Special lssues
Table of Content

Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice

Submission Deadline: 20 December 2023 (closed)

Guest Editors

Prof. Honghao Gao, Shanghai University, China
Prof. Walayat Hussain, Australian Catholic University, Australia

Summary

With the rapidly development of 5G communication, edge device and artificial intelligence (AI) technology, it is a very meaningful thing to process and mine the value of big data by using AI technology. It has applied to multiple fields such as intelligent driving, recommendation system, scientific computing, and Smart Ocean. With the widespread application of AI technology, the scale of AI model is getting larger and larger, and the parameters are increasing exponentially, such as GPT-3, Huawei Pangu model, Enlightenment, etc. And the datasets are also getting larger and larger, such as ImageNet-1K, Google Open Images and Tencent ML-Images, etc.

 

While the development of AI technology and big data bring opportunities, it also brings some challenges. Such as, how to conduct distributed high-performance training and inference? How to protect data privacy when training AI model? How to store and read AI training data efficiently, and so on. Therefore, distributed training and inference system or framework, data privacy, data processing and storage, and algorithms for AI should be researched in depth. However, it is currently in its early stage for research on the next generation large-scale AI and needs a special communication for the recent advances of the next generation large-scale AI.

 

The focus for this special issue is on advances in Distributed ML. Researchers from academic fields and industries worldwide are encouraged to submit high quality unpublished original research articles as well as review articles in broad areas relevant to theories, technologies, and emerging applications.

 

Topics:

• Cloud and edge distributed training framework and algorithm

• Security and privacy issues for edge AI

• Federated Learning framework

• Federated Learning algorithm

• Data processing algorithm for AI

• Distributed storage system, algorithm for AI

• Distributed machine learning algorithm and application

• Distributed ML on programmability, representations of parallelisms, performance optimizations, and system architectures

• Resource management and scheduling for AI

• Scaling and accelerating machine learning, deep learning, and computer vision applications.

• Big data and machine learning techniques for distributed and parallel systems

• Fault tolerance, reliability, and availability

• Datacenter, HPC, cloud, serverless, and edge/IoT computing platforms



Published Papers


  • Open Access

    ARTICLE

    Generative Multi-Modal Mutual Enhancement Video Semantic Communications

    Yuanle Chen, Haobo Wang, Chunyu Liu, Linyi Wang, Jiaxin Liu, Wei Wu
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046837
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission. With it,… More >

  • Open Access

    REVIEW

    A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

    Yuming Tang, Yitian Zhang, Tao Niu, Zhen Li, Zijian Zhang, Huaping Chen, Long Zhang
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.030084
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which… More >

  • Open Access

    ARTICLE

    Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

    Rui Wang, Yao Zhou, Guangchun Luo, Peng Chen, Dezhong Peng
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.047065
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data. Due to the challenges associated with annotating anomaly events, time series reconstruction has become a prevalent approach for unsupervised anomaly detection. However, effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series. In this paper, we propose a cross-dimension attentive feature fusion network for time series anomaly detection, referred to as CAFFN. Specifically, a series and feature mixing block is introduced to learn representations in 1D space. Additionally, a… More >

  • Open Access

    ARTICLE

    A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

    Kai Jiang, Bin Cao, Jing Fan
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046348
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Multimodal sentiment analysis utilizes multimodal data such as text, facial expressions and voice to detect people’s attitudes. With the advent of distributed data collection and annotation, we can easily obtain and share such multimodal data. However, due to professional discrepancies among annotators and lax quality control, noisy labels might be introduced. Recent research suggests that deep neural networks (DNNs) will overfit noisy labels, leading to the poor performance of the DNNs. To address this challenging problem, we present a Multimodal Robust Meta Learning framework (MRML) for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously. Specifically, we… More >

  • Open Access

    ARTICLE

    Performance Prediction Based Workload Scheduling in Co-Located Cluster

    Dongyang Ou, Yongjian Ren, Congfeng Jiang
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2043-2067, 2024, DOI:10.32604/cmes.2023.029987
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster, where the resources can be pooled in order to maximize data center resource utilization. Due to resource competition between batch jobs and online services, co-location frequently impairs the performance of online services. This study presents a quality of service (QoS) prediction-based scheduling model (QPSM) for co-located workloads. The performance prediction of QPSM consists of two parts: the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on random forest. On-line service… More >

  • Open Access

    ARTICLE

    C-CORE: Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

    Wei Zhou, Xincong Jiang, Chuan Qin
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2069-2093, 2024, DOI:10.32604/cmes.2023.043248
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Edge devices, due to their limited computational and storage resources, often require the use of compilers for program optimization. Therefore, ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI. One widely used testing method for this purpose is fuzz testing, which detects bugs by inputting random test cases into the target program. However, this process consumes significant time and resources. To improve the efficiency of compiler fuzz testing, it is common practice to utilize test case prioritization techniques. Some researchers use machine learning to predict the code coverage of test… More >

  • Open Access

    ARTICLE

    Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart Grids

    Fengyong Li, Weicheng Shen, Zhongqin Bi, Xiangjing Su
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2095-2115, 2024, DOI:10.32604/cmes.2023.044431
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract False data injection attack (FDIA) is an attack that affects the stability of grid cyber-physical system (GCPS) by evading the detecting mechanism of bad data. Existing FDIA detection methods usually employ complex neural network models to detect FDIA attacks. However, they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse, making it difficult for neural network models to obtain sufficient samples to construct a robust detection model. To address this problem, this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge, which can effectively bypass the detection model to… More >

  • Open Access

    ARTICLE

    A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

    Ahmed Barnawi, Krishan Kumar, Neeraj Kumar, Bander Alzahrani, Amal Almansour
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2117-2137, 2024, DOI:10.32604/cmes.2023.044184
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Landmines continue to pose an ongoing threat in various regions around the world, with countless buried landmines affecting numerous human lives. The detonation of these landmines results in thousands of casualties reported worldwide annually. Therefore, there is a pressing need to employ diverse landmine detection techniques for their removal. One effective approach for landmine detection is UAV (Unmanned Aerial Vehicle) based Airborne Magnetometry, which identifies magnetic anomalies in the local terrestrial magnetic field. It can generate a contour plot or heat map that visually represents the magnetic field strength. Despite the effectiveness of this approach, landmine removal remains a challenging… More >

  • Open Access

    ARTICLE

    A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting Sites

    Tianming Zhang, Zebin Chen, Haonan Guo, Bojun Ren, Quanmin Xie, Mengke Tian, Yong Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2139-2154, 2024, DOI:10.32604/cmes.2023.043822
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract The data analysis of blasting sites has always been the research goal of relevant researchers. The rise of mobile blasting robots has aroused many researchers’ interest in machine learning methods for target detection in the field of blasting. Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience, which has aroused people’s interest in how to use it in the field of machine learning. In this paper, we design a distributed machine learning training application based on the AWS Lambda platform. Based on data parallelism, the data aggregation and training synchronization… More >

  • Open Access

    REVIEW

    Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

    Weisi Chen, Walayat Hussain, Francesco Cauteruccio, Xu Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and… More >

    Graphic Abstract

    Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

  • Open Access

    ARTICLE

    CALTM: A Context-Aware Long-Term Time-Series Forecasting Model

    Canghong Jin, Jiapeng Chen, Shuyu Wu, Hao Wu, Shuoping Wang, Jing Ying
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 873-891, 2024, DOI:10.32604/cmes.2023.043230
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to… More >

  • Open Access

    ARTICLE

    Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

    Yu Li, Mingxiao Li, Dongyang Ou, Junjie Guo, Fangyuan Pan
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 893-909, 2024, DOI:10.32604/cmes.2023.031350
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract With the rapid development of mobile Internet, spatial crowdsourcing has become more and more popular. Spatial crowdsourcing consists of many different types of applications, such as spatial crowd-sensing services. In terms of spatial crowd-sensing, it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models. Besides collecting sensing data, spatial crowdsourcing also includes spatial delivery services like DiDi and Uber. Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications. Previous research conducted task assignments via traditional matching approaches or using simple network models. However, advanced mining… More >

    Graphic Abstract

    Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

  • Open Access

    ARTICLE

    Improved Convolutional Neural Network for Traffic Scene Segmentation

    Fuliang Xu, Yong Luo, Chuanlong Sun, Hong Zhao
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2691-2708, 2024, DOI:10.32604/cmes.2023.030940
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract In actual traffic scenarios, precise recognition of traffic participants, such as vehicles and pedestrians, is crucial for intelligent transportation. This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants. The algorithm incorporates long and short-term memory networks and the fused attention module (GSAM, GCT, and Spatial Attention Module) to enhance the algorithm’s capability to process both global and local information. Additionally, to increase the network’s initial operation stability, the original network activation function was replaced with Gaussian error linear unit. Experiments were conducted using the publicly available Cityscapes dataset.… More >

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