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arXiv 2024年

作者： Zhang, Yulong Yao, Yuan Chen, Shuhao Jin, Pengrong Zhang, Yu Jin, Jian Lu, Jiangang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning. However, EntMin emphasizes prediction discriminability while neglecting prediction diversity. To alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled samples. By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding. Inspired by this finding, we propose a Label-Encoding Risk Minimization (LERM). It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label encodings. As a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a plugin. Theoretically, we analyze the relationships between LERM and ERM as well as EntMin. Empirically, we verify the superiority of the LERM under several label insufficient scenarios. The codes are available at https://***/zhangyl660/LERM. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Predicting Steady-State Behavior in Complex Networks with Graph Neural Networks

arXiv

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arXiv 2025年

作者： Pradhan, Priodyuti Reza, Amit Department of Computer Science & Engineering Indian Institute of Information Technology Karnataka Raichur584135 India Space Research Institute Austrian Academy of Sciences Schmiedlstrasse 6 Graz8042 Austria Nikhef Science Park 105 Amsterdam1098 XG Netherlands

In complex systems, information propagation can be defined as diffused or delocalized, weakly localized, and strongly localized. This study investigates the application of graph neural network models to learn the behavior of a linear dynamical system on networks. A graph convolution and attention-based neural network framework has been developed to identify the steady-state behavior of the linear dynamical system. We reveal that our trained model distinguishes the different states with high accuracy. Furthermore, we have evaluated model performance with real-world data. In addition, to understand the explainability of our model, we provide an analytical derivation for the forward and backward propagation of our framework. © 2025, CC BY.

关键词： Neural network models

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Renewable Energy Frontiers Exploring Sustainable Energy Sources And Preservation Techniques for Ecosystem Maintenance and Biodiversity Conservation 4

Renewable Energy Frontiers Exploring Sustainable Energy Sour...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhabliya, Dharmesh Singh, K. Ranjith Minhas, Deepak Nishanthi Gupta, Shikhar Lakshman, K. Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Cse Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - UG Management Karnataka Bangalore India

ISBN: (纸本)9798331518981

We introduce Sustainable Energy Optimization via Eco Synthesis (SEOTES), a novel paradigm that promises to revolutionize green energy planning. EcoOptimize, BioDiversityGuard, and Community Empower are SEOTES's complicated initiatives that reduce waste, increase efficiency, and engage people in green energy. The EcoOptimize algorithm determines the ideal location and size for green energy projects to maximize energy production and environmental benefit. BioDiversityGuard utilizes machine learning to predict animal impacts from green energy projects. It emphasizes environmental safety. Community Empower shares the advantages of green energy projects fairly via social network analysis and collaboration. SEO techniques function better and have less impact on the bottom line and environment than outdated techniques. SEOTES is the most innovative and effective renewable energy improvement. This abstract discusses SEOTES's whole technique and its importance for tackling the energy crisis's biggest issues. It emphasizes how SEOTES may improve renewable energy project planning, implementation, and participation. © 2024 IEEE.

关键词： Clean energy

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PointGait: Boosting End-to-End 3D Gait Recognition with Point Clouds via Spatiotemporal Modeling

PointGait: Boosting End-to-End 3D Gait Recognition with Poin...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Rui Wang Chuanfu Shen Chao Fan George Q. Huang Shiqi Yu Department of Computer Science and Engineering Southern University of Science and Technology WeBank Institute of Financial Technology Shenzhen Audencia Financial Technology Institute Shenzhen University Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Department of Industrial and Systems Engineering The Hong Kong Polytechnic University

LiDAR is a new type of sensor used for gait recognition. Previous LiDAR-based state-of-the-art methods mostly exploit gait features from the depth maps generated by projecting point clouds in a 3D-to-2D manner, rather than directly using the raw 3D point data. However, these projection-based methods require an additional preprocessing step, which obstructs the universality of the method among different types of LiDARs. On the other hand, while existing point-based methods have achieved promising results in 3D object recognition, they have underperformed in 3D gait recognition, indicating the presence of a domain gap between coarse-grained 3D object classification and fine-grained 3D pedestrians recognition. By analyzing the success achieved by camera-based methods, we perceive that point-based gait recognition fails mainly because of neglecting to capture local representation. To address this issue, we propose an end-to-end 3D gait recognition framework named PointGait, which can directly capture informative gait features from point cloud data. Specifically, PointGait is a multi-stream model consisting of a Global and Local Gait Feature Extractor to extract holistic and fine-grained spatial features. Besides, a Personalized Motion Extractor is introduced to capture inter-frame motion features. Our experimental results on a LiDAR gait dataset, SUSTech1K, outperform all popular point-based methods, demonstrating the effectiveness and potential of our approach. In conclusion, the proposed PointGait promotes the development of point-based gait recognition by highlighting the importance of incorporating fine-grained spatiotemporal information.

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Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI

arXiv

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arXiv 2024年

作者： Kim, Jonghun Kim, Mansu Park, Hyunjin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of AI Graduate School Gwanju Institute of Science and Technology Gwangju Korea Republic of

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for 3D medical imaging data. Motivated by this gap, we propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI). Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. We pretrain our model using large-scale brain MRI data of 13,687 samples spanning several large-scale databases. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer’s disease classification, Parkinson’s disease classification, and age prediction tasks. The ablation study of the proposed pretext tasks shows the effectiveness of our pretext tasks. Our code is available at ***/jongdory/DAMT. © 2024, CC BY-NC-SA.

关键词： Magnetic resonance imaging

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Reputation in public goods cooperation under double Q-learning protocol

arXiv

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arXiv 2025年

作者： Xie, Kai Szolnoki, Attila School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Institute of Technical Physics and Materials Science Centre for Energy Research P.O. Box 49 BudapestH-1525 Hungary

Understanding and resolving cooperation dilemmas are key challenges in evolutionary game theory, which have revealed several mechanisms to address them. This paper investigates the comprehensive influence of multiple reputation-related components on public cooperation. In particular, cooperative investments in public goods game are not fixed but simultaneously depend on the reputation of group organizers and the population’s cooperation willingness, hence indirectly impacting on the players’ income. Additionally, individual payoff can also be directly affected by their reputation via a weighted approach which effectively evaluates the actual income of players. Unlike conventional models, the reputation change of players is non-monotonic, but may transform abruptly due to specific actions. Importantly, a theoretically supported double Q-learning algorithm is introduced to avoid overestimation bias inherent from the classical Q-learning algorithm. Our simulations reveal a significantly improved cooperation level, that is explained by a detailed Q-value analysis. We also observe the lack of massive cooperative clusters in the absence of network reciprocity. At the same time, as an intriguing phenomenon, some actors maintain moderate reputation and are continuously flipping between cooperation and defection. The robustness of our results are validated by mean-field *** Codes 91 Copyright © 2025, The Authors. All rights reserved.

关键词： Game theory

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Optimizing Cloud IoT Applications with Machine Learning Driven Hybrid Key Management Protocols for WSN Performance

Optimizing Cloud IoT Applications with Machine Learning Driv...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Simhadri Mallikarjuna Rao B. Adinarayana Kasani Vaddikasulu Satya Prasad Dontamsetti Peesapati Surya Venkata Ranganadh Research Scholar Department of Computer Science and Engineering Aacharya Nagarjuna University Guntur Andhra Pradesh India R &D Department of EEE & CSE Ravi Technology Vishakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Eluru college of engineering and technology Eluru Andhra Pradesh India Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Software Engineer USA

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

AI-IoT integration is significant, changing the future of networking, particularly in terms of performance and security enhancements. The collection and transmission of sensitive data across secure networks is crucial for the IoT, which combines embedded systems and Networks of Wireless Sensors (WSNs). Ensuring the efficacy and security of these networks is crucial. In this research, a novel hybrid key Hybrid management protocol for Internet of Things applications using WSNs is proposed. The maximizes network performance of efficiency and security by utilizing cloud services and machine learning, specifically XGBoost. The suggested protocol creates a secure communication framework for WSNs by combining symmetric and asymmetric key management strategies. Furthermore, the centralized functions and scalability of cloud-based services enable network-wide updates and effective key distribution. The protocol offers anomaly detection, intelligent network load balancing, dynamic key management, predictive key distribution, and intelligent network load balancing by employing XGBoost-based machine learning algorithms. By using network behavior modelling and historical data analysis, the system forecasts the optimal times and locations for important updates, reducing overhead and enhancing security. Additionally, anomaly detection based on XGBoost improves the protocol's ability to recognize anomalies and potential security risks. This hybrid architecture creates a highly flexible and effective protocol for IoT-enabled WSNs by combining cloud integration, centralized key management, and AI-driven intelligence. The proposed approach performs better on the network due to proven methods and algorithms.

关键词： Support vector machines Wireless sensor networks Cloud computing Protocols Accuracy Machine learning algorithms Scalability Internet of Things Security Anomaly detection

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A Review on the Use of AI and Robotics in Agricultural Practices

A Review on the Use of AI and Robotics in Agricultural Pract...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Mohan K. Warbhe Prateek Verma Rajendra Rewatkar Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Robotics and artificial intelligence are becoming a part of the agricultural sector in ways bringing a change in it. It is evident that different technologies can help improve the yields, increase sustainability and efficiency in farming. This paper aims at offering a brief on the use of AI and robotics in agriculture; this will include using technologies such as Exact Hybrid Precision Farming, AI in livestock farming, AI-based resource supply chain applications, and others. In this literature review, the ideas of other authors are compiled and compare the concepts of culture as a factor in tackling the issues affecting agriculture and the possibility of utilizing artificial intelligence in the process. The theoretical literature review of the application of AI and robotics in precision farming aims at presenting literature evidence showcasing the use of activities such as predictive analytics, decision support systems, automated crop monitoring, and robotic harvesting. Other discussed issues are threats including high capital investment during the setting up and implementation of the system, replacement of labor by machines, as well as data insecurities.

关键词： Training Costs Bibliographies Government Supply chains Agriculture Artificial intelligence Sustainable development Robots Farming

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AI Technological Interference in Court Proceedings: Right to Fair Trial Decision 3

AI Technological Interference in Court Proceedings: Right to...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Kumar, Santosh Joshi, Kapil Singh, Sumeshwar Anandaram, Harishchander Sindhu, Kawerinder Singh Mahariya, Satish Kumar Kaushik, Vishal Sharda School of Basic Sciences and Research Sharda University Department of Mathematics UP Greater Noida India Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Amrita School of Artificial Intelligence Centre for Computational Engineering and Networking Coimbatore India Uttaranchal University Dehradun India Gsss Mahawa Neemkathana Rajasthan Sikar India School of Computer Science Upes India

ISBN: (纸本)9798350330724

The given study focuses on the influence of artificial intelligence (AI) on adjudicatory procedures mostly on observance of such right to an impartial trial in several law-related fields with respect to existing decisions. The consideration of such substance of the right to an impartial trial serves as the beginning stage for the debate, with a special focus on the autonomy and impartiality of the judiciary. Following that, the article analyses the prospect of minimizing the individual role in court processes in charge of making decisions using information technologies. This paper analyses the admissibility of using AI tools in the judiciary and contains considerations on ethical aspects of AI application in judicial proceedings and whether an AI system can take over the role of a decision-maker in judicial proceedings, thereby replacing or supporting the judge. The paper with a view of its legal compliance, non-discrimination, transparency, and efficiency of legal proceedings. The study employs the method of analyzing the law in force and investigating theories of law, taking into account the critical analysis of the literature at the decision level. © 2023 IEEE.

关键词： Decision making

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Automation Intervention for Monitoring Agriculture Farming Scale: A Review 14

Automation Intervention for Monitoring Agriculture Farming S...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Joshi, Kapil Kumar, Santosh Bhatla, Shruti Kaushik, Vishal Anandaram, Harishchander Kumar, Anil Uttaranchal University Department of Cse Uttaranchal Institute of Technology Dehradun India Sharda University Department of Mathematics Sharda School of Basic Sciences and Research UP Greater Noida India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

Agricultural data is only valuable when it has been managed successfully. As data has evolved into the fundamental ingredient in contemporary agriculture to assist farmers with vital decision, latest developments in managing data are causing Smart Agriculture to rise dramatically. With factual information obtained by sensors, significant advantages emerge with the goal of enhancing production and ecology. This type of data-driven farm management is built on information that can boost efficiency by preventing resource waste and contamination of the ecosystem. Data-driven farming, with the assistance of automated solutions using artificial intelligence techniques, lays the groundwork for potential sustainable *** article analyses the current state of sophisticated agricultural systems by rehashing each critical step, from data processing in field crops to variable interest rate implementations, so that farmers can make optimised decision making to save funds while safeguarding the natural environment and converting how meal would be generated to meet the upcoming population explosion. © 2023 IEEE.

关键词： Big data

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