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2022 Workshop on Scientific Document Understanding, SDU 2022

作者： Rana, Ashish Khanna, Deepanshu Ghosal, Tirthankar Singh, Muskaan Singh, Harpreet Rana, Prashant Singh School of Business Informatics and Mathematics University of Mannheim Germany Department of Electronics & Communication Engineering Thapar Institute of Engineering & Technology India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Czech Republic Department of Computer Science & Engineering Thapar Institute of Engineering & Technology India

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. © 2022 Copyright for this paper by its authors.

关键词： Abstracting

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Revolutionizing Oil Spill Detection: A Machine Learning Approach for Satellite Image Classification

Revolutionizing Oil Spill Detection: A Machine Learning Appr...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Sherif, Khaled Rizk, Faris H. Zaki, Ahmed Mohamed Eid, Marwa M. Khodadadi, Nima Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara Research Center Jadara University Irbid Jordan

ISBN: (纸本)9798350351408

Identifying and labeling oil spills in satellite imagery is an essential activity of both environmental monitoring and disaster response actions. This work is dedicated to applying an Artificial Neural Network (ANN) model for gathering oil spill data by using a dataset that is specially curated for this reason. Our dataset was developed from satellite pictures of the ocean, some of which depict oil spills and some that do not. The features were extracted from each picture using computer vision algorithms. Our ANN model is trained to distinguish between two classes: The metrics that are looked at consist of accuracy, sensitivity, specificity, PPV, NPV, and statistical significance, and they illustrate how the model performs. As a result, the ANN model gets an accuracy of 96.88% and a sensitivity of 92.86% at the same time, while the specificity is 99.88%. The sensitivity of this diagnostic test is 96.30%, and the specificity is 94.74%. A p-value of 0.985997 means that the reported finding reaches a statistical significance, which is enough to support our hypothesis. This can be concluded from the results of ANN, showing the potential of this model to successfully classify the image patches into two sets, namely the ones covered by oil spills and the oil spill-free ones. The research work is a great contribution to the development of the area of environmental monitoring through the machine learning methods used for quick and appropriate detection of environmental hazards. © 2024 IEEE.

关键词： Environmental monitoring

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Optimization of Transmit Beamforming Using Channel Covariances for MISO Downlink Assisted by Reconfigurable Intelligent Surfaces

Optimization of Transmit Beamforming Using Channel Covarianc...

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International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Khin Thandar Kyaw Wiroonsak Santipach Kritsada Mamat Kamol Kaemarungsi Kazuhiko Fukawa Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Information and Communications Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

We propose an unsupervised beamforming neural network (BNN) to optimize transmit beamforming in downlink multiple input single output (MISO) channels. Reconfigurable intelligent surface (RIS) panels are used to assist user equipment that does not receive sufficiently s trong s ignals f rom t he base station. To avoid frequent beam updates, the proposed BNN is based on slow-changing channel covariances and is different from most other BNNs that utilize instant channel state information. Numerical simulations show that the proposed BNN can achieve much higher sum rates than zeroforcing beamforming does when the system is heavily loaded and can drastically reduce computation time for large-scale communication systems.

关键词： Array signal processing Neural networks Reconfigurable intelligent surfaces MISO communication Downlink Numerical simulation Telecommunications

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Comparison of *** and Spring Boot in Web Development

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International Conference on electronics, computers and Artificial Intelligence (ECAI)

作者： Ovidiu Constantin Novac David Ghiurău Mihaela Cornelia Novac Cornelia Emilia Gordan Mihai Oproescu Gyongyi Bujdoso Vlad Ovidiu Mihalca Department of Computers and Information Technology Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Management in Information Technology Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Electrical Engineering Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Electronics and Telecommunications Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Electronics Computers and Electrical Engineering Faculty of Electronics Communication and Computer Science University of Pitesti Pitesti Romania Department of Computer Science and Library and Information Science Faculty of Informatics University of Debrecen Debrecen Hungary Electrical Engineering and Information Technology Faculty School of Engineering Sciences University of Oradea Oradea Romania

In this paper we present two types of server implementation, we will look at the advantages and disadvantages of each technology. *** server application, built with ***, a single threaded server which will treat the requests in an asynchronous manner, with a non-blocking principle, whilst Spring Boot is a more robust and more common approach of a server technology with the benefits of having a strongly typed architecture and multiple threads available. These types of application will behave the same, making almost impossible to tell the difference between a *** and a Spring Boot web server.

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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Urban Mobility Optimization with IoT and Deep Q Networks for Traffic Density Management 3

Urban Mobility Optimization with IoT and Deep Q Networks for...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Ranganathan, Chitra Sabapathy Jayanthi, L.N. Babu, J. Jagan Manikandan, M. Sivakamy, N. Mphasis Corporation Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

Traffic density and mobility optimization in urban areas are becoming more difficult to manage to improve transportation efficiency. Urban traffic management is complicated, but this research provides a revolutionary technique using the Internet of Things (IoT) and Deep Q Networks (DQN). Traffic lights, sensors, and vehicles may be monitored in real-time using IoT technology to provide a complete picture of urban mobility. As an intelligent decision-making agent, the DQN uses reinforcement learning to adapt to changing traffic circumstances and optimize traffic density management. The model is educated on historical data and refines its tactics via environmental interaction, solving urban transportation problems. A complex data fusion mechanism processes data from different IoT devices. A reinforcement learning framework uses DQN for decision-making, and a feedback loop updates and optimizes traffic management tactics. The research uses simulations and urban case studies to prove that the technique works. The findings show improved traffic flow, congestion, and mobility efficiency. It uses IoT and DQN to develop intelligent urban transport systems that scale and respond to traffic density in complex and dynamic urban environments. © 2024 IEEE.

关键词： Traffic congestion

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Traffic Surveillance System Through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation

Traffic Surveillance System Through Capsule Gated Graph Atte...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Mahesh Prasanna K Devashree Marotka Muralidharan J Rachit Adhvaryu Vani V Ramya Maranan Department of Electronics and Communication Engineering Vivekananda College of Engineering and Technology Puttur Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering and Management Nagpur Maharashtra Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Gujarat Department of Information Science and Engineering Bangalore Institute of Technology Bangalore Karnataka Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Internet of Things and Industrial Internet of Things have seen rapid adoption and have transformed industries through smart automation, constant monitoring and better connected systems. The progressive inclusion of such solutions has contributed to the growth in exposure to complex and changing global threats. The primary challenge lies in accurately detecting and classifying cyber threats from high-volume, unstructured network traffic data in real-time. Existing methods are not very accurate and cannot be easily scaled due to constraints in computational resources and the nature of IIoT network systems. This paper introduces Traffic Surveillance System through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation (Cyber-CapG). The system uses raw traffic data from Edge-IIoTset and uses the Tunable Transformer for feature extraction. The extracted features are passed through the Capsule Gated Graph Attention Network to model the traffic as a graph based on communication hierarchy and being selective to essential traffic patterns. Furthermore, weights and loss functions are tuned based on the Progressive Crested Porcupine Optimization so as to achieve maximum optimization. The proposed system exhibits outstanding performance with 99.21 % detection rate, 99.07% precision, 99.15% recall, 99.18% F1 measure, and it provides an effective way of identifying threats like DDoS, SQL Injection, and Ransomware. In conclusion, Cyber-CapG provides a solid solution for traffic surveillance in IoT/IIoT, providing the high detection and prevention of various cyber threats, as well as optimizing the cybersecurity and reliability of industrial networks.

关键词： Surveillance Prevention and mitigation Telecommunication traffic Traffic control Logic gates SQL injection Feature extraction Real-time systems Optimization Industrial Internet of Things

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Enhancing Hate Speech Classification in Myanmar Language through Lexicon-Based Filtering

Enhancing Hate Speech Classification in Myanmar Language thr...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Nang Aeindray Kyaw Ye Kyaw Thu Thazin Myint Oo Hutchatai Chanlekha Manabu Okumura Thepchai Supnithi Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer Technology Center Pathumthani Thailand Language Understanding Lab Myanmar Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Detecting hate speech in low-resource languages, like Myanmar, is a critical yet challenging task due to limited linguistic resources. In this study, we propose a methodology leveraging a hate speech dictionary derived from annotated data to enhance classification accuracy. Our contributions include manually collecting data from Facebook and annotating them to build a comprehensive corpus. Utilizing fastText models, we investigate the impact of filtering hate speech content from sen-tences, comparing classifiers trained on unfiltered long sentences against those trained on hate speech-filtered short sentences (which are obtained by lexicon-based filtering). Our experiments demonstrate notable accuracy enhancements achieved through the incorporation of the hate speech lexicon, with the best accu-racy reaching 0.771. This research underscores the effectiveness of lexicon-based filtering in augmenting hate speech detection capabilities in low-resource language settings.

关键词： Training computer science Accuracy Dictionaries Filtering Social networking (online) Hate speech

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Securing loT Systems with AI-Infused Software and Virtual Replica Models

Securing loT Systems with AI-Infused Software and Virtual Re...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： K. Sangeetha Supriya Jeno Lovesum S P Renuka Arora Rishu Bhatia Sunita Yadwad Natrayan L Department of Computer Science & Engineering Christ university Bengaluru India Department of Computer Science & Engineering Jagannath University NCR Bahadurgarh India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Jhajjar India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The proliferation of Internet of Things (loT) devices has introduced significant security challenges due to the increased attack surface and the inherent vulnerabilities of interconnected systems. This paper proposes a novel security framework that integrates Artificial Intelligence (AI)-infused software and Virtual Replica Models, or Digital Twins, to enhance the robustness of loT systems. The framework leverages AI-driven anomaly detection and predictive analysis to identify potential threats in real-time, while Digital Twins provide a virtual replica of the loT ecosystem for continuous monitoring and simulation-based threat assessment. By fusing these technologies, the proposed solution enables proactive security measures, ensuring rapid response to cyberattacks and continuous system optimization. Extensive simulations and experimental results demonstrate the system's ability to reduce attack detection times by 30%, while improving overall system resilience and minimizing false positive rates. This approach provides a scalable and adaptable solution for securing loT infrastructures in various industries.

关键词： Industries Transport protocols Adaptation models Biological system modeling Software Real-time systems Threat assessment Digital twins Artificial intelligence Anomaly detection

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Towards Real Time Traffic Analysis with Reduced Image Quality

Towards Real Time Traffic Analysis with Reduced Image Qualit...

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IEEE India Conference (INDICON)

作者： Rohit Mohanraj Erunika Oshani Dayaratna Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India Department of Statistics and Computer Science Faculty or science University of peradeniya SriLanka

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

This paper presents a detailed study of two computationally efficient object recognition models-YOLOv5 and Fast R-CNN with a MobileNet backbone-focusing on their performance under different degrees of image / video qualities. Blurring is a common problem in autonomous driving due to motion, weather conditions, and other factors, which can greatly reduce picture quality and, consequently, the accuracy of object recognition. Also, the low-quality images produced by on-car devices suffer when trying to utilize them for advanced detection applications. Utilizing the Zenseact Open Dataset (ZOD), images are generated by varying kernel sizes of Gaussian blur to examine how each model's recognition accuracy is affected by picture degradation. Our study shows key differences in the stability of these models, showing the trade-offs between processing speed and recognition confidence in poor visual settings. The results provide helpful guidance for choosing object recognition systems that keep performance in difficult situations, crucial for the safety and efficiency of driverless cars.

关键词： YOLO Performance evaluation Degradation Training Adaptation models Visualization Accuracy Real-time systems Object recognition Video recording

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