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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Chao Zhao Ali Al-Bashabsheh Chung Chan Department of Computer Science City University of Hong Kong. Big Data and Brain Computing (BDBC) center at Beihang University Beijing China

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

Identifying locally dense subgraphs aims to pinpoint subgraphs characterized by tight internal connectivity. However, existing methods for identifying dense subgraphs based on density can lead to loose internal connections. This paper addresses this issue by introducing a concept of strength to detect strong subsets. Our approach encompasses the existing work that finds a nested chain of densest k-subgraphs as a special case and reveals subgraphs that have tight internal connections overlooked by existing methods. The strong subsets exhibit a laminar structure and can be computed in polynomial time. In contrast to previous works defining locally densest subgraphs without a natural extension to weighted graphs, our method accommodates both weighted and unweighted, directed and undirected graphs, as well as hypergraphs. Furthermore, it extends to a broader notion of information density, surpassing the scope of weighted graphs.

关键词： Conferences Minimization Polynomials Information theory

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RIS-Assisted Localization: A Novel Conditional Sample Mean Approach without CSI

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL United States

Reconfigurable intelligent surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. In this work, we propose a novel multiple RISs aided localization approach to address these challenges. The proposed method first estimates the angle-of-directions (AoDs) between the RISs and the user using the conditional sample mean approach, and then uses the estimated multiple AoD pairs to determine the user’s position. This approach only requires measuring the received signal strength at the BS for a set of randomly generated phase shifts across all RISs, thereby eliminating the need for real-time RIS phase shift design or user-to-BS pilot transmissions. Numerical results show that the proposed localization approach improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

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A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

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Computer Modeling in Engineering & sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur Rahman Ahmad Firdaus Mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

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Application of Generative Artificial Intelligence for Epidemic Modeling

Application of Generative Artificial Intelligence for Epidem...

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Simulation Winter Conference

作者： Hannah Danielle Ladera Villaplana Jaeyoung Kwak Michael H. Lees Hongying Li Wentong Cai College of Computing and Data Science Nanyang Technological University Singapore Singapore Informatics Institute University of Amsterdam Amsterdam The Netherlands Department of Fluid Dynamics Institute of High Performance Computing Singapore Singapore

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Epidemic models have become increasingly useful, especially in the wake of the recent COVID-19 pandemic, emphasizing the crucial role of human behavior in the spread of disease. There has been a recent rise in the usage and popularity of generative artificial intelligence (GenAI), such as ChatGPT especially with its ability to mimic human behavior. In this study, we demonstrate a novel application of GenAI for epidemic modeling. We employed GenAI for creating agents living in a hypothetical town in simulations and simulating their behavior within the context of an ongoing pandemic. We performed a series of simulations to quantify the impact of agent traits and the availability of information for health condition, virus, and government guidelines on the disease spread patterns in terms of peak time and epidemic duration. We also characterized the most influential factors in agents' decision-making using random forest model.

关键词： Pandemics Generative AI Computational modeling Government Decision making Behavioral sciences Random forests Diseases Guidelines Context modeling

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Color-Based Ripeness Classification of Oil Palm Fresh Fruit Bunches Using Convolution Neural Networks and data Augmentation

Color-Based Ripeness Classification of Oil Palm Fresh Fruit ...

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Juhaida Abu Bakar Mahmoud A M Kehail Muhamad Syafiq Mat Kassim Ranjithkumar Saravanan Zhao Wei Cheng Ali Ghazi Dahham Al-Marsoomi Data Science Research Lab School of Computing Universiti Utara Malaysia Kedah Malaysia Department of Mechanical Engineering UCSI University Cheras Malaysia

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

Proper ripeness categorization of oil palm fresh fruit bunches is essential to ensure they are harvested at the optimal stage to maximize oil output. The current manual method of ripeness assessment, which relies on harvesters visually inspecting the fruit's color, is both labor-intensive and prone to human error. To address these limitations, there is a growing need for an automated classification system that leverages artificial intelligence. The manual classification process is cumbersome and susceptible to inaccuracies due to its reliance on subjective visual inspection. This labor-intensive approach increases the likelihood of human error, underscoring the necessity for a more reliable and efficient automated solution. Thus, this study aims to evaluate the feasibility of using Convolutional Neural Networks for automating the grading based on color. A dataset was compiled from various sources and augmented using a data generator. The model was constructed incorporating a transfer learning approach with an Xception model. The model achieved an overall accuracy of 80.4%. Visualization of the model using activation maps confirmed that the model can effectively classify the color, indicating its potential viability for the task.

关键词： Analytical models Accuracy Image color analysis Oils Computational modeling Transfer learning data visualization Manuals data models Graphical user interfaces

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Federated model heterogeneous Matryoshka representation learning 24

Federated model heterogeneous Matryoshka representation lear...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Liping Yi Han Yu Chao Ren Gang Wang Xiaoguang Liu Xiaoxiao Li College of Computer Science TMCC SysNet DISSec GTIISC Nankai University China and College of Computing and Data Science Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science TMCC SysNet DISSec GTIISC Nankai University China Department of Electrical and Computer Engineering The University of British Columbia Canada and Vector Institute Canada

ISBN: (纸本)9798331314385

Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in limited knowledge exchange. To address this limitation, we propose the Federated model heterogeneous Matryoshka Representation Learning (FedMRL) approach for supervised learning tasks. It adds an auxiliary small homogeneous model shared by clients with heterogeneous local models. (1) The generalized and personalized representations extracted by the two models' feature extractors are fused by a personalized lightweight representation projector. This step enables representation fusion to adapt to local data distribution. (2) The fused representation is then used to construct Matryoshka representations with multi-dimensional and multi-granular embedded representations learned by the global homogeneous model header and the local heterogeneous model header. This step facilitates multi-perspective representation learning and improves model learning capability. Theoretical analysis shows that FedMRL achieves a O(1/T) non-convex convergence rate. Extensive experiments on benchmark datasets demonstrate its superior model accuracy with low communication and computational costs compared to seven state-of-the-art baselines. It achieves up to 8.48% and 24.94% accuracy improvement compared with the state-of-the-art and the best same-category baseline, respectively.

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Understanding Graph databases: A Comprehensive Tutorial and Survey

arXiv

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arXiv 2024年

作者： Anuyah, Sydney Bolade, Emmanuel Agbaakin, Oluwatosin Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States Data Science Department Edyah Consulting Anthony Lagos Nigeria

This tutorial is curated as a one-stop shop for understanding graph databases, as it emphasizes the foundations of graph theory and explores practical applications across multiple fields. The paper begins with foundational concepts, and further explains the structure of graphs through nodes and edges, including various types such as undirected, directed, weighted, and unweighted graphs. The tutorial outlines essential graph properties, terminologies, and key algorithms for network analysis, including Dijkstra’s shortest path algorithm and techniques for calculating node centrality and graph connectivity. Graph databases, as discussed in the tutorial, offer advantages over traditional relational databases by enabling efficient management of complex, interconnected data. Overall, this paper discusses prominent graph database systems like Neo4j, Amazon Neptune, and ArangoDB, each with unique features for handling large datasets. There are practical instructions on implementing graph operations using NetworkX and Neo4j cover node and edge creation, attribute assignment, and advanced queries with Cypher. Furthermore, the paper includes common graph visualization techniques using tools such as Plotly and Neo4j Bloom which enhance the usability of graph data, while also focusing on community detection algorithms, such as the Louvain method, which support clustering in large networks. We conclude by providing future directions to researchers who are looking to enter into the world of graphs. © 2024, CC BY.

关键词： Graph databases

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Phantom Events: Demystifying the Issues of Log Forgery in Blockchain

arXiv

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arXiv 2025年

作者： Liu, Yixuan Dong, Yuxin Liu, Ye Luo, Xiapu Li, Yi College of Computing and Data Science Nanyang Technological University Singapore School of Software and Microelectronics Peking University Beijing China School of Computing and Information Systems Singapore Management University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

With the rapid development of blockchain technology, transaction logs play a central role in various applications, including decentralized exchanges, wallets, cross-chain bridges, and other third-party services. However, these logs, particularly those based on smart contract events, are highly susceptible to manipulation and forgery, creating substantial security risks across the ecosystem. To address this issue, we present the first in-depth security analysis of transaction log forgery in EVM-based blockchains, a phenomenon we term Phantom Events. We systematically model five types of attacks and propose a tool designed to detect event forgery vulnerabilities in smart contracts. Our evaluation demonstrates that our approach outperforms existing tools in identifying potential phantom events. Furthermore, we have successfully identified real-world instances for all five types of attacks across multiple decentralized applications. Finally, we call on community developers to take proactive steps to address these critical security vulnerabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Phantoms

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3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering 39

3DMambaIPF: A State Space Model for Iterative Point Cloud Fi...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Qingyuan Yang, Weidong Fei, Ben Xu, Jingyi Zhang, Rui Liu, Keyi Luo, Yeqi He, Ying School of Computer Science Fudan University China Department of Information Engineering The Chinese University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on commonly used datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for dense and large-scale point clouds and their inclination to generate noisy outliers after denoising. To deal with this challenge, we introduce 3DMambaIPF, for the first time, exploiting Selective State Space Models (SSMs) architecture to handle highly-dense and large-scale point clouds, capitalizing on its strengths in selective input processing and large context modeling capabilities. Additionally, we present a robust and fast differentiable rendering loss to constrain the noisy points around the surface. In contrast to previous methodologies, this differentiable rendering loss enhances the visual realism of denoised geometric structures and aligns point cloud boundaries more closely with those observed in real-world objects. Extensive evaluations on commonly used datasets (typically with up to 50K points) demonstrate that 3DMambaIPF achieves state-of-the-art results. Moreover, we showcase the superior scalability and efficiency of 3DMam-baIPF on highly dense and large-scale point clouds with up to 500K points compared to off-the-shelf methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： State space methods

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Game Ai Evolution: Uniting Unity and Machine Learning for Enhanced Player Interaction and Experience

SSRN

引用

SSRN 2024年

作者： Supli, Ahmad Affandi Siqi, Xu Digital Media Technology Department School of Computing and Data Science Xiamen UniversityMalaysia Jalan Sunsuria Bandar Sunsuria Selangor Sepang43900 Malaysia

With the development of information technology and computer hardware and software, players' interests have evolved away from purely chasing visual and aural experiences in favor of internal performance, gameplay, and interactive components. Since it makes player contact and communication easier, game AI has gained creators' attention and has become an essential part of game content. The task of designing outstanding game AI is still difficult. Traditional methods, such as finite state machines and behavior trees, require a lot of work to implement and are difficult to maintain. This study explores the use of machine learning to build a flexible and compliant gaming AI to handle these issues. To train game AI in Unity scenarios, the study specifically uses deep reinforcement learning using the ML-Agents toolbox and Python programming language. A game is created utilizing behavior trees and machine learning to assess the efficacy of the technique. This allows for a comparison of design concepts and implementation methods. The study seeks to advance machine learning in game research for increased operational effectiveness. The suggested game encourages future study in boosting machine learning for more effective and simple game design, ultimately enhancing player experience, by incorporating pertinent ideas and rules. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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