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In the last two decades, Wireless Sensor Networks (WSNs) are gaining more popularity, where the concept of WSN always exists when everything connects. Almost of WSN applications cover wide area and large spaces for assessing and monitoring certain phenomenon. Moreover, WSN components have been integrated in daily life objects or things (object, place, and person), so that they could be monitored and controlled. As a result, a new paradigm called the Internet of Things (IoT) connects WSN components to the Internet to be globally monitored and controlled representing the surrounding environmental events and conditions. The future IoT is called the Web of Things (WoT), which visualizes the IoT data (sensory data) using current web tools and services (HTTP, RESTful services). This paper presents an overview of the WSNs, the IoT and its future paradigm (WoT) discussing key elements, main layers, main challenges, and annotation formats.

Semantic textual similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. Several approaches have been proposed in the literature to determine the semantic similarity between texts. The most promising work recently presented in the literature was supervised approaches. Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations. Word alignment has been widely used in the state-of-the-art approaches. From this point, this paper has three contributions. First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet. Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL). Third, some limitations of the state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach. The UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance. The experimental results proved that the participation of the proposed aligner in STS is effective. Over all the evaluation data sets, the proposed UESTS outperforms the state-of-the-art unsupervised approaches, which is a promising result.

This research presents a new visualization for code clones using EgyCD code clone detector which is based on sequential pattern mining. EgyCD presents a new graph design in which no lines has been drawn, this simplify the graph, no need for the lines since the main objective is to ease the manual management. EgyCD is independent in its visualization in which no graph tools are required for visualizing its code clones, finally supports a very nice way to ease the manual code clone management by the user. .

Parsers are used in many applications such as compilers, NLP and other applications. Parsers that are developed by hand are a complex task and require a generator to automatically generate the parser. The generator reads a grammar and generates a fully working parser. This paper proposes separating the semantic actions’ execution from the parsing phase. The parser generates a queue of semantic actions attached with grammar rules to be visited in case of successful parsing. By this separation, the execution time of the parsing phase can be enhanced. More importantly, this will avoid the incorrect execution of semantic actions when dealing with non-deterministic grammars. Investigating an implementation for the parallelization of the parsing phase for non-deterministic rules is also another contribution of the paper. A previous theoretical work of this paper was made in [1]. The experimental work shows that working with single threaded backtracking with storage of intermediate results, as well as following the Fork/Join parallel execution model without intermediate storage perform in most cases better than working with raw threads execution or by predicting rules as in ANTLR V4. The generator assumes that the grammar is left-recursive free. General Terms Compiler, parsers and parser generator

This paper presents a new technique for clone detection using sequential pattern mining titled EgyCD. Over the last decade many techniques and tools for software clone detection have been proposed such as textual approaches, lexical approaches, syntactic approaches, semantic approaches …, etc. In this paper, we explore the potential of data mining techniques in clone detection. In particular, we developed a clone detection technique based on sequential pattern mining (SPM). The source code is treated as a sequence of transactions processed by the SPM algorithm to find frequent itemsets. We run three experiments to discover code clones of Type I, Type II and Type III and for plagiarism detection. We compared the results with other established code clone detectors. Our technique discovers all code clones in the source code and hence it is slower than the compared code clone detectors since they discover few code clones compared with EgyCD. Keywords

Tasks such as clustering and classification assume the existence of a similarity measure to assess the similarity (or dissimilarity) of a pair of observations or clusters. The key difference between most clustering methods is in their similarity measures. This article proposes a new similarity measure function called PWO “Probability of the Weights between Overlapped items ”which could be used in clustering categorical dataset; proves that PWO is a metric; presents a framework implementation to detect the best similarity value for different datasets; and improves the F-tree clustering algorithm with Semi-supervised method to refine the results. The experimental evaluation on real categorical datasets, such as “Mushrooms, KrVskp, Congressional Voting, Soybean-Large, Soybean-Small, Hepatitis, Zoo, Lenses, and Adult-Stretch” shows that PWO is more effective in measuring the similarity between categorical data than state-of-the-art algorithms; clustering based on PWO with pre-defined number of clusters results a good separation of classes with a high purity of average 80% coverage of real classes; and the overlap estimator perfectly estimates the value of the overlap threshold using a small sample of dataset of around 5% of data size.

Daily life integration of objects or things equipped with smart devices like sensors and actuators converts their surrounding environment (e.g., places) to Smart Spaces (SS) and things to Smart Things (SThs). SThs and Entities of Interest (EoIs) represent environmental events and things states, so that they could be monitored and controlled through the Internet. This new paradigm is called the Internet of Things (IoT); the application layer that visualizes the IoT is called the Web of Things (WoT) or the future IoT. This chapter addresses the problem of designing and implementing testbeds for the WoT research and course projects. Building a WoT testbed is implemented in two main axes; the first axis is to configure and connect hardware components that simulate the set of environmental events (IoT layer), and the second axis is to build the application layer in terms of mini-projects on top of the IoT layer. Mini-projects of this chapter include building prototypes of the WoT using simple components (e.g., Arduino and XBee modules). A smart home prototype will be discussed with details starting from the process of converting things to SThs until monitoring and controlling SThs using current web tools and services (REST, ASP.net and WCF).

This research presents a parallel and distributed data mining approach to code clone detection. It aims to prove the value and importance of deploying parallel and distributed computing for real-time large scale code clone detection. It is implemented this approach in a family of clone detectors, called PD EgyCD (Parallel and Distributed Egypt Clone Detector). In this approach, This research builds on an earlier work of the authors for code clone and plagiarism detection using sequential pattern mining by adding parallelism and distribution to our earlier tool EgyCD. Our approach uses data mining through a tailored Apriori-based algorithm for code clone detection. And it uses parallelization and distribution to achieve excellent performance to scale up to clone detection on very large systems. This approach has been implemented as a database application which leverages the capabilities of modern database tools. Two versions have been developed of this distributed technique. The first one uses client-server technique in which all clients and the server deal with only one database. The second one uses agents where each client acts as a separate agent and has its own database and after working on a sub-problem, it submits its partial solution to the server to finally get the complete solution (set of code clones). Experiments show that agents technique is faster than clientserver one. Distribution enhances performance very much. Speed improvement is a function of the number of clients/agents used. Our conclusion is that data mining, combined with parallel and distributed computing, can efficiently be deployed for code clone detection of very large systems.

The grid vision, of sharing diverse resources in a flexible, coordinated and secure manner, strongly depends on metadata. Currently, grid metadata is generated and used in an ad-hoc fashion, much of it buried in the grid middleware code libraries and database schemas. This ad-hoc expression and use of metadata causes chronic dependency on human intervention during the operation of grid machinery. Therefore, the Semantic Grid is emerged as an extension of the grid in which rich resource metadata is exposed and handled explicitly, and shared and managed via grid protocols. The layering of an explicit semantic infrastructure over the grid infrastructure potentially leads to increase interoperability and flexibility. In this paper, we present PSG framework architecture that offers semantic-based grid services. PSG architecture allows the explicit use of semantics and defining the associated grid services. PSG architecture is originated from the integration of Peer-to-Peer (P2P) computing with semantics and agents. Ontologies are used in annotating each grid component, developing users/nodes profiles and organizing framework agents. While, P2P is responsible for organizing and coordinating the grid nodes and resources.

Sentiment and emotional analyses have recently become effective tools to discover peoples attitudes towards real-life events. While Many corners of the emotional analysis research have been conducted, time emotional analysis at expression and aspect levels is yet to be intensively explored. This paper aims to analyse people emotions from tweets extracted during the Arab Spring and the recent Egyptian Revolution. Analysis is done on tweet, expression and aspect levels. In this research, we only consider surprise, happiness, sadness, and anger emotions in addition to sarcasm expression. We propose a time emotional analysis framework that consists of four components namely annotating tweets, classifying at tweet/expression levels, clustering on some aspects, and analysing the distributions of people emo-tions,expressions, and aspects over specific time. Our contribution is two-fold. First, our framework effectively analyzes people emotional trends over time, at different fine-granularity levels (tweets, expressions, and aspects) while being easily adaptable to other languages. Second, we developed a lightweight clustering algorithm that utilizes the short length of tweets. On this problem, the developed clustering algorithm achieved higher results compared to state-of-the-art clustering algorithms. Our approach achieved 70.1% F-measure in classification, compared to 85.4% which is the state of the art results on English. Our approach also achieved 61.45% purity in clustering.

Phrase-level polarity disambiguation recently became attractive. Nowadays, most corners of the sentiment analysis research have been investigated while the core and hard parts are not yet intensively explored, among which polarity disambiguation is one. In this research, we propose the integration between two SentiWordNet-based resources on phrase-level polarity disambiguation: (1) its score for the best word sense identified by a word sense disambiguation algorithm and (2) its examples having the non-zero scores of the prior polarities for the related subjectivity lexicon words. The integration of subjectivity lexicon word prior polarities together with Senti-WordNet scores and examples of word contexts were presented as classifier features to reduce the classification disambiguation. Our empirical evaluation is twofold. First, an experimental analysis was intrinsically conducted using various lexical resource settings coupled with feature selection algorithms to improve the classifier contextual subjective and sentiment polarity recognition. Second, the integration was extrinsically applied to improve the opinion question answering ranking task. These evaluations prove the superiority of the proposed integration in comparison with the state-of-the-art seminal work and baselines.

Internet of Things (IoT) is an emerging field of research, in which heterogeneous objects effectively communicate with each other over the Internet and each object has a unique address. The fact that objects can access each other makes security a major issue. One of the serious security threats is Denial-of-Service (DoS) attack. DoS attacks overwhelm their victims with fake requests to consume their resources so that they become incapable of serving their legitimate clients. One category of proposed DoS defense techniques uses the Group Testing theory for detecting the identities of DoS attackers among the client group quickly and with low state overhead. In this paper, a group-testing-based DoS defense algorithm, namely Live Baiting, is tested in action. A concrete implementation is used to verify the algorithm's feasibility, effectiveness and weaknesses. The algorithm is modified by introducing dynamic threshold and tolerance degree to enhance its accuracy. Under a high volume of HTTP traffic, the modified algorithm exhibited a detection accuracy (in terms of F-measure) that is improved by up to 300% as compared to the original algorithm.

The extension of logic programming with abduction (ALP) allows a form of hypothetical reasoning. The advantages of abduction lie in the ability to reason with incomplete information and the enhancement of the declarative representation of problems. On the other hand, concurrent logic programming is a framework which explores AND-parallelism and/or OR-parallelism in logic programs in order to efficiently execute them on multi-processor / distributed machines. The aim of our work is to study a way to model abduction within the framework of concurrent logic programming, thus taking advantage of the latter's potential for parallel and/or distributed execution. In particular, we describe Abductive Pandora, a syntactic sugar on top of the concurrent logic programming language Pandora, which provides the user with an abductive behavior for a concurrent logic program. Abductive Pandora programs are then transformed into Pandora programs which support the concurrent abductive behavior through a simple programming technique while at the same time taking advantage of the underlying Pandora machine infrastructure.

This paper presents a tool for automatically generating test data for Pascal programs that satisfy the data flow criteria. Unlike existing tools, our tool is not limited to Pascal programs whose program flow graph contains read statements in only one node but rather deals with read statements appearing in any node in the program flow graph. Moreover, our tool handles loops and arrays, these two features are traditionally difficult to handle in test data generation systems. This allows us to generate tests for larger programs than those previously reported in the literature.

Clustering is an important data mining technique that groups similar data records. In this research, the problem of clustering categorical (or transactional) data is studied. An algorithm for clustering transactional data, called F-Tree (frequent tree), is proposed, and a new similarity measure function, called PWO (probability of the weights of overlapped items), is introduced. Then, a framework to detect the best similarity value for different datasets is developed. Based on the items' frequencies in transactions, the F-Tree algorithm first generates small but highly pure clusters. It then merges similar clusters using the PWO similarity measure function, which depends on the probability of weights of overlapped items. One advantage of PWO is that it outputs the percentage of similarity between items; this is a clearer and more accurate measure of similarity than other similarity measures. Our experimental evaluation on real categorical datasets such as (Mushrooms, KrVskp, Congressional Voting, Soybean-Large, Soybean-Small, Hepatitis, Zoo, Lenses, and Adult-Stretch) shows that: Firstly, F-Tree is effective in finding rare or interesting clusters, and produces clusters with higher purity than LargeItem, WCD and CLOPE. Secondly, PWO is more effective in measuring the similarity between categorical data than LargeItem and CLOPE. Thirdly, clustering using the similarity measure PWO with pre-defined number of clusters results in separate classes with a good purity of average 80% coverage of real classes. Fourthly, the overlap estimator perfectly estimates the value of the overlap threshold using a small sample of dataset of around 5%. Finally, it is seen that the process of merging pure and small clusters increases the purity of resulted clusters as well as reduces time of clustering better than the process of generating clusters directly from the dataset then refining clusters. Based on the items' frequencies in transactions, The F-Tree algorithm first generates small but highly pure clusters. It then merges similar clusters using the PWO similarity measure function, which depends on the probability of weights of overlapped items. One advantage of PWO is that it outputs the percentage of similarity between items; this is a clearer and more accurate measure of similarity than other similarity measures. Our experimental evaluation on real categorical datasets such as "Mushrooms, KrVskp, Congressional Voting, Soybean-Large, Soybean-Small, Hepatitis, Zoo, Lenses, and Adult-Stretch" shows that: Firstly, F-Tree is effective in finding rare or interesting clusters, and produces clusters with higher purity than LargeItem, WCD and CLOPE. Secondly, PWO is more effective in measuring the similarity between categorical data than LargeItem and CLOPE. Thirdly, Clustering using the similarity measure PWO with pre-defined number of clusters results in separate classes with a good purity of average 80% coverage of real classes. Fourthly, the overlap estimator perfectly estimates the value of the overlap threshold using a small sample of dataset of around 5 percent. Finally, It is seen that the process of merging pure and small clusters increases the purity of resulted clusters as well as reduces time of clustering better than the process of generating clusters directly from the dataset then refining clusters.

Email Retrieval task has recently taken much attention to help the user retrieve the email(s) related to the submitted query. Up to our knowledge, existing email retrieval ranking approaches sort the retrieved emails based on some heuristic rules, which are either search clues or some predefined user criteria rooted in email fields. Unfortunately, the user usually does not know the effective rule that acquires best ranking related to his query. This paper presents a new email retrieval ranking approach to tackle this problem. It ranks the retrieved emails based on a scoring function that depends on crucial email fields, namely subject, content, and sender. The paper also proposes an architecture to allow every user in a network/group of users to be able, if permissible, to know the most important network senders who are interested in his submitted query words. The experimental evaluation on Enron corpus prove that our approach outperforms known email retrieval ranking approaches, 20 pages

This paper presents performance comparisons between the CORBA and Jini middleware. The goal of this work is to investigate the behavior of each middleware and discover classes of applications that may perform better under each one of them.

Unified Modeling Language (UML) is the standard notation technique used for modeling object-oriented (OO) systems. Previous efforts exist to extend UML for representing multi-agent systems (MAS). Due to the UML widespread, the resulting extensions are therefore easy to use and to adopt by software engineers. Unfortunately, most of these attempts are based on applying stereotypes for the object-oriented entities. However, heavyweight extension is a more adequate choice to handle different capabilities of the MAS over the OO systems. In this work a heavyweight extension to UML metamodel is proposed for modeling MAS according to our unified MAS conceptual model. It is not limited to a specific application domain or specific agent architecture. A proposed MAS-UML tool is built based on the extended metamodel.

This paper presents a tool for automatically generating test data for Pascal programs that satisfy the data flow criteria. Unlike existing tools, our tool is not limited to Pascal programs whose program flow graph contains read statements in only one node but rather deals with read statements appearing in any node in the program flow graph. Moreover, our tool handles loops and arrays, these two features are traditionally difficult to handle in test data generation systems. This allows us to generate tests for larger programs than those previously reported in the literature., Bu makalede veri akış kriterini sağlayan Pascal programları için otomatik test verisi üreten bir yazılım programı sunulmuştur. Mevcut programların aksine, bizim programımız Pascal programında tek bir düğümdeki okuma komutuna bağlı kalma­makta, bunun yerine herhangi bir düğümde bulunan okuma komutuyla ilgilenmek­tedir. Ayrıca test veri üretim sistemlerinde incelenmesi zor olan çevrim ve dizileri de ele almaktadır. Bu metod, literatürde mevcut programlardan daha kapsamlı prog­ramlar için test üretimini mümkün kılmaktadır