Abstract: Checkpointing is one of the commonly used techniques to provide fault-tolerance in distributed systems so that the system can operate even if one or more components have failed. However, mobile computing systems are constrained by low bandwidth, mobility, lack of stable storage, frequent disconnections and limited battery life. Hence, checkpointing protocols having lesser number of synchronization messages and fewer checkpoints are preferred in mobile environment. There are two different approaches, although not orthogonal, to checkpoint mobile computing systems namely, time-based and index-based. Our protocol is a fusion of these two approaches, though not first of its kind. In the present exposition, an index-based checkpointing protocol has been developed, which uses time to indirectly coordinate the creation of consistent global checkpoints for mobile computing systems. The proposed algorithm is non-blocking, adaptive, and does not use any control message. Compared to other contemporary checkpointing algorithms, it is computationally more efficient because it takes lesser number of checkpoints and does not need to compute dependency relationships. A brief account of important and relevant works in both the fields, time-based and index-based, has also been included in the presentation.
Abstract: Mining frequent tree patterns have many useful
applications in XML mining, bioinformatics, network routing, etc.
Most of the frequent subtree mining algorithms (i.e. FREQT,
TreeMiner and CMTreeMiner) use anti-monotone property in the
phase of candidate subtree generation. However, none of these
algorithms have verified the correctness of this property in tree
structured data. In this research it is shown that anti-monotonicity
does not generally hold, when using weighed support in tree pattern
discovery. As a result, tree mining algorithms that are based on this
property would probably miss some of the valid frequent subtree
patterns in a collection of trees. In this paper, we investigate the
correctness of anti-monotone property for the problem of weighted
frequent subtree mining. In addition we propose W3-Miner, a new
algorithm for full extraction of frequent subtrees. The experimental
results confirm that W3-Miner finds some frequent subtrees that the
previously proposed algorithms are not able to discover.
Abstract: Many real-world data sets consist of a very high dimensional feature space. Most clustering techniques use the distance or similarity between objects as a measure to build clusters. But in high dimensional spaces, distances between points become relatively uniform. In such cases, density based approaches may give better results. Subspace Clustering algorithms automatically identify lower dimensional subspaces of the higher dimensional feature space in which clusters exist. In this paper, we propose a new clustering algorithm, ISC – Intelligent Subspace Clustering, which tries to overcome three major limitations of the existing state-of-art techniques. ISC determines the input parameter such as є – distance at various levels of Subspace Clustering which helps in finding meaningful clusters. The uniform parameters approach is not suitable for different kind of databases. ISC implements dynamic and adaptive determination of Meaningful clustering parameters based on hierarchical filtering approach. Third and most important feature of ISC is the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation.
Abstract: Blind Signature were introduced by Chaum. In this
scheme, a signer can “sign” a document without knowing the
document contain. This is particularly important in electronic voting.
CryptO-0N2 is an electronic voting protocol which is development of
CryptO-0N. During its development this protocol has not been
furnished with the requirement of blind signature, so the choice of
voters can be determined by counting center. In this paper will be
presented of implementation of blind signature using RSA algorithm.
Abstract: In this paper, the potential security issues brought by the virtualization of a Software Defined Networks (SDN) would be analyzed. The virtualization of SDN is achieved by FlowVisor (FV). With FV, a physical network is divided into multiple isolated logical networks while the underlying resources are still shared by different slices (isolated logical networks). However, along with the benefits brought by network virtualization, it also presents some issues regarding security. By examining security issues existing in an OpenFlow network, which uses FlowVisor to slice it into multiple virtual networks, we hope we can get some significant results and also can get furtherdiscussions among the security of SDN virtualization.
Abstract: Fossil fuel-firing power plants dominate electric
power generation in Taiwan, which are also the major contributor to
Green House gases (GHG). CO2 is the most important greenhouse
gas that cause global warming. This paper penetrates the relationship
between carbon trading for GHG reduction and power generation
expansion planning (GEP) problem for the electrical utility. The
Particle Swarm Optimization (PSO) Algorithm is presented to deal
with the generation expansion planning strategy of the utility with
independent power providers (IPPs). The utility has to take both the
IPPs- participation and environment impact into account when a new
generation unit is considering expanded from view of supply side.
Abstract: In these days, multimedia data is transmitted and
processed in compressed format. Due to the decoding procedure and
filtering for edge detection, the feature extraction process of MPEG-7
Edge Histogram Descriptor is time-consuming as well as
computationally expensive. To improve efficiency of compressed
image retrieval, we propose a new edge histogram generation
algorithm in DCT domain in this paper. Using the edge information
provided by only two AC coefficients of DCT coefficients, we can get
edge directions and strengths directly in DCT domain. The
experimental results demonstrate that our system has good
performance in terms of retrieval efficiency and effectiveness.
Abstract: Music segmentation is a key issue in music information
retrieval (MIR) as it provides an insight into the
internal structure of a composition. Structural information about
a composition can improve several tasks related to MIR such
as searching and browsing large music collections, visualizing
musical structure, lyric alignment, and music summarization.
The authors of this paper present the MTSSM framework, a twolayer
framework for the multi-track segmentation of symbolic
music. The strength of this framework lies in the combination of
existing methods for local track segmentation and the application
of global structure information spanning via multiple tracks.
The first layer of the MTSSM uses various string matching
techniques to detect the best candidate segmentations for each
track of a multi-track composition independently. The second
layer combines all single track results and determines the best
segmentation for each track in respect to the global structure of
the composition.
Abstract: Mobile Learning (M-Learning) is a new technology
which is to enhance current learning practices and activities for all
people especially students and academic practitioners UTP is
currently, implemented two types of learning styles which are
conventional and electronic learning. In order to improve current
learning approaches, it is necessary for UTP to implement m-learning
in UTP. This paper presents a study on the students- perceptions on
mobile utilization in the learning practices in UTP. Besides, this
paper also presents a survey that was conducted among 82 students
from System Analysis and Design (SAD) course in UTP. The survey
includes basic information of mobile devices that have been used by
the students, opinions on current learning practices and also the
opinions regarding the m-learning implementation in the current
learning practices especially in SAD course. Based on the results of
the survey, majority of the students are using the mobile devices that
can support m-learning environment. Other than that, students also
agreed that current learning practices are ineffective and they believe
that m-learning utilization can improve the effectiveness of current
learning practices.
Abstract: Ambient Intelligence (AmI) environments bring
significant potential to exploit sophisticated computer technology in
everyday life. In particular, the educational domain could be
significantly enhanced through AmI, as personalized and adapted
learning could be transformed from paper concepts and prototypes to
real-life scenarios. In this paper, an integrated framework is
presented, named ClassMATE, supporting ubiquitous computing and
communication in a school classroom. The main objective of
ClassMATE is to enable pervasive interaction and context aware
education in the technologically augmented classroom of the future.
Abstract: The goal of steganography is to avoid drawing
suspicion to the transmission of a hidden message. If suspicion is
raised, steganography may fail. The success of steganography
depends on the secrecy of the action. If steganography is detected,
the system will fail but data security depends on the robustness of the
applied algorithm. In this paper, we propose a novel plausible
deniability scheme in steganography by using a diversionary message
and encrypt it with a DES-based algorithm. Then, we compress the
secret message and encrypt it by the receiver-s public key along with
the stego key and embed both messages in a carrier using an
embedding algorithm. It will be demonstrated how this method can
support plausible deniability and is robust against steganalysis.
Abstract: Multimedia security is an incredibly significant area of concern. The paper aims to discuss a robust image watermarking scheme, which can withstand geometric attacks. The source image is initially moment normalized in order to make it withstand geometric attacks. The moment normalized image is wavelet transformed. The first level wavelet transformed image is segmented into blocks if size 8x8. The product of mean and standard and standard deviation of each block is computed. The second level wavelet transformed image is divided into 8x8 blocks. The product of block mean and the standard deviation are computed. The difference between products in the two levels forms the watermark. The watermark is inserted by modulating the coefficients of the mid frequencies. The modulated image is inverse wavelet transformed and inverse moment normalized to generate the watermarked image. The watermarked image is now ready for transmission. The proposed scheme can be used to validate identification cards and financial instruments. The performance of this scheme has been evaluated using a set of parameters. Experimental results show the effectiveness of this scheme.
Abstract: A multilayer self organizing neural neural network
(MLSONN) architecture for binary object extraction, guided by a beta
activation function and characterized by backpropagation of errors
estimated from the linear indices of fuzziness of the network output
states, is discussed. Since the MLSONN architecture is designed to
operate in a single point fixed/uniform thresholding scenario, it does
not take into cognizance the heterogeneity of image information in
the extraction process. The performance of the MLSONN architecture
with representative values of the threshold parameters of the beta
activation function employed is also studied. A three layer bidirectional
self organizing neural network (BDSONN) architecture
comprising fully connected neurons, for the extraction of objects from
a noisy background and capable of incorporating the underlying image
context heterogeneity through variable and adaptive thresholding,
is proposed in this article. The input layer of the network architecture
represents the fuzzy membership information of the image scene to
be extracted. The second layer (the intermediate layer) and the final
layer (the output layer) of the network architecture deal with the self
supervised object extraction task by bi-directional propagation of the
network states. Each layer except the output layer is connected to the
next layer following a neighborhood based topology. The output layer
neurons are in turn, connected to the intermediate layer following
similar topology, thus forming a counter-propagating architecture
with the intermediate layer. The novelty of the proposed architecture
is that the assignment/updating of the inter-layer connection weights
are done using the relative fuzzy membership values at the constituent
neurons in the different network layers. Another interesting feature
of the network lies in the fact that the processing capabilities of
the intermediate and the output layer neurons are guided by a beta
activation function, which uses image context sensitive adaptive
thresholding arising out of the fuzzy cardinality estimates of the
different network neighborhood fuzzy subsets, rather than resorting to
fixed and single point thresholding. An application of the proposed
architecture for object extraction is demonstrated using a synthetic
and a real life image. The extraction efficiency of the proposed
network architecture is evaluated by a proposed system transfer index
characteristic of the network.
Abstract: This paper presents a design of source encoding
calculator software which applies the two famous algorithms in the
field of information theory- the Shannon-Fano and the Huffman
schemes. This design helps to easily realize the algorithms without
going into a cumbersome, tedious and prone to error manual
mechanism of encoding the signals during the transmission. The
work describes the design of the software, how it works, comparison
with related works, its efficiency, its usefulness in the field of
information technology studies and the future prospects of the
software to engineers, students, technicians and alike. The designed
“Encodia" software has been developed, tested and found to meet the
intended requirements. It is expected that this application will help
students and teaching staff in their daily doing of information theory
related tasks. The process is ongoing to modify this tool so that it can
also be more intensely useful in research activities on source coding.
Abstract: In this paper we present the first Arabic sentence
dataset for on-line handwriting recognition written on tablet pc. The
dataset is natural, simple and clear. Texts are sampled from daily
newspapers. To collect naturally written handwriting, forms are
dictated to writers. The current version of our dataset includes 154
paragraphs written by 48 writers. It contains more than 3800 words
and more than 19,400 characters. Handwritten texts are mainly
written by researchers from different research centers. In order to use
this dataset in a recognition system word extraction is needed. In this
paper a new word extraction technique based on the Arabic
handwriting cursive nature is also presented. The technique is applied
to this dataset and good results are obtained. The results can be
considered as a bench mark for future research to be compared with.
Abstract: The general purpose processors that are used in
embedded systems must support constraints like execution time,
power consumption, code size and so on. On the other hand an
Application Specific Instruction-set Processor (ASIP) has advantages
in terms of power consumption, performance and flexibility. In this
paper, a 16-bit Application Specific Instruction-set processor for the
sensor data transfer is proposed. The designed processor architecture
consists of on-chip transmitter and receiver modules along with the
processing and controlling units to enable the data transmission and
reception on a single die. The data transfer is accomplished with less
number of instructions as compared with the general purpose
processor. The ASIP core operates at a maximum clock frequency of
1.132GHz with a delay of 0.883ns and consumes 569.63mW power
at an operating voltage of 1.2V. The ASIP is implemented in Verilog
HDL using the Xilinx platform on Virtex4.
Abstract: The way music is interpreted by the human brain is a very interesting topic, but also an intricate one. Although this domain has been studied for over a century, many gray areas remain in the understanding of music. Recent advances have enabled us to perform accurate measurements of the time taken by the human brain to interpret and assimilate a sound. Cognitive computing provides tools and development environments that facilitate human cognition simulation. ACT-R is a cognitive architecture which offers an environment for implementing human cognitive tasks. This project combines our understanding of the music interpretation by a human listener and the ACT-R cognitive architecture to build SINGER, a computerized simulation for listening and recalling songs. The results are similar to human experimental data. Simulation results also show how it is easier to remember short melodies than long melodies which require more trials to be recalled correctly.
Abstract: Many multimedia communication applications require a
source to transmit messages to multiple destinations subject to quality
of service (QoS) delay constraint. To support delay constrained
multicast communications, computer networks need to guarantee an
upper bound end-to-end delay from the source node to each of
the destination nodes. This is known as multicast delay problem.
On the other hand, if the same message fails to arrive at each
destination node at the same time, there may arise inconsistency and
unfairness problem among users. This is related to multicast delayvariation
problem. The problem to find a minimum cost multicast
tree with delay and delay-variation constraints has been proven to
be NP-Complete. In this paper, we propose an efficient heuristic
algorithm, namely, Economic Delay and Delay-Variation Bounded
Multicast (EDVBM) algorithm, based on a novel heuristic function,
to construct an economic delay and delay-variation bounded multicast
tree. A noteworthy feature of this algorithm is that it has very high
probability of finding the optimal solution in polynomial time with
low computational complexity.
Abstract: In this paper, we introduce an effective strategy for
subgoal division and ordering based upon recursive subgoals and
combine this strategy with a genetic-based planning approach. This
strategy can be applied to domains with conjunctive goals. The main
idea is to recursively decompose a goal into a set of serializable
subgoals and to specify a strict ordering among the subgoals.
Empirical results show that the recursive subgoal strategy reduces the
size of the search space and improves the quality of solutions to
planning problems.
Abstract: User satisfaction is one of the most used success
indicators in the research of information system (IS). Literature
shows user expectations have great influence on user satisfaction.
Both expectation and satisfaction of users are important for Hospital
Information Systems (HIS). Education, IS experience, age, attitude
towards change, business title, sex and working unit of the hospital,
are examined as the potential determinant of the medical users’
expectations. Data about medical user expectations are collected by
the “Expectation Questionnaire” developed for this study.
Expectation data are used for calculating the Expectation Meeting
Ratio (EMR) with the evaluation framework also developed for this
study. The internal consistencies of the answers to the questionnaire
are measured by Cronbach´s Alpha coefficient. The multivariate
analysis of medical user’s EMRs of HIS is performed by forward
stepwise binary logistic regression analysis. Education and business
title is appeared to be the determinants of expectations from HIS.