2011 ieee projects

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S.NO TITLE -2010 ABSTRACT DOMAIN PLATFORM

1. A Machine Learning TCP throughput prediction is an important capability for Networking .net
Approach to TCP networks where multiple paths exist between data
Throughput senders and receivers. In this paper, we describe a new
Prediction lightweight method for TCP throughput prediction. Our
predictor uses Support Vector Regression (SVR);
prediction is based on both prior file transfer history and
measurements of simple path properties. We evaluate
our predictor in a laboratory setting where ground truth
can be measured with perfect accuracy. We report the
performance of our predictor for oracular and practical
measurements of path properties over a wide range of
traffic conditions and transfer sizes. For bulk transfers in
heavy traffic using oracular measurements, TCP
throughput is predicted within 10% of the actual value
87% of the time, representing nearly a threefold
improvement in accuracy over prior history-based
methods. For practical measurements of path properties,
predictions can be made within 10% of the actual value
nearly 50% of the time, approximately a 60%
improvement over history-based methods, and with
much lower measurement traffic overhead. We
implement our predictor in a tool called PathPerf, test it
in the wide area, and show that PathPerf predicts TCP
throughput accurately over diverse wide area paths.
2. Feedback-Based A framework for designing feedback-based scheduling .net
Scheduling for Load- algorithms is proposed for elegantly solving the
Balanced Two-Stage notorious packet missequencing problem of a load-
Switches balanced switch. Unlike existing approaches, we show
that the efforts made in load balancing and keeping
packets in order can complement each other. Specifically,
at each middle-stage port between the two switch fabrics
of a load-balanced switch, only a single-packet buffer for
each virtual output queueing (VOQ) is required. Although
packets belonging to the same flow pass through
different middle-stage VOQs, the delays they experience
at different middle-stage ports will be identical. This is
made possible by properly selecting and coordinating
the two sequences of switch configurations to form a
joint sequence with both staggered symmetry property
and in-order packet delivery property. Based on the
staggered symmetry property, an efficient feedback
mechanism is designed to allow the right middle-stage
port occupancy vector to be delivered to the right input
port at the right time. As a result, the performance of
load balancing as well as the switch throughput is
significantly improved. We further extend this feedback
mechanism to support the multicabinet implementation
of a load-balanced switch, where the propagation delay
between switch linecards and switch fabrics is
nonnegligible. As compared to the existing load-balanced
switch architectures and scheduling algorithms, our
solutions impose a modest requirement on switch
hardware, but consistently yield better delay-throughput
performance. Last but not least, some extensions and
refinements are made to address the scalability,
implementation, and fairness issues of our solutions.



3. Trust management in In this paper, we propose a human-based model which .net
mobile ad hoc networks builds a trust relationship between nodes in an ad hoc
using a scalable maturity network. The trust is based on previous individual
based model experiences and on the recommendations of others. We
present the Recommendation Exchange Protocol (REP)
which allows nodes to exchange recommendations about
their neighbors. Our proposal does not require
disseminating the trust information over the entire
network. Instead, nodes only need to keep and exchange
trust information about nodes within the radio range.
Without the need for a global trust knowledge, our
proposal scales well for large networks while still
reducing the number of exchanged messages and
therefore the energy consumption. In addition, we
mitigate the effect of colluding attacks composed of liars
in the network. A key concept we introduce is the
relationship maturity, which allows nodes to improve
the efficiency of the proposed model for mobile
scenarios. We show the correctness of our model in a
single-hop network through simulations. We also extend
the analysis to mobile multihop networks, showing the
benefits of the maturity relationship concept. We
evaluate the impact of malicious nodes that send false
recommendations to degrade the efficiency of the trust
model. At last, we analyze the performance of the REP
protocol and show its scalability. We show that our
implementation of REP can significantly reduce the
number messages.
4. Online social networks OSNs applications, it is a location-based social network Network .net
services, security and privacy

of OSNs, and human mobility models based on social
network OSNs online service site focuses of social
networks or social relations among people, e.g., who
share interests and activities. A social network service
essentially consists of a representation of each user
(often a profile), his/her social links, and a variety of
additional services. Most social network services are web
based and provide means for users to interact over the
internet, such as e-mail and instant messaging. Although
online community services are sometimes considered as
a social network online community services are group-
centered. Social networking sites allow users to share
ideas, activities, events, and interests within their
individual networks.

5. SYNCHRONIZATION OF File synchronization in computing is the process of
LOCAL DESKTOP TO making sure that files in two or more locations are
INTERNET USING FILE updated through certain rules. In one-way file
TRANSFER PROTOCOL synchronization, also called mirroring, updated files are
copied from a 'source' location to one or more 'target'
locations, but no files are copied back to the source
location. In two-way file synchronization, updated files
are copied in both directions, usually with the purpose of
keeping the two locations identical to each other. In this
article, the term synchronization refers exclusively to
two-way file synchronization.



6. Intrusion Detection for Providing security in a distributed system requires more
Grid and Cloud than user authentication with passwords or digital
Computing certificates and confidentiality in data transmission. The
Grid and Cloud Computing Intrusion Detection System
integrates knowledge and behavior analysis to detect
intrusions.

7. Adaptive Physical Transmit power and carrier sense threshold are key
Carrier Sense in MAC/PHY parameters in carrier sense multiple access
Topology-Controlled (CSMA) wireless networks. Transmit power control has
Wireless Networks been extensively studied in the context of topology
control. However, the effect of carrier sense threshold on
topology control has not been properly investigated in
spite of its crucial role. Our key motivation is that the
performance of a topology-controlled network may
become worse than that of a network without any
topology control unless carrier sense threshold is
properly chosen. In order to remedy this deficiency of
conventional topology control, we present a framework
on how to incorporate physical carrier sense into
topology control. We identify that joint control of
transmit power and carrier sense threshold can be
efficiently divided into topology control and carrier
sense adaptation. We devise a distributed carrier sense
update algorithm (DCUA), by which each node drives its
carrier sense threshold toward a desirable operating
point in a fully distributed manner. We derive a sufficient
condition for the convergence of DCUA. To demonstrate
the utility of integrating physical carrier sense into
topology control, we equip a localized topology control
algorithm, LMST, with the capability of DCUA. Simulation
studies show that LMST-DCUA significantly outperforms
LMST and the standard

8. On the Quality of Service of We model the probabilistic behavior of a system Dependable .net
Crash-Recovery Failure comprising a failure detector and a monitored crash- and Security
Detectors recovery target. We extend failure detectors to take
account of failure recovery in the target system. This
involves extending QoS measures to include the
recovery detection speed and proportion of failures
detected. We also extend estimating the parameters of
the failure detector to achieve a required QoS to
configuring the crash-recovery failure detector. We
investigate the impact of the dependability of the
monitored process on the QoS of our failure detector.
Our analysis indicates that variation in the MTTF and
MTTR of the monitored process can have a significant
impact on the QoS of our failure detector. Our analysis
is supported by simulations that validate our
theoretical results.



9. Layered Approach using Intrusion detection faces challenges an intrusion
conditional random field detection system must constantly detect malicious
activities in a network and must perform efficiently to
cope with the large amount of network traffic. These
two issues of Accuracy and Efficiency using
Conditional Random Fields and Layered Approach. We
show that high attack detection accuracy can be
achieved by using Conditional Random Fields and high
efficiency by implementing the Layered Approach.
Experimental results on the benchmark KDD ’99
intrusion data set show that our proposed system
based on Layered Conditional Random Fields
outperforms other well-known methods such as the
decision trees and the naive Bayes. The improvement
in attack detection accuracy is very high, particularly,
for the U2R attacks (34.8 percent improvement) and
the R2L attacks (34.5 percent improvement).
Statistical Tests also demonstrate higher confidence in
detection accuracy for our method. Finally, we show
that our system is robust and is able to handle noisy

data without compromising performance.

10. Privacy-Preserving Sharing Privacy-preserving sharing of sensitive information Security and .net
of Sensitive Information (PPSSI) is motivated by the increasing need for entities privacy
(organizations or individuals) that don't fully trust
each other to share sensitive information. Many types
of entities need to collect, analyze, and disseminate
data rapidly and accurately, without exposing
sensitive information to unauthorized or untrusted
parties. Although statistical methods have been used
to protect data for decades, they aren't foolproof and
generally involve a trusted third party. Recently, the
security research community has studied—and, in a
few cases, deployed—techniques using secure,
multiparty function evaluation, encrypted keywords,
and private information retrieval. However, few
practical tools and technologies provide data privacy,
especially when entities have certain common goals
and require (or are mandated) some sharing of
sensitive information. To this end, PPSSI technology
aims to enable sharing information, without exposing
more than the minimum necessary to complete a
common task.

11. PEACE Security and privacy issues are of most concern in
pushing the success of WMNs(Wireless Mesh
Networks) for their wide deployment and for
supporting service-oriented applications. Despite the
necessity, limited security research has been
conducted toward privacy preservation in WMNs. This
motivates us to develop PEACE, a novel Privacy-
Enhanced
yet Accountable security framework, tailored for
WMNs



12. The Phish-Market Protocol: One way banks mitigate phishing's effects is to remove .net
Secure Sharing Between fraudulent websites or suspend abusive domain
Competitors names. The removal process, called a "take-down," is
often subcontracted to specialist firms, who refuse to
share feeds of phishing website URLs with each other.
Consequently, many phishing websites aren't
removed. The take-down companies are reticent to
exchange feeds, fearing that competitors with less
comprehensive lists might free-ride off their efforts.
Here, the authors propose the Phish-Market protocol,
which enables companies to be compensated for
information they provide to their competitors,
encouraging them to share. The protocol is designed
so that the contributing firm is compensated only for
those websites affecting its competitor's clients and
only those previously unknown to the receiving firm.
The receiving firm, on the other hand, is guaranteed
privacy for its client list. The protocol solves a more
general problem of sharing between competitors;
applications to data brokers in marketing, finance,
energy exploration, and beyond could also benefit.

13. Internet Filtering Issues Various governments have been considering .net
and Challenges mechanisms to filter out illegal or offensive Internet
material. The accompanying debate raises a number of
questions from a technical perspective. This article
explores some of these questions, such as, what
filtering techniques exist,are they effective in filtering
out the specific content, how easy is circumventing
them ,where should they be placed in the Internet
architecture.

14. Can Public-Cloud Security Because cloud-computing environments' security .net
Meet Its Unique vulnerabilities differ from those of traditional data
Challenges? centers, perimeter-security approaches will no longer
work. Security must move from the perimeter to the
virtual machines.

15. Encrypting Keys Securely Encryption keys are sometimes encrypted themselves; .net
doing that properly requires special care. Although it
might look like an oversight at first, the broadly
accepted formal security definitions for cryptosystems
don't allow encryption of key-dependent messages.
Furthermore, key-management systems frequently
use key encryption or wrapping, which might create
dependencies among keys that lead to problems with
simple access-control checks. Security professionals
should be aware of this risk and take appropriate
measures. Novel cryptosystems offer protection for
key-dependent messages and should be considered for
practical use. Through enhanced access control in key-
management systems, you can prevent security-
interface attacks.

16. Auto-Context and Its The notion of using context information for solving Pattern .net
Application to High-Level high-level vision and medical image segmentation Analysis and
Vision Tasks and 3D Brain problems has been increasingly realized in the field. Machine
Image Segmentation However, how to learn an effective and efficient Intelligence
context model, together with an image appearance



model, remains mostly unknown. The current
literature using Markov Random Fields (MRFs) and
Conditional Random Fields (CRFs) often involves
specific algorithm design in which the modeling and
computing stages are studied in isolation. In this
paper, we propose a learning algorithm, auto-context.
Given a set of training images and their corresponding
label maps, we first learn a classifier on local image
patches. The discriminative probability (or
classification confidence) maps created by the learned
classifier are then used as context information, in
addition to the original image patches, to train a new
classifier. The algorithm then iterates until
convergence. Auto-context integrates low-level and
context information by fusing a large number of low-
level appearance features with context and implicit
shape information. The resulting discriminative
algorithm is general and easy to implement. Under
nearly the same parameter settings in training, we
apply the algorithm to three challenging vision
applications: foreground/background segregation,
human body configuration estimation, and scene
region labeling. Moreover, context also plays a very
important role in medical/brain images where the
anatomical structures are mostly constrained to
relatively fixed positions. With only some slight
changes resulting from using 3D instead of 2D
features, the auto-context algorithm applied to brain
MRI image segmentation is shown to outperform
state-of-the-art algorithms specifically designed for
this domain. Furthermore, the scope of the proposed
algorithm goes beyond image analysis and it has the
potential to be used for a wide variety of problems for
structured prediction problems.

17. CSMA protocol Mitigating This system is developed to show the descriptive java
Performance Degradation management of dreadful conditions in Congested
in Congested Sensor Sensor Networks. The dreadful conditions in sensor
Networks networks or any other wired networks will happen
when bandwidth differs from receiving and sending
points. The channel capacity of the network may not
be sufficient enough to handle the speed of packets
sent. In this system, we are presenting a view, how the
data can be sent through the congested channel and
also the safe delivery of the packets to the destination.
This System is developed using java swing technology
with jdk1.6. All the nodes are developed as swing
API‘s.Multiple API‘s form a sink to the destination. The
packets will be sent from Source to destination, via
sink. In the sink, a node will be made congested and
using channel capacity, the path of data will be
calculated. Based on the result of the calculation, the
congestion in the sink will be dissolved and data is set
free to the destination.This system is an application to
maintain the free flow of data in congested sensor
networks using Differentiated Routing Protocol and
Priority Queues, which maintain priority in data-types.



18. Feature Analysis and The definition of parameters is a crucial step in the Multimedia .net
Evaluation for Automatic development of a system for identifying emotions in
Emotion Identification in speech. Although there is no agreement on which are
Speech the best features for this task, it is generally accepted
that prosody carries most of the emotional
information. Most works in the field use some kind of
prosodic features, often in combination with spectral
and voice quality parametrizations. Nevertheless, no
systematic study has been done comparing these
features. This paper presents the analysis of the
characteristics of features derived from prosody,
spectral envelope, and voice quality as well as their
capability to discriminate emotions. In addition, early
fusion and late fusion techniques for combining
different information sources are evaluated. The
results of this analysis are validated with experimental
automatic emotion identification tests. Results suggest
that spectral envelope features outperform the
prosodic ones. Even when different parametrizations
are combined, the late fusion of long-term spectral
statistics with short-term spectral envelope
parameters provides an accuracy comparable to that
obtained when all parametrizations are combined.

19. Automatic Detection of Off- Identifying off-task behaviors in intelligent tutoring Learning .net
Task Behaviors in systems is a practical and challenging research topic. Technologie
Intelligent Tutoring This paper proposes a machine learning model that s
Systems with Machine can automatically detect students' off-task behaviors.
Learning Techniques The proposed model only utilizes the data available
from the log files that record students' actions within
the system. The model utilizes a set of time features,
performance features, and mouse movement features,
and is compared to 1) a model that only utilizes time
features and 2) a model that uses time and
performance features. Different students have
different types of behaviors; therefore, personalized
version of the proposed model is constructed and
compared to the corresponding nonpersonalized
version. In order to address data sparseness problem,
a robust Ridge Regression algorithm is utilized to
estimate model parameters. An extensive set of
experiment results demonstrates the power of using
multiple types of evidence, the personalized model,
and the robust Ridge Regression algorithm.

20. Web-Application Security: Here's a sobering thought for all managers responsible IT .net
From Reactive to Proactive for Web applications: Without proactive consideration
for an application's security, attackers can bypass
nearly all lower-layer security controls simply by
using the application in a way its developers didn't
envision. Learn how to address vulnerabilities
proactively and early on to avoid the devastating
consequences of a successful attack.

21. Trust and Reputation Trust and reputation management research is highly INTERNET .net
Management interdisciplinary, involving researchers from COMPUTING
networking and communication, data management
and information systems, e-commerce and service
computing, artificial intelligence, and game theory, as



well as the social sciences and evolutionary biology.
Trust and reputation management has played and will
continue to play an important role in Internet and
social computing systems and applications. This
special issue addresses key issues in the field, such as
representation, recommendation aggregation, and
attack-resilient reputation systems.

22. Multi-body Structure-and- An efficient and robust framework is proposed for Image .net
Motion Segmentation by two-view multiple structure-and-motion segmentation Processing
Branch-and-Bound Model of unknown number of rigid objects. The segmentation
Selection problem has three unknowns, namely the object
memberships, the corresponding fundamental
matrices, and the number of objects. To handle this
otherwise recursive problem, hypotheses for
fundamental matrices are generated through local
sampling. Once the hypotheses are available, a
combinatorial selection problem is formulated to
optimize a model selection cost which takes into
account the hypotheses likelihoods and the model
complexity. An explicit model for outliers is also added
for robust segmentation. The model selection cost is
minimized through the branch-and-bound technique
of combinatorial optimization. The proposed branch-
and-bound approach efficiently searches the solution
space and guaranties optimality over the current set of
hypotheses. The efficiency and the guarantee of
optimality of the method is due to its ability to reject
solutions without explicitly evaluating them. The
proposed approach was validated with synthetic data,
and segmentation results are presented for real
images.

23. Active Image Re ranking Image search reranking methods usually fail to .net
capture the user's intention when the query term is
ambiguous. Therefore, reranking with user
interactions, or active reranking, is highly demanded
to effectively improve the search performance. The
essential problem in active reranking is how to target
the user's intention. To complete this goal, this paper
presents a structural information based sample
selection strategy to reduce the user's labeling efforts.
Furthermore, to localize the user's intention in the
visual feature space, a novel local-global
discriminative dimension reduction algorithm is
proposed. In this algorithm, a submanifold is learned
by transferring the local geometry and the
discriminative information from the labelled images to
the whole (global) image database. Experiments on
both synthetic datasets and a real Web image search
dataset demonstrate the effectiveness of the proposed
active reranking scheme, including both the structural
information based active sample selection strategy
and the local-global discriminative dimension
reduction algorithm.

24. Content Based Image An innovative approach based on an evolutionary .net
Retrieval using PSO stochastic algorithm, namely the Particle Swarm
Optimizer (PSO), is proposed in this paper as a



solution to the problem of intelligent retrieval of
images in large databases. The problem is recast to an
optimization one, where a suitable cost function is
minimized through a customized PSO. Accordingly, the
relevance-feedback is used in order to exploit the
information of the user with the aim of both guiding
the particles inside the search space and dynamically
assigning different weights to the features.

25. Automatic Composition of This paper presents a novel approach for semantic .net
Semantic Web Services An web service composition based on traditional state
Enhanced State Space space search approach. We regard automatic web
Search Approach service composition problem as an AI problem-solving
problem and propose an enhanced state space search
approach toward web service composition domain.
This approach can not only be used for automatic
service composition, but also for general problem-
solving domain. In addition, in order to validate the
feasibility of our approach, a prototype system is
implemented.

26. Knowledge-first web Although semantic technologies aren't used in current .net
services an E-Government software systems on a large scale yet, they offer high
example potential to significantly improve the quality of
electronic services especially in the E-Government
domain. This paper therefore presents an approach
that not only incorporates semantic technologies but
allows to create E-Government services solely based
on semantic models. This multiplies the benefits of the
ontology modeling efforts, minimizes development
and maintenance time and costs, improves user
experience and enforces transparency.

27. The Applied Research of This paper firstly introduces the characteristics of the Cloud .net
Cloud Computing Platform current E-Learning, and then analyzes the concept and
Architecture In the E- characteristics of cloud computing, and describes the computing
Learning Area architecture of cloud computing platform; by
combining the characteristics of E-Learning and
learning from current major infrastructure approach
of cloud computing platform, this paper structures a
relatively complete set of integration and use in one of
the E-Learning platform, puts the cloud computing
platform apply to the study of E-Learning, and focus on
the application in order to improve the resources'
stability, balance and utilization; under the conditions,
this platform will meet the demand for the current
teaching and research activities, improve the greatest
value of the E-Learning.

28. Cloud Computing System Cloud computing provides people a way to share large .net
Based on Trusted mount of distributed resources belonging to different
Computing Platform organizations. That is a good way to share many kinds
of distributed resources, but it also makes security
problems more complicate and more important for
users than before. In this paper, we analyze some
security requirements in cloud computing
environment. Since the security problems both in
software and hardware, we provided a method to
build a trusted computing environment for cloud



computing by integrating the trusted computing
platform (TCP) into cloud computing system. We
propose a new prototype system, in which cloud
computing system is combined with Trusted Platform
Support Service (TSS) and TSS is based on Trusted
Platform Module (TPM). In this design, better effect
can be obtained in authentication, role based access
and data protection in cloud computing environment.

29.

IT Auditing to Assure a In this paper we discuss the evolvement of cloud .net
Secure Cloud Computing. computing paradigm and present a framework for
secure cloud computing through IT auditing. Our
approach is to establish a general framework using
checklists by following data flow and its lifecycle. The
checklists are made based on the cloud deployment
models and cloud services models. The contribution of
the paper is to understand the implication of cloud
computing and what is meant secure cloud computing
via IT auditing rather than propose a new
methodology and new technology to secure cloud
computing. Our holistic approach has strategic value
to those who are using or consider using cloud
computing because it addresses concerns such as
security, privacy and regulations and compliance.

30. Performance Evaluation of Advanced computing on cloud computing .net
Cloud Computing Offerings infrastructures can only become viable alternative for
the enterprise if these infrastructures can provide
proper levels of nonfunctional properties (NPFs). A
company that focuses on service-oriented
architectures (SOA) needs to know what configuration
would provide the proper levels for individual services
if they are deployed in the cloud. In this paper we
present an approach for performance evaluation of
cloud computing configurations. While cloud
computing providers assure certain service levels, this
it typically done for the platform and not for a
particular service instance. Our approach focuses on
NFPs of individual services and thereby provides a
more relevant and granular information. An
experimental evaluation in Amazon Elastic Compute
Cloud (EC2) verified our approach.

31. Providing Privacy People can only enjoy the full benefits of Cloud .net
Preserving in cloud computing if we can address the very real privacy and
computing security concerns that come along with storing
sensitive personal information in databases and
software scattered around the Internet. There are
many service provider in the internet, we can call each
service as a cloud, each cloud service will exchange
data with other cloud, so when the data is exchanged
between the clouds, there exist the problem of
disclosure of privacy. So the privacy disclosure
problem about individual or company is inevitably
exposed when releasing or sharing data in the cloud
service. Privacy is an important issue for cloud
computing, both in terms of legal compliance and user



trust, and needs to be considered at every phase of
design. Our paper provides some privacy preserving
technologies used in cloud computing services.

32. VEBEK: Virtual Energy- Designing cost-efficient, secure network protocols for Wireless .net
Based Encryption and Wireless Sensor Networks (WSNs) is a challenging Computing
Keying for Wireless Sensor problem because sensors are resource-limited
Networks wireless devices. Since the communication cost is the
most dominant factor in a sensor's energy
consumption, we introduce an energy-efficient Virtual
Energy-Based Encryption and Keying (VEBEK) scheme
for WSNs that significantly reduces the number of
transmissions needed for rekeying to avoid stale keys.
In addition to the goal of saving energy, minimal
transmission is imperative for some military
applications of WSNs where an adversary could be
monitoring the wireless spectrum. VEBEK is a secure
communication framework where sensed data is
encoded using a scheme based on a permutation code
generated via the RC4 encryption mechanism. The key
to the RC4 encryption mechanism dynamically
changes as a function of the residual virtual energy of
the sensor. Thus, a one-time dynamic key is employed
for one packet only and different keys are used for the
successive packets of the stream. The intermediate
nodes along the path to the sink are able to verify the
authenticity and integrity of the incoming packets
using a predicted value of the key generated by the
sender's virtual energy, thus requiring no need for
specific rekeying messages. VEBEK is able to efficiently
detect and filter false data injected into the network by
malicious outsiders. The VEBEK framework consists of
two operational modes (VEBEK-I and VEBEK-II), each
of which is optimal for different scenarios. In VEBEK-I,
each node monitors its one-hop neighbors where
VEBEK-II statistically monitors downstream nodes.
We have evaluated VEBEK's feasibility and
performance analytically and through simulations. Our
results show that VEBEK, without incurring
transmission overhead (increasing packet size or
sending control messages for rekeying), is able to
eliminate malicious data from the network in an
energy-efficient manner. We also show that our
framework performs be- - tter than other comparable
schemes in the literature with an overall 60-100
percent improvement in energy savings without the
assumption of a reliable medium access control layer.

33. Secure Data Collection in Compromised node and denial of service are two key .net
Wireless Sensor Networks attacks in wireless sensor networks (WSNs). In this
Using Randomized paper, we study data delivery mechanisms that can
Dispersive Routes with high probability circumvent black holes formed
by these attacks. We argue that classic multipath
routing approaches are vulnerable to such attacks,
mainly due to their deterministic nature. So once the
adversary acquires the routing algorithm, it can
compute the same routes known to the source, hence,
making all information sent over these routes
vulnerable to its attacks. In this paper, we develop



mechanisms that generate randomized multipath
routes. Under our designs, the routes taken by the ??
shares?? of different packets change over time. So even
if the routing algorithm becomes known to the
adversary, the adversary still cannot pinpoint the
routes traversed by each packet. Besides randomness,
the generated routes are also highly dispersive and
energy efficient, making them quite capable of
circumventing black holes. We analytically investigate
the security and energy performance of the proposed
schemes. We also formulate an optimization problem
to minimize the end-to-end energy consumption under
given security constraints. Extensive simulations are
conducted to verify the validity of our mechanisms.

34. Aging Bloom Filter with A Bloom filter is a simple but powerful data structure Data Mining .net
Two Active Buffers for that can check membership to a static set. As Bloom
Dynamic Sets. filters become more popular for network applications,
a membership query for a dynamic set is also required.
Some network applications require high-speed
processing of packets. For this purpose, Bloom filters
should reside in a fast and small memory, SRAM. In
this case, due to the limited memory size, stale data in
the Bloom filter should be deleted to make space for
new data. Namely the Bloom filter needs aging like
LRU caching. In this paper, we propose a new aging
scheme for Bloom filters. The proposed scheme
utilizes the memory space more efficiently than double
buffering, the current state of the art. We prove
theoretically that the proposed scheme outperforms
double buffering. We also perform experiments on real
Internet traces to verify the effectiveness of the
proposed scheme.

35. Bayesian Classifiers The Bayesian classifier is a fundamental classification .net
Programmed in SQL technique. In this work, we focus on programming
Bayesian classifiers in SQL. We introduce two
classifiers: naive Bayes and a classifier based on class
decomposition using K-means clustering. We consider
two complementary tasks: model computation and
scoring a data set. We study several layouts for tables
and several indexing alternatives. We analyze how to
transform equations into efficient SQL queries and
introduce several query optimizations. We conduct
experiments with real and synthetic data sets to
evaluate classification accuracy, query optimizations,
and scalability. Our Bayesian classifier is more
accurate than naive Bayes and decision trees. Distance
computation is significantly accelerated with
horizontal layout for tables, denormalization, and
pivoting. We also compare naive Bayes
implementations in SQL and C++: SQL is about four
times slower. Our Bayesian classifier in SQL achieves
high classification accuracy, can efficiently analyze
large data sets, and has linear scalability.



36. Using a web-based tool to Top-down process improvement approaches provide a java
define and implement high-level model of what the process of a software
software process development organisation should be. Such models are
improvement initiatives in based on the consensus of a designated working group
a small industrial setting on how software should be developed or maintained.
They are very useful in that they provide general
guidelines on where to start improving, and in which
order, to people who do not know how to do it.
However, the majority of models have only worked in
scenarios within large companies. The authors aim to
help small software development organisations adopt
an iterative approach by providing a process
improvement web-based tool. This study presents
research into a proposal which states that a small
organisation may use this tool to assess and improve
their software process, identifying and implementing a
set of agile project management practices that can be
strengthened using the CMMI-DEV 1.2 model as
reference.

37. An Online Monitoring
2 Web service technology aims to enable the Java
Approach for Web Service interoperation of heterogeneous systems and the
Requirements reuse of distributed functions in an unprecedented
(An Online Monitoring scale and has achieved significant success. There are
Approach for Web Service still, however, challenges to realize its full potential.
Requirements –web One of these challenges is to ensure the behaviour of
services(ME)) Web services consistent with their requirements.
Monitoring events that are relevant to
Web service requirements is, thus, an important
technique. This paper introduces an online monitoring
approach for Web service requirements. It includes a
pattern-based specification of service constraints that
correspond to service requirements, and a monitoring
model that covers five kinds of system events relevant
to client request, service response, application,
resource, and management, and a monitoring
framework in which different probes and agents
collect events and data that are sensitive to
requirements. The framework analyzes the collected
information against the prespecified constraints, so as
to evaluate the behaviour and use of Web
services. The prototype implementation and
experiments with a case study shows that our
approach is effective and flexible, and the monitoring
cost is affordable.



S.NO TITLE -2011 ABSTRACT DOMAIN PLATFORM

1. Exploiting Dynamic In recent years ad hoc parallel data processing has Parallel
Resource Allocation emerged to be one of the killer applications for Distribution
for Efficient Parallel Infrastructure-as-a-Service (IaaS) clouds. Major Cloud
Data Processing in computing companies have started to integrate
the Cloud frameworks for parallel data processing in their product
portfolio, making it easy for customers to access these
services and to deploy their programs. However, the
processing frameworks which are currently used have
been designed for static, homogeneous cluster setups
and disregard the particular nature of a cloud.
Consequently, the allocated compute resources may be
inadequate for big parts of the submitted job and
unnecessarily increase processing time and cost. In this
paper, we discuss the opportunities and challenges for
efficient parallel data processing in clouds and present
our research project Nephele. Nephele is the first data
processing framework to explicitly exploit the dynamic
resource allocation offered by today's IaaS clouds for
both, task scheduling and execution. Particular tasks of a
processing job can be assigned to different types of
virtual machines which are automatically instantiated
and terminated during the job execution. Based on this
new framework, we perform extended evaluations of
MapReduce-inspired processing jobs on an IaaS cloud
system and compare the results to the popular data
processing framework Hadoop.
2. Data integrity proofs Cloud computing has been envisioned as the de-facto Communicat
in cloud storage solution to the rising storage costs of IT Enterprises. ion System &
With the high costs of data storage devices as well as the network
rapid rate at which data is being generated it proves
costly for enterprises or individual users to frequently
update their hardware. Apart from reduction in storage
costs data outsourcing to the cloud also helps in reducing
the maintenance. Cloud storage moves the user’s data to
large data centers, which are remotely located, on which
user does not have any control. However, this unique
feature of the cloud poses many new security challenges
which need to be clearly understood and resolved. One of
the important concerns that need to be addressed is to
assure the customer of the integrity i.e. correctness of his
data in the cloud. As the data is physically not accessible
to the user the cloud should provide a way for the user to
check if the integrity of his data is maintained or is
compromised. In this paper we provide a scheme which
gives a proof of data integrity in the cloud which the
customer can employ to check the correctness of his data
in the cloud. This proof can be agreed upon by both the
cloud and the customer and can be incorporated in the
Service level agreement (SLA). This scheme ensures that
the storage at the client side is minimal which will be
beneficial for thin clients.

3. Efficient Computing In many applications, including location based services, Knowledge
of Range Aggregates queries are not precise. In this paper, we study the & data
against Uncertain problem of efficiently computing range aggregates in a engineering
Location Based multi-dimensional space when the query location is
uncertain. That is, for a set of data points P, an uncertain



Collections location based query Q with location described by a
probabilistic density function, we want to calculate the
aggregate information (e.g., count, average} and sum) of
the data points within distance gamma to Q with
probability at least theta. We propose novel, efficient
techniques to solve the problem based on a filtering-and-
verification framework. In particular, two novel filtering
techniques are proposed to effectively and efficiently
remove data points from verification. Finally, we show
that our techniques can be immediately extended to
solve the range query problem. Comprehensive
experiments conducted on both real and synthetic data
demonstrate the efficiency and scalability of our
techniques.

4. Exploring Natural phenomena show that many creatures form Knowledge
Application-Level large social groups and move in regular patterns. & Data
Semantics for Data However, previous works focus on finding the movement Engineering
Compression patterns of each single object or all objects. In this paper,
we first propose an efficient distributed mining
algorithm to jointly identify a group of moving objects
and discover their movement patterns in wireless sensor
networks. Afterward, we propose a compression
algorithm, called 2P2D, which exploits the obtained
group movement patterns to reduce the amount of
delivered data. The compression algorithm includes a
sequence merge and an entropy reduction phases. In the
sequence merge phase, we propose a Merge algorithm to
merge and compress the location data of a group of
moving objects. In the entropy reduction phase, we
formulate a Hit Item Replacement (HIR) problem and
propose a Replace algorithm that obtains the optimal
solution. Moreover, we devise three replacement rules
and derive the maximum compression ratio. The
experimental results show that the proposed
compression algorithm leverages the group movement
patterns to reduce the amount of delivered data
effectively and efficiently.
5. Improving Aggregate Recommender systems are becoming increasingly Knowledge
Recommendation important to individual users and businesses for & Data
Diversity Using providing personalized recommendations. However, Engineering
Ranking-Based while the majority of algorithms proposed in
Techniques recommender systems literature have focused on
improving recommendation accuracy (as exemplified by
the recent Netflix Prize competition), other important
aspects of recommendation quality, such as the diversity
of recommendations, have often been overlooked. In this
paper, we introduce and explore a number of item
ranking techniques that can generate recommendations
that have substantially higher aggregate diversity across
all users while maintaining comparable levels of
recommendation accuracy. Comprehensive empirical
evaluation consistently shows the diversity gains of the
proposed techniques using several real-world rating
datasets and different rating prediction algorithms.
6. Monitoring Service Business processes are increasingly distributed and Service
Systems from a open, making them prone to failure. Monitoring is, Computing
Language-Action therefore, an important concern not only for the
processes themselves but also for the services that



Perspective comprise these processes. We present a framework for
multilevel monitoring of these service systems. It
formalizes interaction protocols, policies, and
commitments that account for standard and extended
effects following the language-action perspective, and
allows specification of goals and monitors at varied
abstraction levels. We demonstrate how the framework
can be implemented and evaluate it with multiple
scenarios that include specifying and monitoring open-
service policy commitments.

7. One Size Does Not Fit With the emergence of the deep Web databases, Knowledge
All Towards User- searching in domains such as vehicles, real estate, etc. & data
and Query- has become a routine task. One of the problems in this engineering
Dependent Ranking context is ranking the results of a user query. Earlier
For Web Databases approaches for addressing this problem have used
frequencies of database values, query logs, and user
profiles. A common thread in most of these approaches is
that ranking is done in a user- and/or query-
independent manner. This paper proposes a novel
query- and user-dependent approach for ranking the
results of Web database queries. We present a ranking
model, based on two complementary notions of user and
query similarity, to derive a ranking function for a given
user query. This function is acquired from a sparse
workload comprising of several such ranking functions
derived for various user-query pairs. The proposed
model is based on the intuition that similar users display
comparable ranking preferences over the results of
similar queries. We define these similarities formally in
alternative ways and discuss their effectiveness both
analytically and experimentally over two distinct Web
databases.
8. Optimal Service Cloud applications that offer data management services Knowledge
Pricing for a Cloud are emerging. Such clouds support caching of data in & data
Cache order to provide quality query services. The users can engineering
query the cloud data, paying the price for the
infrastructure they use. Cloud management necessitates
an economy that manages the service of multiple users in
an efficient, but also, resource-economic way that allows
for cloud profit. Naturally, the maximization of cloud
profit given some guarantees for user satisfaction
presumes an appropriate price-demand model that
enables optimal pricing of query services. The model
should be plausible in that it reflects the correlation of
cache structures involved in the queries. Optimal pricing
is achieved based on a dynamic pricing scheme that
adapts to time changes. This paper proposes a novel
price-demand model designed for a cloud cache and a
dynamic pricing scheme for queries executed in the
cloud cache. The pricing solution employs a novel
method that estimates the correlations of the cache
services in an time-efficient manner. The experimental
study shows the efficiency of the solution.
9. A Personalized As a model for knowledge description and formalization, Knowledge
Ontology Model for ontologies are widely used to represent user profiles in & data
Web Information personalized web information gathering. However, when engineering
Gathering representing user profiles, many models have utilized
only knowledge from either a global knowledge base or a



user local information. In this paper, a personalized
ontology model is proposed for knowledge
representation and reasoning over user profiles. This
model learns ontological user profiles from both a world
knowledge base and user local instance repositories. The
ontology model is evaluated by comparing it against
benchmark models in web information gathering. The
results show that this ontology model is successful.
10. A Branch-and-Bound In branch-and-bound (B&B) schemes for solving a Computers
Algorithm for Solving minimization problem, a better lower bound could prune
the Multiprocessor many meaningless branches which do not lead to an
Scheduling Problem optimum solution. In this paper, we propose several
with Improved techniques to refine the lower bound on the makespan in
Lower Bounding the multiprocessor scheduling problem (MSP). The key
Techniques idea of our proposed method is to combine an efficient
quadratic-time algorithm for calculating the Fernández's
bound, which is known as the best lower bounding
technique proposed in the literature with two
improvements based on the notions of binary search and
recursion. The proposed method was implemented as a
part of a B&B algorithm for solving MSP, and was
evaluated experimentally. The result of experiments
indicates that the proposed method certainly improves
the performance of the underlying B&B scheme. In
particular, we found that it improves solutions generated
by conventional heuristic schemes for more than 20
percent of randomly generated instances, and for more
than 80 percent of instances, it could provide a
certification of optimality of the resulting solutions, even
when the execution time of the B&B scheme is limited by
one minute.
11. Design and Peer-to-peer (P2P) systems generate a major fraction of Computers
Evaluation of a Proxy the current Internet traffic, and they significantly
Cache for Peer-to- increase the load on ISP networks and the cost of
Peer Traffic running and connecting customer networks (e.g.,
universities and companies) to the Internet. To mitigate
these negative impacts, many previous works in the
literature have proposed caching of P2P traffic, but very
few (if any) have considered designing a caching system
to actually do it. This paper demonstrates that caching
P2P traffic is more complex than caching other Internet
traffic, and it needs several new algorithms and storage
systems. Then, the paper presents the design and
evaluation of a complete, running, proxy cache for P2P
traffic, called pCache. pCache transparently intercepts
and serves traffic from different P2P systems. A new
storage system is proposed and implemented in pCache.
This storage system is optimized for storing P2P traffic,
and it is shown to outperform other storage systems. In
addition, a new algorithm to infer the information
required to store and serve P2P traffic by the cache is
proposed. Furthermore, extensive experiments to
evaluate all aspects of pCache using actual
implementation and real P2P traffic are presented.
12. Robust Feature Feature selection often aims to select a compact feature Computation
Selection for subset to build a pattern classifier with reduced al Biology
Microarray Data complexity, so as to achieve improved classification and
Based on performance. From the perspective of pattern analysis, Bioinformati
producing stable or robust solution is also a desired cs



Multicriterion Fusion property of a feature selection algorithm. However, the
issue of robustness is often overlooked in feature
selection. In this study, we analyze the robustness issue
existing in feature selection for high-dimensional and
small-sized gene-expression data, and propose to
improve robustness of feature selection algorithm by
using multiple feature selection evaluation criteria.
Based on this idea, a multicriterion fusion-based
recursive feature elimination (MCF-RFE) algorithm is
developed with the goal of improving both classification
performance and stability of feature selection results.
Experimental studies on five gene-expression data sets
show that the MCF-RFE algorithm outperforms the
commonly used benchmark feature selection algorithm
SVM-RFE.
13. Image-Based Surface Emerging technologies for structure matching based on Computation
Matching Algorithm surface descriptions have demonstrated their al Biology
Oriented to effectiveness in many research fields. In particular, they and
Structural Biology can be successfully applied to in silico studies of Bioinformati
structural biology. Protein activities, in fact, are related cs
to the external characteristics of these macromolecules
and the ability to match surfaces can be important to
infer information about their possible functions and
interactions. In this work, we present a surface-matching
algorithm, based on encoding the outer morphology of
proteins in images of local description, which allows us
to establish point-to-point correlations among
macromolecular surfaces using image-processing
functions. Discarding methods relying on biological
analysis of atomic structures and expensive
computational approaches based on energetic studies,
this algorithm can successfully be used for
macromolecular recognition by employing local surface
features. Results demonstrate that the proposed
algorithm can be employed both to identify surface
similarities in context of macromolecular functional
analysis and to screen possible protein interactions to
predict pairing capability
14. Iris matching using Iris recognition is one of the most widely used biometric Computer
multi-dimensional technique for personal identification. This identification Vision, IET
artificial neural is achieved in this work by using the concept that, the iris
network patterns are statistically unique and suitable for
biometric measurements. In this study, a novel method
of recognition of these patterns of an iris is considered
by using a multidimensional artificial neural network.
The proposed technique has the distinct advantage of
using the entire resized iris as an input at once. It is
capable of excellent pattern recognition properties as the
iris texture is unique for every person used for
recognition. The system is trained and tested using two
publicly available databases (CASIA and UBIRIS). The
proposed approach shows significant promise and
potential for improvements, compared with the other
conventional matching techniques with regard to time
and efficiency of results.
15. Real-time tracking Many vision problems require fast and accurate tracking Computer
using A* heuristic of objects in dynamic scenes. In this study, we propose Vision, IET
search and template an A* search algorithm through the space of
transformations for computing fast target 2D motion.



updating Two features are combined in order to compute efficient
motion: (i) Kullback??Leibler measure as heuristic to
guide the search process and (ii) incorporation of target
dynamics into the search process for computing the most
promising search alternatives. The result value of the
quality of match computed by the A* search algorithm
together with the more common views of the target
object are used for verifying template updates. A
template will be updated only when the target object has
evolved to a transformed shape dissimilar with respect
to the actual shape. The study includes experimental
evaluations with video streams demonstrating the
effectiveness and efficiency for real-time vision based
tasks with rigid and deformable objects.
16. Integral image The large amount of image data from the captured three- Computer
compression based dimensional integral image requires to be presented Vision, IET
on optical with adequate resolution. It is therefore necessary to
characteristic develop compression algorithms that take advantage of
the characteristics of the recorded integral image. In this
study, the authors propose a new compression method
that is adapted to integral imaging. According to the
optical characteristics of integral imaging, most of the
information of each elemental image is overlapped with
that of its adjacent elemental images. Thus, the method is
to achieve image compression by taking a sample from
the elemental image sequence for every m elemental
image to get image compression. Experimental results
that are presented to illustrate the proposed
compression technique prove that the proposed
technique can improve the compression ratio of integral
imaging
17. A Variational Model In this paper, we propose a variational formulation for Image
for Histogram histogram transfer of two or more color images. We Processing
Transfer of Color study an energy functional composed by three terms:
Images one tends to approach the cumulative histograms of the
transformed images, the other two tend to maintain the
colors and geometry of the original images. By
minimizing this energy, we obtain an algorithm that
balances equalization and the conservation of features of
the original images. As a result, they evolve while
approaching an intermediate histogram between them.
This intermediate histogram does not need to be
specified in advance, but it is a natural result of the
model. Finally, we provide experiments showing that the
proposed method compares well with the state of the art.
18. Nonlocal Mumford- We propose here a class of restoration algorithms for Image
Shah Regularizers color images, based upon the Mumford-Shah (MS) model Processing
for Color Image and nonlocal image information. The Ambrosio-
Restoration Tortorelli and Shah elliptic approximations are defined
to work in a small local neighborhood, which are
sufficient to denoise smooth regions with sharp
boundaries. However, texture is nonlocal in nature and
requires semilocal/non-local information for efficient
image denoising and restoration. Inspired from recent
works (nonlocal means of Buades, Coll, Morel, and
nonlocal total variation of Gilboa, Osher), we extend the
local Ambrosio-Tortorelli and Shah approximations to
MS functional (MS) to novel nonlocal formulations, for
better restoration of fine structures and texture. We



present several applications of the proposed nonlocal MS
regularizers in image processing such as color image
denoising, color image deblurring in the presence of
Gaussian or impulse noise, color image inpainting, color
image super-resolution, and color filter array
demosaicing. In all the applications, the proposed
nonlocal regularizers produce superior results over the
local ones, especially in image inpainting with large
missing regions. We also prove several characterizations
of minimizers based upon dual norm Formulations.
19. A Majorize–Minimize This paper proposes accelerated subspace optimization Image
Strategy for methods in the context of image restoration. Subspace Processing
Subspace optimization methods belong to the class of iterative
Optimization Applied descent algorithms for unconstrained optimization. At
to Image Restoration each iteration of such methods, a stepsize vector
allowing the best combination of several search
directions is computed through a multidimensional
search. It is usually obtained by an inner iterative
second-order method ruled by a stopping criterion that
guarantees the convergence of the outer algorithm. As an
alternative, we propose an original multidimensional
search strategy based on the majorize-minimize
principle. It leads to a closed-form stepsize formula that
ensures the convergence of the subspace algorithm
whatever the number of inner iterations. The practical
efficiency of the proposed scheme is illustrated in the
context of edge-preserving image restoration.
20. A Variational Model We propose a variant of the Mumford-Shah model for the Image
for Segmentation of segmentation of a pair of overlapping objects with Processing
Overlapping Objects additive intensity value. Unlike standard segmentation
With Additive models, it does not only determine distinct objects in the
Intensity Value. image, but also recover the possibly multiple
membership of the pixels. To accomplish this, some a
priori knowledge about the smoothness of the object
boundary is integrated into the model. Additivity is
imposed through a soft constraint which allows the user
to control the degree of additivity and is more robust
than the hard constraint. We also show analytically that
the additivity parameter can be chosen to achieve some
stability conditions. To solve the optimization problem
involving geometric quantities efficiently, we apply a
multiphase level set method. Segmentation results on
synthetic and real images validate the good performance
of our model, and demonstrate the model's applicability
to images with multiple channels and multiple objects.
21. Image Segmentation This paper presents a multiphase fuzzy region Image
Using Fuzzy Region competition model that takes into account spatial and Processing
Competition and frequency information for image segmentation. In the
Spatial/Frequency proposed energy functional, each region is represented
Information by a fuzzy membership function and a data fidelity term
that measures the conformity of spatial and frequency
data within each region to (generalized) Gaussian
densities whose parameters are determined jointly with
the segmentation process. Compared with the classical
region competition model, our approach gives soft
segmentation results via the fuzzy membership
functions, and moreover, the use of frequency data
provides additional region information that can improve
the overall segmentation result. To efficiently solve the



minimization of the energy functional, we adopt an
alternate minimization procedure and make use of
Chambolle's fast duality projection algorithm. We apply
the proposed method to synthetic and natural textures
as well as real-world natural images. Experimental
results show that our proposed method has very
promising segmentation performance compared with the
current state-of-the-art approaches.
22. H.264 video More people are studying digital video stream Image
watermarking with transference via networks. However, frequent Internet Processing,
secret image sharing use increases the requirement for copyright protection IET
and security. As a consequence, to prevent video streams
that belong to rightful owners from being intentionally
or unknowingly used by others, information protection is
indispensable. The authors propose a novel method for
video watermarking that is specifically designed for
H.264 video. For the experiment, a low-energy signal can
relatively guard against low-pass filter attacks.
Conversely, a high-energy signal in the host signal can
relatively guard against the high-frequency noise attack.
In view of these facts, the proposed system design
embedding algorithm provides high-energy and low-
energy blocks. The blocks in the host image frame are
divided into two different groups by estimating the block
energy. The existing singular value decomposition
methods were employed to calculate the watermark
information. In order to enhance the security, the
proposed system also employs torus automorphisms to
encrypt the watermark. To achieve better robustness,
the encrypted results use secret image sharing
technology embedded into different I-frames in the video
stream.
23. Rotation, scaling, and Traditional watermarking schemes are sensitive to Image
translation resilient geometric distortions, in which synchronisation for Processing
watermarking for recovering embedded information is a challenging task
images because of the disorder caused by rotation, scaling or
translation (RST). The existing RST-resistant
watermarking methods still have limitations with
respect to robustness, capacity or fidelity. In this study,
the authors address several major problems in RST-
invariant watermarking. The first point is how to take
advantage of the high RST resilience of scale-invariant
feature transform (SIFT) features, which show good
performance in terms of RSTresistant pattern
recognition. Since many keypoint-based watermarking
methods do not discuss cropping attacks, the second
issue discussed in this study is how to resist cropping
using a human visual system (HVS), which also helps us
to eliminate computational complexity. The third issue is
the investigation of an HVS-based watermarking strategy
for extracting only feature points in the human attentive
area. Lastly, a variable-length watermark
synchronisation algorithm using dynamic programming
is proposed. Experimental results show that the
proposed algorithms are practical and show superior
performance in comparison with many existing works in
terms of watermark capacity, watermark transparency,
and the resistance to RST attacks.
24. Improvements on For classification problems, the generalized eigenvalue Neural



Twin Support Vector proximal support vector machine (GEPSVM) and twin Networks
Machines support vector machine (TWSVM) are regarded as
milestones in the development of the powerful SVMs, as
they use the nonparallel hyperplane classifiers. In this
brief, we propose an improved version, named twin
bounded support vector machines (TBSVM), based on
TWSVM. The significant advantage of our TBSVM over
TWSVM is that the structural risk minimization principle
is implemented by introducing the regularization term.
This embodies the marrow of statistical learning theory,
so this modification can improve the performance of
classification. In addition, the successive overrelaxation
technique is used to solve the optimization problems to
speed up the training procedure. Experimental results
show the effectiveness of our method in both
computation time and classification accuracy, and
therefore confirm the above conclusion further.
25. Feature Selection This paper presents a new wrapper-based feature Neural
Using Probabilistic selection method for support vector regression (SVR) Networks
Prediction of Support using its probabilistic predictions. The method computes
Vector Regression the importance of a feature by aggregating the difference,
over the feature space, of the conditional density
functions of the SVR prediction with and without the
feature. As the exact computation of this importance
measure is expensive, two approximations are proposed.
The effectiveness of the measure using these
approximations, in comparison to several other existing
feature selection methods for SVR, is evaluated on both
artificial and real-world problems. The result of the
experiments show that the proposed method generally
performs better than, or at least as well as, the existing
methods, with notable advantage when the dataset is
sparse.
26. Energy-Efficient In cooperative networks, transmitting and receiving Networking
Protocol for nodes recruit neighboring nodes to assist in
Cooperative communication. We model a cooperative transmission
Networks link in wireless networks as a transmitter cluster and a
receiver cluster. We then propose a cooperative
communication protocol for establishment of these
clusters and for cooperative transmission of data. We
derive the upper bound of the capacity of the protocol,
and we analyze the end-to-end robustness of the
protocol to data-packet loss, along with the tradeoff
between energy consumption and error rate. The
analysis results are used to compare the energy savings
and the end-to-end robustness of our protocol with two
non-cooperative schemes, as well as to another
cooperative protocol published in the technical
literature. The comparison results show that, when
nodes are positioned on a grid, there is a reduction in the
probability of packet delivery failure by two orders of
magnitude for the values of parameters considered. Up
to 80% in energy savings can be achieved for a grid
topology, while for random node placement our
cooperative protocol can save up to 40% in energy
consumption relative to the other protocols. The
reduction in error rate and the energy savings translate
into increased lifetime of cooperative sensor networks.
27. Parametric Methods This paper develops parametric methods to detect Networking



for Anomaly network anomalies using only aggregate traffic statistics,
Detection in in contrast to other works requiring flow separation,
Aggregate Traffic even when the anomaly is a small fraction of the total
traffic. By adopting simple statistical models for
anomalous and background traffic in the time domain,
one can estimate model parameters in real time, thus
obviating the need for a long training phase or manual
parameter tuning. The proposed bivariate parametric
detection mechanism (bPDM) uses a sequential
probability ratio test, allowing for control over the false
positive rate while examining the tradeoff between
detection time and the strength of an anomaly.
Additionally, it uses both traffic-rate and packet-size
statistics, yielding a bivariate model that eliminates most
false positives. The method is analyzed using the bit-rate
signal-to-noise ratio (SNR) metric, which is shown to be
an effective metric for anomaly detection. The
performance of the bPDM is evaluated in three ways.
First, synthetically generated traffic provides for a
controlled comparison of detection time as a function of
the anomalous level of traffic. Second, the approach is
shown to be able to detect controlled artificial attacks
over the University of Southern California (USC), Los
Angeles, campus network in varying real traffic mixes.
Third, the proposed algorithm achieves rapid detection
of real denial-of-service attacks as determined by the
replay of previously captured network traces. The
method developed in this paper is able to detect all
attacks in these scenarios in a few seconds or less.
28. Peering Equilibrium It is generally admitted that interdomain peering links Networking
Multipath Routing: A represent nowadays the main bottleneck of the Internet,
Game Theory particularly because of lack of coordination between
Framework for providers, which use independent and “selfish” routing
Internet Peering policies. We are interested in identifying possible “light”
Settlements coordination strategies that would allow carriers to
better control their peering links while preserving their
independence and respective interests. We propose a
robust multipath routing coordination framework for
peering carriers, which relies on the multiple-exit
discriminator (MED) attribute of Border Gateway
Protocol (BGP) as signaling medium. Our scheme relies
on a game theory modeling, with a non-cooperative
potential game considering both routing and congestions
costs. Peering equilibrium multipath (PEMP)
coordination policies can be implemented by selecting
Pareto-superior Nash equilibria at each carrier. We
compare different PEMP policies to BGP Multipath
schemes by emulating a realistic peering scenario. Our
results show that the routing cost can be decreased by
roughly 10% with PEMP. We also show that the stability
of routes can be significantly improved and that
congestion can be practically avoided on the peering
links. Finally, we discuss practical implementation
aspects and extend the model to multiple players
highlighting the possible incentives for the resulting
extended peering framework.
29. Impact of File Traditionally, it had been assumed that the efficiency Networking
Arrivals and requirements of TCP dictate that the buffer size at the
Departures on Buffer router must be of the order of the bandwidth-delay (C ×



Sizing in Core RTT) product. Recently, this assumption was questioned
Routers in a number of papers, and the rule was shown to be
conservative for certain traffic models. In particular, by
appealing to statistical multiplexing, it was shown that
on a router with N long-lived connections, buffers of size
O([(C × RTT)/(√N)]) or even O(1) are sufficient. In this
paper, we reexamine the buffer-size requirements of
core routers when flows arrive and depart. Our
conclusion is as follows: If the core-to-access-speed ratio
is large, then O(1) buffers are sufficient at the core
routers; otherwise, larger buffer sizes do improve the
flow-level performance of the users. From a modeling
point of view, our analysis offers two new insights. First,
it may not be appropriate to derive buffer-sizing rules by
studying a network with a fixed number of users. In fact,
depending upon the core-to-access-speed ratio, the
buffer size itself may affect the number of flows in the
system, so these two parameters (buffer size and
number of flows in the system) should not be treated as
independent quantities. Second, in the regime where the
core-to-access-speed ratio is large, we note that the O(1)
buffer sizes are sufficient for good performance and that
no loss of utilization results, as previously believed.
30. Dynamic Traffic monitoring is a critical network operation for the Networking
measurement-aware purpose of traffic accounting, debugging or
routing in practice troubleshooting, forensics, and traffic engineering.
Existing techniques for traffic monitoring, however, tend
to be suboptimal due to poor choice of monitor location
or constantly evolving monitoring objectives and traffic
characteristics. One way to counteract these limitations
is to use routing as a degree of freedom to enhance
monitoring efficacy, which we refer to as measurement-
aware routing. Traffic sub-populations can be routed
(rerouted) on the fly to optimally leverage existing
monitoring infrastructures. Implementing dynamic
measurementaware routing (DMR) in practice is riddled
with challenges. Three major challenges are how to
dynamically assess the importance of traffic flows; how
to aggregate flows (and hence take a common action for
them) in order to conserve routing table entries; and
how to achieve traffic routing/rerouting in a manner that
is least disruptive to normal network performance while
maximizing the measurement utility. This article takes a
closer look at these challenges and discusses how they
manifest for different types of networks. Through an
OpenFlow prototype, we show how DMR can be applied
in enterprise networks. Using global iceberg detection
and capture as a driving application, we demonstrate
how our solutions successfully route suspected iceberg
flows to a DPI box for further processing, while
preserving balanced load distribution in the overall
network.
31. Measurement and Through measurement study, we discover an interesting Networking
diagnosis of address phenomenon, P2P address misconfiguration, in which a
misconfigured P2P large number of peers send P2P file downloading
traffic requests to a ??random?? target on the Internet. Through
measuring three large datasets spanning four years and
across five different /8 networks, we find address-
misconfigured P2P traffic on average contributes 38.9



percent of Internet background radiation, increasing by
more than 100 percent every year. To detect and
diagnose such unwanted traffic, we design the P2PScope,
a measurement tool. After analyzing about 2 Tbytes of
data and tracking millions of peers, we found that in all
the P2P systems, address misconfiguration is caused by
resource mapping contamination: the sources returned
for a given file ID through P2P indexing are not valid.
Different P2P systems have different reasons for such
contamination. For eMule, we find that the root cause is
mainly a network byte-order problem in the eMule
Source Exchange protocol. For BitTorrent
misconfiguration, one reason is that anti-P2P companies
actively inject bogus peers into the P2P system. Another
reason is that the KTorrent implementation has a byte-
order problem.
32. Packet traffic: a good The wireless sensor network (WSN) has emerged as a Networking
data source for promising technology. In WSNs, sensor nodes are
wireless sensor distributedly deployed to collect interesting information
network modeling from the environment. Because of the mission of WSNs,
and anomaly most node-wide as well as network-wide activities are
detection manifested in packet traffic. As a result, packet traffic
becomes a good data source for modeling sensor node as
well as sensor network behaviors. In this article, the
methodology of modeling node and network behavior
profiles using packet traffic is exemplified. In addition,
node as well as network anomalies are shown to be
detectable by monitoring the evolution of node/network
behavior profiles.
33. Experiences of Since the early days of the Internet, network traffic Networking
Internet traffic monitoring has always played a strategic role in
monitoring with tstat understanding and characterizing users?? activities. In
this article, we present our experience in engineering
and deploying Tstat, an open source passive monitoring
tool that has been developed in the past 10 years. Started
as a scalable tool to continuously monitor packets that
flow on a link, Tstat has evolved into a complex
application that gives network researchers and
operators the possibility to derive extended and complex
measurements thanks to advanced traffic classifiers.
After discussing Tstat capabilities and internal design,
we present some examples of measurements collected
deploying Tstat at the edge of several ISP networks in
past years. While other works report a continuous
decline of P2P traffic with streaming and file hosting
services rapidly increasing in popularity, the results
presented in this article picture a different scenario.
First, P2P decline has stopped, and in the last months of
2010 there was a counter tendency to increase P2P
traffic over UDP, so the common belief that UDP traffic is
negligible is not true anymore. Furthermore, streaming
and file hosting applications have either stabilized or are
experiencing decreasing traffic shares. We then discuss
the scalability issues software-based tools have to cope
with when deployed in real networks, showing the
importance of properly identifying bottlenecks.
34. Network traffic Modern computer networks are increasingly pervasive, Networking
monitoring, analysis complex, and ever-evolving due to factors like enormous
and anomaly growth in the number of network users, continuous



detection [Guest appearance of network applications, increasing amount
Editorial] of data transferred, and diversity of user behaviors.
Understanding and measuring such a network is a
difficult yet vital task for network management and
diagnosis. Network traffic monitoring, analysis, and
anomaly detection provide useful tools for
understanding network behavior and determining
network performance and reliability so as to effectively
and promptly troubleshoot and resolve various issues in
practice.
35. Scheduling Grid Grid scheduling is essential to Quality of Service Network and
Tasks in Face of provisioning as well as to efficient management of grid Service
Uncertain resources. Grid scheduling usually considers the state of Management
Communication the grid resources as well application demands.
Demands However, such demands are generally unknown for
highly demanding applications, since these often
generate data which will be transferred during their
execution. Without appropriate assessment of these
demands, scheduling decisions can lead to poor
performance. Thus, it is of paramount importance to
consider uncertainties in the formulation of a grid
scheduling problem. This paper introduces the IPDT-
FUZZY scheduler, a scheduler which considers the
demands of grid applications with such uncertainties.
The scheduler uses fuzzy optimization, and both
computational and communication demands are
expressed as fuzzy numbers. Its performance was
evaluated, and it was shown to be attractive when
communication requirements are uncertain. Its efficacy
is compared, via simulation, to that of a deterministic
counterpart scheduler and the results reinforce its
adequacy for dealing with the lack of accuracy in the
estimation of communication demands.
36. Improving Dynamic application placement for clustered web Network and
Application applications heavily influences system performance and Service
Placement for quality of user experience. Existing approaches claim Management
Cluster-Based Web that they strive to maximize the throughput, keep
Applications resource utilization balanced across servers, and
minimize the start/stop cost of application instances.
However, they fail to minimize the worst case of server
utilization; the load balancing performance is not
optimal. What's more, some applications need to
communicate with each other, which we called
dependent applications; the network cost of them also
should be taken into consideration. In this paper, we
investigate how to minimize the resource utilization of
servers in the worst case, aiming at improving load
balancing among clustered servers. Our contribution is
two-fold. First we propose and define a new optimization
objectives: limiting the worst case of each individual
server's utilization, formulated by a min-max problem. A
novel framework based on binary search is proposed to
detect an optimal load balancing solution. Second, we
define system cost as the weighted combination of both
placement change and inter-application communication
cost. By maximizing the number of instances of
dependent applications that reside in the same set of
servers, the basic load-shifting and placement-change
procedures are enhanced to minimize whole system cost.



Extensive experiments have been conducted and
effectively demonstrate that: 1) the proposed framework
achieves a good allocation for clustered web
applications. In other words, requests are evenly
allocated among servers, and throughput is still
maximized; 2) the total system cost maintains at a low
level; 3) our algorithm has the capacity of approximating
an optimal solution within polynomial time and is
promising for practical implementation in real
deployments.
37. Efficient Network When a link or node fails, flows are detoured around the Network and
Modification to failed portion, so the hop count of flows and the link load Service
Improve QoS could change dramatically as a result of the failure. As Management
Stability at Failure real-time traffic such as video or voice increases on the
Internet, ISPs are required to provide stable quality as
well as connectivity at failures. For ISPs, how to
effectively improve the stability of these qualities at
failures with the minimum investment cost is an
important issue, and they need to effectively select a
limited number of locations to add link facilities. In this
paper, efficient design algorithms to select the locations
for adding link facilities are proposed and their
effectiveness is evaluated using the actual backbone
networks of 36 commercial ISPs.
38. Spectral Models for In network measurement systems, packet sampling Network and
Bitrate Measurement techniques are usually adopted to reduce the overall Service
from Packet Sampled amount of data to collect and process. Being based on a Management
Traffic subset of packets, they introduce estimation errors that
have to be properly counteracted by using a fine tuning
of the sampling strategy and sophisticated inversion
methods. This problem has been deeply investigated in
the literature with particular attention to the statistical
properties of packet sampling and to the recovery of the
original network measurements. Herein, we propose a
novel approach to predict the energy of the sampling
error in the real time estimation of traffic bitrate, based
on spectral analysis in the frequency domain. We start by
demonstrating that the error introduced by packet
sampling can be modeled as an aliasing effect in the
frequency domain. Then, we derive closed-form
expressions for the Signal-to-Noise Ratio (SNR) to
predict the distortion of traffic bitrate estimates over
time. The accuracy of the proposed SNR metric is
validated by means of real packet traces. Furthermore, a
comparison with respect to an analogous SNR expression
derived using classic stochastic tools is proposed,
showing that the frequency domain approach grants for
a higher accuracy when traffic rate measurements are
carried out at fine time granularity
39. Vulnerability Systems proposed in academic research have so far Security &
Detection Systems: failed to make a significant impact on real-world Privacy
Think Cyborg, Not vulnerability detection. Most software bugs are still
Robot found by methods with little input from static-analysis
and verification research. These research areas could
have a significant impact on software security, but first
we need a shift in research goals and approaches. We
need systems that incorporate human code auditors'
knowledge and abilities, and we need evaluation
methods that actually test proposed systems' usability in



real situations. Without changes, academic research will
continue to be ignored by the security community, and
opportunities to build better tools for finding bugs and
understanding software will be missed.
40. Dynamic QoS Service-based systems that are dynamically composed at Software
Management and runtime to provide complex, adaptive functionality are Engineering
Optimization in currently one of the main development paradigms in
Service-Based software engineering. However, the Quality of Service
Systems (QoS) delivered by these systems remains an important
concern, and needs to be managed in an equally adaptive
and predictable way. To address this need, we introduce
a novel, tool-supported framework for the development
of adaptive service-based systems called QoSMOS (QoS
Management and Optimization of Service-based
systems). QoSMOS can be used to develop service-based
systems that achieve their QoS requirements through
dynamically adapting to changes in the system state,
environment, and workload. QoSMOS service-based
systems translate high-level QoS requirements specified
by their administrators into probabilistic temporal logic
formulae, which are then formally and automatically
analyzed to identify and enforce optimal system
configurations. The QoSMOS self-adaptation mechanism
can handle reliability and performance-related QoS
requirements, and can be integrated into newly
developed solutions or legacy systems. The effectiveness
and scalability of the approach are validated using
simulations and a set of experiments based on an
implementation of an adaptive service-based system for
remote medical assistance.
41. Seeking Quality of Ranking and optimization of web service compositions Knowledge
Web Service represent challenging areas of research with significant and Data
Composition in a implications for the realization of the “Web of Services” Engineering
Semantic Dimension vision. “Semantic web services” use formal semantic
descriptions of web service functionality and interface to
enable automated reasoning over web service
compositions. To judge the quality of the overall
composition, for example, we can start by calculating the
semantic similarities between outputs and inputs of
connected constituent services, and aggregate these
values into a measure of semantic quality for the
composition. This paper takes a specific interest in
combining semantic and nonfunctional criteria such as
quality of service (QoS) to evaluate quality in web
services composition. It proposes a novel and extensible
model balancing the new dimension of semantic quality
(as a functional quality metric) with a QoS metric, and
using them together as ranking and optimization criteria.
It also demonstrates the utility of Genetic Algorithms to
allow optimization within the context of a large number
of services foreseen by the “Web of Services” vision. We
test the performance of the overall approach using a set
of simulation experiments, and discuss its advantages
and weaknesses.
42. Mining Cluster-Based Researches on Location-Based Service (LBS) have been Knowledge
Temporal Mobile emerging in recent years due to a wide range of potential and Data
Sequential Patterns applications. One of the active topics is the mining and Engineering
in Location-Based prediction of mobile movements and associated
Service transactions. Most of existing studies focus on



Environments discovering mobile patterns from the whole logs.
However, this kind of patterns may not be precise
enough for predictions since the differentiated mobile
behaviors among users and temporal periods are not
considered. In this paper, we propose a novel algorithm,
namely, Cluster-based Temporal Mobile Sequential
Pattern Mine (CTMSP-Mine), to discover the Cluster-
based Temporal Mobile Sequential Patterns (CTMSPs).
Moreover, a prediction strategy is proposed to predict
the subsequent mobile behaviors. In CTMSP-Mine, user
clusters are constructed by a novel algorithm named
Cluster-Object-based Smart Cluster Affinity Search
Technique (CO-Smart-CAST) and similarities between
users are evaluated by the proposed measure, Location-
Based Service Alignment (LBS-Alignment). Meanwhile, a
time segmentation approach is presented to find
segmenting time intervals where similar mobile
characteristics exist. To our best knowledge, this is the
first work on mining and prediction of mobile behaviors
with considerations of user relations and temporal
property simultaneously. Through experimental
evaluation under various simulated conditions, the
proposed methods are shown to deliver excellent
performance.
43. Locally Consistent Previous studies have demonstrated that document Knowledge
Concept clustering performance can be improved significantly in and Data
Factorization for lower dimensional linear subspaces. Recently, matrix Engineering
Document Clustering factorization-based techniques, such as Nonnegative
Matrix Factorization (NMF) and Concept Factorization
(CF), have yielded impressive results. However, both of
them effectively see only the global euclidean geometry,
whereas the local manifold geometry is not fully
considered. In this paper, we propose a new approach to
extract the document concepts which are consistent with
the manifold geometry such that each concept
corresponds to a connected component. Central to our
approach is a graph model which captures the local
geometry of the document submanifold. Thus, we call it
Locally Consistent Concept Factorization (LCCF). By
using the graph Laplacian to smooth the document-to-
concept mapping, LCCF can extract concepts with respect
to the intrinsic manifold structure and thus documents
associated with the same concept can be well clustered.
The experimental results on TDT2 and Reuters-21578
have shown that the proposed approach provides a
better representation and achieves better clustering
results in terms of accuracy and mutual information.

44. Knowledge Service mashup is the act of integrating the resulting Knowledge
Discovery in Services data of two complementary software services into a and Data
(KDS): Aggregating common picture. Such an approach is promising with Engineering
Software Services to respect to the discovery of new types of knowledge.
Discover Enterprise However, before service mashup routines can be
Mashups executed, it is necessary to predict which services (of an
open repository) are viable candidates. Similar to
Knowledge Discovery in Databases (KDD), we introduce
the Knowledge Discovery in Services (KDS) process that
identifies mashup candidates. In this work, the KDS



process is specialized to address a repository of open
services that do not contain semantic annotations. In
these situations, specialized techniques are required to
determine equivalences among open services with
reasonable precision. This paper introduces a bottom-up
process for KDS that adapts to the environment of
services for which it operates. Detailed experiments are
discussed that evaluate KDS techniques on an open
repository of services from the Internet and on a
repository of services created in a controlled
environment.

45. Design and The intrusion response component of an overall Knowledge
Implementation of intrusion detection system is responsible for issuing a and Data
an Intrusion suitable response to an anomalous request. We propose Engineering
Response System for the notion of database response policies to support our
Relational Databases intrusion response system tailored for a DBMS. Our
interactive response policy language makes it very easy
for the database administrators to specify appropriate
response actions for different circumstances depending
upon the nature of the anomalous request. The two main
issues that we address in context of such response
policies are that of policy matching, and policy
administration. For the policy matching problem, we
propose two algorithms that efficiently search the policy
database for policies that match an anomalous request.
We also extend the PostgreSQL DBMS with our policy
matching mechanism, and report experimental results.
The experimental evaluation shows that our techniques
are very efficient. The other issue that we address is that
of administration of response policies to prevent
malicious modifications to policy objects from legitimate
users. We propose a novel Joint Threshold
Administration Model (JTAM) that is based on the
principle of separation of duty. The key idea in JTAM is
that a policy object is jointly administered by at least k
database administrator (DBAs), that is, any modification
made to a policy object will be invalid unless it has been
authorized by at least k DBAs. We present design details
of JTAM which is based on a cryptographic threshold
signature scheme, and show how JTAM prevents
malicious modifications to policy objects from
authorized users. We also implement JTAM in the
PostgreSQL DBMS, and report experimental results on
the efficiency of our techniques.

46. Automatic Discovery An individual is typically referred by numerous name Knowledge
of Personal Name aliases on the web. Accurate identification of aliases of a and Data
Aliases from the Web given person name is useful in various web related tasks Engineering
such as information retrieval, sentiment analysis,
personal name disambiguation, and relation extraction.
We propose a method to extract aliases of a given
personal name from the web. Given a personal name, the
proposed method first extracts a set of candidate aliases.
Second, we rank the extracted candidates according to
the likelihood of a candidate being a correct alias of the



given name. We propose a novel, automatically extracted
lexical pattern-based approach to efficiently extract a
large set of candidate aliases from snippets retrieved
from a web search engine. We define numerous ranking
scores to evaluate candidate aliases using three
approaches: lexical pattern frequency, word co-
occurrences in an anchor text graph, and page counts on
the web. To construct a robust alias detection system, we
integrate the different ranking scores into a single
ranking function using ranking support vector machines.
We evaluate the proposed method on three data sets: an
English personal names data set, an English place names
data set, and a Japanese personal names data set. The
proposed method outperforms numerous baselines and
previously proposed name alias extraction methods,
achieving a statistically significant mean reciprocal rank
(MRR) of 0.67. Experiments carried out using location
names and Japanese personal names suggest the
possibility of extending the proposed method to extract
aliases for different types of named entities, and for
different languages. Moreover, the aliases extracted
using the proposed method are successfully utilized in an
information retrieval task and improve recall by 20
percent in a relation-detection task.

47. Classification and Most existing data stream classification techniques Knowledge
Novel Class Detection ignore one important aspect of stream data: arrival of a and Data
in Concept-Drifting novel class. We address this issue and propose a data Engineering
Data Streams under stream classification technique that integrates a novel
Time Constraints class detection mechanism into traditional classifiers,
enabling automatic detection of novel classes before the
true labels of the novel class instances arrive. Novel class
detection problem becomes more challenging in the
presence of concept-drift, when the underlying data
distributions evolve in streams. In order to determine
whether an instance belongs to a novel class, the
classification model sometimes needs to wait for more
test instances to discover similarities among those
instances. A maximum allowable wait time Tc is imposed
as a time constraint to classify a test instance.
Furthermore, most existing stream classification
approaches assume that the true label of a data point can
be accessed immediately after the data point is classified.
In reality, a time delay Tl is involved in obtaining the true
label of a data point since manual labeling is time
consuming. We show how to make fast and correct
classification decisions under these constraints and
apply them to real benchmark data. Comparison with
state-of-the-art stream classification techniques prove
the superiority of our approach.
48. A Machine Learning The Machine Learning (ML) field has gained its Knowledge
Approach for momentum in almost any domain of research and just and Data
Identifying Disease- recently has become a reliable tool in the medical Engineering
Treatment Relations domain. The empirical domain of automatic learning is
in Short Texts used in tasks such as medical decision support, medical
imaging, protein-protein interaction, extraction of
medical knowledge, and for overall patient management
care. ML is envisioned as a tool by which computer-



based systems can be integrated in the healthcare field in
order to get a better, more efficient medical care. This
paper describes a ML-based methodology for building an
application that is capable of identifying and
disseminating healthcare information. It extracts
sentences from published medical papers that mention
diseases and treatments, and identifies semantic
relations that exist between diseases and treatments.
Our evaluation results for these tasks show that the
proposed methodology obtains reliable outcomes that
could be integrated in an application to be used in the
medical care domain. The potential value of this paper
stands in the ML settings that we propose and in the fact
that we outperform previous results on the same data
set.



MATLAB 2011
1. Face Recognition by Information jointly contained in image space, scale and Image
Exploring Information orientation domains can provide rich important clues not seen in Processing
Jointly in Space, Scale and either individual of these domains. The position, spatial
frequency and orientation selectivity properties are believed to
Orientation
have an important role in visual perception. This paper proposes
a novel face representation and recognition approach by
exploring information jointly in image space, scale and
orientation domains. Specifically, the face image is first
decomposed into different scale and orientation responses by
convolving multiscale and multi-orientation Gabor filters.
Second, local binary pattern analysis is used to describe the
neighboring relationship not only in image space, but also in
different scale and orientation responses. This way, information
from different domains is explored to give a good face
representation for recognition. Discriminant classification is then
performed based upon weighted histogram intersection or
conditional mutual information with linear discriminant analysis
techniques. Extensive experimental results on FERET, AR, and
FRGC ver 2.0 databases show the significant advantages of the
proposed method over the existing ones.

2. Detection of Architectural We present methods for the detection of sites of architectural Image
Distortion in Prior distortion in prior mammograms of interval-cancer cases. We Processing
Mammograms hypothesize that screening mammograms obtained prior to the
detection of cancer could contain subtle signs of early stages of
breast cancer, in particular, architectural distortion. The
methods are based upon Gabor filters, phase portrait analysis, a
novel method for the analysis of the angular spread of power,
fractal analysis, Laws' texture energy measures derived from
geometrically transformed regions of interest (ROIs), and
Haralick's texture features. With Gabor filters and phase portrait
analysis, 4224 ROIs were automatically obtained from 106 prior
mammograms of 56 interval-cancer cases, including 301 true-
positive ROIs related to architectural distortion, and from 52
mammograms of 13 normal cases. For each ROI, the fractal
dimension, the entropy of the angular spread of power, 10 Laws'
measures, and Haralick's 14 features were computed. The areas
under the receiver operating characteristic curves obtained
using the features selected by stepwise logistic regression and
the leave-one-ROI-out method are 0.76 with the Bayesian
classifier, 0.75 with Fisher linear discriminant analysis, and 0.78
with a single-layer feed-forward neural network. Free-response
receiver operating characteristics indicated sensitivities of 0.80
and 0.90 at 5.8 and 8.1 false positives per image, respectively,
with the Bayesian classifier and the leave-one-image-out
method.



3. Enhanced Assessment of the With the widespread use of digital cameras, freehand wound Image
Wound-Healing Process by imaging has become common practice in clinical settings. There Processing
Accurate Multiview Tissue is however still a demand for a practical tool for accurate wound
Classification healing assessment, combining dimensional measurements and
tissue classification in a single user-friendly system. We achieved
the first part of this objective by computing a 3-D model for
wound measurements using uncalibrated vision techniques. We
focus here on tissue classification from color and texture region
descriptors computed after unsupervised segmentation. Due to
perspective distortions, uncontrolled lighting conditions and
view points, wound assessments vary significantly between
patient examinations. The main contribution of this paper is to
overcome this drawback with a multiview strategy for tissue
classification, relying on a 3-D model onto which tissue labels are
mapped and classification results merged. The experimental
classification tests demonstrate that enhanced repeatability and
robustness are obtained and that metric assessment is achieved
through real area and volume measurements and wound outline
extraction. This innovative tool is intended for use not only in
therapeutic follow-up in hospitals but also for telemedicine
purposes and clinical research, where repeatability and accuracy
of wound assessment are critical.

4. A New Supervised This paper presents a new supervised method for blood vessel Image
Method for Blood Vessel detection in digital retinal images. This method uses a neural Processing
Segmentation in Retinal network (NN) scheme for pixel classification and computes a 7-D
Images by Using Gray-
vector composed of gray-level and moment invariants-based
Level and Moment
Invariants-Based Features features for pixel representation. The method was evaluated on
the publicly available DRIVE and STARE databases, widely used
for this purpose, since they contain retinal images where the
vascular structure has been precisely marked by experts.
Method performance on both sets of test images is better than
other existing solutions in literature. The method proves
especially accurate for vessel detection in STARE images. Its
application to this database (even when the NN was trained on
the DRIVE database) outperforms all analyzed segmentation
approaches. Its effectiveness and robustness with different
image conditions, together with its simplicity and fast
implementation, make this blood vessel segmentation proposal
suitable for retinal image computer analyses such as automated
screening for early diabetic retinopathy detection.

5. Graph Run-Length Matrices The histopathological examination of tissue specimens is Image
for Histopathological Image essential for cancer diagnosis and grading. However, this Processing

Segmentation examination is subject to a considerable amount of observer
variability as it mainly relies on visual interpretation of
pathologists. To alleviate this problem, it is very important to
develop computational quantitative tools, for which image
segmentation constitutes the core step. In this paper, we
introduce an effective and robust algorithm for the



segmentation of histopathological tissue images. This algorithm
incorporates the background knowledge of the tissue
organization into segmentation. For this purpose, it quantifies
spatial relations of cytological tissue components by
constructing a graph and uses this graph to define new texture
features for image segmentation. This new texture definition
makes use of the idea of gray-level run-length matrices.
However, it considers the runs of cytological components on a
graph to form a matrix, instead of considering the runs of pixel
intensities. Working with colon tissue images, our experiments
demonstrate that the texture features extracted from “graph
run-length matrices” lead to high segmentation accuracies, also
providing a reasonable number of segmented regions.
Compared with four other segmentation algorithms, the results
show that the proposed algorithm is more effective in
histopathological image segmentation.

6. X-ray Categorization and In this study we present an efficient image categorization and Image
Retrieval on the Organ and retrieval system applied to medical image databases, in Processing

Pathology Level, Using particular large radiograph archives. The methodology is based
on local patch representation of the image content, using a “bag
Patch-Based Visual Words
of visual words” approach. We explore the effects of various
parameters on system performance, and show best results using
dense sampling of simple features with spatial content, and a
nonlinear kernel-based support vector machine (SVM) classifier.
In a recent international competition the system was ranked first
in discriminating orientation and body regions in X-ray images. In
addition to organ-level discrimination, we show an application to
pathology-level categorization of chest X-ray data, the most
popular examination in radiology. The system discriminates
between healthy and pathological cases, and is also shown to
successfully identify specific pathologies in a set of chest
radiographs taken from a routine hospital examination. This is a
first step towards similarity-based categorization, which has a
major clinical implications for computer-assisted diagnostics

7. Standard Deviation for This letter proposes a new technique of restoring images Image
Obtaining the Optimal distorted by random-valued impulse noise. The detection Processing
Direction in the Removal of process is based on finding the optimum direction, by calculating
Impulse Noise the standard deviation in different directions in the filtering
window. The tested pixel is deemed original if it is similar to the
pixels in the optimum direction. Extensive simulations prove that
the proposed technique has superior performance, when
compared to other existing methods, especially at high noise
rates.

8. Removal of High Density Salt A modified decision based unsymmetrical trimmed median filter Image
and Pepper Noise Through algorithm for the restoration of gray scale, and color images that Processing
Modified Decision Based are highly corrupted by salt and pepper noise is proposed in this
Unsymmetric Trimmed paper. The proposed algorithm replaces the noisy pixel by



Median Filter trimmed median value when other pixel values, 0's and 255's are
present in the selected window and when all the pixel values are
0's and 255's then the noise pixel is replaced by mean value of
all the elements present in the selected window. This proposed
algorithm shows better results than the Standard Median Filter
(MF), Decision Based Algorithm (DBA), Modified Decision Based
Algorithm (MDBA), and Progressive Switched Median Filter
(PSMF). The proposed algorithm is tested against different
grayscale and color images and it gives better Peak Signal-to-
Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

9. IMAGE Resolution In this correspondence, the authors propose an image resolution Image
Enhancement by Using enhancement technique based on interpolation of the high Processing
Discrete and Stationary frequency subband images obtained by discrete wavelet
Wavelet Decomposition transform (DWT) and the input image. The edges are enhanced
by introducing an intermediate stage by using stationary wavelet
transform (SWT). DWT is applied in order to decompose an input
image into different subbands. Then the high frequency
subbands as well as the input image are interpolated. The
estimated high frequency subbands are being modified by using
high frequency subband obtained through SWT. Then all these
subbands are combined to generate a new high resolution image
by using inverse DWT (IDWT). The quantitative and visual results
are showing the superiority of the proposed technique over the
conventional and state-of-art image resolution enhancement
techniques.

10. Automatic Optic Disc Under the framework of computer-aided eye disease diagnosis, Image
Detection From Retinal this paper presents an automatic optic disc (OD) detection Processing
Images by a Line Operator technique. The proposed technique makes use of the unique
circular brightness structure associated with the OD, i.e., the OD
usually has a circular shape and is brighter than the surrounding
pixels whose intensity becomes darker gradually with their
distances from the OD center. A line operator is designed to
capture such circular brightness structure, which evaluates the
image brightness variation along multiple line segments of
specific orientations that pass through each retinal image pixel.
The orientation of the line segment with the
minimum/maximum variation has specific pattern that can be
used to locate the OD accurately. The proposed technique has
been tested over four public datasets that include 130, 89, 40,
and 81 images of healthy and pathological retinas, respectively.
Experiments show that the designed line operator is tolerant to
different types of retinal lesion and imaging artifacts, and an
average OD detection accuracy of 97.4% is obtained.

11. Wavelet-Based Image Texture In this letter, we propose an efficient one-nearest-neighbor Image
Classification Using Local classifier of texture via the contrast of local energy histograms of Processing
Energy Histograms all the wavelet subbands between an input texture patch and
each sample texture patch in a given training set. In particular,



the contrast is realized with a discrepancy measure which is just
a sum of symmetrized Kullback-Leibler divergences between the
input and sample local energy histograms on all the wavelet
subbands. It is demonstrated by various experiments that our
proposed method obtains a satisfactory texture classification
accuracy in comparison with several current state-of-the-art
texture classification approaches.

12. A Ringing-Artifact This paper proposes a new ringing-artifact reduction Image
Reduction Method for method for image resizing in a block discrete cosine Processing

Block-DCT-Based Image transform (DCT) domain. The proposed method reduces
Resizing ringing artifacts without further blurring, whereas previous
approaches must find a compromise between blurring and
ringing artifacts. The proposed method consists of DCT-
domain filtering and image-domain post-processing, which
reduces ripples on smooth regions as well as overshoot
near strong edges. By generating a mask map of the
overshoot regions, we combine a ripple-reduced image
and an overshoot-reduced image according to the mask
map in the image domain to obtain a ringing-artifact
reduced image. The experimental results show that the
proposed method is computationally faster and produces
visually finer images than previous ringing-artifact
reduction approaches.

2011 ieee projects

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2011 ieee projects