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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

Frontiers of computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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UNSUPERVISED SAMPLE SELECTION FOR ACTIVE LEARNING WITH QUADRATIC PROGRAMMING

Journal of Applied and Numerical Optimization

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Journal of Applied and Numerical Optimization 2024年第3期6卷 339-350页

作者： Wang, Yunbin Song, Na Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350108 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China

Graph Neural Networks (GNNs), which gained popularity recently, is facing the problem of reducing the cost of acquiring large datasets. Although a portion of the work combining GNN with active learning has been moderately successful, there are still some shortcomings in this research area. Most of the studies using clustering methods can only obtain local optima, and some of them suffer from the problem of difficult convergence. Moreover, the result of clustering is often undesired if the amount of data in each class is not balanced. Some methods obtain higher performance by combining multiple metrics, but are limited by the adverse effects of under-training the initial network. For these reasons, we propose an active learning framework based on quadratic programming in this paper. This framework transforms the sample sampling process into an optimal solution problem, which can obtain the global optimal solution and avoid the problem of hard convergence. Experimental results on several datasets demonstrate that the proposed method outperforms other baselines. © 2024 Journal of Applied and Numerial Optimization.

关键词： Active learning

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Deep Learning-Based Multi-Criteria Recommender System: Leveraging Autoencoders for Improved Personalization 1

Deep Learning-Based Multi-Criteria Recommender System: Lever...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Chouti, Nithish Kusale, Sakshi Mishra, Sanket Rajora, Shashank Prasad, Prabhu Sinha, Bam Bahadur Vanahalli, Manjunath IIIT Dharwad Computer Science Engineering Karnataka580009 India NIT Sikkim Computer Science Engineering Ravangla737139 India IIIT Dharwad Data Science & Artificial Intelligence Karnataka580009 India

ISBN: (纸本)9798350353648

Multi-Criteria Recommender Systems (MCRSs) are a promising method that takes into account several elements of the user's preferences in order to improve recommendation accuracy. However, most existing research has focused on single-criterion ratings, overlooking the potential of multi-criteria considerations in deep learning-based recommender systems. This paper introduces a novel deep learning-based algorithm for MCRSs, leveraging deep autoencoders to uncover complex user-item interactions based on multi-criteria preferences. The proposed algorithm, evaluated using the TripAdvisor multi-criteria dataset, demonstrates superior recommendation capabilities compared to traditional methods, achieving a significantly lower RMSE of 0.975. The results highlight the algorithm's effectiveness in providing accurate and personalized recommendations tailored to diverse user preferences, marking a significant advancement in MCRS performance. © 2024 IEEE.

关键词： Collaborative filtering

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Cryptocurrency Forecasting using Financial Indicators 15

Cryptocurrency Forecasting using Financial Indicators

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Deb, Sagar Sankhe, Atharva Dalvi, Indrajit Vartak, Pooja Dwarkadas J. Sanghvi College of Engineering Department of Computer Engineering and Data Science Mumbai India

ISBN: (纸本)9798350370249

With the rise of digital financial systems, cryptocurrencies have become a prime example of blockchain's potential. This paper presents a deep learning approach targeting the time series data of Bitcoin, which is known for its high volatility. By implementing a long short-term memory (LSTM) model, this study aims to capture the underlying trends in cryptocurrency pricing for future price prediction. © 2024 IEEE.

关键词： Decentralized finance

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Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

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computers, Materials & Continua 2023年第12期77卷 3289-3304页

作者： Tamal Kumar Kundu Dinesh Kumar Anguraj S.V.Sudha Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAndhra PradeshIndia Department of Artificial Intelligence and Data Science KPR Institute of Engineering and TechnologyCoimbatoreTamil Nadu641407India

A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral *** existing approaches fail to predict the malaria parasitic features and reduce the prediction *** trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online *** Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to *** features are extracted from the images in the proposed system to train the DNN for initializing the visible *** smear images show the concatenated feature to be utilized as the feature vector in the proposed ***,hyper-parameters are used to fine-tune DNN to calculate the class labels’*** suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood.

关键词： Malaria parasite classification hyper-parameter deep neural network the feature vector

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Image Captioning with Audio Reinforcement using RNN and CNN

Image Captioning with Audio Reinforcement using RNN and CNN

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Patankar, Rohan Sethi, Harshik Sadhukha, Aditi Banjade, Nishant Mathur, Ashwini Jain University Department of Computer Science and Engineering Specialization in Data Science Bengaluru India Jain University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350333602

Image captioning involves generating a descriptive text that encapsulates the visual information contained in an image. This ppt proposes a deep learning model for image captioning that utilizes a Vision Transformer (ViT) and a GPT-2 network. Specifically, the ViT is employed to extract image features, which are then used by the GPT-2 network to generate captions for the images. To evaluate the effectiveness of the model, experiments are conducted on a Stanford dataset comprising images and their corresponding captions, and demonstrate that our model achieves performance comparable to other state-of-the-art image captioning models. Proposed results suggest that our model has the potential to generate accurate and semantically meaningful captions for images, and could be useful for various applications such as improving the accessibility of visual media. © 2023 IEEE.

关键词： Image enhancement

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Product Quality Enhancement in Social Media using Emotion-Enhanced Bert (EE-BERT) Model 2

Product Quality Enhancement in Social Media using Emotion-En...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Raj Kumar, V.S. Kumaresan, T. Mythily, V. Raghavendran, N. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data science Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Erode India Rmk college of Engineering and Technology Department of Artificial Intelligence and Data science Chennai India

ISBN: (纸本)9798350368413

Twitter plays an important role in understanding the consumer sentiment about the products. The advanced analytics and Natural Language Processing (NLP) are used to extract actionable insights from this data and useful for a business to improve features and address issues on time for better customer satisfaction and brand loyalty. Most of the current models suffer from a lack of effective ways to handle subtle sentiment analysis because of a lack of contextual understanding and proper feature extraction. Most traditional models cannot combine and optimize features that lead to obtaining sentiments. This leads to overfitting and problems caused by local minima. The proposed Emotion-Enhanced BERT (EE-BERT) model is an extension of the standard BERT to learn contextual embeddings in user sentiment. In this work, data preprocessing includes text cleaning, tokenization, dealing with emojis and emoticons, and lemmatization with the best quality data. The proposed work is basically a dual-stream architecture extended with butterfly optimizer. First, the advanced tokenization and semantic understanding techniques is used for extracting textual features. Second, the emotion-enhanced features extracted through emotion-hash vectorization to enhance the model's contextual awareness in social media data. This provides a unified framework that combines the feature extraction methods to enhance the accuracy in sentiment analysis. It demonstrated with an impressive accuracy of 98.2% on sentiment in posts and comments with 0.15 losses. The proposed work is free from overfitting and local minima in product quality enhancement. © 2024 IEEE.

关键词： Tweets

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Decentralized Generative AI Model Deployment Using Microservices

Decentralized Generative AI Model Deployment Using Microserv...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Jhingran, Sushant Bansal, Nidhi Chaturvedi, Rekha Singh, Ajeet Arora, Yojna School of Engineering and Technology Sharda University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Pune India School of Information Security and Data Science Manipal University Jaipur Department of Data Science and Engineering Jaipur India Moradabad Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Moradabad India

ISBN: (纸本)9798331517953

The microservices are continuously growing in domains of communication using cloud and AI. Access to microservice can be done using any respective cloud environment. Access microservices using cloud require multiple configurations. These configurations can be set up in Kubernetes or containerized environments to lightweight any application which increases the performance of microservices. Multiple requests can create a concurrent call, which causes slow response. Generative AI models can be used to solve concurrent calls. Generative AI used a modular approach for microservice communications with API gateway. This allows microservices to function in a variety of contexts, including cloud, edge devices, and containers. This process refers to a decentralized approach which can only be implemented in the case of microservices and provide improvement in latency along with bandwidth. © 2024 IEEE.

关键词： Cloud Container Docker Generative AI Microservices

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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Control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

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computers, Materials & Continua 2024年第3期78卷 4343-4361页

作者： Yunchang Liu Fei Wan Chengwu Liang School of Computer and Data Science Henan University of Urban ConstructionPingdingshan467036China School of Electrical and Control Engineering Henan University of Urban ConstructionPingdingshan467036China

Traffic flow prediction plays a key role in the construction of intelligent transportation ***,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very *** of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between ***,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial *** paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic *** combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic *** on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

关键词： Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism

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