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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. This paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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A Novel Machine Learning Approach to Delay Efficient Offloading Strategy for Mobile Edge Computing 1st

A Novel Machine Learning Approach to Delay Efficient Offload...

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1st International Conference on Microelectronics, Communication Systems, Machine Learning, and Internet of Things, MCMI 2020

作者： Bharti, Piyush Chaudhary, Srishti Snigdh, Itu Department of Computer Science and Engineering Birla Institute of Technology Mesra India Software Development Microland Limited Bengaluru India

ISBN: (纸本)9789811919053

There have been studies on the problems experienced in offloading. But, how to leverage machine learning for resource-efficient computation offloading is not completely explored yet. We present this paper with the motivation to leverage machine learning and backward induction to suggest a method for the resource-efficient computation offloading in IoT devices. In particular, we use machine learning to predict the most efficient device to which a task should be offloaded through the use of a two-model machine learning system deployed in the edge cloud itself, since most high power IoT devices are fixed in their positions and display a certain pattern in their CPU utilization data. This is followed by using backward induction to determine the optimal execution location for each task component. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computation offloading

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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Frontiers of computer science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

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Enhancing Viewer Engagement: Exploring Generative Long Short Term Memory in Dynamic Ad Customization for Streaming Media 5

Enhancing Viewer Engagement: Exploring Generative Long Short...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Troy University Software Development & Engineering in Content Discovery & Ai Department of computer Science Computer Science Montgomery Campus AL United States C & Rs Platform services DT Amtrak United States University of south alabama Department of electrical engineering Electrical engineering United States

ISBN: (纸本)9798331504403

In Ad customization, several approaches are employed to improve the relevance level of the message for monitoring the user performance according to the prior search history (e.g., retargeting) or by gathering particular information related to the customer (e.g., micro-targeting). Occasionally, the consumers view the ad developed by the other user, in this situation, the ads are viewed as something that can be related by the user. The quantity of information that is gathered openly or secretly can affect the user choice for the content displayed. However, the users remain as recipients of a strategy, that decides the best ad content. Therefore, an exploring Generative Artificial Intelligence-based Dynamic Ad Customization model for enhancing viewer engagement from streaming media is developed. In this paper, the Generative Long Short-Term Memory (GenLSTM) model is developed for examining customized advertising then includes the customization of specific message elements by individuals, variation in the level of ad customization, and in some cases perceived collaboration with others. Here, the customization classification is defined based on the modification in the marketing mix element that is created by the customer proactively as opposed to the company. In regards to customized advertising, 'ad is something that happens with the viewer'. The customization increases based on the sense of task engagement, strength of the perceived customer relationship with the company, attitude toward the ad, and perceptions of company trust/integrity. Finally, the experimental analysis is conducted based on the relationship with the company, company care, company trust, and task engagement to attain the overall impact on ad customization. © 2024 IEEE.

关键词： Sales

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UAV Communication and Localization Network Deployment for Content Distribution in Emergency Scenarios 6

UAV Communication and Localization Network Deployment for Co...

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6th International Conference on Electronics and Communication, Network and computer Technology, ECNCT 2024

作者： Wang, Kailiang Yu, Ran Zhang, Donghui Zhang, Yuhao Lu, Jie Yu, Xinyu Men, Baoxia Zhang, Yi Ji, Yutong State Grid Jibei Information & Telecommunication Company Beijing China School of Computer Science National Pilot Software Engineering School Bupt Beijing China

ISBN: (纸本)9798350366143

Unmanned Aerial Vehicles (UAVs) are increasingly recognized for their potential to revolutionize emergency response communications and localization, especially when traditional infrastructure is damaged or non-existent. This study presents an iterative deployment strategy for UAVs equipped with directional antennas to optimize communication and localization in disaster-stricken areas. By integrating a line-of-sight coverage model with real-world environmental data, and utilizing a greedy algorithm alongside an improved Particle Swarm Optimization algorithm, the strategy refines UAV placement and antenna orientations iteratively. The results of simulations show that our method significantly enhances communication rates and localization accuracy, proving effective in complex emergency scenarios. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Identification of the Diabetic Retinopathy Using ResNet-18 2

Identification of the Diabetic Retinopathy Using ResNet-18

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ahmed, Fahad Fatima, Areej Mamoon, Muhammed Khan, Shan National College Of Business Administration Economics Department Of Computer Science Lahore Pakistan Lahore Garrison University Department Of Computer Science Lahore Pakistan Islamia University Bhawalpur Department Of Software Engineering Bahawalpur Pakistan Lahore Pakistan

ISBN: (纸本)9798350394962

In the realm of healthcare, trauma, aging, and diseases like diabetes can cause a variety of abnormalities in human eyes. Diabetic retinopathy stands out as a prevalent global cause of blindness. Early identification and diagnosis of diabetic retinopathy is essential for prompt treatment and blindness prevention. The analysis of medical images can contribute to the identification of diabetic retinopathy, assisting in the diagnostic process. This study uses a deep learning (DL) model, ResNet-18, to identify diabetic retinopathy. The dataset used in this study acquired from Kaggle is divided into training and testing. ResNet-18 accomplished a training accuracy of 99.91% and a testing accuracy of 96.65%. The findings demonstrate the effectiveness of utilizing DL models like ResNet-18 for the early identification of diabetic retinopathy, potentially revolutionizing the screening process in clinical settings. By streamlining the diagnostic process, this proposed model has the potential to significantly enhance early intervention and treatment strategies, ultimately mitigating the risk of blindness in diabetic patients. Incorporating such technology into the healthcare system could improve patient outcomes and resource allocation. © 2024 IEEE.

关键词： Diagnosis

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Predicting Associations between circRNAs and Drug Sensitivity using Heterogeneous Graphs and Graph Attention Networks

Predicting Associations between circRNAs and Drug Sensitivit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xiao, Xiaojun Qian, Yurong Huang, Zhijian Zheng, Rongtao Deng, Lei Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Discovering associations between circular RNAs (circRNAs) and cellular drug sensitivity is essential for understanding drug efficacy and therapeutic resistance. Traditional experimental methods to verify such associations are costly and time-consuming. Thus, the development of efficient computational methods for predicting circRNA-drug associations is crucial. In this study, we introduce a novel computational predictor called HETACDA, aimed at predicting potential circRNA-drug sensitivity associations. HETACDA constructs a heterogeneous graph network, incorporating the characteristic structure graph of drugs and circRNAs, along with the circRNA-drug sensitivity topology graph. By employing a graph convolutional network, the drug embedding vector is computed from the molecular structure of the drug. Through a graph attention mechanism, HETACDA assigns distinct attention weights to nodes in order to emphasize the contribution of various neighborhood nodes to the central node. Subsequently, an association score between circRNA-drug sensitivity is predicted using a three-layer fully connected neural network. Extensive experimental comparisons against several state-of-the-art methods highlight the effectiveness of our proposed framework. The availability of our source code and datasets on GitHub (https://***/xiaoxiaojun131421/HETACDA) facilitates replication and further research in this area. © 2023 IEEE.

关键词： circRNA-drug sensitivity associations graph attention mechanism heterogeneous graph network

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3D Atrous Spatial Pyramid Pooling based Multi-Scale Feature Fusion Network for Hyperspectral Image Classification

3D Atrous Spatial Pyramid Pooling based Multi-Scale Feature ...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Zhu, Tianxing Liu, Qin Zhang, Lixiang School of Software Engineering Tongji University Shanghai China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781510668898

The convolutional neural network (CNN) has been widely adopted in the processing of hyperspectral images (HSI) due to its remarkable capabilities in feature extraction. However, CNN-based methods for HSI classification still face the challenge of extracting spectral-spatial features that are more effective while considering all spectral bands. This paper reviews various spectral and spectral-spatial classifiers that have been developed to overcome the challenges of HSI classification, including the limitations of handcrafted features and the problems caused by spectral variability and nonlinearity. The paper proposes a novel method, the 3D ASPP Multi-Scale Feature Fusion Network (3A-MFFN), which simultaneously preserves high-resolution spatial detail information and high-level semantic information of HSI. The proposed network extracts and fuses multi-scale features for HSI classification using a multi-scale 3D ASPP convolution block to fuse different levels of feature output. The paper presents experiments and analysis to demonstrate the effectiveness of the proposed method. The results suggest that the 3A-MFFN algorithm can significantly enhance the classification accuracy of HSI with limited samples, making it a valuable contribution to the hyperspectral remote sensing community. © 2023 SPIE.

关键词： Convolution

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TTAG+R: A Dataset of Google Play Store's Top Trending Android Games and User Reviews 22

TTAG+R: A Dataset of Google Play Store's Top Trending Androi...

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22nd IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2022

作者： Chand, Raheela Rehman Khan, Saif Ur Hussain, Shahid Wang, Wenli Department of Computer Science Islamabad Pakistan School of Engineering Penn State University Department of Computer Science and Software Engineering United States

ISBN: (纸本)9798350319910

Context: Android games are gaining wide attention from users in recent years. However, the existing literature reports alarming statistics about banning popular and top-trending Android apps. The popular gaming apps have been removed from Google Play Store due to various user concerns. Objectives: The goal of this work is twofold: (i) to assist the researchers and practitioners in identifying the state-of-the-art challenges, constraints, and compliments about Android apps for future Android-specific studies, and (ii) to encourage active users' perspectives on the Android development process because usability remains a core deciding factor about the success or failure of Android apps. Method: To accomplish this, we introduce a novel open-source dataset, Top Trending Android apps with their user Reviews (TTAG+R) in GitHub. Results and Contributions: Briefly, TTAG+R presents information about 245 top trending Android Free Games, 97 top trending Android Grossing Games, and 52 top trending Android Paid Games with a total of 8,423 user reviews in 12 different. csv files. The main contributions of this paper are: (i) provides one-place comprehensive data on Android Apps, (ii) describes various features of Android apps and their user reviews, (iii) reports the updated and latest knowledge about Android apps, and (iv) provides the data in an unfiltered form so that researchers may not find difficulty in using this dataset in their data-driven experimentation. From a research implication viewpoint, the dataset supports: (i) understanding the usability characteristics of Android apps, (ii) discovering current trends and pitfalls in Android apps, and (iii) analyzing the Android financial market. Conclusion and Future Work: Thus, TTAG+R is freely available to the research community, and useful for future enhancements in the Android domain. In the future, we plan to keep the data up-to-date with the most recent information for the continued usage of the dataset. © 2022 IEEE.

关键词： Android (operating system)

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