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arXiv 2025年

作者： Wang, Fen Wang, Bomiao Shu, Xueli Liu, Zhen Shao, Zekai Liu, Chao Chen, Siming Henan Institute of Advanced Technology Zhengzhou University Zhengzhou Zhongke Institute of Integrated Circuit and System Application China Fudan University China School of Computer Science Fudan University Zhengzhou Zhongke Institute of Integrated Circuit and System Application China

Effective chart summary can significantly reduce the time and effort decision makers spend interpreting charts, enabling precise and efficient communication of data insights. Previous studies have faced challenges in generating accurate and semantically rich summaries of time-series data charts. In this paper, we identify summary elements and common hallucination types in the generation of time-series chart summaries, which serve as our guidelines for automatic generation. We introduce ChartInsighter, which automatically generates chart summaries of time-series data, effectively reducing hallucinations in chart summary generation. Specifically, we assign multiple agents to generate the initial chart summary and collaborate iteratively, during which they invoke external data analysis modules to extract insights and compile them into a coherent summary. Additionally, we implement a self-consistency test method to validate and correct our summary. We create a high-quality benchmark of charts and summaries, with hallucination types annotated on a sentence-by-sentence basis, facilitating the evaluation of the effectiveness of reducing hallucinations. Our evaluations using our benchmark show that our method surpasses state-of-the-art models, and that our summary hallucination rate is the lowest, which effectively reduces various hallucinations and improves summary quality. The benchmark is available at https://***/wangfen01/ChartInsighter. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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An Efficient Two-stage Networking Topology Design for Mega-constellation of Low Earth Orbit Satellites

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IEEE Transactions on Mobile Computing 2025年

作者： Hu, Han Lyu, Yifeng Song, Kaifeng Fan, Rongfei Zhan, Cheng Yang, Jian Beijing Institute of Technology School of Information and Electronics Beijing100081 China Norinco Group Institute of Computer Application Technology Beijing100089 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Southwest University School of Computer and Information Science Chongqing400715 China University of Science and Technology of China School of Information Science and Technology Hefei230027 China

Low Earth Orbit (LEO) satellites play a crucial role in providing high-speed internet to remote areas and ensuring network resilience during outages. The design of efficient satellite constellations requires optimizing network topology, which is a complex task due to the large solution space and the need for fault tolerance. This paper presents the AlphaSat algorithm, a two-phase approach to improve latency and network robustness in LEO constellations. In the initialization phase, Monte Carlo Tree Search (MCTS) is used to generate an initial topology by selecting links from a vast search space. In the refinement phase, an edge-switching method is applied to enhance network resilience and performance. AlphaSat is evaluated on OneWeb, Starlink, and Telesat mega-constellations, demonstrating superior performance over existing algorithms. The results show significant reductions in latency ranging from 4.7% to 44.5% and improvements in network robustness, increasing by 3.3% to 28.3%. Furthermore, AlphaSat effectively balances network load and optimizes power consumption, offering a promising solution for efficient and resilient LEO satellite network design. © 2025 IEEE.

关键词： Trees (mathematics)

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Archer: Adaptive Memory Compression with Page-Association-Rule Awareness for High-Speed Response of Mobile Devices 23

Archer: Adaptive Memory Compression with Page-Association-Ru...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

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CiGeN 1.0: A Generative AI Enabled Medical Image Augmentation Pipeline for Deep Network Models

CiGeN 1.0: A Generative AI Enabled Medical Image Augmentatio...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Archana Dash Debasish Swapnesh Kumar Nayak Tripti Swarnkar Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of Computer Science and Engineering Centurion University of Technology and Management Bhubaneswar India Department of Computer Application National Institute of Technology Raipur India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Deep Learning methods have gained a significant momentum in medical image classification tasks, including many ranges of datasets. However, these datasets often suffer from class imbalance, where the minority class (e.g., diseased cases) is underrepresented. This imbalance leads to biased predictions, affecting the model's performance. Generative Adversarial Networks (GANs) offer a potential solution by generating synthetic images for the minority class, helping balance the dataset. Methodology: In this study we further evaluated our Deep Learning Model CoVaD-GAN with separate set of datasets to test the dynamics behind it. We applied a Deep Convolutional GAN (DCGAN) to generate synthetic images for underrepresented classes in the chest X-ray (CXR) and Break His histopathological dataset. Augmented synthetic images were made a part of training set, and the model's performance was evaluated before and after augmentation using VGG-16, ResNet-50 with necessary metric measures as accuracy, sensitivity, and specificity were compared pre- and post-GAN augmentation. We also evaluated the quality of GAN-generated images using Fréchet Inception Distance (FID) and manual expert reviews. The results demonstrate significant improvements in model accuracy, sensitivity, and F1-scores after applying GAN-based augmentation. The use of GANs for data augmentation enhances the ability of deep learning models to generalize, particularly for minority classes. GAN-generated images were of high quality and contributed positively to model performance. The Proposed model (CiGeN 1.0) dynamically generalizes the other medical dataset introduced to it and derived a fair resulting diagnosis.

关键词： Deep learning Training Sensitivity Accuracy Generative adversarial networks Data augmentation Stability analysis Convolutional neural networks Medical diagnostic imaging X-ray imaging

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search

arXiv

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arXiv 2025年

作者： Luo, Haoran Haihong, E. Guo, Yikai Lin, Qika Wu, Xiaobao Mu, Xinyu Liu, Wenhao Song, Meina Zhu, Yifan Tuan, Luu Anh Beijing University of Posts and Telecommunications China Nanyang Technological University Singapore Beijing Institute of Computer Technology and Application China National University of Singapore Singapore

Knowledge Base Question Answering (KBQA) aims to answer natural language questions with a large-scale structured knowledge base (KB). Despite advancements with large language models (LLMs), KBQA still faces challenges in weak KB awareness, imbalance between effectiveness and efficiency, and high reliance on annotated data. To address these challenges, we propose KBQA-o1, a novel agentic KBQA method with Monte Carlo Tree Search (MCTS). It introduces a ReAct-based agent process for stepwise logical form generation with KB environment exploration. Moreover, it employs MCTS, a heuristic search method driven by policy and reward models, to balance agentic exploration’s performance and search space. With heuristic exploration, KBQA-o1 generates high-quality annotations for further improvement by incremental fine-tuning. Experimental results show that KBQA-o1 outperforms previous low-resource KBQA methods with limited annotated data, boosting Llama-3.1-8B model’s GrailQA F1 performance to 78.5% compared to 48.5% of the previous sota method with GPT-3.5-turbo. Our code is publicly available at https://***/LHRLAB/KBQA-o1. © 2025, CC BY-NC-SA.

关键词： Trees (mathematics)

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Energy-Proficient Cluster Enrichment in Wireless Sensor Networks via Categorized Fuzzy Clustering and Multi-Hop Routing Optimization

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SN computer Science 2025年第1期6卷 1-12页

作者： Shakya, Harish Kumar Chandanan, Amit Kumar Subbalakshmi, Chatti Khambra, Geetanjli Ansari, Mohd Shahnawaz Kushwaha, Ajay Roy, Vandana Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya (A Central University) Chhattisgarh Bilaspur India Department of Computer Science and Engineerings Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of Computer Science and Application The Bhopal School of Social Sciences (BSSS) College M.P. Bhopal India Department of Computer Science and Engineering Eklavya University Damoh MP Damoh India Computer Science and Engineering Rungta College of Engineering and Technology Bhilai India Department of Electronics Communication Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

The WSN's lifetime can be extended by practicing energy saving. By using the appropriate routing mechanism when executing clustering, it can be done successfully. For improved energy conservation, clustering, and routing work together. Clustering lowers duplicate data transmission, while superior routing enables energy-saving travel along the quickest route to the destination. Nodes can be grouped via clustering, and each group will have CH. CH provides data to the BS either directly or via the intermediary nodes. By reducing the workload of resource-limited nodes, clustering chooses CH from resource-rich nodes. The entire network needs to undergo re-clustering in the traditional WSN clustering approach. The suggested protocol creates an energy-efficient cluster head selection approach applying FIS in hierarchical WSNs, which in turn increases sensing network lifetime, power consumption rate, and network communication performance. The proposed FBUCA and Modified minimum spanning tree with fuzzy logic can efficiently route data packets. The load-balancing among clusters and decreased energy usage demonstrated by the proposed model in MATLAB R2016 simulations are key to increasing throughput and extending the network's lifetime. The proposed approach takes into account six of the aforementioned use scenarios. The experimental results confirmed the adaptability and sturdiness of the proposed method, showing that it works well with a large number of WSNs. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CH FBUCA Fuzzy logic WSN

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CiGeN 1.0: A Generative AI Enabled Medical Image Augmentation Pipeline for Deep Network Models

CiGeN 1.0: A Generative AI Enabled Medical Image Augmentatio...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Dash, Archana Nayak, Debasish Swapnesh Kumar Swarnkar, Tripti Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Bhubaneswar751030 India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar752050 India National Institute of Technology Department of Computer Application Raipur492010 India

ISBN: (纸本)9798331530259

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Optimizing sustainability and resilience of composite construction materials using life cycle assessment and advanced artificial intelligence techniques

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Asian Journal of Civil Engineering 2025年第2期26.0卷 471-489页

作者： Pande, Prashant B. Dhengare, Sagar W. Raut, Jayant M. Bhagat, Rajesh M. Bahoria, Boskey V. Shelke, Nilesh Upadhye, Sachin D. Vairagade, Vikrant S. Department of Civil Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India Department of Computer Science and Application Ramdeobaba University Maharashtra Nagpur 440013 India Department of Civil Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India

The need for sustainable and resilient composite construction materials that can cope with increasing environmental and structural demands of modern construction is becoming urgently critical. The proposed model will handle the lack of sustainability and mechanical performance of the existing approaches. Specifically, they are not capable of dynamically adapting up to the changing environmental conditions and the intrinsic complexity of optimizing the material properties for the composites. To overcome these constraints, the present study develops an innovative multimethod framework by integrating the implementation of several state-of-the-art optimization and machine-learning techniques in order to enhance the sustainability and resilience of composite materials. The work is initialized by proposing a Multiple Objective Genetic Algorithm (MOGA) with dynamic fitness functions for the optimization of material designs, by balancing environmental impacts with mechanical performance in real time. This approach, hence, fits different environmental conditions and material requirements at the same time while importantly enhancing the design stage itself. At the same time, Gaussian Process Regression is the method that enables future LCA outcome prognoses undertaken using RL;it is possible to deal with the sustainability prediction as uncertain, and hence it is incorporated in the ongoing process of material optimization. In this way, RL will adaptively optimize processing parameters for the manufacturing of composites: both material resilience and goals regarding sustainability are realized through self-learning. Finally, a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm is introduced to probe and polish the solution space for composite material designs to leap over local optima hurdles. The overall improvement in the integrated attributes is 15% of the carbon footprint decrease, 20% in the tensile strength, and 12% decrease in energy consum

关键词： Composite Material Design Life Cycle Assessment Multiple Objective Genetic Algorithm Reinforcement Learning Sustainable Materials

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A deep learning approach for reconstructing hourly surface air temperature in Qinghai for the period 2006-2015

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Earth Science Informatics 2025年第2期18卷 1-19页

作者： Zhang, Qiyuan Zhang, Xiaodan Zhang, Yongkun Zhao, Tong You, Shaojie Huo, Wei Hu, Naihao School of Computer Technology and Applications Qinghai University Xining 810016 China Qinghai Provincial Laboratory for Intelligent Computing and Application Xining 810016 China Qinghai Provincial Institute of Meteorological Sciences Xining 810001 China

The Qinghai-Tibet Plateau, often referred to as the “Roof of the World”, has temperature variations that significantly affect both the local and surrounding ecosystems. Qinghai Province, located at the northeastern gateway to the plateau, serves as a microcosm of the plateau’s broader temperature changes. Therefore, it is crucial to study the temperature changes in Qinghai Province. However, existing meteorological data, including ground observations, atmospheric reanalysis data, and satellite remote sensing data, suffer from low accuracy due to limitations in time and space. In this research, we introduce a deep learning approach to integrate multi-source temperature data and reconstruct a 10-year near-surface temperature dataset with a temporal resolution of 1 hour and a spatial resolution of 0.01°. The reconstruction of near-surface temperature was performed by inputting two atmospheric reanalysis products-European Centre for Medium-Range Weather Forecasts Regional Reanalysis Version 5 (ERA5) and Modern-Era Retrospective Analysis for Research and applications, Version 2 (MERRA-2)-along with auxiliary variables that influence temperature, into the newly developed Temporal Gated Positional Model (TGPM) deep learning model. The reconstructed dataset from the TGPM model was then compared to ground observation data and evaluated against datasets generated by five other machine learning methods. The results show that the fusion dataset generated by the TGPM model closely fits the target data, outperforming the products from the other five methods across all evaluation metrics. The correlation coefficient (CC), coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and relative bias (RB) for the TGPM near-surface temperature dataset were 0.9949, 0.9896, 0.7713°C, 1.0901°C, 1.1888 °C2, and 4.02%, respectively. Overall, the deep learning method proposed in this study is more suitable for near-surface temperatu

关键词： Data fusion Deep learning Near-surface temperature Reconstruction

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