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A Landscape-Aware Differential Evolution for Multimodal Optimization Problems

IEEE Transactions on Evolutionary Computation 2025年

作者： Lin, Guo-Yun Chen, Zong-Gan Liu, Chuanbin Jiang, Yuncheng Kwong, Sam Zhang, Jun Zhan, Zhi-Hui South China Normal University School of Computer Science Guangzhou510631 China Ministry of Education Center for Scientific Research and Development in Higher Education Institutes China Lingnan University Department of Computing and Decision Science Hong Kong Nankai University College of Artificial Intelligence Tianjin300350 China ERICA Hanyang University 15588 Korea Republic of

How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this article, a landscape-aware differential evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape knowledge to maintain sufficient diversity and provide efficient search guidance. In detail, the landscape knowledge is efficiently utilized in the following three aspects. First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual re-locating an already found peak. Second, a landscape-aware peak distinction distinguishes whether an individual locates a new global peak, a new local peak, or an already found peak. Accuracy refinement can thus only be conducted on the global peaks to enhance the search efficiency. Third, a landscape-aware reinitialization specifies the initial position of an individual adaptively according to the distribution and distinction of the found peaks, which helps explore more peaks. The experiments are conducted on the widely-used benchmark MMOPs and multimodal nonlinear equation system problems. Experimental results show that LADE obtains generally better or competitive performance compared with seven well-performing recent algorithms and four winner algorithms in the IEEE CEC competitions for multimodal optimization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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The Case for Non-Volatile RAM in Cloud HPCaaS

arXiv

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arXiv 2022年

作者： Fridman, Yehonatan Harel, Re'em Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Israel Atomic Energy Commission Scientific Computing Center Nuclear Research Center Negev Israel Department of Physics Nuclear Research Center Negev Israel Department of Computer Science Technion Israel Institute of Technology Israel

HPC as a service (HPCaaS) is a new way to expose HPC resources via cloud services. However, continued effort to port large-scale tightly coupled applications with high interprocessor communication to multiple (and many) nodes synchronously, as in on-premise supercomputers, is still far from satisfactory due to network latencies. As a consequence, in said cases, HPCaaS is recommended to be used with one or few instances. In this paper we take the claim that new piece of memory hardware, namely Non-Volatile RAM (NVRAM), can allow such computations to scale up to an order of magnitude with marginalized penalty in comparison to RAM. Moreover, we suggest that the introduction of NVRAM to HPCaaS can be cost-effective to the users and the suppliers in numerous forms. © 2022, CC BY.

关键词： Random access storage

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Syncopated Dynamical Decoupling for Suppressing Crosstalk in Quantum Circuits

arXiv

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arXiv 2024年

作者： Evert, Bram Izquierdo, Zoe Gonzalez Sud, James Hu, Hong-Ye Grabbe, Shon Rieffel, Eleanor G. Reagor, Matthew J. Wang, Zhihui Rigetti Computing BerkeleyCA United States NASA Ames Research Center Moffett FieldCA United States USRA Moffett FieldCA United States University of Chicago Computer Science Department IL United States

Theoretically understanding and experimentally characterizing and modifying the underlying Hamiltonian of a quantum system is of utmost importance in achieving high-fidelity quantum gates for quantum computing. In this work, we explore the use of dynamical decoupling (DD) in characterizing undesired two-qubit couplings as well as the underlying single-qubit decoherence, and in suppressing them. We develop a syncopated dynamical decoupling technique which protects against decoherence and selectively targets unwanted two-qubit interactions, overcoming both significant hurdles to achieving precise quantum control and realizing quantum computing on many hardware prototypes. On a transmon-qubit-based superconducting quantum device, we identify separate white and 1/f noise components underlying the single-qubit decoherence and a static ZZ coupling between pairs of qubits. We suppress these errors using syncopated dynamical decoupling in two-qubit benchmarking experiments and significantly boost performance in a realistic algorithmic quantum circuit. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Text-Driven Video Style Transfer with State-Space Models: Extending StyleMamba for Temporal Coherence

arXiv

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arXiv 2025年

作者： Li, Chao Park, Minsu Rossi, Cristina Li, Zhuang Dept. of Computer Science Eastern Asia Institute of Technology Beijing China AI Research Center Seoul National Technical Labs Seoul Korea Republic of School of Computing University of South Europe Rome Italy Department of Data Science Western Frontier University CA United States

StyleMamba has recently demonstrated efficient text-driven image style transfer by leveraging state-space models (SSMs) and masked directional losses. In this paper, we extend the StyleMamba framework to handle video sequences. We propose new temporal modules, including a Video State-Space Fusion Module to model inter-frame dependencies and a novel Temporal Masked Directional Loss that ensures style consistency while addressing scene changes and partial occlusions. Additionally, we introduce a Temporal Second-Order Loss to suppress abrupt style variations across consecutive frames. Our experiments on DAVIS and UCF101 show that the proposed approach outperforms competing methods in terms of style consistency, smoothness, and computational efficiency. We believe our new framework paves the way for real-time text-driven video stylization with state-of-the-art perceptual results. Copyright © 2025, The Authors. All rights reserved.

关键词： Video recording

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A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

arXiv

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arXiv 2025年

作者： Wazir, Hafsa Ahmad, Jawad Khan, Muazzam A. Jan, Sana Ullah Khan, Fadia Ali Khan, Muhammad Shahbaz ICESCO Chair for Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Department of Computer Science HITEC University Taxila Pakistan

lectromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. EMG signals measure the electrical activity of muscles during different motions. EMG signals play a key role in gesture recognition studies, such as hand gesture recognition. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DBl dataset. The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. The fast inference time and lightweight architecture of the proposed model makes it suitable for resource constrained loT devices. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data. © 2025, CC BY-NC-ND.

关键词： Convolutional neural networks

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Performance Evaluation of Threshold Signing Schemes in Cryptography

arXiv

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arXiv 2025年

作者： Faneela Ahmad, Jawad Ghaleb, Baraq Khan, Imdad Ullah Buchanan, William J. Jan, Sana Ullah Khan, Muhammad Shahbaz School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia Department of Computer Science Lahore University of Management Sciences Punjab Lahore Pakistan

Threshold Signature Scheme (TSS) protocols have gained significant attention over the past ten years due to their widespread adoption in cryptocurrencies. The adoption is mainly boosted by Gennaro and Goldfedder's TSS protocol. Since then, various TSS protocols have been introduced with different features, such as security and performance, etc. Large organizations are using TSS protocols to protect many digital assets, such as cryptocurrency. However, the adoption of these TSS protocols requires an understanding of state-of-the-art research in threshold signing. This study describes the holistic view of TSS protocols, evaluates cutting-edge TSS protocols, highlights their characteristics, and compares them in terms of security and performance. The evaluation of these TSS protocols will help the researchers address real-world problems by considering the relevant merits of different TSS protocols. © 2025, CC BY-NC-ND.

关键词： Authentication Protocol

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Yifan Zhang Haolong Xiang Xuyun Zhang Xiaolong Xu Wei Fan Qin Zhang Lianyong Qi School of software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia Uffield Department of Women's & Reproductive Health University of Oxford England College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering University of Petroleum (East China) China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

关键词： Learning systems Training Accuracy Clustering algorithms Forestry Network security Fraud Time complexity Parallel algorithms Anomaly detection

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Enhancing Weakly Supervised Semantic Segmentation with Multi-modal Foundation Models: An End-to-End Approach

arXiv

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arXiv 2024年

作者： Ravanbakhsh, Elham Niu, Cheng Liang, Yongqing Ramanujam, J. Li, Xin Louisiana State University Baton RougeLA70803 United States Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States Section of Visual Computing and Interactive Media Texas A&M University College StationTX77843 United States

Semantic segmentation is a core computer vision problem, but the high costs of data annotation have hindered its wide application. Weakly-Supervised Semantic Segmentation (WSSS) offers a cost-efficient workaround to extensive labeling in comparison to fully-supervised methods by using partial or incomplete labels. Existing WSSS methods have difficulties in learning the boundaries of objects leading to poor segmentation results. We propose a novel and effective framework that addresses these issues by leveraging visual foundation models inside the bounding box. Adopting a two-stage WSSS framework, our proposed network consists of a pseudo-label generation module and a segmentation module. The first stage leverages Segment Anything Model (SAM) to generate high-quality pseudo-labels. To alleviate the problem of delineating precise boundaries, we adopt SAM inside the bounding box with the help of another pre-trained foundation model (e.g., Grounding-DINO). Furthermore, we eliminate the necessity of using the supervision of image labels, by employing CLIP in classification. Then in the second stage, the generated high-quality pseudo-labels are used to train an off-the-shelf segmenter that achieves the state-of-the-art performance on PASCAL VOC 2012 and MS COCO 2014. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Socialized learning: making each other better through multi-agent collaboration 24

Socialized learning: making each other better through multi-...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Xinjie Yao Yu Wang Pengfei Zhu Wanyu Lin Jialu Li Weihao Li Qinghua Hu College of Intelligence and Computing Tianjin University Tianjin China and Engineering Research Center of City Intelligence and Digital Governance Ministry of Education of the People's Republic of China Tianjin China and Haihe Lab of ITAI Tianjin China Department of Computing The Hong Kong Polytechnic University Hong Kong China Department of Computer Science Boston University Boston

Learning new knowledge frequently occurs in our dynamically changing world, e.g., humans culturally evolve by continuously acquiring new abilities to sustain their survival, leveraging collective intelligence rather than a large number of individual attempts. The effective learning paradigm during cultural evolution is termed socialized learning (SL). Consequently, a straightforward question arises: Can multi-agent systems acquire more new abilities like humans? In contrast to most existing methods that address continual learning and multi-agent collaboration, our emphasis lies in a more challenging problem: We prioritize the knowledge in the original expert classes, and as we adeptly learn new ones, the accuracy in the original expert classes stays superior among all in a directional manner. Inspired by population genetics and cognitive science, leading to unique and complete development, we propose Multi-Agent Socialized Collaboration (MASC), which achieves SL through interactions among multiple agents. Specifically, we introduce collective collaboration and reciprocal altruism modules, organizing collaborative behaviors, promoting information sharing, and facilitating learning and knowledge interaction among individuals. We demonstrate the effectiveness of multi-agent collaboration in an extensive empirical study. Our code will be publicly available at https://***/yxjdarren/SL.

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Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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