Search

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

The past few years have witnessed a dramatic growth in the number and variety of graphics intensive mobile applications, which allow users to interact and navigate through large scenes such as historical sites, museums and virtual cities. These applications support many clients and impose a heavy requirement on network resources and computational resources. One key issue in the design of cost efficient mobile walkthrough applications is the data transmission between servers and mobile?client devices. In this paper, we propose an effective progressive mesh transmission framework that stores and divide scene objects into different resolutions. In this approach, each mobile device progressively receives and processes only the object's details matching its display resolution which improves the overall system's response time and the user's perception. A fine grained cache mechanism is used to keep the most frequently requested objects' details in the device memory and consequently reduce the network traffic. Experiments, in simulated and real world environment, are used to illustrate the effectiveness of the proposed framework under various settings of the virtual scene and mobile device configuration. Experimental results show that the proposed framework can improve the walkthrough system performance in mobile devices, with a relatively small overhead.

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).

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.