Publications

2018

Ali Daud, Jamal Ahmad Khan, Jamal Abdul Nasir, Jalal S. Alowibdi, Rabeeh Ayaz Abbasi, Naif Radi Aljohani, Latent Dirichlet Allocation and POS Tags Based Method for External Plagiarism Detection: LDA and POS Tags Based Plagiarism Detection, Volume 14, Issue 3, July-September 2018

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In this article we present a new semantic and syntactic-based method for external plagiarism detection. In the proposed approach, latent dirichlet allocation (LDA) and parts of speech (POS) tags are used together to detect plagiarism between the sample and a number of source documents. The basic hypothesis is that considering semantic and syntactic information between two text documents may improve the performance of the plagiarism detection task. Our method is based on two steps, naming, which is a pre-processing where we detect the topics from the sentences in documents using the LDA and convert each sentence in POS tags array; then a post processing step where the suspicious cases are verified purely on the basis of semantic rules. For two types of external plagiarism (copy and random obfuscation), we empirically compare our approach to the state-of-the-art N-gram based and stop-word N-gram based methods and observe significant improvements. Read Less

Rabeeh Ayaz Abbasi, Onaiza Maqbool, Mubashar Mushtaq, Naif R Aljohani, Ali Daud, Jalal S. Alowibdi, Basit Shahzad, Saving lives using social media: Analysis of the role of twitter for personal blood donation requests and dissemination, Volume 35, Issue 4, 2018/7/1

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Social media has an impact on many aspects of human life ranging from sharing personal information to revolutionizing political systems of entire countries. One not so well studied aspect of social media is analyzing its usage and efficacy in healthcare, particularly in developing countries which lack state-of-the-art healthcare systems and processes. In such countries, social media may be used to facilitate patient-centric healthcare by involving the patient for fulfilling personal healthcare needs. This article provides an in-depth analysis of one such need, that is, how people use social media to request for blood donations. We study the request and dissemination behavior of people using social media to fulfill blood donation requests. We focus on twitter, and blood donation accounts in India. Our study reveals that each of the seven twitter accounts we studied have a large followership of more than 35,000 users on an average and receive a substantial number (more than 900) of donation requests in a day on an average. We analyze the requests in various ways to present an outlook for healthcare providers to make their systems more patient-centric through a better understanding of the needs of people requesting for blood donations. Our study also identifies areas where future social media enabled automated healthcare systems can focus on the needs of individual patients. These systems can provide support for saving more lives by reducing the gap between blood donors and the people in need. Read Less

Imtiaz Awan, Wajid Aziz, Imran Hussain Shah, Nazneen Habib, Jalal S. Alowibdi, Sharjil Saeed, Malik Sajjad Ahmed Nadeem, Syed Ahsin Ali Shah, Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis, Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

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Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features. Read Less

Muhammad Usman Ilyas, Jalal S. Alowibdi, Disease Tracking in GCC Region Using Arabic Language Tweets, Companion of the The Web Conference 2018 on The Web Conference 2018, Pages 417-423, 2018/4/23

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Several prior studies have demonstrated the possibility of tracking the outbreak and spread of diseases using public tweets and other social media platforms. However, almost all such prior studies were restricted to geographically filtered English language tweets only. This study is the first to attempt a similar approach for Arabic language tweets originating from the Gulf Cooperation Council (GCC) countries. We obtained a list of commonly occurring diseases in the region from the Saudi Ministry of Health. We used both the English disease names as well as their Arabic translations to filter the stream of tweets. We acquired old tweets for a period spanning 29 months. All tweets were geographically filtered for the Middle East and the list of disease names in both English and Arabic languages. We observed that only a small fraction of tweets were in English, demonstrating that prior approaches to disease tracking relying on English language features are less effective for this region. We also demonstrate how Arabic language tweets can be used rather effectively to track the spread of some infectious diseases in the region. We verified our approach by demonstrating that a high degree of correlation between the occurrence of MERS-Coronavirus cases and Arabic language tweets on the disease. We also show that infectious diseases generating fewer tweets and non-infectious diseases do not exhibit the same high correlation. We also verify the usefulness of tracking cases using Twitter mentions by comparing against a ground truth data set of MERS-CoV cases obtained from the Saudi Ministry of Health. Read Less

Dirk Ahlers, Erik Wilde, Rossano Schifanella, Jalal S. Alowibdi, Muhammad Zubair Shafiq, LocWeb2018 Chairs' Welcome & Organization, Companion of the The Web Conference 2018 on The Web Conference 2018, Pages 1188-1189, 2018/4/23

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It is our great pleasure to welcome you to the 8th International Workshop on Location and the Web (LocWeb2018) at WWW 2018. LocWeb 2018 will continue a successful workshop series at the intersection of location-based services and Web architecture. It focuses on Web-scale services and systems facilitating location-aware information access as well as on Spatial Social Behavior Analytics on the Web as part of social computing. The location topic is seen as a cross-cutting issue equally concerning information access, semantics and standards, social analysis and mining, and Web-scale systems and services. The workshop is an integrated venue where location and spatio-social aspects can be discussed in depth with an interested community. New application areas for Web architecture, such as the Internet of Things (IoT) and the Web of Things (WoT), will lead to increasingly rich and large sets of applications for which location is highly relevant as the connection to the physical world. Location has high importance in Web-based designs, and it continues to provide challenging research questions. Read Less

Naif Radi Aljohani, Ali Daud, Rabeeh Ayaz Abbasi, Jalal S. Alowibdi, Mohammad Basheri, Muhammad Ahtisham Aslam, An integrated framework for course adapted student learning analytics dashboard, Computers in Human Behavior, 2018/4/4

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The advanced learning analytics research of the last years converges with the industry demand to enhance famous learning management systems with learning analytics capabilities promoting the efficiency of higher education. The exploitation of big volume learning data, is a critical challenge for the design of personalized curricula and learning experiences. The purpose of this research paper is to communicate a framework for Learning Analytics aiming to support the integrated management of end-to-end learning data. We present the research foundations of a research prototype for the integration of a Learning Analytics Dashboard: The AMBA Prototype with famous Learning Management Systems. Finally, we present the main findings of an empirical study that proves the capacity of learning analytics to enhance the learners' ecosystem with value adding learning services. The proposed framework exploits cognitive computing for the enhancement of decision making in education by proving the capacity of Learning Analytics to reveal hidden patterns of learners’ behaviour and attitude. Read Less

Jawad Ahmed, Aqsa Malik, Muhammad U Ilyas, Jalal S. Alowibdi, Instance launch-time analysis of OpenStack virtualization technologies with control plane network errors, Computing, Pages 1-26, 2018

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We analyzed the performance of a multi-node OpenStack cloud amid different types of controlled and self-induced network errors between controller and compute-nodes on the control plane network. These errors included limited bandwidth, delays and packet losses of varying severity. This study compares the effects of network errors on spawning times of batches of instances created using three different virtualization technologies supported by OpenStack, i.e., Docker containers, Linux containers and KVM virtual machines. We identified minimum/maximum thresholds for bandwidth, delay and packet-loss rates below/beyond which instances fail to launch. To the authors’ best knowledge, this is the first comparative measurement study of its kind on OpenStack. The results will be of particular interest to designers and administrators of distributed OpenStack deployments. Read Less

2017

Lal Hussain, Wajid Aziz, Jalal S. Alowibdi, Nazneen Habib, Muhammad Rafique, Sharjil Saeed, Syed Zaki Hassan Kazmi, Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states, Volume 36, Issue 1, December 2017

Sohaib Ghani, Jalal S. Alowibdi, Method for language-independent gender classification on twitter, 2017/11/30

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Online Social Networks (OSNs) allow users to share knowledge, opinions, interests, activities, relationships and friendships with each other. Gender classification of users of an OSN such as Twitter may be difficult to ascertain because gender is not necessarily provided. The present invention relates to a computer-implemented method for predicting gender classification of users of an OSN such as Twitter. The computer-implemented method may predict gender using five color-based features extracted from Twitter profiles such as the background color in a user's profile page. This is in contrast with most existing methods for gender prediction that are language dependent. Those methods use high-dimensional spaces consisting of unique words extracted from such text fields as postings, user names, and profile descriptions. The present method is independent of the user's language, efficient, scalable, and computationally tractable, while attaining a good level of accuracy. Read Less

Mustansar Ali Ghazanfar, Hina Iqbal, Muhammad Awais Azam, Naif Radi Aljohani, Jalal S. Alowibdi, Building scalable and accurate hybrid kernel mapping recommender systems., Intelligent Systems Conference (IntelliSys), Pages 488-493, 2017

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Recommender systems are intelligent systems that can overcome information overload problem. Kernel Mapping Recommender (KMR) systems have been proposed, which give state-of-the-art performance. However, the performance of the KMR algorithm suffers under cold-start and sparse problems. From this line of research, this paper proposes a hybrid framework that can efficiently integrate different versions, namely, item-based and user-based KMR — of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results reveal that the proposed algorithm yields robust results under cold-start and sparse scenarios. Read Less

Muhammad Ali Masood, Rabeeh Ayaz Abbasi, Onaiza Maqbool, Mubashar Mushtaq, Naif R Aljohani, Ali Daud, Muhammad Ahtisham Aslam, Jalal S. Alowibdi, MFS-LDA: a multi-feature space tag recommendation model for cold start problem, Volume 51, Issue 3, 2017/9/5

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This paper presents a novel model for tag recommendation, called multi-feature space latent Dirichlet allocation (MFS-LDA), for cold start problem which exploits multiple feature spaces, such as title, contents, and tags, for recommending tags. Read Less

Muneeb Ahmad, Jalal S. Alowibdi, Muhammad U Ilyas, vIoT: A first step towards a shared, multi-tenant IoT Infrastructure architecture, Communications Workshops (ICC Workshops), 2017 IEEE International Conference on, Pages 308-313, 2017/5/21

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This paper describes a virtualized Internet of Things (vIoT) testbed. We argue in favor of an IoT Infrastructure-as-a-Service as a possible deployment model for future IoTs. The vIoT testbed is being built from open source components, most notably comprising of OpenStack, Linux containers and Raspberry Pi computers. Results demonstrates vIoT infrastructure configured to be shared by multiple users using with LXC/LXD running containers of Ubuntu Trusty Tahr, Ubuntu Xenial Xerus and CirrOS. Read Less

Saima Nazir, Mustansar Ali Ghazanfar, Naif Radi Aljohani, Muhammad Awais Azam, Jalal S. Alowibdi, Data analysis to uncover intruder attacks using data mining techniques, Information and Communication Technology (ICoIC7), 2017 5th International Conference on, 1-6, 2017/5/17

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Radicalism is becoming an increasingly potential concern. Intruder groups are using handful tactics and radicalism has disparaging effects, particularly in Gulf and Pakistan region. Forecasting the pattern of attacks is a complex task. This research paper presents new insights on intruder groups and targets using data mining algorithms. We propose a framework, which uses historical data to train machine-learning classifiers and can predict intruder groups and attack types based on selected features. We analyzed that the major victims of intruder groups would be citizen and property, government, police, and military sectors. We figured out that J48 and IBK learning algorithms perform consistently well under various experimental settings. Read Less

Nabila Shahid, Muhammad U Ilyas, Jalal S. Alowibdi, Naif R Aljohani, Word cloud segmentation for simplified exploration of trending topics on Twitter, Volume 11, Issue 5, 2017/5/5

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Twitter is a popular microblogging platform, with 310 million monthly active users as of the first quarter of 2016. It is a rapidly growing microblogging platform where people share opinions, news on any topic of their interest. More than 7000 tweets are posted every second. Due to the enormous volume of data being generated, it becomes difficult to extract useful/meaningful information. Tweets collected from Twitter on a certain topic may consist of numerous conversation threads about relevant sub-topics. However, it is difficult to discern these sub-topics if the data is visualised as a single word cloud. The authors transform a corpus of tweets to a spectral domain and evaluate the results from a number of clustering algorithms, including K-means, latent semantic indexing and non-negative matrix factorisation to construct clustered word clouds that helps identify sub-topics under a broader topic. Read Less

Azeem Iqbal, Uzzam Javed, Saad Saleh, Jongwon Kim, Jalal S. Alowibdi, Muhammad Usman Ilyas, Analytical modeling of end-to-end delay in openflow based networks, IEEE Access, Volume 5, Pages 6859-6871, 2017

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OpenFlow enabled networks split and separate the data and control planes of traditional networks. This design commodifies network switches and enables centralized control of the network. Control decisions are made by an OpenFlow controller, and locally cached by switches, as directed by controllers. This can significantly impact the forwarding delay incurred by packets in switches, because controllers are not necessarily co-located with switches. Only very few studies have been conducted to evaluate the performance of OpenFlow in terms of end-to-end delay. In this paper, we develop a stochastic model for the end to end delay in OpenFlow switches based on measurements made in Internet-scale experiments performed on three different platforms, i.e., Mininet, the GENI testbed, and the OF@TEIN testbed. Read Less

osÈ-RamÛn Cano, Naif R. Aljohani, Rabeeh Ayaz Abbasi, Jalal S. Alowibdi, Salvador GarcÌa, Prototype selection to improve monotonic nearest neighbor, Engineering Applications of Artificial Intelligence, Volume 60, April 2017, Pages 128-135, ISSN 0952-1976

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Student surveys occupy a central place in the evaluation of courses at teaching institutions. At the end of each course, students are requested to evaluate various aspects such as activities, methodology, coordination or resources used. In addition, a final qualification is given to summarize the quality of the course. The prediction of this final qualification can be accomplished by using monotonic classification techniques. The outcome offered by these surveys is particularly significant for faculty and teaching staff associated with the course.
The monotonic nearest neighbor classifier is one of the most relevant algorithms in monotonic classification. However, it does suffer from two drawbacks, (a) inefficient execution time in classification and (b) sensitivity to no monotonic examples. Prototype selection is a data reduction process for classification based on nearest neighbor that can be used to alleviate these problems. This paper proposes a prototype selection algorithm called Monotonic Iterative Prototype Selection (MONIPS) algorithm. Our objective is two-fold. The first one is to introduce MONIPS as a method for obtaining monotonic solutions. MONIPS has proved to be competitive with classical prototype selection solutions adapted to monotonic domain. Besides, to further demonstrate the good performance of MONIPS in the context of a student survey about taught courses. Keywords: Monotonic classification; Prototype selection; Monotone nearest neighbor; Data reduction; Opinion surveys. Read Less

Shanshan Ruan, Rashid Mehmood, Ali Daud, Hussain Dawood, and Jalal S. Alowibdi. 2017. An Adaptive Method for Clustering by Fast Search-and-Find of Density Peaks: Adaptive-DP.In Proceedings of the 26th International Conference on World Wide Web Companion (WWW '17 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 119-127.

Ali Daud, Naif Radi Aljohani, Rabeeh Ayaz Abbasi, Miltiadis D. Lytras, Farhat Abbas, and Jalal S. Alowibdi. 2017. Predicting Student Performance using Advanced Learning Analytics. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW '17 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 415-421.

Mehreen Gillani, Muhammad U. Ilyas, Saad Saleh, Jalal S. Alowibdi, Naif Aljohani, and Fahad S. Alotaibi. 2017. Post Summarization of Microblogs of Sporting Events. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW '17 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 59-68.

Jarwar, Muhammad Aslam, Rabeeh Ayaz Abbasi, Mubashar Mushtaq, Onaiza Maqbool, Naif R. Aljohani, Ali Daud, Jalal S. Alowibdi, J.R. Cano, S. GarcÌa and Ilyoung Chong. "CommuniMents: A Framework for Detecting Community Based Sentiments for Events," International Journal on Semantic Web and Information Systems (IJSWIS) 13 (2017): 2, accessed (May 14, 2017)

Hikmat Ullah Khan, Ali Daud, Umer Ishfaq, Tehmina Amjad, Naif Aljohani, Rabeeh Ayyaz Abbasi, Jalal S. Alowibdi, Modelling to identify influential bloggers in the blogosphere: A survey, Computers in Human Behavior, Volume 68, March 2017, Pages 64-82, ISSN 0747-5632

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The user participatory nature of the social web has revolutionized the use of the conventional web. The social web is an integral part of our daily life. Due to the resulting exponential growth of the social web, a number of research domains have emerged, involving research activities that aim to study human nature, to analyse human sentiments and emotions, and to find the impact of various users in the social networks. Recently, the research focus has shifted to identifying a user's influence on other users in a social network. In the recent literature, we find a number of models proposed to find the most influential users in the blogging community. In this paper, we review the models to find these influential bloggers. The existing models are classified into feature-based and network-based categories. The feature-based models consider the salient factors to measure bloggers' influence. The network models, on the other hand, consider the graph-based social network structure of the bloggers to identify those who have the most impact on fellow members. This survey introduces each model with its features, novel aspects, and the datasets used. In addition to the discussion about the model, a comparative analysis of the datasets is presented. We conclude by discussing applications of the relevant literature, exploring open research issues and challenges, and sharing possible future directions in this active area of research. Keywords: Social web; Blog; Model; Influential bloggers; Blogosphere. Read Less

Rabeeh Ayaz Abbasi, Onaiza Maqbool, Mubashar Mushtaq, Naif R. Aljohani, Ali Daud, Jalal S. Alowibdi, Basit Shahzad, Saving lives using social media: Analysis of the role of twitter for personal blood donation requests and dissemination, Telematics and Informatics, Available online 3 February 2017, ISSN 0736-5853

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Social media has an impact on many aspects of human life ranging from sharing personal information to revolutionizing political systems of entire countries. One not so well studied aspect of social media is analyzing its usage and efficacy in healthcare, particularly in developing countries which lack state-of-the-art healthcare systems and processes. In such countries, social media may be used to facilitate patient-centric healthcare by involving the patient for fulfilling personal healthcare needs. This article provides an in-depth analysis of one such need, that is, how people use social media to request for blood donations. We study the request and dissemination behavior of people using social media to fulfill blood donation requests. We focus on twitter, and blood donation accounts in India. Our study reveals that each of the seven twitter accounts we studied have a large followership of more than 35,000 users on an average and receive a substantial number (more than 900) of donation requests in a day on an average. We analyze the requests in various ways to present an outlook for healthcare providers to make their systems more patient-centric through a better understanding of the needs of people requesting for blood donations. Our study also identifies areas where future social media enabled automated healthcare systems can focus on the needs of individual patients. These systems can provide support for saving more lives by reducing the gap between blood donors and the people in need. Read Less

Muhammad Aslam Jarwar, Rabeeh Ayaz Abbasi, Mubashar Mushtaq, Jalal S. Alowibdi, Onaiza Maqbool, Naif R. Aljohani, Ali Daud, J.R. Cano, S. García, Ilyoung Chong, CommuniMents: A Framework for Detecting Community Based Sentiments for Events, Volume 13, Issue 2, April-June 2017

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Social media has revolutionized human communication and styles of interaction. Due to its effectiveness and ease, people have started using it increasingly to share and exchange information, carry out discussions on various events, and express their opinions. Various communities may have diverse sentiments about events and it is an interesting research problem to understand the sentiments of a particular community for a specific event. In this article, the authors propose a framework CommuniMents which enables us to identify the members of a community and measure the sentiments of the community for a particular event. CommuniMents uses automated snowball sampling to identify the members of a community, then fetches their published contents (specifically tweets), pre-processes the contents and measures the sentiments of the community. The authors perform qualitative and quantitative evaluation for a variety of real world events to validate the effectiveness of the proposed framework. Read Less

2016

Sohaib Ghani, Mohamed Mokbel, Jalal S. Alowibdi, Using online social networks to find trends of top vacation destinations, 2016/12/8

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A system, method, and apparatus for destination trend determination is provided. The method includes receiving a user query, accessing a database, the database including spatiotemporal content from a plurality of users, generating a first dataset by filtering the database according to the user query, generating a second dataset by filtering the database according to the user query, comparing the first dataset and the second dataset to determine one or more unique users associated with spatiotemporal content in both the first dataset and the second dataset, analyzing the spatiotemporal content of the one or more unique users to determine one or more locations of the spatiotemporal content corresponding to the second dataset, and controlling a display of the analyzed content. Read Less

Suzan Almutairi, Saoucene Mahfoudh, Jalal S. Alowibdi, Peer to peer botnet detection based on network traffic analysis, New Technologies, Mobility and Security (NTMS), 2016 8th IFIP International Conference on, Pages 1-4, 2016/11/21

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IOne of the most serious cyber-security threats is the botnet. The botnet runs in the background of the compromised machine and maintains the communication with the C&C server to receive malicious commands. Botnet master uses botnet to launch dangerous attacks. %such as Distributed Denial of Service (DDoS), data stealing and spamming. This paper addresses the problem of detecting P2P botnet flow records from P2P application within Netflow traces and activities in the network. We propose a technique that is capable of detecting a new P2P botnet in early stage. This technique has been evaluated with a collection of real malicious and legitimate datasets. Our algorithm preprocesses and extracts features to differentiate the botnet behavior from the legitimate behavior. The results of our experiment show a high level of accuracy and a low positive rate. Read Less

2015

Jalal S. Alowibdi, Ugo A Buy, S Yu Philip, Sohaib Ghani, Mohamed Mokbel, Deception detection in Twitter, Volume 5, Issue 1, 2015/12/1

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Online Social Networks (OSNs) play a significant role in the daily life of hundreds of millions of people. However, many user profiles in OSNs contain deceptive information. Existing studies have shown that lying in OSNs is quite widespread, often for protecting a user’s privacy. In this paper, we propose a novel approach for detecting deceptive profiles in OSNs. We specifically define a set of analysis methods for detecting deceptive information about user genders and locations in Twitter. First, we collected a large dataset of Twitter profiles and tweets. Next, we defined methods for gender guessing from Twitter profile colors and names. Subsequently, we apply Bayesian classification and K-means clustering algorithms to Twitter profile characteristics (e.g., profile layout colors, first names, user names, and spatiotemporal information) and geolocations to analyze the user behavior. We establish the overall accuracy of each indicator through extensive experimentation with our crawled dataset. Based on the outcomes of our approach, we are able to detect deceptive profiles about gender and location with a reasonable accuracy. Read Less

2014

Jalal S. Alowibdi, Ugo A Buy, S Yu Philip, Leon Stenneth, Detecting deception in online social networks, Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, Pages 383-390, 2014/8/17

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Over the past decade Online Social Networks (OSNs) have been helping hundreds of millions of people develop reliable computer-mediated relations. However, many user profiles in OSNs contain misleading, inconsistent or false information. Existing studies have shown that lying in OSNs is quite widespread, often for protecting a user's privacy. In order for OSNs to continue expanding their role as a communication medium in our society, it is crucial for information posted on OSNs to be trusted. Here we define a set of analysis methods for detecting deceptive information about user genders in Twitter. In addition, we report empirical results with our stratified data set consisting of 174,600 Twitter profiles with a 50-50 breakdown between male and female users. Our automated approach compares gender indicators obtained from different profile characteristics including first name, user name, and layout colors. We establish the overall accuracy of each indicator and the strength of all possible values for each indicator through extensive experimentations with our data set. We define male trending users and female trending users based on two factors, namely the overall accuracy of each characteristic and the relative strength of the value of each characteristic for a given user. We apply a Bayesian classifier to the weighted average of characteristics for each user. We flag for possible deception profiles that we classify as male or female in contrast with a self-declared gender that we obtain independently of Twitter profiles. Finally, we use manual inspections on a subset of profiles that we identify as potentially deceptive in order to verify the correctness of our predictions. Read Less

Jalal S. Alowibdi, Sohaib Ghani, Mohamed F Mokbel, VacationFinder: A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots, Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, Pages 9-12, 2014/11/4

Jalal S. Alowibdi, Ugo A Buy, S Yu Philipi, Say it with colors: Language-independent gender classification on twitter, Online Social Media Analysis and Visualization, Pages 74-62, 2014

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Online Social Networks (OSNs) have spread at stunning speed over the past decade. They are now a part of the lives of dozens of millions of people. The onset of OSNs has stretched the traditional notion of community to include groups of people who have never met in person but communicate with each other through OSNs to share knowledge, opinions, interests and activities. Here we explore in depth language independent gender classification. Our approach predicts gender using five color-based features extracted from Twitter profiles such as the background color in a user’s profile page. This is in contrast with most existing methods for gender prediction that are language dependent. Those methods use high-dimensional spaces consisting of unique words extracted from such text fields as postings, user names, and profile descriptions. Our approach is independent of the user’s language, efficient, scalable, and computationally tractable, while attaining a good level of accuracy. Read Less

2013

Jalal S. Alowibdi, Ugo A Buy, Philip Yu, Empirical evaluation of profile characteristics for gender classification on twitter, Machine Learning and Applications (ICMLA), 2013 12th International Conference on, Pages 365-369, 2013/12/4

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Online Social Networks (OSNs) provide reliable communication among users from different countries. The volume of texts generated by OSNs is huge and highly informative. Gender classification can serve commercial organizations for advertising, law enforcement for legal investigation, and others for social reasons. Here we explore profile characteristics for gender classification on Twitter. Unlike existing approaches to gender classification that depend heavily on posted text such as tweets, here we study the relative strengths of different characteristics extracted from Twitter profiles (eg, first name and background color in a user’s profile page). Our goal is to evaluate profile characteristics with respect to their predictive accuracy and computational complexity. In addition, we provide a novel technique to reduce the number of features of text-based profile characteristics from the order of millions to a few thousands and, in some cases, to only 40 features. We prove the validity of our approach by examining different classifiers over a large dataset of Twitter profiles. Read Less

Jalal S. Alowibdi, Leon Stenneth, An empirical study of data race detector tools, Control and Decision Conference (CCDC), 2013 25th Chinese, Pages 3951-3955, 2013/5/25

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The industry of software applications has been increased significantly because of the high demand of using the software applications. This revolution leads on developing many concurrent software systems. Noticeably, some of these concurrent software systems have falsely report data race condition to one or more of their shared variables. Debugging such concurrent software systems to find the race condition is a challenge, especially for large and complex software systems. Since the race condition concerned mostly ignored in the concurrent software systems, adopting it could help to ensure the efficiency of these software systems. There are few detector tools that have been known in the industry focusing on data race detectors. This paper aims to study those tools. We are going to conduct empirical study of data race using well known tools in order to measure the correctness, performances and effectiveness of those tools in practical by using some benchmarks. Those benchmarks will be tested on each tool and compare it with others to see the similarity and differentiate. Read Less

Jalal S. Alowibdi, Ugo Buy, Philip Yu, Language Independent Gender Classification on Twitter, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM'13, Pages 739-743, 2013

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Online Social Networks (OSNs) generate a huge volume of user-originated texts. Gender classification can serve multiple purposes. For example, commercial organizations can use gender classification for advertising. Law enforcement may use gender classification as part of legal investigations. Others may use gender information for social reasons. Here we explore language independent gender classification. Our approach predicts gender using five color-based features extracted from Twitter profiles (e.g., the background color in a user's profile page). Most other methods for gender prediction are typically language dependent. Those methods use high-dimensional spaces consisting of unique words extracted from such text fields as postings, user names, and profile descriptions. Our approach is independent of the user's language, efficient, and scalable, while attaining a good level of accuracy. We prove the validity of our approach by examining different classifiers over a large dataset of Twitter profiles. Read Less

2012

Leon Stenneth, Kenville Thompson, Waldin Stone, Jalal S. Alowibdi, Automated transportation transfer detection using GPS enabled smartphones, Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, Pages 802-807, 2012/9/16

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Understanding the mobility of a traveller from mobile sensor data is an important area of work in context aware and ubiquitous computing. Given a multimodal GPS trace, we will identify where in the GPS trace the traveller changed transportation modes. For example, where in the GPS trace the traveller alight a bus and boards a train, or where did the client stop running and start walking. Using data mining schemes to understand mobility data, in conjunction with real world observations, we propose an algorithm to identify mobility transfer points automatically. We compared the proposed algorithm against the state of the art that is used in the previously proposed work. Evaluation on real world data collected from GPS enabled mobile phones indicate that the proposed algorithm is accurate, has a good coverage, and a good asymptotic run time complexity. Read Less

2011

Jalal S. Alowibdi, Adopting Knowledge Based Security System for Software Development Life Cycle, 2011

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The high-demand from the software industry led to the development of many Software Development Life Cycle (SDLC) models that help produce high quality software within budget and time constraints. Most of these SDLC models do not completely cover security as early as possible in the development cycle. Since security is a major concern to the users and the developers, adopting it at the early stages of the SDLC could help to ensure integrity, accessibility and confidentiality in future systems. It is still unclear how to achieve a perfectly secured software system by modifying the SDLC models. In this paper, the Knowledge Based Security System (KBSS) model is proposed to help in modeling and specifying security at all stages of SDLC in an effort to achieve a maximally secured software system. KBSS is a system that categorizes, clusters, monitors, alerts, and controls the Security Knowledge Management by the knowledge of the Security Expert Team, who are able to identify, collect, organize, manage, retrieve, provide and store all aspects of security functions and issues. Read Less

Leon Stenneth, Waldin Stone, Jalal S. Alowibdi, Reducing Travel Time by Incident Reporting via CrowdSourcing, Volume 11, 2011

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The contribution of this work is the creation of a novel system that enables motorists who witness incidents to submit reports to our system via the web. These reports are aggregated, validated and verified automatically. Then, they are used to update the road network graph. In this work, we designed and implemented an incident reporting system whereby users can report an incident such as an accident or construction on a road network. We extended the FreeSim simulator to accommodate our incident reporting system. Experimental results showed that our system is capable of reducing the travel time of users. We also presented our verification algorithms that are used to verify that reports are fact. Current approaches to congestion detection such as loop detectors probe vehicles, and video image detection may not be available on arterial streets. These technologies may not be available in countries whose transportation budget is low. Our model is less expensive, easy to implement and can work in any environment (eg extreme weather). This work is dependent on people’s incident response input and not from sensor signals converted to traffic measurements. To the best of our knowledge, this approach is the first to consider web-based incident Crowd Sourcing with automatic incident verification. Other driver based models are telephoned based. Read Less

2009

Jalal S. Alowibdi, Managing online requirements elicitation in ultra-large software systems, 2009

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A recent report issued by the Software Engineering Institute entitled “Ultra-Large-Scale Systems: The Future Challenge of Software Engineering,” outlines the concept of Ultra-Large-Scale (ULS) systems for which size and complexity is expected to increase exponentially from most of the systems we build today [1]. Traditional requirements elicitation techniques are unlikely to scale well to handle the thousands of stakeholders in involved in ULS systems. A new framework has therefore been proposed for managing large-scaled and distributed requirements elicitation processes [2]. The framework involves a requirements gathering phase in which stakeholders describe their needs for the project. Unsupervised clustering techniques are then used to identify dominant topics and then to generate corresponding discussion forums at which project stakeholders work collaboratively to gather information, generate ideas … Read Less