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arXiv 2023年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT’s performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (scienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT (Zhang et al., 2023) on the scienceQA test set. Our code is publicly available at https://***/Zoeyyao27/Graphof-Thought © 2023, CC BY.

关键词： Rats

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DtS-IoT Resource Allocation Analysis Framework: Assessing DQ and RESS-IoT

DtS-IoT Resource Allocation Analysis Framework: Assessing DQ...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Jason Gerard Juan A. Fraire Raydel Ortigueira Sandra Céspedes Department of Computer Science & Software Engineering Concordia University Montreal Canada CONICET - Universidad Nacional de Córdoba Córdoba Argentina Advanced Center for Electrical and Electronic Engineering Universidad Técnica Federico Santa María Valparaíso Chile

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The demand for expanding connectivity to remote and isolated areas has intensified the need for global Internet of Things (IoT) solutions. Direct-to-Satellite IoT (DtS-IoT), enabled by advances in nanosatellite technology, allows cost-effective global and direct connectivity between IoT devices and low Earth orbit satellites. However, the most cost-effective DtSIoT networks face resource limitations in user, ground, and space segments, demanding scalable solutions to fully realize their scalability potential. While several resource allocation protocols have been proposed for DtS-IoT, they have mainly been evaluated using static topologies or simplified simulations, overlooking the time-evolving orbital and channel dynamics crucial to DtS-IoT performance. This work bridges this gap by introducing the DtS-IoT Resource Allocation Analysis Framework (DRAAF), a comparison methodology and toolchain for evaluating resource allocation mechanisms in realistic DtS-IoT scenarios. Our method is framed in scalability, energy efficiency, throughput, delay, fairness, and overhead metrics. Utilizing FLoRaSat, a realistic LoRa-based DtS-IoT network simulator, we showcase the methodology with a use case investigating two energy-efficient MAC protocols for DtS-IoT: RESS-IoT and Distributed Queuing (DQ). Results indicate that DQ excels in throughput and node energy efficiency, whereas RESS-IoT reduces delay. Both protocols maintain satellite energy efficiency and enhance fairness in uplink transmissions. These findings underscore the value of our approach in evaluating resource allocation within complex DtS-IoT systems.

关键词： Measurement Systematics Scalability Satellite broadcasting Throughput Energy efficiency Delays Resource management Internet of Things Uplink

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization

arXiv

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arXiv 2025年

作者： Huang, Xiaohan Wang, Dongjie Ning, Zhiyuan Qiao, Ziyue Long, Qingqing Zhu, Haowei Du, Yi Wu, Min Zhou, Yuanchun Xiao, Meng Computer Network Information Center Chinese Academy of Sciences University of the Chinese Academy of Sciences China Computer Network Information Center Chinese Academy of Sciences China Department of Electrical Engineering and Computer Science University of Kansas United States School of Computing and Information Technology Great Bay University Dongguan China School of Software Tsinghua University China Institute for Infocomm Research Agency for Science Technology and Research Singapore

Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. To address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework’s core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operation, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. To demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Network Modeling Approach for the Nonlinear Dynamics of the Osseous Bio-Physics and Bio-Mathematics

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Biophysical Reviews and Letters 2024年第4期19卷 391-408页

作者： Khaled A. Al-Utaibi M. Idrees Sadiq M. Sait S. Iqbal Computer Science and Software Engineering Department University of Ha’il Ha’il Saudi Arabia Department of Mathematics and Statistics The University of Lahore Lahore Pakistan Center for Communications and IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Shahdrah Medical Hospital Lahore 54000 Pakistan

Bone is termed the smart material, and its modeling is of great interest in biomechanics, biochemistry, endocrinology, and oncology. The bone health of cancer patients is directly affected by hormonal imbalances, receptor-mediated tumor-targeted processes, and disturbed bone mineral density. Researchers have used different therapeutic approaches to monitor bone health during and after cancer treatment. This paper describes a reverse process of bone rebuilding after the resorption of bone via cancer treatment. A detailed model is used for hormonal therapy, which leads to the physical changes in trabecular structure. These changes are demonstrated with the aid of artificial neural networks and Petri nets. This paper connects the structural modeling of the bone trabecula with chemical kinetics. The main goal of this study is to provide a PN model of bone metastasis and an analysis of its structural properties. These properties are very helpful in demonstrating the complex dynamics of bone metastasis. Although both ANNs and PNs are well organized in the areas of machine learning and network modeling, neither technique is without limitations. ANNs, for example, are very efficient machine-learning applications, but their utter lack of explanation capabilities classifies them as a “black-box” technique. On the other hand, PNs are an effective modeling technique, but their theory does not include machine learning. This paper provides a hybrid approach to address the two approaches in a novel manner.

关键词： Discrete mathematics crossover biomechanics t -invariants hormonal treatment multi-physics simulations

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Identifying Dna-Binding Proteins Using Local Features Dpc-Pssm and Ab-Pssm with Primary Sequences Based on 2dcnn

SSRN

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SSRN 2024年

作者： Sikander, Rahu Rahu, Ali Ghulam Rahu, Mujeeb -Ur-Rehman Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women Karachi Pakistan Information Technology Centre Sindh Agriculture University Sindh Tandojam Pakistan School of Information and Communication Engineering Guilin University of Electronic Technology Guilin China

DBPs, also known as DNA-binding proteins, play a crucial role in the evolutionary and genetic mechanisms of humans. Understanding the features of DNA-binding peptides might develop our comprehension of protein roles inside cellular genetic processes. Investigation on predicting the function of DNA-binding proteins can advance the study of drug proteins and facilitate the development of computer-assisted medications. Deep learning methods have become the major approach for protein prediction in recent years. While current approaches can yield impressive expect performance, further research is necessary to enhance this performance. This work proposed the prediction of DNA-binding proteins using two-dimensional Convolutional Neural Network (CNN) named 2DCNN-DBP. The prominent characteristics are obtained through innovative techniques, specifically position specific scoring matrix-dipeptide composition dipeptide composition (PSSM-DPC), Second, we employed Average Block novel approach to representing proteins using position-specific scoring matrices (AB-PSSM) in images. The DPC-PSSM feature extraction approach achieved a prediction accuracy of 0.9309% for the validation set, which is notably superior to the AB-PSSM. Based on the analysis, DPC-PSSM shown superior performance compared to the other two feature selection approaches in classification using 2DCNN. Specifically, AB-PSSM attained a higher accuracy level of 73.50% using independent test datasets and a training dataset accuracy score of 87.52%. The results demonstrate the superior performance of our innovative predictor compared to previous studies. Utilizing PSSM-DPC could lead to the development of more effective therapeutic approaches for the treatment of life-threatening diseases. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Edge-Guided Multilevel Feature Fusion Network for Lightweight Camouflaged Object Detection

Edge-Guided Multilevel Feature Fusion Network for Lightweigh...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xingpeng Zhang Meilin Gao Guohai Gao Xin Wang Qiuli Wang School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Engineering Research Center for Intelligent Oil & Gas Exploration and Development of Sichuan Province Chengdu China Department of Radiology The First Affiliated Hospital of Army Medical University Chongging China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Camouflaged object detection (COD) aims to accurately recognize targets in intricate environments that blend into the background. Although numerous camouflage object identification techniques have demonstrated effectiveness, they often possess a substantial number of parameters. Therefore, we propose a new lightweight edge-guided multilevel feature fusion camouflaged object detection network, codenamed as LEMFNet. Initially, we adopt a lightweight CNN network model for feature extraction to reduce model complexity. Subsequently, we introduced a neighborhood feature association module (NFAM) to integrate feature information from different stages to obtain complementary feature representations and enhance the overall model performance. Furthermore, to obtain a more complete object structure, we introduce a boundary aggregation module (BAM) to delve into the edge semantics associated with the target and integrate the edge features into the proposed edge-guided aggregation module (EGAM). Experiments on three challenging benchmarks demonstrate our approach, with fewer parameters, achieves comparable or superior performance, effectively balancing resource utilization and accuracy.

关键词： Accuracy Target recognition Image edge detection Semantics Neural networks Object detection Benchmark testing

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MURDOC: Transforming Pixels into Perception for Camouflage Detection

MURDOC: Transforming Pixels into Perception for Camouflage D...

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Conference on Digital Avionics Systems (DASC)

作者： Debra Hogue Zak Kastl Joseph Karch Don Nguyen Lacey Schley Justin Lewis Shane Connelly Chris Weaver School of Computer Science at the University of Oklahoma. US Air Force's 76th Software Engineering Group. Oklahoma Aerospace and Defense Innovation Institute at the University of Oklahoma. Department of Psychology and Center for Applied Social Research the University of Oklahoma School of Computer Science at the University of Oklahoma

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

The MURDOC project introduces an application to enhance trustworthiness and explainability in computer vision models, focusing on camouflage detection. It aims to address the need for transparent and interpretable AI systems in sensitive domains such as security and defense. MURDOC integrates advanced eXplainable AI techniques including off-ramps for collecting interpretable insights, attention mechanisms for high-lighting relevant features, and a user-centric image pre-processing tool within the visualization interface. This paper offers an assessment of MURDOC's potential impact on trustworthiness and explainability in camouflaged object detection tasks. It discusses and assesses the potential effectiveness of off-ramps in gathering XAI output at various model stages, such as feature maps, attention mechanisms, and activation maps. Additionally, the paper investigates how user-driven image pre-processing mechanisms may enhance the trustworthiness of the model's predictions and decisions, allowing users to modify the input and observe prediction changes. As a work in progress, the development of MURDOC shows promise in bridging the gap for transparency and interpretability in camouflage detection. The paper also discusses the challenges, future directions, and potential applications of MURDOC's capabilities in diverse domains requiring transparent and trustworthy AI systems. The source code for the version of MURDOC in this paper is available on GitHub at: https://***/department-70/DASC_2024.

关键词： Visualization Attention mechanisms Uncertainty Source coding Buildings Security Artificial intelligence Graphical user interfaces Testing software development management

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Real-Time Domestic Garbage Detection Method Based on Improved YOLOv5 8th

Real-Time Domestic Garbage Detection Method Based on Improve...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Kan, Shengqi Fang, Wei Wu, Jiayi Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067662

With the substantial improvement of people’s living standards, the amount of domestic garbage is increasing rapidly, and intelligent waste classification has become an urgent need in modern society. In this paper, we propose a real-time garbage detection model based on the improved YOLOv5 (you only look once version 5) algorithm. Firstly, mosaic data enhancement is introduced to enrich the background of the detection object and improve the robustness of the network. Secondly, Distance-IOU Non-Maximum Suppression is used to replace the traditional Non-Maximum Suppression to improve the suppression effect of prediction boxes. Finally, the network is further optimized from the aspect of activation function. The experimental results show that among the four versions of YOLOv5, their mean average precision(mAP) all reach more than 84%, The improved YOLOv5x has the best recognition effect, whose mAP reaches 89.4%, which is 2.1% higher than that of YOLOv5x and 5.3% higher than that of YOLOv5s. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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A Systematic Literature Review of Decentralized Applications in Web3: Identifying Challenges and Opportunities for Blockchain Developers

A Systematic Literature Review of Decentralized Applications...

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IEEE International Conference on Big Data

作者： Md Jobair Hossain Faruk Pratusha Raya Md Kamrul Siam Jerry Q. Cheng Hossain Shahriar Alfredo Cuzzocrea Pablo Garcia Bringas Department of Computer Science New York Institute of Technology USA Department of Software Engineering Kennesaw State University USA Center for Cybersecurity University of West Florida USA D4K Group University of Deusto Bilbao Spain iDEA Lab University of Calabria Rende Italy Department of Computer Science University of Paris City Paris France

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The Internet has opened the floor to stakeholders by redefining the way of organizing, communicating, and collaborating that was initiated by the Web’s development. The advancement of the World Wide Web is an outright phenomenon and significant and we witnessed the evolution of the Web. As decentralized technologies continue to gain traction, Web3, or the decentralized internet, has emerged as a promising approach to enable a more secure, transparent, and privacy-preserved digital landscape. In this paper, we thoroughly conduct a systematic study to explore the challenges and opportunities encountered by blockchain developers in the context of decentralized applications (dApps) in Web3. We analyze a set of peer-reviewed research articles, whitepapers, and technical reports and present an in-depth understanding of the current state of Web3 development and its implications. Our finding indicates the opportunities that Web3 can facilitate, such as expanded use cases, enhanced security and privacy, decentralized infrastructure, and the potential for enabling inclusive development resources for blockchain developers. Additionally, we highlight various challenges that blockchain developers deal with including scalability, security, privacy, interoperability, and the need for standardized tools and frameworks along with various challenges in the software development lifecycle (SDLC). While there are significant challenges to overcome, the potential benefits of Web3 are substantial and could lead to a more inclusive, secure, and transparent digital ecosystem. Furthermore, we emphasize the importance of continued research, collaboration, and innovation among stakeholders to address the identified challenges and capitalize on Web3’s opportunities.

关键词： Privacy Ecosystems Writing Decentralized applications Blockchains Internet Stakeholders Security Web sites software development management

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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