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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Jiawei Wang, Dawei Zhang, Jiankang Alfarraj, Osama He, Yixin Al-Rubaye, Saba Yu, Keping Mumtaz, Shahid Northwestern Polytechnical University School of Electronics and Information Xi'an710072 China Bournemouth University Department of Computing and Informatics United Kingdom Jiaxing University College of Information Science and Engineering Jiaxing314001 China Cranfield University School of Aerospace Transport and Manufacturing United Kingdom King Saud University Community College Department of Computer Science Riyadh11437 Saudi Arabia Hosei University School of Science and Engineering Tokyo184-8584 Japan Silesian University of Technology Department of Applied Informatics Gliwice44-100 Poland Nottingham Trent University Departement of Computer Sciences United Kingdom

ISBN: (纸本)9798350351255

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Efficient Vehicle Detection and Classification using YOLO v8 for Real-Time Applications

Efficient Vehicle Detection and Classification using YOLO v8...

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Information Technologies and Communications (GCITC), Global Conference on

作者： M. Ravichandran Kumar Laxmikant Annamalai Muthu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electrical and Electronics Engineering University of Technology & Applied Science Nizwa Oman

In the realm of real-time applications such as autonomous driving and surveillance, efficient vehicle detection stands as a paramount concern. The revolutionary You Only Look Once (YOLO) framework, renowned for its rapid object recognition, has reshaped this landscape. This study delves into the fusion of real-time vehicle detection and YOLO v8, optimizing its architecture for swift and precise identification. Empirical validation underscores the model's prowess, achieving a remarkable accuracy of 97.9% on mAP50 and 91.3% on mAP50-95. This achievement not only attests to the model's accuracy but also reaffirms its real-time processing capabilities. The implications resonate across transportation safety and urban planning, promising transformative potential. This research underscores the power of YOLO v8 in intelligent object detection, poised to reshape and enhance various domains.

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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Electrical Electronics and computing Technologies (ICEECT), International Conference on

作者： Suresh Palarimath Pyingkodi Maran R. Venkateswaran Wilfred Blessing N.R K. V Shiny S. Renuga College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence’s framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce.

关键词： Technological innovation Education Machine learning Transforms Real-time systems Problem-solving Artificial intelligence Pupils Creativity STEM

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Remote Volume Rendering with a Decoupled, Ray-Traced Display Phase 8

Remote Volume Rendering with a Decoupled, Ray-Traced Display...

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8th Smart Tools and Applications in Graphics Conference, STAG 2021

作者： Zellmann, Stefan University of Cologne Department of Computer Science Germany Bonn-Rhein-Sieg University of Applied Sciences Institute of Visual Computing Germany

ISBN: (纸本)9783038681656

We propose an image warping-based remote rendering technique for volumes that decouples the rendering and display phases. For that we build on prior work where we sample the volume on the client using ray casting and reconstruct z-values based on heuristics. Color and depth buffers are then sent to the client, which reuses this depth image as a stand-in for subsequent frames by warping it to reflect the current camera position and orientation until new data was received from the server. The extension we propose in this work represents the depth pixels as spheres and ray traces them on the client side. In contrast to the reference method, this representation adapts the footprint of the depth pixels to the distance to the camera origin, which is more effective at hiding warping artifacts, particularly when applied to volumetric data sets. © 2021 The Author(s)

关键词： Pixels

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Retraction Note: Pedestrian identification using motion-controlled deep neural network in real-time visual surveillance (Soft computing, (2023), 27, 1, (453-469), 10.1007/s00500-021-05701-9)

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Soft computing 2024年第2期28卷 1029-1029页

作者： Zahid, Muhammad Khan, Muhammad Attique Azam, Faisal Sharif, Muhammad Kadry, Seifedine Mohanty, Jnyana Ranjan Department of Computer Science COMSATS University Islamabad Wah Campus Islamabad Pakistan Department of Computer Science HITEC University Museum Road Taxila Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway School of Computer Applications KIIT Deemed to be University Bhubaneswar India

The publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher no longer has confidence in the results and conclusions of this article. Muhammad Attique Khan, Muhammad Sharif, Seifedine Kadry, and Jnyana Ranjan Mohanty disagree with this retraction. Faisal Azam did not explicitly state whether they agree or disagree with this retraction. Muhammad Zahid has not responded to correspondence regarding this retraction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Counselors network for intrusion detection

Counselors network for intrusion detection

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作者： Quincozes, Silvio E. Raniery, Carlos Ceretta Nunes, Raul Albuquerque, Célio Passos, Diego Mossé, Daniel Department of Computer Science Fluminense Federal University Rio de Janeiro Brazil Department of Applied Computing Federal University of Santa Maria Santa Maria Brazil Department of Applied Computing University of Pittsburgh PittsburghPA United States

Intrusion detection systems (IDSs) are a fundamental component of defense solutions. In particular, IDSs aim to detect malicious activities on computer systems and networks by relying on data classification models built from a training dataset. However, classifiers' performance can vary for each attack pattern. A common technique to overcome this issue is to use ensemble methods, where multiple classifiers are employed and a final decision is taken combining their outputs. Despite the potential advantages of such an approach, its usefulness is limited in scenarios where (i) multiple expert classifiers present divergent results, (ii) all classifiers present poor results due to lack of representative features, or (iii) detectors have insufficient labeled signatures to train their classifiers for a specific attack pattern. In this work, we introduce the concept of a counselors network to deal with conflicts from different classifiers by exploiting the collaboration among IDSs that analyze multiple and heterogeneous data sources. Empirical results demonstrate the feasibility of the proposed architecture in improving the accuracy of the intrusion detection process. © 2020 John Wiley & Sons, Ltd.

关键词： Intrusion detection

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Incorporating Artificial Intelligence Powered Immersive Realities to Improve Learning using Virtual Reality (VR) and Augmented Reality (AR) Technology

Incorporating Artificial Intelligence Powered Immersive Real...

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applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： Jasim Sharki Ghulam Al Balushi Malak Ibrahim Ahmed Al Jabri Suresh Palarimath Pyingkodi Maran K Thenmozhi C. Balakumar Information Technology Department College of Computing and Information Sciences Univesity of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This study analyzes the transformative potential of combining artificial intelligence (AI) with immersive technologies, specifically virtual reality (VR) and augmented reality (AR), to elevate educational outcomes significantly. The synergy of AI and immersive technologies presents a unique opportunity to enhance students’ motivation, memory retention, and comprehension of complex subjects, creating a distinct and impactful learning experience. The research comprehensively explores the current understanding of the effectiveness of VR and AR in educational settings, placing special emphasis on their applications in diverse fields such as science, Technology, Engineering, And Mathematics (STEM), medicine, language acquisition, and the development of interpersonal skills. Beyond highlighting the immense potential, the study acknowledges and addresses the obstacles that include the costs associated with equipment, training, and content creation. It underscores the importance of collaborative efforts among technology suppliers, educators, and content producers to overcome these challenges effectively. By recognizing and navigating these hurdles, the study aims to provide practical insights for educational institutions and policymakers, advocating for the integration of AI-powered immersive technology to enhance the overall learning experience for students. The findings underscore the transformative impact of such technologies, emphasizing their role in shaping the future of education and preparing students for the dynamic challenges of the modern world.

关键词： Training Costs Navigation Collaboration Learning (artificial intelligence) Augmented reality STEM

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Scaling Unicyclic Graph Analysis: Automated Generation, Augmentation, and Machine Learning-Driven Prediction

Scaling Unicyclic Graph Analysis: Automated Generation, Augm...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Khalid Munir Nadia Khan Mansoor Iqbal Nasser Shebka Saad Alahmari Daniyal Shakir Department of Computer Science University of Alabama at Birmingham Alabama USA School of Computing Sciences Pak-Austria Fachhochschule IAST Haripur Pakistan Department of Computer Science University of Wah Wah Cantt Pakistan Department of Computer Science Applied College Northern Border University Arar Saudi Arabia S&P Global Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Unicyclic graphs featuring a single cycle within a connected structure are crucial in modeling chemistry, biology, and information science networks. Traditional analysis methods, reliant on combinatorics, struggle with scalability for larger graphs. This paper introduces an automated framework for generating, augmenting, and analyzing unicyclic graphs using machine learning to predict key indices like degree distance. The framework enhances training datasets by systematically generating configurations and introducing structural diversity through edge perturbation and node reattachment. Evaluating models such as XGBoost and Support Vector Regression, we show that predictions closely match exact values, particularly for larger, unseen graphs. This approach improves scalability and accuracy, advancing graph analysis and network science.

关键词： Training Support vector machines Information science Chemistry Scalability Perturbation methods Estimation Machine learning Predictive models Periodic structures

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Building Narratives and Probing Concepts: Preparing Materials for Co-Design with Autistic Livestreamers

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ACM Transactions on Accessible computing 2025年第2期18卷 1-25页

作者： Terrance Mok Tyson Hartley Anthony Tang Adam McCrimmon Lora Oehlberg Department of Computer Science University of Calgary Calgary Alberta Canada School of Computing and Information Systems Singapore Management University Singapore Singapore Werklund School of Education Specialization School and Applied Child Psychology University of Calgary Calgary Alberta Canada

Based on 10 semi-structured interviews with autistic Twitch streamers, we introduce a series of scenario-based design narratives coupled with technology design concepts as a starting point for co-design discussion about autistic streaming. This work builds on prior thematic analysis of the unique intersection between autism and livestreaming. Our user-centered scenarios highlight the needs, goals, and challenges of autistic individuals in livestreaming contexts. By using evocative narratives, the scenarios serve to facilitate empathy and deeper engagement with the needs of autistic users, and help facilitate and support co-creative dialogues and discussions about new technology designs. We contribute this starting point for researchers and technology designers to co-create more inclusive and supportive online environments for autistic users.

关键词： autism autistic live streaming Twitch co-design scenario-based design

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Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs

arXiv

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arXiv 2024年

作者： Zahid, Anwar Hossain Laguna, Ignacio Le, Wei Department of Computer Science Iowa State University AmesIA United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States

As scientific codes are ported between GPU platforms, continuous testing is required to ensure numerical robustness and identify potential numerical differences between platforms. Compiler-induced numerical differences can occur when a program is compiled and run on different GPUs and compilers, and the numerical outcomes are different for the same input. We present a study of compiler-induced numerical differences between NVIDIA and AMD GPUs, two widely used GPUs in HPC clusters. Our approach uses a random program generator (Varity) to generate thousands of short numerical tests in CUDA and HIP, and their inputs;then, we use differential testing to check if the program produced a numerical inconsistency when run on NVIDIA and AMD GPUs, using the same compiler optimization level. We also use the AMD’s HIPIFY tool to convert CUDA tests into HIP tests and test if there are numerical inconsistencies induced by HIPIFY. In our study, we generated more than 600,000 tests and found subtle numerical differences occurring between the two classes of GPUs. We found that some of the differences come from (1) math library calls, (2) differences in floating-point precision (FP64 versus FP32), and (3) converting code to HIP with HIPIFY. © 2024, CC BY.

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