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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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LithoHoD: A Litho Simulator-Powered Framework for IC Layout Hotspot Detection

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1098-1111页

作者： Shao, Hao-Chiang Chen, Guan-Yu Lin, Yu-Hsien Lin, Chia-Wen Fang, Shao-Yun Tsai, Pin-Yian Liu, Yan-Hsiu National Chung Hsing University Institute of Data Science and Information Computing Taichung402202 Taiwan Novatek Microelectronics Corporation Hsinchu300092 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300044 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106335 Taiwan United Microelectronics Corporation Product Engineering Department Hsinchu300094 Taiwan United Microelectronics Corporation Development of Smart Manufacturing Hsinchu300094 Taiwan

Recent advances in VLSI fabrication technology have led to die shrinkage and increased layout density, creating an urgent demand for advanced hotspot detection techniques. However, by taking an object detection network as the backbone, recent learning-based hotspot detectors learn to recognize only the problematic layout patterns in the training data. This fact makes these hotspot detectors difficult to generalize to real-world scenarios. We propose a novel lithography simulator-powered hotspot detection framework to overcome this difficulty. Our framework integrates a lithography simulator with an object detection backbone, merging the extracted latent features from both the simulator and the object detector via well-designed cross-attention blocks. Consequently, the proposed framework can be used to detect potential hotspot regions based on 1) the variation of possible circuit shape deformation estimated by the lithography simulator and 2) the problematic layout patterns already known. To this end, we utilize RetinaNet with a feature pyramid network as the object detection backbone and leverage LithoNet as the lithography simulator. Extensive experiments demonstrate that our proposed simulator-guided hotspot detection framework outperforms the previous state-of-the-art methods on real-world data. © 2024 IEEE.

关键词： Integrated circuit layout

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First-principles electron-phonon interactions and polarons in the parent cuprate La2CuO4

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Physical Review Research 2025年第1期7卷 L012073-L012073页

作者： Benjamin K. Chang Iurii Timrov Jinsoo Park Jin-Jian Zhou Nicola Marzari Marco Bernardi Department of Applied Physics and Materials Science and Department of Physics California Institute of Technology Pasadena California 91125 USA PSI Center for Scientific Computing Theory and Data Paul Scherrer Institut 5232 Villigen PSI Switzerland Department of Physics Pohang University of Science and Technology Pohang 37673 Korea School of Physics Beijing Institute of Technology Beijing 100081 China Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL) École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland

Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon (e-ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here, we study e-ph interactions and polarons in a prototypical parent (undoped) cuprate, La2CuO4 (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with the band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e-ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e-ph coupling to hole states. These modes consist of bond stretching and bond bending in the Cu-O plane as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e-ph coupling, exhibit broad quasiparticle peaks with a small spectral weight (Z≈0.25) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e-ph coupling distribution function not explained by existing models. These results show that the universal strong e-ph coupling found experimentally in doped lanthanum cuprates is also present in the parent compound, and elucidate its microscopic origin.

关键词： High temperature superconductors

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Mobile Captured Document Classification based on Blind Image Quality Assessment Model using Brisque

Mobile Captured Document Classification based on Blind Image...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Koushik K S Bipin Nair B J N. Shobha Rani Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Artificial Intelligence and Data Science GITAM School of Technology Bengaluru GITAM

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Assessment of document image quality helps in applying suitable pre-processing methods for enhancing document images. The result of enhancement leads to improved recognition rates by OCR. This paper proposes a mobile-captured document classification model based on the blind image quality score. The quality scores are generated by fitting the document wise opinion scores with the Brisque metric. Document wise opinion scores are collected using subjective method. Initially, learning models are created based on the document wise opinion scores. Two methods are proposed as part of this study to generate the document opinion scores, one is using a subjective assessment method, and the other through a random value generation function. The attempt to fit the created learning models using the most dominantly used Brisque's blind image quality measure. The classification of mobile-captured handwritten documents is performed solely using of blind quality score generated by Brisque using simple linear classifiers. datasets of mobile-captured handwritten documents of shadow, blur, and good readability are considered for experimentation. The evaluation of proposed models for training and test sets shows that the learning models created with subjectively assessed opinion scores produced improved results.

关键词： Image quality Measurement Training Analytical models Text analysis Computational modeling Optical character recognition Fitting

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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GPU-accelerated Evolutionary Many-objective Optimization Using Tensorized NSGA-III

arXiv

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arXiv 2025年

作者： Li, Hao Liang, Zhenyu Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

NSGA-III is one of the most widely adopted algorithms for tackling many-objective optimization problems. However, its CPU-based design severely limits scalability and computational efficiency. To address the limitations, we propose TensorNSGA-III, a fully tensorized implementation of NSGA-III that leverages GPU parallelism for large-scale many-objective optimization. Unlike conventional GPU-accelerated evolutionary algorithms that rely on heuristic approximations to improve efficiency, TensorNSGA-III maintains the exact selection and variation mechanisms of NSGA-III while achieving significant acceleration. By reformulating the selection process with tensorized data structures and an optimized caching strategy, our approach effectively eliminates computational bottlenecks inherent in traditional CPU-based and naïve GPU implementations. Experimental results on widely used numerical benchmarks show that TensorNSGA-III achieves speedups of up to 3629× over the CPU version of NSGA-III. Additionally, we validate its effectiveness in multiobjective robotic control tasks, where it discovers diverse and high-quality behavioral solutions. Furthermore, we investigate the critical role of large population sizes in many-objective optimization and demonstrate the scalability of TensorNSGA-III in such scenarios. The source code is available at https://***/EMI-Group/evomo. Copyright © 2025, The Authors. All rights reserved.

关键词： Cellular automata

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Linguistic Resources for Extremism Detection on Social Media 1st

Linguistic Resources for Extremism Detection on Social Media

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1st International Conference on computing and Emerging Technologies, ICCET 2023

作者： Hussain, Muhammad Anwar Shahzad, Muhammad Khurram Sulaiman, Sarina School of Computing Universiti of Teknologi Malaysia Johor Skudai Malaysia Department of Data Science University of the Punjab Lahore Pakistan

ISBN: (纸本)9783031776168

Social networks, such as Facebook, Twitter and Youtube, are becoming robust sources that radical groups use to spread the propaganda of extremist group to gather resources for their cause. Therefore, locating radical social media content has become an priority task for counter-terrorism agencies, technology companies and governments. This study has employed a systematic literature review protocol to identify the publicly available corpora that can be used for training machine learning techniques for extremism detection in text. Furthermore, the snowballing approach is employed to identify the studies that have used these corpora for extremism detection. The identified datasets and the studies have been identified to develop an understanding of landscape of extremism detection. Based on the observations we have developed recommendations for processing this important area of research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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EvoGP: A GPU-accelerated Framework for Tree-based Genetic Programming

arXiv

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arXiv 2025年

作者： Wang, Lishuang Wu, Zhihong Sun, Kebin Li, Zhuozhao Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

Tree-based Genetic Programming (TGP) is a key evolutionary algorithm widely used in symbolic regression, feature engineering, and scientific modeling. Its high computational demands make GPU acceleration essential for scalable and high-performance evolutionary computation. However, GPU acceleration of TGP faces three key challenges: inefficient tree encoding, highly heterogeneous genetic operations, and limited parallelism in fitness evaluation. To address these challenges, we introduce EvoGP, a comprehensive GPU-accelerated TGP framework. First, we design a tensorized encoding scheme to represent tree with different structures as tensors with the same shape, optimizing memory access and enabling efficient parallel execution. Second, we propose a unified parallel framework for genetic operations by leveraging shared computational primitives and implementing dedicated CUDA kernels for scalable performance. Third, we present a fully parallel fitness evaluation strategy for symbolic regression, exploiting both population-level and data-level parallelism to maximize GPU utilization. Moreover, we implement a comprehensive library to provide rich algorithm operators and benchmark problems. EvoGP is extensively tested on various tasks, including symbolic regression, classification, and robotics control, demonstrating its versatility and effectiveness across diverse application scenarios. Experimental results show that EvoGP achieves up to a 140.89× speedup over the state-of-the-art GPU-based TGP implementation, while maintaining or exceeding the accuracy of baseline methods. EvoGP is open-source and accessible at: https://***/EMI-Group/evogp. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization

arXiv

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arXiv 2025年

作者： Liang, Zhenyu Li, Hao Yu, Naiwei Sun, Kebin Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limitations due to insufficient parallelism and scalability. While most work has focused on algorithm design to address these challenges, little attention has been given to hardware acceleration, thereby leaving a clear gap between EMO algorithms and advanced computing devices, such as GPUs. To bridge the gap, we propose to parallelize EMO algorithms on GPUs via the tensorization methodology. By employing tensorization, the data structures and operations of EMO algorithms are transformed into concise tensor representations, which seamlessly enables automatic utilization of GPU computing. We demonstrate the effectiveness of our approach by applying it to three representative EMO algorithms: NSGA-III, MOEA/D, and HypE. To comprehensively assess our methodology, we introduce a multiobjective robot control benchmark using a GPU-accelerated physics engine. Our experiments show that the tensorized EMO algorithms achieve speedups of up to 1113× compared to their CPU-based counterparts, while maintaining solution quality and effectively scaling population sizes to hundreds of thousands. Furthermore, the tensorized EMO algorithms efficiently tackle complex multiobjective robot control tasks, producing high-quality solutions with diverse behaviors. Source codes are available at https://***/EMI-Group/evomo. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models

arXiv

引用

arXiv 2025年

作者： Zhang, Yuxuan Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University China Department of Computing Science University of Aberdeen United Kingdom

With the rapid development of large language models (LLMs), fully fine-tuning(FT) these models is becoming increasingly infeasible due to high computational demands. Moreover, FT also increases the risk of catastrophic forgetting. As an alternative, Low-Rank Adaptation (LoRA) has been proposed. By fine-tuning only a small subset of parameters, LoRA achieves performance similar to FT while significantly reducing resource requirements. However, since LoRA inherits FT’s design, the issue of catastrophic forgetting still remains. To address these limitations, we propose SECURA: Sigmoid-Enhanced CUR Decomposition LoRA, a novel PEFT variant designed to mitigate catastrophic forgetting while improving fine-tuning performance. Our method introduces a novel normalization technique, Sigmoid based Magnitude Norm(SMagNorm), which enhances parameter retention and improve fine-tuning performance. SECURA has been evaluated on a diverse range of tasks, including mathematical problem-solving (GSM8K), complex question-answering (CNNDM), translation (NewsDE), and complex multiple-choice reasoning (LogiQA). Experimental results demonstrate that it achieves an average fine-tuning improvement of 3.59% across 4 MCQ sphere tasks and a 2.51% improvement in 5 QA sphere tasks across Gemma2 2b, Qwen2 1.5b, Qwen2 7b,Llama3 8b,Llama3.1 8b compared to DoRA. Additionally, SECURA demonstrates superior knowledge retention capabilities, achieves state-of-the-art performance in 16 continual learning tests maintaining more than 70% accuracy on LLMs’ basic knowledge compared with Experience Replay(ER), sequential learning(SEQ),EWC, I-LoRA and CUR-LoRA. © 2025, CC BY.

关键词： Problem solving

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