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arXiv 2023年

作者： Bera, Asish Nasipuri, Mita Krejcar, Ondrej Bhattacharjee, Debotosh The Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Campus Rajasthan Pilani333031 India The Department of Computer Science and Engineering Jadavpur University Kolkata32 India The Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://***/view/syd-net/home. © 2023, CC BY.

关键词： Sports

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Development and implementation of unmanned vehicles through artificial intelligence involving communication system with sensors and control parameters

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Measurement: Sensors 2024年 33卷

作者： Srinivas, T. Aditya Sai Yadav, Ranjeet Gowri, V. Chandraprabha, K. Ponnusamy, Sivakumar Mavaluru, Dinesh Department of AIML Jayaprakash Narayan College of Engineering Telangana Mahabubnagar India Government Polytechnic Daman UT Administration of Dadra and Nagar Haveli and Daman and Diu Varkund Nani Daman 396210 India Electronics and Communication Engineering J.J.College of Engineering and Technology Ammapettai Trichy620009 India Department of Information Technology Bannari Amman Institute of Technology Tamilnadu Sathyamangalam India Department of Computer Science and Engineering KSR College of Engineering Tiruchengode TamilNadu Namakkal India Department of Information Technology College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

Physical situations that are duplicated in virtual settings are what the term "digital twin" refers to. Utilizing the Internet of Things and sensors to improve microgrid operation, the Microgrid Digital Twin (MGDT) exhibits bidirectional data sharing and sophisticated communication characteristics. Smart grid functionality is enhanced, limits are set in real-time, and data is securely stored by MGDT. It directs the planning and execution phases of microgrid operations. Concurrently, industry and transportation innovation is propelled by the Industrial Revolution. AI-driven ground-dependent controls, communication systems, and sensors are essential for breakthroughs in unmanned vehicle safety. This abstract clarifies how important digital twins more specifically, MGDTs are to system operations optimization. In addition to highlighting technological advancement and the use of AI to ensure the safety of unmanned vehicles, it provides insights into the potentially revolutionary applications of digital twins across a range of industries. © 2024 The Authors

关键词： Accident prevention

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Enhancing Acceptance of Automated Vehicles Through Gamification: Insights from a Driving Simulator Study

SSRN

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SSRN 2025年

作者： Li, Chenchang Yin, Rao Yang, Bo Guan, Muhua Xie, Kun Nakano, Kimihiko Institute of Industrial Science University of Tokyo Tokyo153-8505 Japan School of Design & Innovation China Academy of Art Hangzhou311100 China School of Computer Science & System Engineering Kyushu Institute of Technology Fukuoka820-8502 Japan Department of Civil and Environmental Engineering Transportation Informatics Lab Old Dominion University NorfolkVA23529 United States

Automated vehicles hold great potential to revolutionize transportation, but subjective user acceptance remains a major obstacle to their widespread adoption and the realization of anticipated safety benefits. Recognizing this challenge, this study explores the role of gamification in enhancing user acceptance of automated vehicles and promoting safer driver engagement. By designing a gamified interaction framework and implementing it in a driving simulator experiment, the research addresses three core questions: Does gamification work? Why does it work? How can it be effectively implemented? In the experiment, participants experienced automated driving scenarios with integrated gamified interactions during overtaking tasks. Data on users' attitudes, behaviors, and eye movements were collected and analyzed through qualitative and quantitative methods. Furthermore, the study incorporated design research techniques, including interviews and user journey mapping, into the scientific framework to derive meaningful and actionable insights. The results reveal that gamification significantly improves user acceptance by enhancing Perceived Ease of Use and strengthening Behavioral Intentions. At a deeper mechanistic level, gamification enhances users' situational awareness and trust by increasing their perception efficiency during interactions and raising their risk tolerance, thereby shifting the balance between safety and reward. Based on these mechanisms and further findings, the study proposes three design strategies to maximize the benefits of gamification: designing system transparency, tailoring experiences to individual user characteristics, and leveraging incentive mechanisms to engage and motivate users effectively. This study's findings provide theoretical foundations and practical design directions for leveraging gamification to enhance human-vehicle interactions, utimately contributing to the development of safer and more efficient automated driving systems. © 2

关键词： Eye movements

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center 2 Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies "Alessandro Faedo" Italy Hacettepe University Department of Computer Engineering 6 Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA1 (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs’ grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2023, CC BY.

关键词： Computational linguistics

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Synergizing Airborne Non-Terrestrial Networks and Reconfigurable Intelligent Surfaces-Aided 6G IoT

arXiv

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arXiv 2023年

作者： Jamshed, Muhammad Ali Kaushik, Aryan Toka, Mesut Shin, Wonjae Shakir, Muhammad Zeeshan Dash, Soumya P. Dardari, Davide The College of Science and Engineering University of Glasgow United Kingdom The School of Engineering & Informatics University of Sussex United Kingdom The Department of Electrical and Computer Engineering Ajou University Korea Republic of The School of Electrical Engineering Korea University Korea Republic of The Department of Electrical and Computer Engineering Princeton University United States The School of Computing Engineering and Physical Sciences University of the West of Scotland United Kingdom The School of Electrical Sciences Indian Institute of Technology Bhubaneswar India The Department of Electrical Electronic and Information Engineering University of Bologna Italy

On the one hand, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising solution to meet the demand for higher data rates, improved coverage, and efficient spectrum utilization. On the other hand, Non-Terrestrial Networks (NTNs) offer unprecedented possibilities for global connectivity. Moreover, the NTN can also support the upsurge in the number of Internet of Things (IoT) devices by providing reliable and ubiquitous connectivity. Although NTNs have shown promising results, there are several challenges associated with their usage, such as signal propagation delays, interference, security, etc. In this article, we have discussed the possibilities of integrating RIS with an NTN platform to overcome the issues associated with NTN. Furthermore, through experimental validation, we have demonstrated that the RIS-assisted NTN can play a pivotal role in improving the performance of the entire communication system. © 2023, CC BY.

关键词： Internet of things

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Evaluating RNN Models for Multi-Step Traffic Matrix Prediction

Evaluating RNN Models for Multi-Step Traffic Matrix Predicti...

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Information technology and Data Science (CITDS), IEEE Conference on

作者： Mohammed Nsaif Gergely Kovásznai Hasanein D. Rjeib Ali Malik Ruairí de Fréin Faculty of informatics University of Debrecen Hungary Faculty of Computer Science and Mathematics University of Kufa Iraq Department of Business Management Al-imam University College Balad Iraq Faculty of informatics Eszterházy Károly Catholic University Hungary Faculty of Engineering University of Kufa Iraq Institute of Information Technology University of Miskolc Hungary School of Electrical and Electronic Engineering Technological University Dublin Ireland

ISBN: (数字)9798350387889

ISBN: (纸本)9798350387896

Network traffic matrix prediction is used to estimate the patterns of future network flows before they are initiated. Traffic matrix prediction is needed by a wide range of network management functions such as network monitoring and it relies on historical data. In this paper, we address the task of multi-time step traffic matrix prediction using Recurrent Neural Networks (RNN). Our objective is to conduct a comparative study of different types of RNNs and to evaluate their ability to predict multi-time step Origin-Destination traffic matrices. Experiments show that RNNs are capable of predicting multiple steps of traffic matrices, however, the RMSE of the predictions increases as we move further away from the last true value. Our primary finding is that the RNN-GRU show has the best prediction ability in the very beginning steps with an RMSE of 0.048, while RNN-LSTM demonstrated higher capability with the last steps, having an RMSE value of 0.058.

关键词： Recurrent neural networks Power demand Telecommunication traffic Predictive models Data science Data models computer networks Numerical models Information technology Monitoring

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer Science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Quantum advantage for noisy channel discrimination

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Physical Review A 2022年第3期105卷 032401-032401页

作者： Zane M. Rossi Jeffery Yu Isaac L. Chuang Sho Sugiura Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Department of Electrical Engineering and Computer Science and Co-Design Center for Quantum Advantage Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. Sunnyvale California 94085 USA

Many quantum mechanical experiments can be viewed as multiround interactive protocols between known quantum circuits and an unknown quantum process. Fully quantum “coherent” access to the unknown process is known to provide an advantage in many discrimination tasks compared to when only incoherent access is permitted, but it is unclear if this advantage persists when the process is noisy. Here, we show that a quantum advantage can be maintained when distinguishing between two noisy single-qubit rotation channels. Numerical and analytical calculations reveal a distinct transition between the performance of fully coherent and fully incoherent protocols as a function of noise strength. Moreover, the size of the region of coherent quantum advantage shrinks inverse polynomially in the number of channel uses, and in an intermediate regime an improved strategy is a hybrid of fully coherent and fully incoherent subroutines. The fully coherent protocol is based on quantum signal processing, suggesting a generalizable algorithmic framework for the study of quantum advantage in the presence of realistic noise.

关键词： Quantum algorithms Quantum channels Quantum computation Quantum information processing Quantum sensing Quantum simulation

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Stability from graph symmetrization arguments in generalized Turán problems

arXiv

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arXiv 2023年

作者： Gerbner, Dániel Karim, Hilal Hama Alfréd Rényi Institute of Mathematics Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

Given graphs H and F, ex(n,H,F) denotes the largest number of copies of H in F-free n-vertex graphs. Let χ(H) 0 there exists δ > 0 such that if an n-vertex F-free graph G contains at least ex(n,H,F) − δnh copies of H, then the edit distance of G and the r-partite Turán graph is at most Εn2. We say that H is weakly F-Turán-stable if the same holds with the Turán graph replaced by any complete r-partite graph T. It is known that such stability implies exact results in several cases. We show that complete multipartite graphs with chromatic number at most r are weakly Kr+1-Turán-stable. Answering a question of Morrison, Nir, Norin, Rza zewski and Wesolek positively, we show that for every graph H, if r is large enough, then H is Kr+1-Turán-stable. Finally, we prove that if H is bipartite, then it is weakly C2k+1-Turán-stable for k large enough. Copyright © 2023, The Authors. All rights reserved.

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Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities

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Measurement: Sensors 2024年 35卷

作者： Mrudula, Sunkara Teena Meenakshi Ritonga, Mahyudin Sivakumar, S. Jawarneh, Malik F, Sammy Keerthika, T. Rane, Kantilal Pitambar Roy, Bhaskar Velagapudiramakrishna Siddhartha engineering College Andhra Pradesh Vijayawada India Apeejay Stya University Haryana Sohna India Universitas Muhammadiyah Sumatera Barat Indonesia Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Avadi Tamilnadu Chennai India College of Computer Science and Informatics Amman Arab University Jordan Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of AIML Asansol Engineering College Asansol India

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595. © 2024 The Authors

关键词： Traffic congestion

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