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البحوث المنشورة

البحث (1):

عنوان البحث:

Developing an Ultra Wideband Indoor Navigation System for Visually Impaired People

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تاريخ النشر:

  22/07/2016

موجز عن البحث:

The number of individuals who suffer from visual impairment is increasing rapidly. The most significant barrier to improving the lives of the blind and visually impaired people is their inability to navigate independently and safely. Indoor navigation systems for blind and visually impaired people aim to guide them in navigating independently in familiar and unfamiliar environments. Our system aims to provide an assistive technology for blind and visually impaired individuals by exploiting popular existing technologies that are often used by blind individuals, such as the smartphone. The system provides users with guidance statements that help them navigate from their current positions to desired destinations. The system consists of four main components: mapping, positioning, navigation, and interface. In order to implement these components, three main applications need to be developed: application in localization server, application in communication server, and application in smartphone, each of which is located in a different place but connected to the others. Functionalities test and blind test were conducted to evaluate the system. The system proved its ability to aid blind individuals effectively.

البحث (2): 

عنوان البحث:

Ultra wideband indoor positioning technologies: Analysis and recent advances 

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تاريخ النشر:

  16/05/2016

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In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

المؤتمرات العلمية:

المؤتمر (1):

عنوان المؤتمر:

  8th annual international ICT-BDCS 2017 

تاريخ الإنعقاد:

  21-22/08/2017 

مكان الإنعقاد:

Singapore.

طبيعة المشاركة:

Paper presentation

عنوان المشاركة:

QT: A Quality Testing Tool for Data-Intensive Applications 

ملخص المشاركة:

We present the DICE Quality Testing (QT) tool, a software for load injection and testing of Data-Intensive Applications (DIAs). The load injection mechanism of the DICE QT tool is designed for Apache Storm – a mature, stable and well-known Big Data technology for stream-based applications – and Apache Kafka. The QT tool consists of two modules. The first module is named QT-GEN and can generate input workload similar, but nevertheless non-identical, to the real workload data supplied to it, with prescribed rates for each type of message. The QT-LIB module is a Java library that provides custom spouts for autonomic workload injection into DIAs. The paper demonstrates the applicability of the tool on two case studies. 

المؤتمر (2):

عنوان المؤتمر:

  2^nd ICISDM 2018 

تاريخ الإنعقاد:

  9-11/04/2018

مكان الإنعقاد:

Florida,USA

طبيعة المشاركة:

Paper presentation

عنوان المشاركة:

Do Words with Certain Part of Speech Tags Improve the Performance of Arabic Text Classification?

ملخص المشاركة:

Feature extraction – the process of choosing feature types that can represent and discriminate between dataset topics – is one of the critical steps in text classification and varies with the language of the texts. Different feature types have been proposed for Arabic text classification, ranging from features based on word orthography (single word and character and word N-grams) to features based on linguistic analysis (roots, stems). To the best of our knowledge, little attention has been paid to investigating the performance of Arabic text classification when Part of Speech (POS) tagging information is used to extract features. In this study, we used a corpus comprising 4900 newspaper texts distributed evenly over seven topics to investigate the effect of using POS tag distribution and words that belong to certain POS tags on Arabic text classification, namely nouns, verbs and adjectives. For feature selection, feature representation and text classification we used Chi-squared, Log-Weighted Term Frequency Inverse Document Frequency with Cosine Normalization (LTC) and support vector machine (SVM) respectively. We used four metrics, namely accuracy, precision, recall and F-measure to measure classification performance. Experiment data suggest that the words achieved the best classification performance when the number of features was low; however, the classification performance can be marginally increased when nouns, verbs and adjectives are used as features, given that the number of features is increased.

المؤتمر (3):

عنوان المؤتمر:

11th International Conference on the Quality of Information and Communications Technology        

تاريخ الإنعقاد:

04/09/2018

مكان الإنعقاد:

Coimbra, Portugal

طبيعة المشاركة:

Paper presentation

عنوان المشاركة:

Publication Preview Source A Neural-Network Driven Methodology for Anomaly Detection in Apache Spark

ملخص المشاركة:

Different types of runtime anomalies can affect the user-perceived performance of batch analytics applications. These anomalies may arise from hardware and software faults, or concurrently executing workloads issued by other tenants in resources such as CPU, memory, disk, or network. Even though techniques exist in the literature for anomaly detection from monitoring data, they are typically blackbox and therefore do not fully exploit available information on the user tasks. We here focus in particular on Spark-based workloads, in which the analytic operations are applied to a resilient distributed dataset (RDD) that can be described as a direct acyclic graph. We develop a neural network based methodology for anomaly detection that is able to improve the accuracy of anomaly detection based on knowledge of the RDD characteristics. Using experiments against multiple workloads and anomaly types, we show that our method improves over other types of classifiers as well as against blackbox performance anomaly detection.