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Advances in Data engineering and Intelligent Computing systems (ADICS), International Conference on

作者： R. Mohandas D.S.John Deva Prasanna Baiju B V S. Nathiya T. Dhiliphan Rajkumar C. Sivashankar Department of Computational Intelligence SRM Institiute of Science and Technology Chennai India Department of Datascience and Business systems SRM Institiute of Science and Technology Chennai India School of Computer Science and Engineering(SCOPE) VIT University Vellore TamilNadu India Department of Electronics and Communication Engineering Prince shri Venkateshwara Padmavathi Engineering College Chennai Dept. Of Computer Science and Engineering Kalasalingam Academy Of Research And Education Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennnai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks; therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities.

关键词： Learning systems Industries Pathology Machine learning algorithms Intrusion detection Machine learning Internet of Things

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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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A Moving Target DDoS Defense Approach in Consortium Blockchain

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Gai, Keke Zhang, Guolei Jiang, Peng Zhu, Liehuang Choo, Kim-Kwang Raymond Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China University of Texas at San Antonio Department of Information Systems and Cyber Security Department of Electrical and Computer Engineering San AntonioTX78249 United States

Although consortium blockchain has an identification mechanism, the captured internal clients are potentially threatening internal blockchain nodes. Internal Distributed Denial-of-Service (DDoS) attacks threaten the specific nodes in consortium blockchain, e.g., the executor, consensus, and committer nodes. Typical attack methods may include SYN Flooding and ACK Flooding and deny normal transaction service by sending many invalid transactions and blocks. In this work, we have proposed an organization collaboration-based DDoS defense approach and a Deep Q-learning (DQN)-based Moving Target Defense (MTD) for changing attack surface of victims in consortium blockchain. On one hand, contracts are used to synchronize attack information obtained from organizations, e.g., bots' IP addresses and public keys. On the other hand, we have developed a DQN-based MTD defense mechanism for organizations to change the attack surface of victims in order to mitigate the malicious traffic, in the case of missing detections of bots. Our approach applies a multi-stage game to reflect interactions between attackers and defenders. The evaluation results have demonstrated that our approach could effectively mitigate DDoS attacks in consortium blockchain. © 2025 IEEE.

关键词： Denial-of-service attack

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Towards Scalable Automated Grading: Leveraging Large Language Models for Conceptual Question Evaluation in engineering 1

Towards Scalable Automated Grading: Leveraging Large Languag...

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1st Workshop on Large Foundation Models for Educational Assessment

作者： Gao, Rujun Guo, Xiaosu Li, Xiaodi Narayanan, Arun Balajiee Lekshmi Thomas, Naveen Srinivasa, Arun R. J. Mike Walker’66 Department of Mechanical Engineering Texas A&M University United States Computer Science Department University of Texas Dallas United States Intelligent Systems Program University of Pittsburgh United States Department of Artificial Intelligence and Informatics Mayo Clinic United States

This study explores the feasibility of using large language models (LLMs), specifically GPT-4o (ChatGPT), for automated grading of conceptual questions in an undergraduate Mechanical engineering course. We compared the grading performance of GPT-4o with that of human teaching assistants (TAs) on ten quiz problems from the MEEN 361 course at Texas A&M University, each answered by approximately 225 students. Both the LLM and TAs followed the same instructor-provided rubric to ensure grading consistency. We evaluated performance using Spearman’s rank correlation coefficient and Root Mean Square Error (RMSE) to assess the alignment between rankings and the accuracy of scores assigned by GPT-4o and TAs under zero- and few-shot grading settings. In the zero-shot setting, GPT-4o demonstrated a strong correlation with TA grading, with Spearman’s rank correlation coefficient exceeding 0.6 in seven out of ten datasets and reaching a high of 0.9387. Our analysis reveals that GPT-4o performs well when grading criteria are straightforward but struggles with nuanced answers, particularly those involving synonyms not present in the rubric. The model also tends to grade more stringently in ambiguous cases compared to human TAs. Overall, ChatGPT shows promise as a tool for grading conceptual questions, offering scalability and consistency. © 2024 R. Gao1, X. Guo2, X. Li4, A.B.L. Narayanan3, N. Thomas1 & A.R. Srinivasa1.

关键词： Mean square error

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Generalized Haldane map from the matrix product state path integral to the critical theory of the J1−J2 chain

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Physical Review Research 2025年第1期7卷 L012037-L012037页

作者： F. Azad Adam J. McRoberts Chris Hooley A. G. Green Electrical Engineering and Computer Science Department Technische Universität Berlin 10587 Berlin Germany London Centre for Nanotechnology University College London Gordon Street London WC1H 0AH United Kingdom International Centre for Theoretical Physics Strada Costiera 11 34151 Trieste Italy Max Planck Institute for the Physics of Complex Systems Nöthnitzer Straße 38 01187 Dresden Germany

We study the J1−J2 spin-1/2 chain using a path integral constructed over matrix product states (MPS). By virtue of its nontrivial entanglement structure, the MPS ansatz captures the key phases of the model even at a semiclassical, saddle-point level, and, as a variational state, is in good agreement with the field theory obtained by Abelian bosonization. Going beyond the semiclassical level, we show that the MPS ansatz facilitates a physically motivated derivation of the field theory of the critical phase: By carefully taking the continuum limit—a generalization of the Haldane map—we recover from the MPS path integral a field theory with the correct topological term and emergent SO(4) symmetry, constructively linking the microscopic states and topological field-theoretic structures. Moreover, the dimerization transition is particularly clear in the MPS formulation—an explicit dimerization potential becomes relevant, gapping out the magnetic fluctuations.

关键词： Topology

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A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

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computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

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Risk-Aware Security-Constrained Unit Commitment: Taming the Curse of Real-Time Volatility and Consumer Exposure

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 536-551页

作者： Bienstock, Daniel Dvorkin, Yury Guo, Cheng Mieth, Robert Wang, Jiayi Columbia University Department of Industrial Engineering and Operations Research New YorkNY10027 United States Johns Hopkins University Department of Electrical and Computer Engineering Department of Civil and System Engineering Ralph O'Connor Sustainable Energy Institute BaltimoreMD21218 United States Clemson University School of Mathematical and Statistical Sciences ClemsonSC29634 United States Rutgers University Department of Industrial and Systems Engineering New BrunswickNJ08901 United States Stanford University Department of Management Science and Engineering StanfordCA94305 United States

We propose an enhancement to wholesale electricity markets to contain the exposure of consumers to increasingly large and volatile consumer payments arising as a byproduct of volatile real-time net loads - i.e., loads minus renewable outputs - and prices, both compared to day-ahead cleared values. We incorporate a trade-off, motivated by portfolio optimization methods, between standard day-ahead payments and a robust estimate of such excess payments into the day-ahead computation and specifically seek to account for volatility in real-time net loads and renewable generation. Our model features a data-driven uncertainty set based on principal component analysis, which accommodates both load and wind production volatility and captures locational correlation of uncertain data. To solve the model more efficiently, we develop a decomposition algorithm that can handle nonconvex subproblems. Our extensive experiments on a realistic NYISO data set show that the risk-aware model protects the consumers from potential high costs caused by adverse circumstances. © 2024 IEEE.

关键词： Principal component analysis

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E3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images

arXiv

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arXiv 2024年

作者： Qi, Yunshan Li, Jia Zhao, Yifan Zhang, Yu Zhu, Lin The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The School of Computer Science Beijing Institute of Technology Beijing100081 China The SenseTime Research and Tetras.AI China

Neural Radiance Fields (NeRF) achieve impressive rendering performance by learning volumetric 3D representation from several images of different views. However, it is difficult to reconstruct a sharp NeRF from blurry input as it often occurs in the wild. To solve this problem, we propose a novel Efficient Event-Enhanced NeRF (E3NeRF) by utilizing the combination of RGB images and event streams. To effectively introduce event streams into the neural volumetric representation learning process, we propose an event-enhanced blur rendering loss and an event rendering loss, which guide the network via modeling the real blur process and event generation process, respectively. Specifically, we leverage spatial-temporal information from the event stream to evenly distribute learning attention over temporal blur while simultaneously focusing on blurry texture through the spatial attention. Moreover, a camera pose estimation framework for real-world data is built with the guidance of the events to generalize the method to practical applications. Compared to previous image-based or event-based NeRF, our framework makes more profound use of the internal relationship between events and images. Extensive experiments on both synthetic data and real-world data demonstrate that E3NeRF can effectively learn a sharp NeRF from blurry images, especially in non-uniform motion and low-light scenes. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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The Power of Second Chance: Personalized Submodular Maximization with Two Candidates 17th

The Power of Second Chance: Personalized Submodular Maximiz...

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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Yuan, Jing Tang, Shaojie Department of Computer Science and Engineering University of North Texas Denton United States Center for AI Business Innovation Department of Management Science and Systems University at Buffalo Buffalo United States

ISBN: (纸本)9789819644476

Most of existing studies on submodular maximization focus on selecting a subset of items that maximizes a single submodular function. However, in many real-world scenarios, we might have multiple user-specific functions, each of which models the utility of a particular type of user. In these settings, our goal would be to choose a set of items that performs well across all the user-specific functions. One way to tackle this problem is to select a single subset that maximizes the sum of all of the user-specific functions. Although this aggregate approach is efficient in the sense that it avoids computation of sets for individual functions, it really misses the power of personalization - for it does not allow to choose different sets for different functions. In this paper, we introduce the problem of personalized submodular maximization with two candidate solutions. For any two candidate solutions, the utility of each user-specific function is defined as the better of these two candidates. Our objective is, therefore, to select the best set of two candidates that maximize the sum of utilities of all the user-specific functions. We have designed effective algorithms for this problem. We also discuss how our approach generalizes to multiple candidate solutions, increasing flexibility and personalization in our solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Mask-guided Multi-scale Spatial-Spectral Transformer for Snapshot Compressive Imaging

Mask-guided Multi-scale Spatial-Spectral Transformer for Sna...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Heyuan Yin Jingxiang Yang Jia Liu Liang Xiao School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Analysis of Mathematical Theory and Modeling of Complex Systems The Ministry of Industry and Information Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Effectively reconstructing 3D hyperspectral images (HSIs) from 2D measurements presents a significant challenge in Coded Aperture Snapshot Spectral Imaging (CASSI) systems. While recent transformers exhibit potential in HSI reconstruction, they often suffer from inadequate exploration of multi-scale spatial-spectral self-similarity, leading to mean effects and information loss. Additionally, these methods struggle with insufficient modeling of the degradation inherent in the compressive imaging process. To address these issues, we propose a novel Mask-guided Multi-scale Spatial-Spectral Transformer (MMSST). Specifically, we introduce a Degradation Aware Mask Attention (DAMA) module to incorporate degradation information of the compressive imaging process. Furthermore, MMSST leverages Local-Regional SpAtial attention (LRSA) and Global-Regional SpEctral attention (GRSE) to effectively exploit multi-scale self-similarity across spatial and spectral dimensions. Extensive experimental results demonstrate the effectiveness of our MMSST.

关键词： Degradation Three-dimensional displays Computational modeling Imaging Transformer cores Signal processing Transformers Speech processing Image reconstruction Hyperspectral imaging

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