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Graph Transformer for Communities Detection in Social Networks

computers, Materials & Continua 2022年第3期70卷 5707-5720页

作者： G.Naga Chandrika Khalid Alnowibet K.Sandeep Kautish E.Sreenivasa Reddy Adel F.Alrasheedi Ali Wagdy Mohamed Department of Computer Science and Engineering ANU College of Engineering and TechnologyGuntur522510India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia LBEF Campus Kathmandu44600Nepal Department of Computer Science and Engineering ANUGuntur522510India Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12588Egypt

Graphs are used in various disciplines such as telecommunication,biological networks,as well as social *** large-scale networks,it is challenging to detect the communities by learning the distinct properties of the *** deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph *** this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to *** advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale *** contributions of the paper are summarized as ***,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the ***,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 ***,the reconstructed model forms communities that present the relationship between the *** proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node *** proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.

关键词： Social networks graph transformer community detection graph classification

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Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

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computers, Materials & Continua 2023年第5期75卷 2491-2508页

作者： Ashir Javeed Ana Luiza Dallora Johan Sanmartin Berglund Arif Ali Peter Anderberg Liaqat Ali Aging Research Center Karolinska InstitutetStockholm17165Sweden Department of Health Blekinge Institute of TechnologyKarlskrona37141Sweden School of Health Sciences University of SkövdeSkövdeSE 54128Sweden Department of Computer Science University of Science and Technology BannuBannu28100Pakistan Department of Electrical Engineering University of Science and Technology BannuBannu28100Pakistan

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity,mortality,and *** there is a consensus that dementia is a multifactorial disorder,which portrays changes in the brain of the affected individual as early as 15 years before its onset,prediction models that aim at its early detection and risk identification should consider these *** study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data,which comprised 75 *** are two automated diagnostic systems developed that use genetic algorithms for feature selection,while artificial neural network and deep neural network are used for dementia *** proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%,sensitivity of 93.15%,specificity of 91.59%,MCC of 0.4788,and performed superior to other 11 machine learning techniques which were presented in the past for dementia *** identified best predictors were:age,past smoking habit,history of infarct,depression,hip fracture,single leg standing test with right leg,score in the physical component summary and history of TIA/*** identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.

关键词： Dementia prediction feature selection genetic algorithm neural networks

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GoT: Effective Graph-of-Thought Reasoning in Language Models

GoT: Effective Graph-of-Thought Reasoning in Language Models

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

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

ISBN: (纸本)9798891761193

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. © 2024 Association for Computational Linguistics.

关键词： Rats

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Generative AI Meets Virtual Reality: A Comprehensive Survey on Applications, Challenges, and Future Direction

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IEEE Access 2025年 13卷 94893-94909页

作者： Fatema Rahimi Abolghasem Sadeghi-Niaraki Soo-Mi Choi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul South Korea

The integration of generative artificial intelligence (AI) with virtual reality (VR) is reshaping how immersive environments are designed, personalized, and experienced. Unlike traditional VR systems that rely on static and pre-scripted content, Generative VR leverages AI-driven content generation, multimodal interaction, and contextual adaptation to create dynamic virtual spaces. This paper presents a comprehensive survey of the core components, applications, and challenges associated with Generative VR. It explores its transformative impact across education, healthcare, industrial training, entertainment, and emerging fields such as environmental conservation and virtual tourism. While Generative VR enhances user engagement and realism, it also introduces critical challenges, including computational scalability, ethical concerns, AI explainability, and real-time performance constraints. The paper further identifies key research gaps and future directions, such as AI-driven multi-user collaboration, cognitive load management, and cross-domain interoperability. By addressing these challenges and advancing the integration of AI with VR, Generative VR has the potential to reshape human-computer interaction, unlocking new possibilities for creativity, accessibility, and intelligent virtual ecosystems.

关键词： Generative AI Artificial intelligence Virtual reality Three-dimensional displays Real-time systems Scalability Ethics Bibliometrics Technological innovation Surveys

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Web-Based Tools Validation for Antimicrobial Resistance Prediction: An Empirical Comparative Analysis

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SN computer science 2024年第1期5卷 1-16页

作者： Routray, Sweta Padma Sahoo, Swayamprabha Nayak, Debasish Swapnesh Kumar Shah, Sejal Swarnkar, Tripti Center for Biotechnology Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar 751003 India Department of Computer Science and Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar 751030 India Department of Computer Science and Bioscience Faculty of Technology Marwadi University Gujarat Rajkot 360003 India Department of Computer Application Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar 751030 India

Antimicrobial resistance (AMR) is a serious threat to global public health, necessitating rapid and precise diagnostic tools. The prevalence of novel antibiotic resistance genes (ARGs) has increased due to microbial sequencing, resulting in the need to extract vital information from vast amounts of data. Although many AMR prediction tools exist, only a few are accurate and scalable. We examined 20 widely used AMR prediction tools and chose 4 web-based tools for antimicrobial resistance surveillance over standalone software due to their easy accessibility, portability, and centralized data management, eliminating the need for complex installation and maintenance. CGE (center for Genomic Epidemiology) provides bioinformatics tools and promotes open data sharing. At the same time, CARD (Comprehensive Antibiotic Resistance Database) is a valuable resource for antibiotic resistance gene information, collectively contributing to our understanding and management of antibiotic resistance. We highlighted web-based AMR prediction tools and performed a case study using the Pseudomonas aeruginosa complete plasmid sequence (CPS) to identify strengths and weaknesses in the system. Our study explored four web-based antibiotic resistance gene prediction tools: ResFinder, KmerResistance, ResFinderFG, and RGI. ResFinder excelled at finding acquired antimicrobial resistance genes as well as maintaining a database up to date. KmerResistance identified resistance genes using k-mer analysis. esFinderFG offered a unique perspective, excelling in detecting a broad range of resistant phenotypes, due to its inclusion of sequences discovered through functional metagenomics. RGI was versatile in detecting a wide range of resistance genes and provided extensive resistance mechanism information. researchers must understand the capabilities and trade-offs of these tools to make well-informed choices for efficient resistance gene identification and surveillance as the antibiotic resistance landsca

关键词： Antibiotic resistance genes (ARGs) Antimicrobial resistance (AMR) KmerResistance ResFinder ResfinderFG

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HDCGD-CBAM:Satellite Interference Recognition Algorithm Based on Improved CLDNN and CBAM

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China Communications 2024年第12期21卷 257-274页

作者： Duan Ruifeng Chen Ziyu Meng Wei Wang Xu Yang Guoting Cheng Peng Li Yonghui School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China Jining Economic and Social Development Research Center Jining 272000China School of Electrical and Information Engineering the University of SydneySydneyNSW 2006Australia Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia

Satellite communication systems are facing serious electromagnetic interference,and interference signal recognition is a crucial foundation for targeted *** this paper,we propose a novel interference recognition algorithm called HDCGD-CBAM,which adopts the time-frequency images(TFIs)of signals to effectively extract the temporal and spectral *** the proposed method,we improve the Convolutional Long Short-Term Memory Deep Neural Network(CLDNN)in two ***,the simpler Gate Recurrent Unit(GRU)is used instead of the Long Short-Term Memory(LSTM),reducing model parameters while maintaining the recognition ***,we replace convolutional layers with hybrid dilated convolution(HDC)to expand the receptive field of feature maps,which captures the correlation of time-frequency data on a larger spatial ***,Convolutional Block Attention Module(CBAM)is introduced before and after the HDC layers to strengthen the extraction of critical features and improve the recognition *** experiment results show that the HDCGD-CBAM model significantly outper-forms existing methods in terms of recognition accuracy and *** Jamming-to-Signal Ratio(JSR)varies from-30dB to 10dB,it achieves an average accuracy of 78.7%and outperforms the CLDNN by 7.29%while reducing the Floating Point Operations(FLOPs)by 79.8%to ***,the proposed model has fewer parameters with 301k compared to several state-of-the-art methods.

关键词： attention mechanism CLDNN HDC interference recognition satellite communication

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Electromagnetic Functions Modulation of Recycled By-Products by Heterodimensional Structure

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Nano-Micro Letters 2025年第6期17卷 264-278页

作者： Ze Nan Wei Wei Zhenhua Lin Ruimei Yuan Miao Zhang Jincheng Zhang Jianyong Ouyang Jingjing Chang Hejun Li Yue Hao State Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology School of MicroelectronicsXidian UniversityXi’an 710071People’s Republic of China State Key Laboratory of Solidification Processing Shaanxi Province Key Laboratory of Fiber Reinforced Light Composite MaterialsCarbon/Carbon Composites Research CenterNorthwestern Polytechnical UniversityXi’an 710072People’s Republic of China Advanced Interdisciplinary Research Center for Flexible Electronics Xidian UniversityXi’an 710071People’s Republic of China Department of Materials Science and Engineering National University of Singapore7 Engineering Drive 1Singapore 117574Singapore

One of the significant technological challenges in safeguarding electronic devices pertains to the modulation of electromagnetic(EM)wave jamming and the recycling of defensive *** synergistic effect of heterodimensional materials can effectively enable the manipulation of EM waves by altering the *** we propose a novel approach for upcycling by-products of silver nanowires that can fabricate shape-tunable aerogels which enable the modulation of its interaction with microwaves by heterodimensional structure of ***-product heterodimensionality was used to design EM-wave-jamming-dissipation structures and therefore two typical tunable aerogel forms were *** first tunable form was aerogel film,which shielded EM interference(EMI shielding effectiveness(EMI SE)>89 dB)and the second tunable form was foam,which performed dual EM functions(SE>30 dB&reflective loss(RL)<-35 dB,effective absorption bandwidth(EAB)>6.7 GHz).We show that secondary recycled aerogels retain nearly all of their EM protection properties,making this type of closed-loop cycle an appealing *** findings pave the way for the development of adaptive EM functions with nanoscale regulation in a green and closed-loop cycle,and they shed light on the fundamental understanding of microwave interactions with heterodimensional structures.

关键词： Silver nanowires Electromagnetic shielding Microwave absorption Recycled aerogels

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Efficient Resource Allocation in Computing Power Networks Considering Similar Task Merging: A Lyapunov Optimization-Based DRL Approach

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IEEE Internet of Things Journal 2025年第13期12卷 23118-23138页

作者： Jia, Zhonghai Xue, Junxiao Shi, Lei Li, Jie He, Mengyang Zhejiang Lab Research Center for Space Computing System Hangzhou310000 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China SongShan Laboratory Zhengzhou450040 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3 Shanghai China

The cloud-edge-terminal architecture relies on hierarchy for resource allocation but lacks global optimization. The computing power network (CPN) introduces a new distributed computing paradigm, integrating cross-domain, heterogeneous resources for global scheduling. However, most CPN research focuses on task optimization during resource allocation, while neglecting the similarity of random tasks before the allocation stage. Additionally, fragmented CPN resources and complex task demands pose challenges to global load balancing. This paper proposes a deep reinforcement learning framework with task merging and congestion avoidance for on-demand resource allocation. Specifically, a low-complexity similar task merging algorithm reduces redundant resource consumption during task preprocessing. In task offloading, the principal neighborhood aggregated graph neural network captures CPN's intricate features. Lyapunov optimization, integrated into a multi-threaded training framework, minimizes resource backlog congestion. A carefully designed reward function balances multiple objectives, enhancing computing resource utilization efficiency and ensuring system stability. Theoretical analysis shows that with control parameter V, the trade-off between resource utilization efficiency and system stability follows the relationship O(1/V), O(V). Extensive experiments demonstrate a 33.5% improvement in resource utilization efficiency and a 62.7% increase in task offloading success rates with respect to those in state-of-the-art algorithms. The proposed algorithm exhibits robustness and effectiveness, particularly in high-load and real network topologies. © 2014 IEEE.

关键词： Computing power

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EMO-Mamba: Multimodal Selective Structured State Space Model for Depression Detection

EMO-Mamba: Multimodal Selective Structured State Space Model...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xing, Tao Dou, Yutao Xie, Xiaolan Zhou, Jiansong Chen, Xianliang Peng, Shaoliang College of Computer Science and Engineering Guilin University of Technology Guilin541006 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The Second Xiangya Hospital Central South University Department of Psychiatry National Clinical Research Center for Mental Disorders National Center for Mental Disorders Changsha410011 China

ISBN: (纸本)9798350386226

Depression is a severe mental disorder that significantly affects health and quality of life, making early detection critical for effective treatment and management. In traditional clinical diagnostics, doctors and mental health professionals typically assess emotional states by observing patients' facial expressions and listening to their speech. Changes in facial expressions can reflect emotional responses, while vocal features such as tone and pace may indicate underlying emotional distress or psychological conditions. Artificial intelligence technologies are adept at efficiently extracting and analyzing these multimedia signals. However, existing methods often overlook critical information when processing long-time series data and struggle to handle multiple modalities simultaneously. To address these issues, we propose the EMO-Mamba, which utilizes selective mechanisms and state space models (SSM) to filter important features and effectively maintain memory over long time-series. Additionally, we introduced a multimodal data fusion framework to integrate key features from various modals, thereby enhancing model performance. Evaluations on the multimodal public datasets D-Vlog showed that our method achieved accuracy of 75.54%, demonstrating its effectiveness across diverse data environments and optimizing detection outcomes. © 2024 IEEE.

关键词： Data fusion

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Secrecy Capacity Analysis with Imperfect Channel State Information and Varying Interference for 6G C-V2X Communication 10

Secrecy Capacity Analysis with Imperfect Channel State Infor...

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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Ayaz, Hina Abbas, Ghulam Abbas, Ziaul Haq Waqas, Muhammad Gik Institute of Engineering Sciences and Technology Facutly of Computer Science and Engineering Topi23460 Pakistan Research Center Topi23460 Pakistan University of Bahrain College of Information Technology Computer Engineering Department 32038 Bahrain

ISBN: (纸本)9798350329674

It has been observed that the use of radio-frequency fingerprinting (RF-FP) for location estimation (LE) of vehicles can significantly improve secrecy capacity (SC) for urban scenarios in 6th generation cellular vehicle-to-everything (6G C-V2X) communication. However, in most of the literature, interference is considered constant for simplicity. Thus, there is a need to investigate the impact of varying levels of interference and incomplete channel state information (CSI) on the performance of SC. This study examines the impact of shadowing and interference in the presence of heavy traffic and obstructions. We have proposed a technique that analyzes the effects of varying levels of interference for LE via RF-FP to improve SC with incomplete CSI. Simulation results demonstrate that interference has a significant impact on SC which depends on the distance and location of the eavesdropping vehicle. However, a decrease in SC is not solely caused by the increase in interference, since other factors, such as the speed and relative position of the illegitimate vehicles as well as incomplete CSI, can also significantly affect the SC performance, as demonstrated through simulations. © 2023 IEEE.

关键词： Vehicle to Everything

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