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23rd International Arab Conference on Information technology, ACIT 2022

作者： Darwish, Omar Herzallah, Sarah Maabreh, Majdi Al-Eidi, Shouroq Al-Maani, Mahmoud Tashtoush, Yahya Eastern Michigan University Information Security and Applied Computing YpsilantiMI United States Eastern Michigan University Electrical and Computer Engineering YpsilantiMI United States The Hashemite University Department of Information Technology Zarqa Jordan Tafila Technical University Computer Science Department Tafila Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan

ISBN: (纸本)9798350320244

One of the most crucial considerations, when considering security vulnerabilities, is network traffic. There is still potential for more research on the inter-arrival time side, even though some studies concentrate on network traffic from the perspective of the packet fields such as packet length and packet number. Inter-arrival timings are crucial to investigate because there are numerous attacks, such as Covert Timing Channels attacks, that heavily rely on them. In this article, we conduct a statistical analysis of the TCP inter-arrival times of two major key streaming programs (Zoom and Skype), which are frequently used, particularly during and following the COVID-19 pandemic. Using two internet-connected devices and the statistical measures of TCP, a dataset of 18,371 instances is created for this use. Five machine learning algorithms are evaluated on balanced and imbalanced forms of the dataset. The results revealed that the traffic of Zoom and Skype calls can be identified by machine learning algorithms with an accuracy of up to 99% by random forest. © 2022 IEEE.

关键词： Transmission control protocol

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Enhancing IoT-Edge Computation with Data Forwarding based Decentralized Deep Neural Networks 7

Enhancing IoT-Edge Computation with Data Forwarding based De...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Prasad, B.M.G. Jopate, Rachappa Savita, Pankaj Basi Reddy, A. Shankar, B. Prabu Arunkumar, M.S. Department of Cse Andhra Pradesh Kurnool518452 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Sagar Institute of Science & Technology Bhopal Organization-Sagar Institute of Science & Technology Department of Cse Madhya Pradesh Bhopal India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Alliance University Alliance College of Engineering And Design Department of Computer Science And Engineering Karnataka Banglore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371406

The incorporation of edge computing has become essential in the big data and Internet of Things age to tackle issues related to latency, real-time processing, and data scale. This paper delves into the imperative need for edge computing within the context of big data environments. Distributed deep learning methodologies are explored, encompassing a diverse array of deep learning models. Particularly, a novel paradigm named Data Forwarding based Decentralized Deep Neural Network (DF-DDNN) is introduced to achieve low latency IoT-Edge computation. In order to reduce latency in Io T ecosystems, the DF-DDNN model makes use of edge devices' processing capabilities. By strategically distributing data processing and computation at the edge, this model seeks to overcome inherent limitations of conventional Io T architectures, with a keen focus on network, processing, and overall latencies. The research provides a thorough performance evaluation that compares the DF-DDNN model's performance versus conventional IoT models in a number of areas. The outcomes demonstrate how much better the suggested DF-DDNN model is than traditional IoT systems. Notably, improvements are evident in terms of reduced network latency, accelerated processing speeds, and overall latency mitigation. This research underscores the critical importance of embracing edge computing in the context of big data and deep learning applications. The DF-DDNN model emerges as a promising avenue for advancing IoT-Edge computation, effectively addressing latency concerns and augmenting the performance of IoT-enabled systems. © 2023 IEEE.

关键词： Edge computing

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Pandemics in silico: Scaling Agent-based Simulations on Realistic Social Contact Networks

arXiv

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arXiv 2024年

作者： Kitson, Joy Costello, Ian Chen, Jiangzhuo Jiménez, Diego Hoops, Stefan Mortveit, Henning Meneses, Esteban Yeom, Jae-Seung Marathe, Madhav V. Bhatele, Abhinav Department of Computer Science University of Maryland College Park United States Google Inc. Mountain View United States Biocomplexity Institute and Initiative University of Virginia Charlottesville United States Max Planck Computing and Data Facility Garching Germany National Advanced Computing Collaboratory National High Technology Center San José Costa Rica Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore United States

Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of the spread of such diseases in the presence of possible interventions. The computational cost of modeling epidemic diffusion through large social contact networks necessitates the use of parallel algorithms and resources in order to achieve quick turnaround times. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete event simulation to model disease spread, and is implemented on top of Charm++, an asynchronous, many-task runtime that enables over-decomposition and adaptive overlap of computation and communication. We demonstrate that Loimos is able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID-19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC. © 2024, CC BY.

关键词： Discrete event simulation

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Improved Attentive Recurrent Network for applied Linguistics-Based Offensive Speech Detection

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Computer Systems Science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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Multi-Photon Subtraction from Squeezed Light toward Full Quantum State Engineering

Multi-Photon Subtraction from Squeezed Light toward Full Qua...

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Quantum 2.0, QUANTUM 2024 in Quantum 2.0 Conference and Exhibition

作者： Endo, Mamoru Nomura, Takefumi Sonoyama, Tatsuki Takahashi, Kazuma Fukui, Kosuke Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Takasu, Sachiko Hattori, Kaori Fukuda, Daiji Nehra, Rajveer Marek, Petr Filip, Radim Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs. NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan National Institute of Advanced Industrial Science and Technology Ibaraki Tsukuba305-8563 Japan AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory Ibaraki Tsukuba305-8563 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States Department of Optics Palacky University 17. listopadu 1192/12 Olomouc77146 Czech Republic

We have applied up to four annihilation operations to squeezed light by photon subtraction. The presence of negative values in the Wigner function for all states indicates the successful implementation of quantum mani... 详细信息

ISBN: (纸本)9781557525185

关键词： Squeezed light

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A Review Paper on Student-Graduate Advisory Expert system

A Review Paper on Student-Graduate Advisory Expert system

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2020 International Conference on computing and Information technology, ICCIT 2020

作者： Tawafak, Ragad M Alfarsi, Ghaliya Romli, Awanis Jabbar, Jasiya Malik, Sohail Iqbal Alsideiri, Abir AlBuraimi College Information Technology Department Buraimi Oman University Malaysia Pahang Faculty of Computing College of Computing and Applied Science Malaysia

ISBN: (纸本)9781728126807

Academic advising of the postgraduate students is considered as a fundamental rule and one of the significant positive services and responsibilities for the academic staff follow up in most of the e-leading educations. The expert system is considered as one of most achievement areas in artificial intelligence and highly appreciated for application with e-learning and teaching methods. The aim of this paper, is to survey the most expert systems used in advisory model and replaced with the manual registration and advising system of face to face communication and discussion in Instructor office. The method used of determined the key factors related to student advising and expert system models though open access databases. The finding of this section reviews out nine selected expert systems in order to explore the strengths, and limitations of existing expert system approaches to advising undergraduate students. © 2020 IEEE.

关键词： Expert systems

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A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

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Computers, Materials & Continua 2022年第1期70卷 1845-1856页

作者： Jari Jussila Eman Alkhammash Norah Saleh Alghamdi Prashanth Madhala Mohammad Ayoub Khan HAMK Smart Research Unit Häme University of Applied SciencesHämeenlinna13100Finland Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Management and Business Tampere UniversityTampere33720Finland College of Computing and Information Technologies University of BishaBisha67714Saudi Arabia

Social media platforms provide new value for markets and research *** article explores the use of social media data to enhance customer value *** case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress *** and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management *** aim is to analyze the tweets about stress management practices and to identify the context from the ***,we map the tweets on pleasure and arousal to elicit customer *** analyzed a case study of a marketing strategy on the Twitter *** in the marketing campaign shared photos and texts about their stress management *** learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign *** computational semantic analysis of the tweets was compared to the text analysis of the *** content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

关键词： Social media stress semantic analysis Twitter context recognition

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Classifying Arabian Gulf Tweets to Detect People's Trends: A case study

Classifying Arabian Gulf Tweets to Detect People's Trends: A...

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International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Khaled Balhaf Omar Darwish Emad Rawashdeh Mohammad Abu Awad Dirar Darweesh Yahya Tashtoush Saif Rawashdeh Department of Computer Science Jordan University of Science and Technology Irbid Jordan Information Security and Applied Computing Eastern Michigan University Ypsilanti USA

Recently, media and business companies are utilizing social media to reach a large set of users to maximize the amount of gained profit. Actually, these companies are looking for the best ways to satisfy their user's requirements. It is very difficult to understand these requirements because of the large set of users on social media like Twitter. For this reason, the goal of our research project is to build a classifier that can detect Arabian trends among Gulf area Twitter users. The new built classifier can assist these companies to deliver the convenient products and media contents like photos and videos according to users' trends. By using our own designed Java-based tool, we have collected a significant dataset of tweets. Also, two experiments of tweet classification have been implemented to compare the effects of balanced and imbalanced training data and to measure the effect of data size on the accuracy of classifiers. In both experiments, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes algorithms are used as classifiers. The first experiment uses small, imbalanced data sets and four classes of data, which are Sport, Politics, Islam and Culture. The Light and Root Stemmers were used with each classifier. The best outcome achieved in our research project by utilizing a Naïve Bayes algorithm with the Light Stemmer technique. It achieved an accuracy reaching 76.27%. In the second experiment, we used a balanced large data set with the same classifiers. In addition, we have added one more class to the new data set which is Economics. The experimental results showed that the best accuracy (81.17%) is obtained by using SVM with the Light Stemmer method. The Light Stemmer achieved the best outcomes for all classifiers since almost all of the tweets were written in dialects.

关键词： Support vector machines Social networking (online) Blogs Training data Companies Media Market research

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Convergence of Blockchain, AI and IoT for Effective Monitoring and Securing Smart City Infrastructure 2

Convergence of Blockchain, AI and IoT for Effective Monitori...

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2nd International Conference on Multidisciplinary Engineering and applied Science, ICMEAS 2023

作者： Agajo, James Imoru, Odunayo Aigbavboa, Clinton Mishra, Amit Onah, Fidelis Ijala, Abdullahi Danjuma Federal University of Technology Department of Computer Engineering Niger State Minna Nigeria University of Johannesburg Department of Electrical and Computer Engineering South Africa University of Johannesburg Faculty of Engineering and the Built Environment South Africa Department of Computer Science School of Computing and Applied Science Baze University Abuja Nigeria Veritas University Faculty of Engineering Abuja Nigeria Nigeria Communication Commission Abuja Nigeria

ISBN: (纸本)9798350358834

The convergence of block chain, artificial intelligence, and the internet of things (IoT) for effective monitoring and securing smart city infrastructure combines these three hot trends in technology and promises to advance the development of smart cities. Blockchain, AI, and IoT represent one of the most notable technological revolutions since the combination of computing and transaction processing systems, which is now having a positive impact. Due to the actions of cyber assault, the applications of these technologies will soon have such a significant impact on our society that it will be difficult to picture life without them. The Internet of Things Infrastructure is vulnerable to assaults since it is hosted on the cloud. Modern methods are thoroughly examined to talk about the advantages and difficulties in each domain. The combination of these three will change technology and advance a number of fields that will benefit academics and experts in industry, fostering improved service delivery and enhancing corporate structures. The convergence will change the game since it will address security and data privacy concerns. © 2023 IEEE.

关键词： Internet of things

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Continual Learning for Autonomous Robots: A Prototype-based Approach

Continual Learning for Autonomous Robots: A Prototype-based ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Elvin Hajizada Balachandran Swaminathan Yulia Sandamirskaya Neuromorphic Computing Lab Intel Labs Munich Germany School of Computation Information and Technology Technical University of Munich Germany Pennsylvania State University Institute of Computational Life Sciences (ICLS) Zurich University of Applied Sciences (ZHAW) Wädenswil Switzerland

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Humans and animals learn throughout their lives from limited amounts of sensed data, both with and without supervision. Autonomous, intelligent robots of the future are often expected to do the same. The existing continual learning (CL) methods are usually not directly applicable to robotic settings: they typically require buffering and a balanced replay of training data. A few-shot online continual learning (FS-OCL) setting has been proposed to address more realistic scenarios where robots must learn from a non-repeated sparse data stream. To enable truly autonomous life-long learning, an additional challenge of detecting novelties and learning new items without supervision needs to be addressed. We address this challenge with our new prototype-based approach called Continually Learning Prototypes (CLP). In addition to being capable of FS-OCL learning, CLP also detects novel objects and learns from them without supervision. To mitigate forgetting, CLP utilizes a novel metaplasticity mechanism that adapts the learning rate individually per prototype. CLP is rehearsal-free, hence does not require a memory buffer, and is compatible with neuromorphic hardware, characterized by ultra-low power consumption, real-time processing abilities, and on-chip learning. Indeed, we have open-sourced both the PyTorch implementation of CLP and a simpler version in the neuromorphic software framework Lava, targetting Intel’s neuromorphic chip Loihi 2. We evaluate CLP on a robotic vision dataset, OpenLORIS. In a low-instance FS-OCL scenario, CLP shows state-of-the-art results. In the open world, CLP detects novelties with superior precision and recall and learns features of the detected novel classes without supervision, achieving a strong baseline of 99% base class and 65% / 76% (5-shot/10-shot) novel class accuracy.

关键词： Continuing education Neuromorphics Memory management Prototypes Training data Robot sensing systems Software Real-time systems System-on-chip Intelligent robots

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