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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.

The web is a network of linked sites whereby each site either forms a physical portal or a standalone page. In the former case, the portal presents an access point to its embedded web pages that coherently present a specific topic. In the latter case, there are millions of standalone web pages, that are scattered throughout the web, having the same topic and could be conceptually linked together to form virtual portals. Search engines have been developed to help users in reaching the adequate pages in an efficient and effective manner. All the known current search engine techniques rely on the web page as the basic atomic search unit. They ignore the conceptual links, that reveal the implicit web related meanings, among the retrieved pages. However, building a semantic model for the whole portal may contain more semantic information than a model of scattered individual pages. In addition, user queries can be poor and contain imprecise terms that do not reflect the real user intention. Consequently, retrieving the standalone individual pages that are directly related to the query may not satisfy the user’s need. In this paper, we propose PREFCA, a P ortal R etrieval E ngine based on F ormal C oncept A nalysis that relies on the portal as the main search unit. PREFCA consists of three phases: First, the information extraction phase that is concerned with extracting portal’s semantic data. Second, the formal concept analysis phase that utilizes formal concept analysis to discover the conceptual links among portal and attributes. Finally, the information retrieval phase where we propose a portal ranking method to retrieve ranked pairs of portals and embedded pages. Additionally, we apply the network analysis rules to output some portal characteristics. We evaluated PREFCA using two data sets, namely the Forum for Information Retrieval Evaluation 2010 and ClueWeb09 (category B) test data, for physical and virtual portals respectively. PREFCA proves higher F-measure accuracy, better Mean Average Precision ranking and comparable network analysis and efficiency results than other search engine approaches, namely Term Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), and BM25 techniques. As well, it gains high Mean Average Precision in comparison with learning to rank techniques. Moreover, PREFCA also gains better reach time than Carrot as a well-known topic-based search engine.

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

This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, in which a proposed alignment similarity method coupled with new usage of a semantic network (BabelNet) is used. Additionally, a previously proposed integration between sense-based and sur-face-based semantic textual similarity approach is applied together with our proposed approach. For all the tracks in Task1, Run1 is a string kernel with alignments metric and Run2 is a sense-based alignment similarity method. The first run is ranked 10th, and the second is ranked 12th in the primary track, with correlation 0.619 and 0.617 respectively

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).

A key challenge in the emerging Web of Things (WoT) paradigm is how the human users and machines look for meaningful and readable information in huge and dynamic datasets in real-time, whereby the datasets are presented in different formats. This paper presents a technique to construct efficient, hierarchical web indices that are efficiently kept up-to-date. Also, a framework for searching in the WoT, namely WoTSF, is proposed and experimentally evaluated using a prototype. The proposed framework was shown to present a tradeoff between search speed and result accuracy as compared to the Dyser WoT search engine.

The growth of social networks has lately attracted both academic and industrial researchers to study the ties between people, and how the social networks evolve with time. Social networks like Facebook, Twitter and Flickr require efficient and accurate methods to recommend friends to their users in the network. Several algorithms have been developed to recommend friends or predict likelihood of future links. Two main approaches are used to utilize those features; Score-based Approaches and Machine Learning Approaches. In a previous work, a score-based method was used based on topological, node and social features to calculate similarity between users and determine the likelihood of forming future links. This work has been extended by moving to a Machine Learning Approach which treats the prediction process as a classification problem. The classifier predicts the class of each edge whether it exists or doesn’t exist. Machine Learning Approaches have the benefit of adding all similarity indices needed as the feature set fed to the classifier. While in Score-based Approach when we used multiple features with associated weights, the performance was sensitive to the values of such weights. When machine learning is applied, the learning process is performed by the classifier which is fed by eight similarity indices representing connectivity, community, interaction and trust in social network. When indices are combined, a much higher accuracy than the previous Score-based Approach is obtained and hence enhancing the prediction accuracy. In order to evaluate the correctness of the proposed model, it has been applied on a real dataset of 2.974k users on the Twitter social network. Experiments show that using both classical and ensemble classifiers outperforms baseline algorithms when applied individually.

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.

There are some problems that need expertise in order to get a satisfactory solution. Ferryboat carries goods, fresh water, diesel oil, luggage and storing rooms up to its permissible draft in order to maintain safety according to the international safety regulations. The best weight distribution on ferryboat needs human expertise to handle many variables, such as the amount of the bunker and fresh water that allow us to use more rooms for charging in order to maximize the profit. This sort of problems can be classified under Configuration Problem. In this paper, we address the development of a ferryboat expert systems (WDFB) using CommonKADS knowledge engineering methodology. We propose a reusable problem-solving approach, which is an enhancement of the structure-oriented approach, capable of solving the ferryboat configuration problem. The proposed model includes heuristics that make the search of suitable configuration more efficient, taking into consideration the transformation knowledge and the optimality criteria. The results of testing the system on a real-world data from National Navigation Company, Suez, Egypt, were satisfactory.

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.

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.

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.

Pandora was first introduced in [1] as a new parallel logic programming language which combines don't-know non-determinism and stream and-parallelism in a unified and efficient manner. In this paper, we present the Pandora deadlock handler relation whose input argument is a list of meta-level representations of suspended goals. One way of utilizing the deadlock handler relation is in implementing (application-dependent) heuristic search. More generally, the deadlock handler relation can be used to manipulate suspended goals in a flexible manner; redundant goals can be removed, new goals can be added, and a group of goals can be replaced by a simpler group. We illustrate the use of the deadlock handler relation by various application programs and outline the features of the Pandora abstract machine which are required to support its functionality.

Combining stream and-parallelism and don't-know non-determinism in a unified logic programming language introduces new difficulties which affect both the language design and implementation. Nevertheless, recent work suggests that it may be feasible to tackle these problems. A language called Pandora was recently introduced, which extends Parlog with a deadlock handling mechanism and a “lazy” form of don't-know non-determinism. In this paper, we present an abstract machine for Pandora targetted towards a shared memory multiprocessor architecture. It is based on the JAM: an abstract machine for committed-choice languages, and modifies it to support features of Pandora that are not in Parlog.