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SSRN 2024年

作者： Lisker, Simona Butman, Ayelet Dubin, Ran Dvir, Amit Hajaj, Chen Department of Computer Science Holon Institute of Technology Holon 52 Golomb Street POB 305 Holon5810201 Israel Department of Computer Science Ariel Cyber Innovation Center Ariel University Kiryat Hamada St. Ariel4070000 Israel Department of Computer and Software Engineering Ariel Cyber Innovation Center Ariel University Kiryat Hamada St. Ariel4070000 Israel Department of Industrial Engineering and Management Data Science and Artificial Intelligence Research Center Ariel University Kiryat Hamada St. Ariel4070000 Israel

In classical malware detection methodologies, digitalforensics and file carving, file type classification holds a pivotalrole. However, it is still a challenge to accurately categorize an ever-growing diversity of file formats and *** approaches to address the issue have been introducedin recent years. Most approches use hand-crafted features, andsome use a compact neural network architectureapproach with explicit feature extraction. Nonetheless, thesemethods encounter some challenges, including training time fornew file types, dealing with an insufficient number of trainingexamples, the complexity of training, and their accuracylevel. Our paper introduces three novel, content-basedretrieval and identification, file type classificationapproaches, proactively addressing these identified *** three approaches significantly enhance accuracy andprioritize an intuitive method for swift integration of new filetypes using pre-trained models, thereby enhancing speed andreliability in file type detection. The first approach is basedon the principles of Natural Language Processing (NLP) andtransformer architecture. The second approach amalgamates anensemble of statistical features, OOXML files identifier and a pre-trained model usingtransfer *** meticulously evaluated the classification task, achieving an accuracy of 72.4% and 69.2%, respectively, for our first two approaches, surpassing the performance of the state-of-the-art convolutional neural network (CNN) based models described in the literature in terms of accuracy. Finally, our third approach of one-shot learning enables the identification of files with minimal examples, facilitating efficient training in a short period, and demonstrating a remarkable 100% accuracy in several scenarios. © 2024, The Authors. All rights reserved.

关键词： Malware

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Everything you wanted to know about ChatGPT: Components, capabilities, applications, and opportunities

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Internet Technology Letters 2024年第6期7卷

作者： Heidari, Arash Navimipour, Nima Jafari Zeadally, Sherali Chamola, Vinay Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan College of Communication and Information University of Kentucky LexingtonKY United States Pilani India

Conversational Artificial Intelligence (AI) and Natural Language Processing have advanced significantly with the creation of a Generative Pre-trained Transformer (ChatGPT) by OpenAI. ChatGPT uses deep learning techniques like transformer architecture and self-attention mechanisms to replicate human speech and provide coherent and appropriate replies to the situation. The model mainly depends on the patterns discovered in the training data, which might result in incorrect or illogical conclusions. In the context of open-domain chats, we investigate the components, capabilities constraints, and potential applications of ChatGPT along with future opportunities. We begin by describing the components of ChatGPT followed by a definition of chatbots. We present a new taxonomy to classify them. Our taxonomy includes rule-based chatbots, retrieval-based chatbots, generative chatbots, and hybrid chatbots. Next, we describe the capabilities and constraints of ChatGPT. Finally, we present potential applications of ChatGPT and future research opportunities. The results showed that ChatGPT, a transformer-based chatbot model, utilizes encoders to produce coherent responses. © 2024 John Wiley & Sons Ltd.

关键词： Taxonomies

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Improving Predictive Navigation Through the Optimization of Counterfactual Track Evaluation

Improving Predictive Navigation Through the Optimization of ...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Alexander Stringer Geoffrey Dolinger Timothy Sharp Debra Hogue Joseph Karch Lesya Borowska Justin G. Metcalf US Air Force's Oklahoma City Air Logistics Complex and the 76 Software Engineering Group School of Electrical and Computer Engineering and the Advanced Radar Research Center at the University of Oklahoma Department of Computer Science University of Oklahoma

Modern navigation applications are confronting the challenge of path identification and prediction. Because of the inherent noisiness of measurement techniques, positional data often contains errors. Over time, when attempting to predict future positions, these errors can compound, causing drift in the target's path. Filtering or regression techniques can use periodic high-fidelity measurements, like those from GPS sensors, to correct positional data and reduce historic errors. These techniques have been much less effective in specialized environments where GPS is intermittent or denied. At the same time, the development of semi- and fully-autonomous systems has increased the need for accurate predictive navigation. To address this problem, previous methods were proposed for repairing tracking data using causality-aware machine learning (ML). It combined a long short-term memory (LSTM) network that predicted target paths with the non-dominated sorting genetic algorithm II (NSGA-II) which identified and evaluated counterfactual paths. Here te authors expand on that work by improving the simulated data used to train and test the system, expanding the GA by adding additional first principles-based objective functions, improving upon the LSTM implementation, and utilizing Extended Kalman Filter (EKF) pre-processing. System testing is conducted on Matlab-generated navigation scenarios, and results are compared to both EKF correction and spline interpolation. The proposed counterfactual track repair (CTR) tool produces paths with less repeatability than traditional approaches. However, it is shown to consistently generate more realistic path corrections, lower error in predictions based on its corrected paths, and is highly configurable, demonstrating the value and utility of this approach.

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A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Khalifa University KU6G Research Center Department of Computer Science Abu Dhabi United Arab Emirates Ecole de Technologie Superieure Department of Software and IT Engineering Montreal Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Concordia University Concordia Institute for Information Systems Engineering MontrealQC Canada Ecole de Technologie Superieure Department of Electrical Engineering Montreal Canada Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Mohamed Bin Zayed University of Artificial Intelligence Department of ML Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 1998-2012 IEEE.

关键词： Resource allocation

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Enhancing road safety with DL vision: Do driver distraction alerts hold the key?

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Neural Computing and Applications 2025年 1-21页

作者： Ali, Luqman Swavaf, Muhammad Alnajjar, Fady Zou, Zhao Mohan Ambali Parambil, Medha Mubin, Omar AlJassmi, Hamad Emirates Center for Mobility Research UAE University Abu Dhabi Al Ain United Arab Emirates Department of Civil and Environmental Engineering College of Engineering UAE University Abu Dhabi Al Ain United Arab Emirates Department of Computer Science and Software Engineering College of IT UAE University Abu Dhabi Al Ain United Arab Emirates School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW Australia

Driver distraction is a significant contributor to road accidents and traffic safety challenges. This study evaluates the performance of various YOLO models trained on a combination of custom and publicly available datasets, with their performance assessed using metrics such as mAP@50, precision, recall, F1-score, inference time, FPS, and model parameters. The aim is to determine the best trade-off between accuracy, speed, and computational efficiency, providing insights into the practical applicability of these models for distracted driving detection. Among the models, YOLOv8l achieves the highest accuracy with a mAP@50 of 0.921, precision of 0.911, recall of 0.895, and an F1-score of 0.899, making it suitable for scenarios prioritizing detection precision. YOLOv5m balances accuracy and efficiency, achieving a mAP@50 of 0.917 and an F1-score of 0.896, while maintaining moderate inference times. For real-time applications, YOLOv5n and YOLOv8n offer the fastest speeds, with inference times of 0.9 ms and FPS of 1111, at the cost of reduced accuracy. YOLOv8m provides a balance between performance and speed, achieving a mAP@50 of 0.915 and F1-score of 0.898, with an inference time of 3.4 ms and FPS of 294. A post-simulation survey reveals that participants find the visual warning system effective in mitigating driver distraction and enhancing perceived road safety. By integrating simulation data and user feedback, this study highlights the potential of advanced deep learning models like YOLO in improving driver assistance systems and promoting safer driving practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Highway accidents

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MABPD: Mobile Agent-Based Prevention and Black Hole Attack Detection in Wireless Sensor Networks

MABPD: Mobile Agent-Based Prevention and Black Hole Attack D...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Humaira Ashraf Fizza Khan Uswa Ihsan Fatima Al-Quayed N.Z. Jhanjhi Mamoona Humayun Department of Computer Science and Software Engineering International Islamic University Islamabad Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia School of Computer Science and Engineering (SCE) Taylor's University Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia

With the advancement and availability of the internet in the present age, everything is being wireless. Be it our home appliances or high defined monitoring systems, Wireless sensor networks play an important role. But with advancement comes a high probability of getting attacked. One of these attacks is the Blackhole attack (BHA), in which a node drains all the traffic towards itself and discards the data traffic without sending it towards the receiver. There exist multiple approaches that help to protect a network against BHA, but existing approaches have some drawbacks. Many proposed schemes make sure that the given technique uses less energy but failed to reduce the packet drop ratio. In the detection of BHA, the proposed scheme should not only detect defected nodes but also manages the efficiency. The proposed methodology used mobile agents with authentication of nodes and trust values to detect black hole nodes. The methodology is tested using different evaluation measures such as energy consumption, latency, network life, and packet delivery ratio. The proposed method uses a detection algorithm that increases energy consumption and hence networks lifetime. Packet delivery rate is increased by 19.51%, the energy consumption is reduced by 53.3%, and network life is increased by 43.3% as compared to the previous technique.

关键词： Wireless communication Energy consumption Wireless sensor networks Mobile agents Energy measurement Clustering algorithms Transportation

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Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II

arXiv

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arXiv 2024年

作者： Wu, Rixin Wang, Ran Hao, Jie Wu, Qiang Wang, Ping Niyato, Dusit The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing Jiangsu210038 China The Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University M3J 1P3 Canada The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem. © 2024, CC BY.

关键词： Reinforcement learning

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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S3-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation

arXiv

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arXiv 2024年

作者： Wang, Gui Li, Yuexiang Chen, Wenting Ding, Meidan Cheah, Wooi Ping Qu, Rong Ren, Jianfeng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Group University Engineering Research Center of Digital Medicine and Healthcare Guangxi Medical University Guangxi Nanning China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong School of Computer Science University of Nottingham Nottingham United Kingdom

Small lesions play a critical role in early disease diagnosis and intervention of severe infections. Popular models often face challenges in segmenting small lesions, as it occupies only a minor portion of an image, while down sampling operations may inevitably lose focus on local features of small lesions. To tackle the challenges, we propose a Small-Size-Sensitive Mamba (S3-Mamba), which promotes the sensitivity to small lesions across three dimensions: channel, spatial, and training strategy. Specifically, an Enhanced Visual State Space block is designed to focus on small lesions through multiple residual connections to preserve local features, and selectively amplify important details while suppressing irrelevant ones through channel-wise attention. A Tensor-based Cross-feature Multi-scale Attention is designed to integrate input image features and intermediate-layer features with edge features and exploit the attentive support of features across multiple scales, thereby retaining spatial details of small lesions at various granularities. Finally, we introduce a novel regularized curriculum learning to automatically assess lesion size and sample difficulty, and gradually focus from easy samples to hard ones like small lesions. Extensive experiments on three medical image segmentation datasets show the superiority of our S3-Mamba, especially in segmenting small lesions. Our code is available at https://***/ErinWang2023/S3-Mamba. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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