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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jin Qian Wei Jin Xuanyuan Wang Ying Yu Mingchen Zheng Duoqian Miao School of Software East China Jiaotong University Nanchang Jiangxi School of Transportation Engineering East China Jiaotong University Nanchang Jiangxi Department Computer Science and Technology Tongji University Shanghai China

In the existing multi-attribute decision making researches, most of the hesitant fuzzy linguistic multi-attribute decision making methods assume the decision makers use the linguistic term set under the same granularity to express their linguistic evaluation informations. However, in actual decision making, due to different cultural and educational backgrounds, decision makers may use different sets of linguistic terms at different granularities, which leads to the mismatch of decision information and inaccuracy of decision results. To address this problem, this paper proposes VIKOR decision making method based on multi-granularity hesitant furzy linguistic term sets, which consider both the overall benefits of the group and the regret values of individuals to select the optimal solution. Specifically, a VIKOR decision making method based on the multi-granularity hesitant fuzzy linguistic term set is first proposed, which employs to convert the multi-granularity hesitant fuzzy linguistic term set of each decision maker to the same granularity for aggregation, and obtains the corresponding membership level linguistic term set. Secondly, using the VIKOR method, the positive and negative ideal solutions are determined, and all alternatives are ranked based on their proximity to the positive ideal solution. This ranking facilitates the selection of the optimal solution. Secondly, combined with the VIKOR method, the positive and negative ideal solutions are determined, and all alternatives are ranked in order of merit according to the magnitude of the closeness of the preference value setting of each alternative to the positive ideal solution, so as to achieve the selection of the optimal solution. Finally, an arithmetic example is used to prove the accuracy, rationality and feasibility of the method, and the stability of the proposed method is further illustrated by experimentally analyzing the decision mechanism coefficients.

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AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection

AutoLog: A Log Sequence Synthesis Framework for Anomaly Dete...

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IEEE International Conference on Automated software engineering (ASE)

作者： Yintong Huo Yichen Li Yuxin Su Pinjia He Zifan Xie Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK Shenzhen) China Huazhong University of Science and Technology Wuhan China

The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AUTOLOG, the first automated log generation methodology for anomaly detection. AUTOLOG uses program analysis to generate runtime log sequences without actually running the system. AUTOLOG starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AUTOLOG propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AUTOLOG acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. AUTOLOG also provides hyper-parameters to adjust the data size, anomaly rate, and component indicator for simulating different real-world scenarios. We further demonstrate AUTOLOG's practicality by showing that AUTOLOG enables log-based anomaly detectors to achieve better performance (1.93%) compared to existing log datasets. We hope AUTOLOG can facilitate the benchmarking and adoption of automated log analysis techniques.

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An explainable deep learning approach for stock market trend prediction

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Heliyon 2024年第21期10卷 e40095页

作者： Muhammad, Dost Ahmed, Iftikhar Naveed, Khwaja Bendechache, Malika CRT-AI and ADAPT Research Centres School of Computer Science University of Galway Ireland Department of Software Engineering University of Europe for Applied Sciences Potsdam Germany Swedish Center for Digital Innovation University of Gothenburg Sweden ADAPT Research Centre School of Computer Science University of Galway Ireland

Given the intricate nature of stock forecasting as well as the inherent risks and uncertainties, analysis of market trends is necessary to capitalize on optimal investment opportunities for profit maximization and timely disinvestment for loss minimization. In this work, we propose a deep learning model for predicting five distinct stock market trends: upward, downward, double top, rounded bottom, and rounded top. The proposed model surpasses common benchmarks, including support vector machine, random forest, and logistic regression, achieving an average accuracy of 94.9%, compared to 85.7% for random forest, 60.07% for support vector machine, and 52.45% for logistic regression. Furthermore the proposed model excels in F1-score, with a 94.85% performance, compared to 77.95% for random forest, 21.02% for support vector machine and 46.23% for logistic regression, across four real world diverse datasets. Additionally, we employ explainable AI (XAI) techniques, SHAP and LIME, to enhance interpretability, enabling stakeholders to understand the key factors driving predictions. The SHAP analysis reveals the top 10 most important/influential features, enabling feature reduction while maintaining performance. Interestingly, while accuracy slightly decreases with top 10 features, precision, recall, and F1-score improve, suggesting a trade-off between comprehensiveness and performance. These results demonstrate the potential for practical application in financial decision-making, providing a balance between interpretability and predictive power that can support investors in risk management and strategic planning. © 2024 The Author(s)

关键词： Deep neural network Explainable AI Machine learning Stock market trend prediction Time series prediction

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Transportation Path Planning Model for Multi-Constraint Unmanned Aerial Vehicle in Healthcare 2

Transportation Path Planning Model for Multi-Constraint Unma...

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2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024

作者： Byeon, Haewon Kumar, Pramod Yogi, Kottala Sri Gudala, Leeladhar Soni, Mukesh Motagi, Samarth Inje University Department of Ai and Software Gimhae 50834 South Korea Ganga Institute of Technology and Management Haryana Jhajjar 124104 India A Constituent College of Symbiosis International University Dept of Operations Pune India Valparaiso University Department of Computer Science and Engineering West Godavari India D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India Collins Aerospace Department of Aircraft Seating Winston-Salem NC United States

ISBN: (纸本)9798350368949

In response to the path planning problem of using Unmanned Aerial Vehicle (UAV) for blood transportation, with the objective of minimizing the total distance travelled by the UAV, a multi-constraint drone blood transportation path planning model is established. Taking into account the limited number of drone take-off and landing platforms and the safety time intervals for continuous drone take-off and landing, a drone take-off scheduling strategy is designed to reduce the total time spent by UAV completing transportation tasks. Additionally, an Imperial Competition Algorithm based on Imperialist Competitive Algorithm is proposed to solve this problem. This algorithm introduces a sine disturbance strategy and adds Imperial Reform stages to improve the search accuracy of the algorithm. It utilizes acceptance criteria related to solution quality to maintain the diversity of the population. Validation is conducted using benchmark examples and instances of drone blood transportation. The results indicate that the proposed algorithm can provide transportation solutions for drone blood transportation tasks that meet various constraints without any conflicts in drone take-off and landing. The drone take-off scheduling strategy effectively reduces the total time spent by UAV in completing tasks. © 2024 IEEE.

关键词： Blood Transportation Healthcare Transportation Imperial Competition Algorithm Path Planning Model UAV

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Mutual Graph Learning Network and Diffusion Probabilistic Model-based Medical Image Segmentation

Mutual Graph Learning Network and Diffusion Probabilistic Mo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huaqiang Su Haijun Lei Guoliang Chen Xin Chen Baiying Lei College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Radiology Guangzhou First People’s Hospital Guangzhou China School of Biomedical Engineering & Shenzhen University Medical School Shenzhen University Shenzhen China

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network’s ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16.

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： D.P.H Arunoda S.R Walpola S.M.I Piumira A.D.M.P Athukorala Thusithanjana Thilakarathna Sanjeevi Chandrasiri Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students’ interactions (attention, emotions) with the system to identify students’ ability to use the learning tool, identifying gaps in students’ knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students’ knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy.

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A Melting Pot of Evolution and Learning

arXiv

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arXiv 2023年

作者： Sipper, Moshe Elyasaf, Achiya Halperin, Tomer Haramaty, Zvika Lapid, Raz Segal, Eyal Tzruia, Itai Tamam, Snir Vitrack Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel DeepKeep Tel-Aviv Israel

We survey eight recent works by our group, involving the successful blending of evolutionary algorithms with machine learning and deep learning: 1. Binary and Multinomial Classification through Evolutionary Symbolic Regression, 2. Classy Ensemble: A Novel Ensemble Algorithm for Classification, 3. EC-KitY: Evolutionary Computation Tool Kit in Python, 4. Evolution of Activation Functions for Deep Learning-Based Image Classification, 5. Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution, 6. An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks, 7. Foiling Explanations in Deep Neural Networks, 8. Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors. Copyright © 2023, The Authors. All rights reserved.

关键词： Genetic programming

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Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey

arXiv

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arXiv 2025年

作者： Tian, Shuang Zhang, Tao Liu, Jiqiang Wang, Jiacheng Wu, Xuangou Zhu, Xiaoqiang Zhang, Ruichen Zhang, Weiting Yuan, Zhenhui Mao, Shiwen Kim, Dong In School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Technology An hui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui University of Technology Anhui243002 China College of Computing and Data Science Nanyang Technological University Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

—With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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Machine Learning Based Container Placement in On-Demand Clustered Fogs

Machine Learning Based Container Placement in On-Demand Clus...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Peter Farhat Sarhad Arisdakessian Omar Abdul Wahab Azzam Mourad Hakima Ould-Slimane Department of Computer Science Lebanese American University Beirut Lebanon Department of Computer Science &#x0026 Engineering Universit&#x00E9 du Quebec en Outaouais Gatineau Canada Division of Science New York University Abu Dhabi UAE Department of Software Engineering IT &#x00C9 cole de Technologie Sup&#x00E9 rieure Montreal Canada

Fog computing extends the concept of cloud computing by allowing services, embedded into virtual machines or containers, to be placed at the edge of the network in the proximity of the end devices. However, due to the huge increase in the number of user requests, placing containers onto fog devices becomes a challenging task. In this work, we address the problem of large-scale container placement in fog computing environments. We propose a machine learning-based K-means clustering solution, which we integrate into the Genetic Algorithm (GA) to improve the selection of the initial population. We first formulate the container placement problem as a multi-objective optimization model with several (conflicting) objectives and then propose a cluster-based GA approach to solve the problem in an efficient manner. Simulation results suggest that our solution outperforms one state-of-the-art approach in terms of effectiveness and efficiency.

关键词： Wireless communication Simulation Sociology Containers Virtual machining Statistics Task analysis

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Specific Emitter Identification Based on Joint Variational Mode Decomposition

arXiv

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arXiv 2024年

作者： Chen, Xiaofang Xu, Wenbo Wang, Yue Huang, Yan Key Laboratory of Universal Wireless Communication Beijing University of Posts and Telecommunications Beijing China Department of Computer Science Georgia State University AtlantaGA United States Department of Software Engineering and Game Development Kennesaw State University MariettaGA30060 United States

Specific emitter identification (SEI) technology is significant in device administration scenarios, such as self-organized networking and spectrum management, owing to its high security. For nonlinear and non-stationary electromagnetic signals, SEI often employs variational modal decomposition (VMD) to decompose the signal in order to effectively characterize the distinct device fingerprint. However, the trade-off of VMD between the robustness to noise and the ability to preserve signal information has not been investigated in the current literature. Moreover, the existing VMD algorithm does not utilize the stability of the intrinsic distortion of emitters within a certain temporal span, consequently constraining its practical applicability in SEI. In this paper, we propose a joint variational modal decomposition (JVMD) algorithm, which is an improved version of VMD by simultaneously implementing modal decomposition on multi-frame signals. The consistency of multi-frame signals in terms of the central frequencies and the inherent modal functions (IMFs) is exploited, which effectively highlights the distinctive characteristics among emitters and reduces noise. Additionally, the complexity of JVMD is analyzed, which is proven to be more computational-friendly than VMD. Simulations of both modal decomposition and SEI that involve real-world datasets are presented to illustrate that when compared with VMD, the JVMD algorithm improves the accuracy of device classification and the robustness towards noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Variational mode decomposition

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