

scheme is applied to bound the accumulated tracking error by fusing DR with

sparse range measurements. Experimental results show that the proposed system is

able to track users much better than DR alone. The system is robust even when: 1)

the initial user location is not available; 2) range updates are noisy; and 3) range

updates are intermittent, both temporally and spatially.

6. A MAC Sensing Protocol Design for Data Transmission with More

Protection to Primary Users

ABSTRACT:

MAC protocols to sense channels for data transmission have been widely

investigated for the secondary users to efficiently utilize and share the spectrum

licensed by the primary user. One important issue associated with MAC protocols

design is how the secondary users determine when and which channel they should

sense and access without causing harmful interference to the primary user. In this

paper, we jointly consider the MAC-layer spectrum sensing and channel access.

Normal Spectrum Sensing (NSS) is required to be carried out at the beginning of

each frame to determine whether the channel is idle. On detecting the available

transmission opportunity, the secondary users employ CSMA for channel

contention. The novelty is that, Fast Spectrum Sensing (FSS) is inserted after

channel contention to promptly detect the return of the primary users. This is

unlike most other MAC protocols which do not incorporate FSS. Having FSS, the


34



primary user can benefit from more protection. A concrete protocol design is

provided in this paper, and the throughput-collision tradeoff and utility-collision

tradeoff problems are formulated to evaluate its performance. Simulation results

demonstrate the efficiency of the proposed MAC protocol with FSS.

7. Cross-Layer Minimum-Delay Scheduling and Maximum-Throughput

Resource Allocation for Multiuser Cognitive Networks

ABSTRACT:

A cognitive network is considered that consists of a base station (BS)

communicating with multiple primary and secondary users. Each secondary user

can access only one of the orthogonal primary channels. A model is considered in

which the primary users can tolerate a certain average delay. A special case is also

considered in which the primary users do not suffer from any delay. A novel cross-

layer scheme is proposed in which the BS performs successive interference

cancellation and thus a secondary user can coexist with an active primary user

without adversely affecting its transmission. A scheduling algorithm is proposed

that minimizes the average packet delay of the secondary user under constraints on

the average power transmitted by the secondary user and the average packet delay

of the primary user. A resource allocation algorithm is also proposed to assign the

secondary users' channels such that the total throughput of the network is

maximized. Our results indicate that the network throughput increases significantly

by increasing the number of transmitted packets of the secondary users and/or by

allowing a small delay for the primary user packets.


35



8. ALERT: An Anonymous Location-Based Efficient Routing Protocol in

MANETs

ABSTRACT:

Mobile Ad Hoc Networks (MANETs) use anonymous routing protocols that hide

node identities and/or routes from outside observers in order to provide anonymity

protection. However, existing anonymous routing protocols relying on either hop-

by-hop encryption or redundant traffic, either generate high cost or cannot provide

full anonymity protection to data sources, destinations, and routes. The high cost

exacerbates the inherent resource constraint problem in MANETs especially in

multimedia wireless applications. To offer high anonymity protection at a low cost,

we propose an Anonymous Location-based Efficient Routing proTocol (ALERT).

ALERT dynamically partitions the network field into zones and randomly chooses

nodes in zones as intermediate relay nodes, which form a nontraceable anonymous

route. In addition, it hides the data initiator/receiver among many

initiators/receivers to strengthen source and destination anonymity protection.

Thus, ALERT offers anonymity protection to sources, destinations, and routes. It

also has strategies to effectively counter intersection and timing attacks. We

theoretically analyze ALERT in terms of anonymity and efficiency. Experimental

results exhibit consistency with the theoretical analysis, and show that ALERT

achieves better route anonymity protection and lower cost compared to other

anonymous routing protocols. Also, ALERT achieves comparable routing

efficiency to the GPSR geographical routing protocol.


36



MULTIMEDIA

1. An Adaptive Cloud Downloading Service

ABSTRACT:

Video content downloading using the P2P approach is scalable, but does not

always give good performance. Recently, subscription-based premium services

have emerged, referred to as cloud downloading. In this service, the cloud storage

and server caches user-interested content and updates the cache based on user

downloading requests. If a requested video is not in the cache, the request is held in

a waiting state until the cache is updated. We call this design server mode. An

alternative design is to let the cloud server serve all downloading requests as soon

as they arrive, behaving as a helper peer. We call this design helper mode. Our

model and analysis show that both these designs are useful for certain operating

regimes. The helper mode is good at handling a high request rate, while the server

mode is good at scaling with video population size. We design an adaptive

algorithm (AMS) to select the service mode automatically. Intuitively, AMS

switches service mode from server mode to helper mode when too many peers

request blocked movies, and vice versa. The ability of AMS to achieve good

performance in different operating regimes is validated by simulation .


37



2. Efficient Resource Provisioning and Rate Selection for Stream Mining

in a Community Cloud

ABSTRACT:

Real-time stream mining such as surveillance and personal health

monitoring, which involves sophisticated mathematical operations, is computation-

intensive and prohibitive for mobile devices due to the hardware/computation

constraints. To satisfy the growing demand for stream mining in mobile networks,

we propose to employ a cloud-based stream mining system in which the mobile

devices send via wireless links unclassified media streams to the cloud for

classification. We aim at minimizing the classification-energy cost, defined as an

affine combination of classification cost and energy consumption at the cloud,

subject to an average stream mining delay constraint (which is important in real-

time applications). To address the challenge of time-varying wireless channel

conditions without a priori information about the channel statistics, we develop an

online algorithm in which the cloud operator can dynamically adjust its resource

provisioning on the fly and the mobile devices can adapt their transmission rates to

the instantaneous channel conditions. It is proved that, at the expense of increasing

the average stream mining delay, the online algorithm achieves a classification-

energy cost that can be pushed arbitrarily close to the minimum cost achieved by

the optimal offline algorithm. Extensive simulations are conducted to validate the

analysis.


38



3. Cloud-Based Image Coding for Mobile Devices—Toward Thousands to

One Compression

ABSTRACT:

Current image coding schemes make it hard to utilize external images for

compression even if highly correlated images can be found in the cloud. To solve

this problem, we propose a method of cloud-based image coding that is different

from current image coding even on the ground. It no longer compresses images

pixel by pixel and instead tries to describe images and reconstruct them from a

large-scale image database via the descriptions. First, we describe an input image

based on its down-sampled version and local feature descriptors. The descriptors

are used to retrieve highly correlated images in the cloud and identify

corresponding patches. The down-sampled image serves as a target to stitch

retrieved image patches together. Second, the down-sampled image is compressed

using current image coding. The feature vectors of local descriptors are predicted

by the corresponding vectors extracted in the decoded down-sampled image. The

predicted residual vectors are compressed by transform, quantization, and entropy

coding. The experimental results show that the visual quality of reconstructed

images is significantly better than that of intra-frame coding in HEVC and JPEG at

thousands to one compression .


39



4. Optimizing Cloud Resources for Delivering IPTV Services Through

Virtualization

ABSTRACT:

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. Our results

show that we are able to reduce the load by ~24%(compared to a possible ~31.3%

as predicted by the optimization framework).


40



5. A Network and Device Aware QoS Approach for Cloud-Based Mobile

Streaming

ABSTRACT:

Cloud multimedia services provide an efficient, flexible, and scalable data

processing method and offer a solution for the user demands of high quality and

diversified multimedia. As intelligent mobile phones and wireless networks

become more and more popular, network services for users are no longer limited to

the home. Multimedia information can be obtained easily using mobile devices,

allowing users to enjoy ubiquitous network services. Considering the limited

bandwidth available for mobile streaming and different device requirements, this

study presented a network and device-aware Quality of Service (QoS) approach

that provides multimedia data suitable for a terminal unit environment via

interactive mobile streaming services, further considering the overall network

environment and adjusting the interactive transmission frequency and the dynamic

multimedia transcoding, to avoid the waste of bandwidth and terminal power.

Finally, this study realized a prototype of this architecture to validate the feasibility

of the proposed method. According to the experiment, this method could provide

efficient self-adaptive multimedia streaming services for varying bandwidth

environments.


41



NETWORK SECURITY

1. To Lie or to Comply: Defending against Flood Attacks in Disruption

Tolerant Networks

ABSTRACT:

Disruption Tolerant Networks (DTNs) utilize the mobility of nodes and the

opportunistic contacts among nodes for data communications. Due to the limitation

in network resources such as contact opportunity and buffer space, DTNs are

vulnerable to flood attacks in which attackers send as many packets or packet

replicas as possible to the network, in order to deplete or overuse the limited

network resources. In this paper, we employ rate limiting to defend against flood

attacks in DTNs, such that each node has a limit over the number of packets that it

can generate in each time interval and a limit over the number of replicas that it

can generate for each packet. We propose a distributed scheme to detect if a node

has violated its rate limits. To address the challenge that it is difficult to count all

the packets or replicas sent by a node due to lack of communication infrastructure,

our detection adopts claim-carry-and-check: each node itself counts the number of

packets or replicas that it has sent and claims the count to other nodes; the

receiving nodes carry the claims when they move, and cross-check if their carried

claims are inconsistent when they contact. The claim structure uses the pigeonhole

principle to guarantee that an attacker will make inconsistent claims which may

lead to detection. We provide rigorous analysis on the probability of detection, and


42



evaluate the effectiveness and efficiency of our scheme with extensive trace-driven

simulations.

2. Design and Implementation of TARF: A Trust-Aware Routing

Framework for WSNs

ABSTRACT:

The multihop 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 multihop routing, we have designed

and implemented TARF, 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


43



effort. Based on TARF, we also demonstrated a proof-of-concept mobile target

detection application that functions well against an antidetection mechanism.

3. Towards Trustworthy Resource Scheduling in Clouds

ABSTRACTS:

Managing the allocation of cloud virtual machines at physical resources is a

key requirement for the success of clouds. Current implementations of cloud

schedulers do not consider the entire cloud infrastructure neither do they consider

the overall user and infrastructure properties. This results in major security,

privacy, and resilience concerns. In this paper, we propose a novel cloud scheduler

which considers both user requirements and infrastructure properties. We focus on

assuring users that their virtual resources are hosted using physical resources that

match their requirements without getting users involved with understanding the

details of the cloud infrastructure. As a proof-of-concept, we present our prototype

which is built on OpenStack. The provided prototype implements the proposed

cloud scheduler. It also provides an implementation of our previous work on cloud

trust management which provides the scheduler with input about the trust status of

the cloud infrastructure.

4. A Hierarchical Approach for the Resource Management of Very Large

Cloud Platforms

ABSTRACT:

Worldwide interest in the delivery of computing and storage capacity as a

service continues to grow at a rapid pace. The complexities of such cloud


44



computing centers require advanced resource management solutions that are

capable of dynamically adapting the cloud platform while providing continuous

service and performance guarantees. The goal of this paper is to devise resource

allocation policies for virtualized cloud environments that satisfy performance and

availability guarantees and minimize energy costs in very large cloud service

centers. We present a scalable distributed hierarchical framework based on a

mixed-integer non-linear optimization for resource management acting at multiple

time-scales. Extensive experiments across a wide variety of configurations

demonstrate the efficiency and effectiveness of our approach.

5. CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring

ABSTRACT:

Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing

mobile communications and cloud computing technologies to provide feedback decision

support, has been considered as a revolutionary approach to improving the quality of

healthcare service while lowering the healthcare cost. Unfortunately, it also poses a

serious risk on both clients' privacy and intellectual property of monitoring service

providers, which could deter the wide adoption of mHealth technology. This paper is to

address this important problem and design a cloud-assisted privacy preserving mobile

health monitoring system to protect the privacy of the involved parties and their data.

Moreover, the outsourcing decryption technique and a newly proposed key private proxy

reencryption are adapted to shift the computational complexity of the involved parties to

the cloud without compromising clients' privacy and service providers' intellectual

property. Finally, our security and performance analysis demonstrates the effectiveness of

our proposed design.


45



NETWORKING

1. An Effective Network Traffic Classification Method with Unknown

Flow Detection

ABSTRACT:

Traffic classification technique is an essential tool for network and system

security in the complex environments such as cloud computing based environment.

The state-of-the-art traffic classification methods aim to take the advantages of

flow statistical features and machine learning techniques, however the

classification performance is severely affected by limited supervised information

and unknown applications. To achieve effective network traffic classification, we

propose a new method to tackle the problem of unknown applications in the crucial

situation of a small supervised training set. The proposed method possesses the

superior capability of detecting unknown flows generated by unknown applications

and utilizing the correlation information among real-world network traffic to boost

the classification performance. A theoretical analysis is provided to confirm

performance benefit of the proposed method. Moreover, the comprehensive

performance evaluation conducted on two real-world network traffic datasets

shows that the proposed scheme outperforms the existing methods in the critical

network environment.


46



2. Hierarchical Trust Management for Wireless Sensor Networks and its

Applications to Trust-Based Routing and Intrusion Detection

ABSTRACT:

We propose a highly scalable cluster-based hierarchical trust management

protocol for wireless sensor networks (WSNs) to effectively deal with selfish or

malicious nodes. Unlike prior work, we consider multidimensional trust attributes

derived from communication and social networks to evaluate the overall trust of a

sensor node. By means of a novel probability model, we describe a heterogeneous

WSN comprising a large number of sensor nodes with vastly different social and

quality of service (QoS) behaviors with the objective to yield "ground truth" node

status. This serves as a basis for validating our protocol design by comparing

subjective trust generated as a result of protocol execution at runtime against

objective trust obtained from actual node status. To demonstrate the utility of our

hierarchical trust management protocol, we apply it to trust-based geographic

routing and trust-based intrusion detection. For each application, we identify the

best trust composition and formation to maximize application performance. Our

results indicate that trust-based geographic routing approaches the ideal

performance level achievable by flooding-based routing in message delivery ratio

and message delay without incurring substantial message overhead. For trust-based

intrusion detection, we discover that there exists an optimal trust threshold for

minimizing false positives and false negatives. Furthermore, trust-based intrusion

detection outperforms traditional anomaly-based intrusion detection approaches in

both the detection probability and the false positive probability.


47



3. Torrents on Twitter: Explore Long-Term Social Relationships in Peer-

to-Peer Systems

ABSTRACT:

Peer-to-peer file sharing systems, most notably BitTorrent (BT), have

achieved tremendous success among Internet users. Recent studies suggest that the

long-term relationships among BT peers can be explored to enhance the

downloading performance; for example, for re-sharing previously downloaded

contents or for effectively collaborating among the peers. However, whether such

relationships do exist in real world remains unclear. In this paper, we take a first

step towards the real-world applicability of peers' long-term relationship through a

measurement based study. We find that 95% peers cannot even meet each other

again in the BT networks; therefore, most peers can hardly be organized for further

cooperation. This result contradicts to the conventional understanding based on the

observed daily arrival pattern in peer-to-peer networks. To better understand this,

we revisit the arrival of BT peers as well as their long-range dependence. We find

that the peers' arrival patterns are highly diverse; only a limited number of stable

peers have clear self-similar and periodic daily arrivals patterns. The arrivals of

most peers are, however, quite random with little evidence of long-range

dependence. To better utilize these stable peers, we start to explore peers' long-

term relationships in specific swarms instead of conventional BT networks.

Fortunately, we find that the peers in Twitter-initialized torrents have stronger

temporal locality, thus offering great opportunity for improving their degree of

sharing. Our PlanetLab experiments further indicate that the incorporation of social


48



relations remarkably accelerates the download completion time. The improvement

remains noticeable even in a hybrid system with a small set of social friends only.

4. Efficient Storage and Processing of High-Volume Network Monitoring

Data

ABSTRACT:

Monitoring modern networks involves storing and transferring huge

amounts of data. To cope with this problem, in this paper we propose a technique

that allows to transform the measurement data in a representation format meeting

two main objectives at the same time. Firstly, it allows to perform a number of

operations directly on the transformed data with a controlled loss of accuracy,

thanks to the mathematical framework it is based on. Secondly, the new

representation has a small memory footprint, allowing to reduce the space needed

for data storage and the time needed for data transfer. To validate our technique,

we perform an analysis of its performance in terms of accuracy and memory

footprint. The results show that the transformed data closely approximates the

original data (within 5% relative error) while achieving a compression ratio of

20%; storage footprint can also be gradually reduced towards the one of the state-

of-the-art compression tools, such as bzip2, if higher approximation is allowed.

Finally, a sensibility analysis show that technique allows to trade-off the accuracy

on different input fields so to accommodate for specific application needs, while a

scalability analysis indicates that the technique scales with input size spanning up

to three orders of magnitude.


49



5. An Efficient and Robust Addressing Protocol for Node

Autoconfiguration in Ad Hoc Networks

ABSTRACT:

Address assignment is a key challenge in ad hoc networks due to the lack of

infrastructure. Autonomous addressing protocols require a distributed and self-

managed mechanism to avoid address collisions in a dynamic network with fading

channels, frequent partitions, and joining/leaving nodes. We propose and analyze a

lightweight protocol that configures mobile ad hoc nodes based on a distributed

address database stored in filters that reduces the control load and makes the

proposal robust to packet losses and network partitions. We evaluate the

performance of our protocol, considering joining nodes, partition merging events,

and network initialization. Simulation results show that our protocol resolves all

the address collisions and also reduces the control traffic when compared to

previously proposed protocols.

6. Complexity Analysis and Algorithm Design for Advance Bandwidth

Scheduling in Dedicated Networks

ABSTRACT:

An increasing number of high-performance networks provision dedicated

channels through circuit switching or MPLS/GMPLS techniques to support large

data transfer. The link bandwidths in such networks are typically shared by

multiple users through advance reservation, resulting in varying bandwidth


50



availability in future time. Developing efficient scheduling algorithms for advance

bandwidth reservation has become a critical task to improve the utilization of

network resources and meet the transport requirements of application users. We

consider an exhaustive combination of different path and bandwidth constraints

and formulate four types of advance bandwidth scheduling problems, with the

same objective to minimize the data transfer end time for a given transfer request

with a prespecified data size: fixed path with fixed bandwidth (FPFB); fixed path

with variable bandwidth (FPVB); variable path with fixed bandwidth (VPFB); and

variable path with variable bandwidth (VPVB). For VPFB and VPVB, we further

consider two subcases where the path switching delay is negligible or

nonnegligible. We propose an optimal algorithm for each of these scheduling

problems except for FPVB and VPVB with nonnegligible path switching delay,

which are proven to be NP-complete and nonapproximable, and then tackled by

heuristics. The performance superiority of these heuristics is verified by extensive

experimental results in a large set of simulated networks in comparison to optimal

and greedy strategies.

7. Distortion-Aware Scalable Video Streaming to Multinetwork Clients

ABSTRACT:

We consider the problem of scalable video streaming from a server to

multinetwork clients over heterogeneous access networks, with the goal of

minimizing the distortion of the received videos. This problem has numerous

applications including: 1) mobile devices connecting to multiple licensed and ISM

bands, and 2) cognitive multiradio devices employing spectrum bonding. In this


51



paper, we ascertain how to optimally determine which video packets to transmit

over each access network. We present models to capture the network conditions

and video characteristics and develop an integer program for deterministic packet

scheduling. Solving the integer program exactly is typically not computationally

tractable, so we develop heuristic algorithms for deterministic packet scheduling,

as well as convex optimization problems for randomized packet scheduling. We

carry out a thorough study of the tradeoff between performance and computational

complexity and propose a convex programming-based algorithm that yields good

performance while being suitable for real-time applications. We conduct extensive

trace-driven simulations to evaluate the proposed algorithms using real network

conditions and scalable video streams. The simulation results show that the

proposed convex programming-based algorithm: 1) outperforms the rate control

algorithms defined in the Datagram Congestion Control Protocol (DCCP) by about

10-15 dB higher video quality; 2) reduces average delivery delay by over 90%

compared to DCCP; 3) results in higher average video quality of 4.47 and 1.92 dB

than the two developed heuristics; 4) runs efficiently, up to six times faster than the

best-performing heuristic; and 5) does indeed provide service differentiation

among users.

8. Exploring the Design Space of Multichannel Peer-to-Peer Live Video

Streaming Systems

ABSTRACT:

Most of the commercial peer-to-peer (P2P) video streaming deployments

support hundreds of channels and are referred to as multichannel systems. Recent


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research studies have proposed specific protocols to improve the streaming quality

for all channels by enabling cross-channel cooperation among multiple channels.

In this paper, we focus on the following fundamental problems in designing

cooperating multichannel systems: 1) what are the general characteristics of

existing and potential designs? and 2) under what circumstances should a particular

design be used to achieve the desired streaming quality with the lowest

implementation complexity? To answer the first question, we propose simple

models based on linear programming and network-flow graphs for three general

designs, namely Naive Bandwidth allocation Approach (NBA), Passive Channel-

aware bandwidth allocation Approach (PCA), and Active Channel-aware

bandwidth allocation Approach (ACA), which provide insight into understanding

the key characteristics of cross-channel resource sharing. For the second question,

we first develop closed-form results for two-channel systems. Then, we use

extensive numerical simulations to compare the three designs for various peer

population distributions, upload bandwidth distributions, and channel structures.

Our analytical and simulation results show that: 1) the NBA design can rarely

achieve the desired streaming quality in general cases; 2) the PCA design can

achieve the same performance as the ACA design in general cases; and 3) the ACA

design should be used for special applications.


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PARALLEL AND DISTRIBUTED SYSTEM

1. Efficient Resource Mapping Framework over Networked Clouds via

Iterated Local Search-Based Request Partitioning

ABSTRACT:

The cloud represents a computing paradigm where shared configurable

resources are provided as a service over the Internet. Adding intra- or intercloud

communication resources to the resource mix leads to a networked cloud

computing environment. Following the cloud infrastructure as a Service paradigm

and in order to create a flexible management framework, it is of paramount

importance to address efficiently the resource mapping problem within this

context. To deal with the inherent complexity and scalability issue of the resource

mapping problem across different administrative domains, in this paper a

hierarchical framework is described. First, a novel request partitioning approach

based on Iterated Local Search is introduced that facilitates the cost-efficient and

online splitting of user requests among eligible cloud service providers (CPs)

within a networked cloud environment. Following and capitalizing on the outcome

of the request partitioning phase, the embedding phase-where the actual mapping

of requested virtual to physical resources is performed can be realized through the

use of a distributed intracloud resource mapping approach that allows for efficient

and balanced allocation of cloud resources. Finally, a thorough evaluation of the

proposed overall framework on a simulated networked cloud environment is


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provided and critically compared against an exact request partitioning solution as

well as another common intradomain virtual resource embedding solution.

2. A Highly Practical Approach toward Achieving Minimum Data Sets

Storage Cost in the Cloud

ABSTRACT:

Massive computation power and storage capacity of cloud computing

systems allow scientists to deploy computation and data intensive applications

without infrastructure investment, where large application data sets can be stored

in the cloud. Based on the pay-as-you-go model, storage strategies and

benchmarking approaches have been developed for cost-effectively storing large

volume of generated application data sets in the cloud. However, they are either

insufficiently cost-effective for the storage or impractical to be used at runtime. In

this paper, toward achieving the minimum cost benchmark, we propose a novel

highly cost-effective and practical storage strategy that can automatically decide

whether a generated data set should be stored or not at runtime in the cloud. The

main focus of this strategy is the local-optimization for the tradeoff between

computation and storage, while secondarily also taking users' (optional)

preferences on storage into consideration. Both theoretical analysis and simulations

conducted on general (random) data sets as well as specific real world applications

with Amazon's cost model show that the cost-effectiveness of our strategy is close


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to or even the same as the minimum cost benchmark, and the efficiency is very

high for practical runtime utilization in the cloud.

3. Optimal Multiserver Configuration for Profit Maximization in Cloud

Computing

ABSTRACT:

As cloud computing becomes more and more popular, understanding the

economics of cloud computing becomes critically important. To maximize the

profit, a service provider should understand both service charges and business

costs, and how they are determined by the characteristics of the applications and

the configuration of a multiserver system. The problem of optimal multiserver

configuration for profit maximization in a cloud computing environment is studied.

Our pricing model takes such factors into considerations as the amount of a

service, the workload of an application environment, the configuration of a

multiserver system, the service-level agreement, the satisfaction of a consumer, the

quality of a service, the penalty of a low-quality service, the cost of renting, the

cost of energy consumption, and a service provider's margin and profit. Our

approach is to treat a multiserver system as an M/M/m queuing model, such that

our optimization problem can be formulated and solved analytically. Two server

speed and power consumption models are considered, namely, the idle-speed

model and the constant-speed model. The probability density function of the

waiting time of a newly arrived service request is derived. The expected service

charge to a service request is calculated. The expected net business gain in one unit


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of time is obtained. Numerical calculations of the optimal server size and the

optimal server speed are demonstrated.

4. Anchor: A Versatile and Efficient Framework for Resource

Management in the Cloud

ABSTRACT:

We present Anchor, a general resource management architecture that uses

the stable matching framework to decouple policies from mechanisms when

mapping virtual machines to physical servers. In Anchor, clients and operators are

able to express a variety of distinct resource management policies as they deem fit,

and these policies are captured as preferences in the stable matching framework.

The highlight of Anchor is a new many-to-one stable matching theory that

efficiently matches VMs with heterogeneous resource needs to servers, using both

offline and online algorithms. Our theoretical analyses show the convergence and

optimality of the algorithm. Our experiments with a prototype implementation on a

20-node server cluster, as well as large-scale simulations based on real-world

workload traces, demonstrate that the architecture is able to realize a diverse set of

policy objectives with good performance and practicality.

5. Load Rebalancing for Distributed File Systems in Clouds

ABSTRACT:

Distributed file systems are key building blocks for cloud computing

applications based on the MapReduce programming paradigm. In such file

systems, nodes simultaneously serve computing and storage functions; a file is


57



partitioned into a number of chunks allocated in distinct nodes so that MapReduce

tasks can be performed in parallel over the nodes. However, in a cloud computing

environment, failure is the norm, and nodes may be upgraded, replaced, and added

in the system. Files can also be dynamically created, deleted, and appended. This

results in load imbalance in a distributed file system; that is, the file chunks are not

distributed as uniformly as possible among the nodes. Emerging distributed file

systems in production systems strongly depend on a central node for chunk

reallocation. This dependence is clearly inadequate in a large-scale, failure-prone

environment because the central load balancer is put under considerable workload

that is linearly scaled with the system size, and may thus become the performance

bottleneck and the single point of failure. In this paper, a fully distributed load

rebalancing algorithm is presented to cope with the load imbalance problem. Our

algorithm is compared against a centralized approach in a production system and a

competing distributed solution presented in the literature. The simulation results

indicate that our proposal is comparable with the existing centralized approach and

considerably outperforms the prior distributed algorithm in terms of load

imbalance factor, movement cost, and algorithmic overhead. The performance of

our proposal implemented in the Hadoop distributed file system is further

investigated in a cluster environment.


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SERVICE COMPUTING

1. Optimization of Resource Provisioning Cost in Cloud Computing

ABSTRACT:

In cloud computing, cloud providers can offer cloud consumers two

provisioning plans for computing resources, namely reservation and on-demand

plans. In general, cost of utilizing computing resources provisioned by reservation

plan is cheaper than that provisioned by on-demand plan, since cloud consumer has

to pay to provider in advance. With the reservation plan, the consumer can reduce

the total resource provisioning cost. However, the best advance reservation of

resources is difficult to be achieved due to uncertainty of consumer's future

demand and providers' resource prices. To address this problem, an optimal cloud

resource provisioning (OCRP) algorithm is proposed by formulating a stochastic

programming model. The OCRP algorithm can provision computing resources for

being used in multiple provisioning stages as well as a long-term plan, e.g., four

stages in a quarter plan and twelve stages in a yearly plan. The demand and price

uncertainty is considered in OCRP. In this paper, different approaches to obtain the

solution of the OCRP algorithm are considered including deterministic equivalent

formulation, sample-average approximation, and Benders decomposition.

Numerical studies are extensively performed in which the results clearly show that

with the OCRP algorithm, cloud consumer can successfully minimize total cost of

resource provisioning in cloud computing environments.


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2. Interacting with the SOA-Based Internet of Things: Discovery, Query,

Selection, and On-Demand Provisioning of Web Services

ABSTRACT:

The increasing usage of smart embedded devices in business blurs the line

between the virtual and real worlds. This creates new opportunities to build

applications that better integrate real-time state of the physical world, and hence,

provides enterprise services that are highly dynamic, more diverse, and efficient.

Service-Oriented Architecture (SOA) approaches traditionally used to couple

functionality of heavyweight corporate IT systems, are becoming applicable to

embedded real-world devices, i.e., objects of the physical world that feature

embedded processing and communication. In such infrastructures, composed of

large numbers of networked, resource-limited devices, the discovery of services

and on-demand provisioning of missing functionality is a significant challenge. We

propose a process and a suitable system architecture that enables developers and

business process designers to dynamically query, select, and use running instances

of real-world services (i.e., services running on physical devices) or even deploy

new ones on-demand, all in the context of composite, real-world business

applications.


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3. Message-Efficient Service Management Schemes for MOM-Based UPnP

Networks

ABSTRACT:

The use of message-oriented middleware (MOM) in pervasive systems has

increased noticeably because of its flexible and failure-tolerant nature. Meanwhile,

decentralized service management protocols such as UPnP are believed to be more

suitable for administrating applications in small-scale pervasive environments such

as smart homes. However, administering MOM-based pervasive systems by UPnP

often suffers from network flood problems due to the replications of too many

unnecessary messages. This paper presents several traffic reduction schemes,

namely, decomposing the multicast traffic, service-based node searching, heartbeat

by decomposing the multicast traffic, and on-demand heartbeat, which reduce the

replications of unnecessary messages in MOM-based UPnP networks. The

analytical predictions agree well with the simulated and experimental results,

which show that the message counts of presence and leave announcements, node

searching, and heartbeat can be greatly reduced.

4. Toward Trustworthy Coordination of Web Services Business Activities

ABSTRACT:

We present a lightweight Byzantine fault tolerance (BFT) algorithm, which

can be used to render the coordination of web services business activities (WS-BA)

more trustworthy. The lightweight design of the BFT algorithm is the result of a

comprehensive study of the threats to the WS-BA coordination services and a


61



careful analysis of the state model of WS-BA. The lightweight BFT algorithm uses

source ordering, rather than total ordering, of incoming requests to achieve

Byzantine fault tolerant, state-machine replication of the WS-BA coordination

services. We have implemented the lightweight BFT algorithm, and incorporated it

into the open-source Kandula framework, which implements the WS-BA

specification with the WS-BA-I extension. Performance evaluation results obtained

from the prototype implementation confirm the efficiency and effectiveness of our

lightweight BFT algorithm, compared to traditional BFT techniques.

5. Dynamic Service Contract Enforcement in Service-Oriented Networks

ABSTRACT:

In recent years, service-oriented architectures (SOA) have emerged as the

main solution for the integration of legacy systems with new technologies in the

enterprise world. A service is usually governed by a client service contract (CSC)

that specifies, among other requirements, the rate at which a service should be

accessed, and limits it to no more than a number of service requests during an

observation period. Several approaches, using both static and dynamic credit-based

strategies, have been developed to enforce the rate specified in the CSC. Existing

approaches have problems related to starvation, approximations used in

calculations, and rapid credit consumption under certain conditions. In this paper,

we propose and validate DoWSS, a doubly weighted algorithm for service traffic

shaping. We show via simulation that DoWSS possesses several advantages: It

eliminates the approximation issues, prevents starvation, and contains the rapid

credit consumption issue in existing credit-based approaches.


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6. A Two-Tiered On-Demand Resource Allocation Mechanism for VM-

Based Data Centers

ABSTRACT:

In a shared virtual computing environment, dynamic load changes as well as

different quality requirements of applications in their lifetime give rise to dynamic

and various capacity demands, which results in lower resource utilization and

application quality using the existing static resource allocation. Furthermore, the

total required capacities of all the hosted applications in current enterprise data

centers, for example, Google, may surpass the capacities of the platform. In this

paper, we argue that the existing techniques by turning on or off servers with the

help of virtual machine (VM) migration is not enough. Instead, finding an

optimized dynamic resource allocation method to solve the problem of on-demand

resource provision for VMs is the key to improve the efficiency of data centers.

However, the existing dynamic resource allocation methods only focus on either

the local optimization within a server or central global optimization, limiting the

efficiency of data centers. We propose a two-tiered on-demand resource allocation

mechanism consisting of the local and global resource allocation with feedback to

provide on-demand capacities to the concurrent applications. We model the on-

demand resource allocation using optimization theory. Based on the proposed

dynamic resource allocation mechanism and model, we propose a set of on-

demand resource allocation algorithms. Our algorithms preferentially ensure

performance of critical applications named by the data center manager when

resource competition arises according to the time-varying capacity demands and


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the quality of applications. Using Rainbow, a Xen-based prototype we

implemented, we evaluate the VM-based shared platform as well as the two-tiered

on-demand resource allocation mechanism and algorithms. The experimental

results show that Rainbow without dynamic resource allocation (Rainbow-NDA)

provides 26 to 324 percent improvements in the application performance, as well

as 26 percent higher average CPU - tilization than traditional service computing

framework, in which applications use exclusive servers. The two-tiered on-demand

resource allocation further improves performance by 9 to 16 percent for those

critical applications, 75 percent of the maximum performance improvement,

introducing up to 5 percent performance degradations to others, with 1 to 5 percent

improvements in the resource utilization in comparison with Rainbow-NDA.

7. A Decentralized Service Discovery Approach on Peer-to-Peer Networks

ABSTRACT:

Service-Oriented Computing (SOC) is emerging as a paradigm for

developing distributed applications. A critical issue of utilizing SOC is to have a

scalable, reliable, and robust service discovery mechanism. However, traditional

service discovery methods using centralized registries can easily suffer from

problems such as performance bottleneck and vulnerability to failures in large

scalable service networks, thus functioning abnormally. To address these

problems, this paper proposes a peer-to-peer-based decentralized service discovery

approach named Chord4S. Chord4S utilizes the data distribution and lookup

capabilities of the popular Chord to distribute and discover services in a

decentralized manner. Data availability is further improved by distributing


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published descriptions of functionally equivalent services to different successor

nodes that are organized into virtual segments in the Chord4S circle. Based on the

service publication approach, Chord4S supports QoS-aware service discovery.

Chord4S also supports service discovery with wildcard(s). In addition, the Chord

routing protocol is extended to support efficient discovery of multiple services with

a single query. This enables late negotiation of Service Level Agreements (SLAs)

between service consumers and multiple candidate service providers. The

experimental evaluation shows that Chord4S achieves higher data availability and

provides efficient query with reasonable overhead.


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SOFTWARE ENGINEERING

1. Ranking and Clustering Software Cost Estimation Models through a

Multiple Comparisons Algorithm

ABSTRACT:

Software Cost Estimation can be described as the process of predicting the

most realistic effort required to complete a software project. Due to the strong

relationship of accurate effort estimations with many crucial project management

activities, the research community has been focused on the development and

application of a vast variety of methods and models trying to improve the

estimation procedure. From the diversity of methods emerged the need for

comparisons to determine the best model. However, the inconsistent results

brought to light significant doubts and uncertainty about the appropriateness of the

comparison process in experimental studies. Overall, there exist several potential

sources of bias that have to be considered in order to reinforce the confidence of

experiments. In this paper, we propose a statistical framework based on a multiple

comparisons algorithm in order to rank several cost estimation models, identifying

those which have significant differences in accuracy, and clustering them in

nonoverlapping groups. The proposed framework is applied in a large-scale setup

of comparing 11 prediction models over six datasets. The results illustrate the

benefits and the significant information obtained through the systematic

comparison of alternative methods.


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2. Reducing Features to Improve Code Change-Based Bug Prediction

ABSTRACT:

Machine learning classifiers have recently emerged as a way to predict the

introduction of bugs in changes made to source code files. The classifier is first

trained on software history, and then used to predict if an impending change causes

a bug. Drawbacks of existing classifier-based bug prediction techniques are

insufficient performance for practical use and slow prediction times due to a large

number of machine learned features. This paper investigates multiple feature

selection techniques that are generally applicable to classification-based bug

prediction methods. The techniques discard less important features until optimal

classification performance is reached. The total number of features used for

training is substantially reduced, often to less than 10 percent of the original. The

performance of Naive Bayes and Support Vector Machine (SVM) classifiers when

using this technique is characterized on 11 software projects. Naive Bayes using

feature selection provides significant improvement in buggy F-measure (21 percent

improvement) over prior change classification bug prediction results (by the

second and fourth authors [28]). The SVM's improvement in buggy F-measure is 9

percent. Interestingly, an analysis of performance for varying numbers of features

shows that strong performance is achieved at even 1 percent of the original number

of features.


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3. Ant Colony Optimization for Software Project Scheduling and Staffing

with an Event-Based Scheduler

ABSTRACT:

Research into developing effective computer aided techniques for planning

software projects is important and challenging for software engineering. Different

from projects in other fields, software projects are people-intensive activities and

their related resources are mainly human resources. Thus, an adequate model for

software project planning has to deal with not only the problem of project task

scheduling but also the problem of human resource allocation. But as both of these

two problems are difficult, existing models either suffer from a very large search

space or have to restrict the flexibility of human resource allocation to simplify the

model. To develop a flexible and effective model for software project planning,

this paper develops a novel approach with an event-based scheduler (EBS) and an

ant colony optimization (ACO) algorithm. The proposed approach represents a

plan by a task list and a planned employee allocation matrix. In this way, both the

issues of task scheduling and employee allocation can be taken into account. In the

EBS, the beginning time of the project, the time when resources are released from

finished tasks, and the time when employees join or leave the project are regarded

as events. The basic idea of the EBS is to adjust the allocation of employees at

events and keep the allocation unchanged at nonevents. With this strategy, the

proposed method enables the modeling of resource conflict and task preemption

and preserves the flexibility in human resource allocation. To solve the planning


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problem, an ACO algorithm is further designed. Experimental results on 83

instances demonstrate that the proposed method is very promising.

4. Locating Need-to-Externalize Constant Strings for Software

Internationalization with Generalized String-Taint Analysis

ABSTRACT:

Nowadays, a software product usually faces a global market. To meet the

requirements of different local users, the software product must be

internationalized. In an internationalized software product, user-visible hard-coded

constant strings are externalized to resource files so that local versions can be

generated by translating the resource files. In many cases, a software product is not

internationalized at the beginning of the software development process. To

internationalize an existing product, the developers must locate the user-visible

constant strings that should be externalized. This locating process is tedious and

error-prone due to 1) the large number of both user-visible and non-user-visible

constant strings and 2) the complex data flows from constant strings to the

Graphical User Interface (GUI). In this paper, we propose an automatic approach

to locating need-to-externalize constant strings in the source code of a software

product. Given a list of precollected API methods that output values of their string

argument variables to the GUI and the source code of the software product under

analysis, our approach traces from the invocation sites (within the source code) of

these methods back to the need-to-externalize constant strings using generalized

string-taint analysis. In our empirical evaluation, we used our approach to locate

need-to-externalize constant strings in the uninternationalized versions of seven


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real-world open source software products. The results of our evaluation

demonstrate that our approach is able to effectively locate need-to-externalize

constant strings in uninternationalized software products. Furthermore, to help

developers understand why a constant string requires translation and properly

translate the need-to-externalize strings, we provide visual representation of the

string dependencies related to the need-to-externalize strings.

Date post:	25-May-2015
Category:	Technology
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