Abductive Network Ensembles for Improved Prediction of Future Change-Prone Classes in Object-Oriente
Abductive Network Ensembles for Improved
Prediction of Future Change-Prone Classes
in Object-Oriented Software
Mojeeb
Al-Khiaty1, Radwan Abdel-Aal2, and Mahmoud Elish1,3
1Information and Computer Science
Department, King Fahd University of Petroleum and Minerals, Saudi Arabia
2Computer Engineering Department,
King Fahd University of Petroleum and Minerals, Saudi Arabia
3Computer Science Department, Gulf
University for Science and Technology, Kuwait
Abstract: Software
systems are subject to a series of changes due to a variety of maintenance
goals. Some parts of the software system are more prone to changes than others.
These change-prone parts need to be identified so that maintenance resources
can be allocated effectively. This paper proposes the use of Group Method of
Data Handling (GMDH)-based abductive networks for modeling and predicting
change proneness of classes in object-oriented software using both software
structural properties (quantified by the C&K metrics) and software change
history (quantified by a set of evolution-based metrics) as predictors. The
empirical results derived from an experiment conducted on a case study of an
open-source system show that the proposed approach improves the prediction
accuracy as compared to statistical-based prediction models.
Keywords: Change-proneness,
software metrics, abductive networks, ensemble classifiers.
Received June 2, 2015; accepted September 20,
2015
Chaotic Encryption Scheme Based on a Fast
Permutation and Diffusion Structure
Jean De Dieu Nkapkop1,2, Joseph Effa1, Monica Borda2,
Laurent Bitjoka3, and Alidou Mohamadou4
1Department of Physics, University
of Ngaoundéré, Cameroon
2Department of
Communications, Technical University of Cluj-Napoca, Romania
3Department of Electrical Engineering, Energetics and Automatics, University of Ngaoundéré, Cameroon
4Department of Physics, University of Maroua, Cameroon
Abstract: The image encryption
architecture presented in this paper employs a novel permutation and diffusion
strategy based on the sorting of chaotic solutions of the Linear Diophantine Equation
(LDE) which aims to reduce the computational time observed in Chong's
permutation structure. In this scheme, firstly, the sequence generated by the
combination of Piece Wise Linear Chaotic Map (PWLCM) with solutions of LDE is
used as a permutation key to shuffle the sub-image. Secondly, the shuffled
sub-image is masked by using diffusion scheme based on Chebyshev map. Finally,
in order to improve the influence of the encrypted image to the statistical
attack, the recombined image is again shuffle by using the same permutation
strategy applied in the first step. The design of the proposed algorithm is
simple and efficient, and based on three phases which provide the necessary
properties for a secure image encryption algorithm. According to NIST
randomness tests the image sequence encrypted by the proposed algorithm passes
all the statistical tests with the high P-values. Extensive cryptanalysis has
also been performed and results of our analysis indicate that the scheme is
satisfactory in term of the superior security and high speed as compared to the
existing algorithms.
Keywords: Fast and secure encryption,
chaotic sequence, linear diophantine equation, NIST test.
Received May 16, 2015;
accepted September 7, 2015
Constructing a Lexicon of Arabic-English Named
Entity using SMT and Semantic Linked Data
Emna Hkiri, Souheyl Mallat, Mounir
Zrigui and Mourad Mars
Faculty of
Sciences of Monastir, University of Monastir, Tunisia
Abstract: Named Entity Recognition (NER) is the problem of locating and
categorizing atomic entities in a given text. In this work, we used DBpedia
Linked datasets and combined existing open source tools to generate from a
parallel corpus a bilingual lexicon of Named Entities (NE). To annotate NE in
the monolingual English corpus, we used linked data entities by mapping them to
Gate Gazetteers. In order to translate entities identified by the gate tool from
the English corpus, we used moses, a Statistical Machine Translation (SMT) system.
The construction of the Arabic-English NE lexicon is based on the results of moses
translation. Our method is fully automatic and aims to help Natural Language
Processing (NLP) tasks such as, Machine Translation (MT) information retrieval,
text mining and question answering. Our lexicon contains 48753 pairs of
Arabic-English NE, it is freely available for use by other researchers.
Keywords: NER, named entity translation, parallel
Arabic-English lexicon, DBpedia, linked data entities, parallel corpus, SMT.
Received April 1, 2015; accepted October 7,
2015
Forecasting of Chaotic Time Series Using RBF
Neural Networks Optimized By Genetic Algorithms
Mohammed Awad
Faculty of Engineering and
Information Technology, Arab American University, Palestine
Abstract: Time series forecasting is an
important tool, which is used to support the areas of planning for both
individual and organizational decisions. This problem consists of forecasting
future data based on past and/or present data. This paper deals with the
problem of time series forecasting from a given set of input/output data. We
present a hybrid approach for time series forecasting using Radial Basis
Functions Neural Network (RBFNs) and Genetic Algorithms (GAs). GAs technique
proposed to optimize centers c and width r of RBFN, the weights w of RBFNs
optimized used traditional algorithm. This method uses an adaptive process of
optimizing the RBFN parameters depending on GAs, which improve the homogenize
during the process. This proposed hybrid approach improves the forecasting
performance of the time series. The performance of the proposed method
evaluated on examples of short-term mackey-glass time series. The results show
that forecasting by RBFNs parameters is optimized using GAs to achieve better
root mean square error than algorithms that optimize RBFNs parameters found by
traditional algorithms.
Keywords: Time series forecasting, RBF neural networks, genetic algorithms, hybrid
approach.
Received March 17, 2015;
accepted October 7, 2015
Contextual Text Categorization: An Improved Stemming Algorithm to Increase the Quality of Categoriza
Contextual Text Categorization: An Improved Stemming Algorithm
to Increase the Quality of Categorization in Arabic Text
Said Gadri and Abdelouahab Moussaoui
Department of Computer Science, University Ferhat
Abbas of Setif, Algeria
Abstract: One of the methods used to reduce the size of
terms vocabulary in Arabic text categorization is to replace the different
variants (forms) of words by their common root. This process is called stemming
based on the extraction of the root. Therefore, the search of the root in
Arabic or Arabic word root extraction is more difficult than in other languages
since the Arabic language has a very different and difficult structure, that is
because it is a very rich language with complex morphology. Many algorithms are
proposed in this field. Some of them are based on morphological rules and
grammatical patterns, thus they are quite difficult and require deep linguistic
knowledge. Others are statistical, so they are less difficult and based only on
some calculations. In this paper we propose an improved stemming algorithm based
on the extraction of the root and the technique of n-grams which permit to
return Arabic words’ stems without using any morphological rules or grammatical
patterns.
Keywords: Root extraction, information retrieval,
bigrams, stemming, Arabic morphological rules, feature selection.
Received February 22, 2015; accepted August 12, 2015
An Architecture of Thin Client-Edge Computing Collaboration for Data Distribution and Resource Alloc
An Architecture of Thin Client-Edge Computing
Collaboration for Data Distribution and Resource Allocation in Cloud
Aymen Alsaffar, Pham Hung,
and Eui-Nam Huh
Department of Computer Science and Engineering, Kyung Hee
University, South Korea
Abstract: These days, Thin-client devices are
continuously accessing the Internet to perform/receive diversity of services in
the cloud. However these devices might either has lack in their capacity (e.g.,
processing, CPU, memory, storage, battery, resource allocation, etc) or in
their network resources which is not sufficient to meet users satisfaction in
using Thin-client services. Furthermore, transferring big size of Big Data over
the network to centralized server might burden the network, cause poor quality
of services, cause long respond delay, and inefficient use of network
resources. To solve this issue, Thin-client devices such as smart mobile device
should be connected to edge computing which is a localized near to user
location and more powerful to perform computing or network resources. In this
paper, we introduce a new method that constructs its architecture on
Thin-client -edge computing collaboration. Furthermore, present our new
strategy for optimizing big data distribution in cloud computing. Moreover, we
propose algorithm to allocate resources to meet Service Level Agreement (SLA)
and Quality of Service (QoS) requirements. Our simulation result shows that our
proposed approach can improve resource allocation efficiently and shows better
performance than other existing methods.
Keywords: Cloud computing, data distribution, edge
computing, resource allocation, and thin client.
Received January 19, 2015; accepted August 12,
2015
TDMCS: An Efficient Method for Mining Closed Frequent Patterns over Data Streams Based on Time Decay
TDMCS: An Efficient Method for Mining
Closed Frequent Patterns over Data Streams Based on Time Decay Model
Meng Han,
Jian Ding, and Juan Li
School
of Computer Science and Engineering,
North Minzu University, China
Abstract: In some data stream applications, the
information embedded in the data arriving in the new recent time period is
important than historical transactions. Because data stream is changing over
time, concept drift problem may appear in data stream mining. Frequent pattern
mining methods always generate useless and redundant patterns. In order to
obtain the result set of lossless compression, closed pattern is needed. A
novel method for efficiently mining closed frequent patterns on data stream is
proposed in this paper. The main works includes: distinguished importance of
recent transactions from historical transactions based on time decay model and
sliding window model; designed the frame minimum support count-maximal support
error rate-decay factor (θ-ε-f) to avoid concept drift; used closure operator
to improve the efficiency of algorithm; design a novel way to set decay factor:
average-decay-factor faverage in order to balance the high recall
and high precision of algorithm. The performance of proposed method is
evaluated via experiments, and the results show that the proposed method is
efficient and steady-state. It applies to mine data streams with high density
and long patterns. It is suitable for different size sliding windows, and it is
also superior to other analogous algorithms.
Keywords: data stream mining, frequent pattern mining, closed pattern
mining, time decay model, sliding window, concept drift.
Received January 15, 2015; accepted August 12,
2015
Internal Model Control to Characterize Human Handwriting Motion
Ines Chihi,
Afef Abdelkrim, and Mohamed Benrejeb
National School of Engineers of Tunisia, Tunis El Manar University, Tunisia
Abstract: The
main purpose of this paper is to consider the human handwriting process as an
Internal Model Control structure (IMC). The proposed approach allows characterizing
the biological process from two muscles activities of the forearm, named
ElectroMyoGraphy signals (EMG). For this, an experimental approach was used to
record the coordinates of a pen-tip moving on (x,y) plane and EMG signals
during the handwriting act. In this sense direct and inverse handwriting models
are proposed to establish the relationship between the muscles activities of
the forearm and the velocity of the pen-tip. Recursive Least Squares algorithm
(RLS) is used to estimate the parameters of both models (direct and inverse).
Simulations show good agreement between the proposed approach results and the
recorded data.
Keywords: Human handwriting
process; IMC; the muscular activities; direct and inverse handwriting models;
velocity of the pen-tip; RLS algorithm.
Received
January 6, 2015; accepted September 22, 2015
Efficient Segmentation of Arabic Handwritten
Characters Using Structural Features
Mazen Bahashwan, Syed Abu-Bakar, and Usman Sheikh
Department of Electronics and
Computer Engineering, Universiti Teknologi Malaysia, Malaysia
Abstract: Handwriting recognition is an important field as it has many practical applications such as for bank cheque processing, post office address processing and zip code recognition. Most applications are developed exclusively for Latin characters. However, despite tremendous effort by researchers in the past three decades, Arabic handwriting recognition accuracy remains low because of low efficiency in determining the correct segmentation points. This paper presents an approach for character segmentation of unconstrained handwritten Arabic words. First, we seek all possible character segmentation points based on structural features. Next, we develop a novel technique to create several paths for each possible segmentation point. These paths are used in differentiating between different types of segmentation points. Finally, we use heuristic rules and neural networks, utilizing the information related to segmentation points, to select the correct segmentation points. For comparison, we applied our method on IESK-arDB and IFN/ENIT databases, in which we achieved a success rate of 91.6% and 90.5% respectively.
Keywords: Arabic handwriting, character segmentation and structural features.
Received December
23, 2014; accepted August 26, 2015
A Novel
Swarm Intelligence Algorithm for the Evacuation Routing Optimization Problem
Jin-long Zhu1, Wenhui Li2, Huiying Li2, Qiong
Wu2, and Liang Zhang2
1Department of Computer Science and Technology, ChangChun Normal University, China
2Department of Computer Science and Technology, Jilin University, China
Abstract: This paper presents a novel swam
intelligence optimization algorithm that combines the evolutionary method of Particle
Swarm Optimization (PSO) with the filled function method in order to
solve the evacuation routing optimization problem. In the proposed algorithm,
the whole process is divided into three stages. In the first stage, we make use
of global optimization of filled function to obtain
optimal solution to set destination of all particles. In the second stage, we
make use of the randomicity and rapidity of PSO to simulate the crowd
evacuation. In the third stage, we propose three methods to manage the
competitive behaviors among the particles. This algorithm makes an evacuation
plan using the dynamic way finding of particles from both a macroscopic and a
microscopic perspective simultaneously. There are three types of experimental
scenes to verify the effectiveness and efficiency of the proposed algorithm: a
single room, a 4-room/1-corridor layout, and a multi-room multi-floor building
layout. The simulation examples demonstrate that the proposed algorithm can
greatly improve upon evacuation clear and congestion times. The experimental
results demonstrate that this method takes full advantage of multiple exits to
maximize the evacuation efficiency.
Keywords: PSO, filled function, global optimum,
local optimum.
Received November 17, 2014; accepted September
10, 2015
The Veracious
Counting Bloom Filter
Brindha Palanisamy1 and
Senthilkumar Athappan2
1Research Scholar, Anna
University, India
2Department of
Electrical and Electronics Engineering, Anna University, India
Abstract: Counting Bloom Filters (CBFs) are widely employed in many
applications for fast membership queries. CBF works on dynamic sets rather than
a static set via item insertions and deletions. CBF allows false positive, but
not false negative. The Bh-Counting Bloom Filter (Bh-CBF)
and Variable Increment Counting Bloom Filter (VI-CBF) are introduced to reduce
the False Positive Probability (FPP), but they suffer from memory overhead and
hardware complexity. In this paper, we proposed a multilevel optimization
approach named as Veracious Bh-Counting Bloom Filter (VBh-CBF)
and Veracious Variable Increment Counting Bloom Filter (VVI-CBF) by
partitioning the counter vector into multiple levels to reduce the FPP and to
limit the memory requirement. The experiment result shows that the FPP and
total memory size are reduced by 65.4%, 67.74% and 20.26%, 41.29%
respectively compared
to basic Bh-CBF and VI-CBF.
Key words: Bloom filter, false positive, counting bloom filter, intrusion detection system.
Received
August 3, 2014; accepted November 25, 2015
A MMDBM Classifier with CPU and CUDA GPU
Computing in Various Sorting Procedures
Sivakumar
Selvarasu1, Ganesan Periyanagounder1, and Sundar Subbiah2
1Department of
Mathematics, Anna University, India
2Department of
Mathematics, Indian Institute of Technology, India
Abstract: A decision tree classifier called Mixed Mode
Database Miner (MMDBM) which is used to classify large number of datasets with
large number of attributes is implemented with different types of sorting
techniques (quick sort and radix sort) in both Central Processing Unit (CPU)
computing and General-Purpose computing
on Graphics Processing Unit (GPGPU) computing and the results are discussed.
This classifier is suitable for handling large number of both numerical and categorical
attributes. The MMDBM classifier has been implemented in CUDA GPUs and the code
is provided. We used the parallelized
algorithms of the two sorting techniques on GPU using Compute Unified Device
Architecture (CUDA) parallel programming platform developed by NVIDIA corporation.
In this paper, we have discussed an efficient parallel (quick sort and radix
sort) sorting procedures on GPGPU computing and compared the results of GPU to
the CPU computing. The main result of
MMDBM is used to compare the classifier with an existing CPU computing results
and GPU computing results. The GPU sorting algorithms provides quick and exact
results with less handling time and offers sufficient support in real time
applications.
Keywords: Classification, Data Mining, CUDA, GPUs,
Decision tree, Quick sort, Radix sort.
Received July
29, 2014; accepted April 12, 2015
Inter-Path OOS Packets Differentiation Based Congestion Control for Simultaneous Multipath Transmiss
Inter-Path OOS Packets
Differentiation Based Congestion Control for Simultaneous Multipath
Transmission
Samiullah Khan and Muhammad Abdul Qadir
Department of Computer Science, Capital University of
Science and Technology, Pakistan
Abstract: An increase in the popularity and usage of
Multimode’s devices for ubiquitous network access creates thrust for
utilization of simultaneous network connections. Unfortunately, the standard
transport layer protocols used single homed congestion control mechanism for multipath
transmission. One major challenge in such multipath transmission is related to
the Receiver Buffer (RBuf) blocking that hinders higher aggregation ratio of
multiple paths. This study proposed Simultaneous Multipath Transmission (SMT)
scheme to avoid the RBuf blocking problem. Realistic simulation scenarios were
designed such as intermediate nodes, cross traffic, scalability or mix of them
to thoroughly analyses SMT performance. The results revealed that SMT has
overcome RBuf blocking with improvement in aggregate throughput up to 95.3 % of
the total bandwidth.
Keywords: Multipath transmission, RBuf blocking, out-of-sequence
arrival, throughput, congestion window.
Received June 14,
2014; accepted September 16, 2015
Method-level Code Clone Detection for Java
through Hybrid Approach
Egambaram
Kodhai1 and Selvadurai Kanmani2
1Department of Computer Science and Engineering, Sri MankulaVinayagar Engineering
College, India
2Department of Information Technology, Pondicherry Engineering
College, India
Abstract: A software clone is an active
research area where several researchers have investigated techniques to
automatically detect duplicated code in programs. However their researches have
limitations either in finding the structural or functional clones. Moreover, all
these techniques detected only the first three types of clones. In this paper,
we propose a hybrid approach combining metric-based approach with textual
analysis of the source code for the detection of both syntactical and
functional clones in a given Java source code. This proposal is also used to
detect all four types of clones. The detection process makes use of a set of
metrics calculated for each type of clones. A tool named CloneManager is developed
based on this method in Java for high portability and platform-independency.
The various types of clones detected by the tool are classified and clustered
as clone clusters. The tool is also tested with seven existing open source
projects developed in Java and compared with the existing approaches.
Keywords: Clone detection, functional clones, source code metrics, string-matching.
Received October 21, 2013; accepted June 24,
2014
Enhancing Cloud Security Based On Group
Signature
Arumugam
Sakthivel
Department of Computer Science and
Engineering, Kalasalingam University, India
Abstract: Using the eccentric of truncated
preservation, cloud computing gives a reasonable and proficient result for
distributing cluster resources among cloud clients. Regrettably, distributing
data in a multi user fashion whereas maintaining data and individuality privacy
from an unfaith cloud is quiet a puzzling concern, because of the recurrent
change of the participation. The proposed system focuses a protected multi user
data distributing method, for active clusters in the cloud. Using group
signature and active broadcast encryption methods, any cloud client can
secretly distribute data among others. Provisionally, the storage load and
encryption calculation cost of the proposed method is liberated from the amount
of repealed clients. Additionally, the security and performance
analysis of the proposed method shows that, much more efficient and secure than
all other existing methods.
Keywords: Active
broadcast encryption, cloud, data distribution, group signature.
Received September 12, 2014; accepted June 18, 2015