Energy and bursty packet loss tradeoff over fading channels: a system-level model

Energy efficiency and quality of service (QoS) guarantees are the key design goals for the 5G wireless communication systems. In this context, we discuss a multiuser scheduling scheme over fading channels for loss tolerant applications. The loss tolerance of the application is characterized in terms of different parameters that contribute to quality of experience (QoE) for the application. The mobile users are scheduled opportunistically such that a minimum QoS is guaranteed. We propose an opportunistic scheduling scheme and address the cross-layer design framework when channel state information (CSI) is not perfectly available at the transmitter and the receiver. We characterize the system energy as a function of different QoS and channel state estimation error parameters. The optimization problem is formulated using Markov chain framework and solved using stochastic optimization techniques. The results demonstrate that the parameters characterizing the packet loss are tightly coupled and relaxation of one parameter does not benefit the system much if the other constraints are tight. We evaluate the energy-performance tradeoff numerically and show the effect of channel uncertainty on the packet scheduler design

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System †

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores.Peer reviewe

A survey on author profiling, deception, and irony detection for the Arabic language

"This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.Qatar National Research Fund, Grant/Award Number: NPRP 9-175-1-033Rosso, P.; Rangel-Pardo, FM.; Hernandez-Farias, DI.; Cagnina, L.; Zaghouani, W.; Charfi, A. (2018). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass. 12(4):1-20. https://doi.org/10.1111/lnc3.12275S120124Abuhakema , G. Faraj , R. Feldman , A. Fitzpatrick , E. 2008 Annotating an arabic learner corpus for error Proceedings of The sixth international conference on Language Resources and Evaluation, LREC 2008Adouane , W. Dobnik , S. 2017 Identification of languages in algerian arabic multilingual documents Proceedings of The Third Arabic Natural Language Processing Workshop (WANLP)Adouane , W. Semmar , N. Johansson , R 2016a Romanized berber and romanized arabic automatic language identification using machine learning Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 53 61Adouane , W. Semmar , N. Johansson , R. 2016b ASIREM participation at the discriminating similar languages shared task 2016 Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 163 169Adouane , W. Semmar , N. Johansson , R. Bobicev , V. 2016c Automatic detection of arabicized berber and arabic varieties Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 63 72Alfaifi , A. Atwell , E. Hedaya , I. 2014 Arabic learner corpus (ALC) v2: A new written and spoken corpus of Arabic learnersAlharbi , K. 2015 The irony volcano explodes black comedyAli , A. Bell , P. Renals , S. 2015 Automatic dialect detection in Arabic broadcast speechAlmeman , K. Lee , M. 2013 Automatic building of Arabic multi dialect text corpora by bootstrapping dialect words 1 6Aloshban , N. Al-Dossari , H. 2016 A new approach for group spam detection in social media for Arabic language (AGSD) 20 23Al-Sabbagh , R. Girju , R. 2012 YADAC: Yet another dialectal Arabic corpusAlsmearat , K. Al-Ayyoub , M. Al-Shalabi , R. 2014 An extensive study of the bag-of-words approach for gender identification of Arabic articlesAlsmearat , K. Shehab , M. Al-Ayyoub , M. Al-Shalabi , R. Kanaan , G. 2015 Emotion analysis of Arabic articles and its impact on identifying the authors genderArfath , P. Al-Badrashiny , M. Diab , M. El Kholy , A. Eskander , R. Habash , N. Pooleery , M. Rambow , O. Roth , R. M. 2014 MADAMIRA: A fast, comprehensive tool for morphological analysis and disambiguation of ArabicBarbieri , F. Basile , V. Croce , D. Nissim , M. Novielli , N. Patti , V. 2016 Overview of the Evalita 2016 sentiment polarity classification taskBarbieri , F. Saggion , H 2014 Modelling irony in twitter 56 64Barbieri , F. Saggion , H. Ronzano , F 2014 Modelling sarcasm in Twitter, a novel approachBasile , V. Bolioli , A. Nissim , M. Patti , V. Rosso , P. 2014 Overview of the Evalita 2014 sentiment polarity classification taskBlanchard, D., Tetreault, J., Higgins, D., Cahill, A., & Chodorow, M. (2013). TOEFL11: A CORPUS OF NON-NATIVE ENGLISH. ETS Research Report Series, 2013(2), i-15. doi:10.1002/j.2333-8504.2013.tb02331.xBosco, C., Patti, V., & Bolioli, A. (2013). Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT. IEEE Intelligent Systems, 28(2), 55-63. doi:10.1109/mis.2013.28Bouamor , H. Habash , N. Salameh , M. Zaghouani , W. Rambow , O. Abdulrahim , D. Oflazer , K. 2018 The MADAR Arabic Dialect Corpus and LexiconBouchlaghem , R. Elkhlifi , A. Faiz , R. 2014 Tunisian dialect Wordnet creation and enrichment using web resources and other Wordnets 104 113 https://doi.org/10.3115/v1/W14-3613Boujelbane , R. BenAyed , S. Belguith , L. H. 2013 Building bilingual lexicon to create dialect Tunisian corpora and adapt language modelCagnina L. Rosso , P 2015 Classification of deceptive opinions using a low dimensionality representationCavalli-Sforza , V. Saddiki , H. Bouzoubaa , K. Abouenour , L. Maamouri , M. Goshey , E. 2013 Bootstrapping a Wordnet for an Arabic dialect from other Wordnets and dictionary resourcesCotterell , R. Callison-Burch , C. 2014 A multi-dialect, multi-genre corpus of informal written ArabicDahlmeier , D. Tou Ng , H. Mei Wu , S. 2013 Building a large annotated corpus of learner English: the NUS corpus of learner English 22 31Darwish , K. Sajjad , H. Mubarak , H. 2014 Verifiably effective Arabic dialect identification 1465 1468Duh , K. Kirchhoff , K. 2006 Lexicon acquisition for dialectal Arabic using transductive learningElfardy , E. Diab , M. T. 2013 Sentence level dialect identification in Arabic 456 461Estival , D. Gaustad , T. Hutchinson , B. Bao-Pham , S. Radford , W. 2008 Author profiling for English and Arabic emailsFitzpatrick, E., Bachenko, J., & Fornaciari, T. (2015). 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Ontology-Based Education/Industry Collaboration System

As the main supplier of the workforce to the industry, higher education is increasingly criticized for not being abreast with the digital revolution and being disconnected from the industry. Competency-based education was developed to address this issue and bridge the gap between what the university is producing and the requirements of the industry. Hence, tools need to be developed that assists in the analysis process. This paper focuses on proposing a system that models the competencies required by occupations in the industry and higher education curricula and assists in matching profiles from the two domains. The different concepts in the domain are modeled as a semantic web ontology, and an inference engine performs the profile matching. In addition to the profile matching, the system calculates a score for the matching degree using the analytic hierarchy process (AHP) method. 2013 IEEE.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) NPRP under Grant NPRP 7-1883-5-289.Scopu

A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems

© 2013 IEEE. Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.This work was supported by NPRP through the Qatar National Research Fund (a member of the Qatar Foundation) under Grant 7-684-1-127