Article
Computer Science, Artificial Intelligence
Hui Zhou, Maoguo Gong, Shanfeng Wang, Yuan Gao, Zhongying Zhao
Summary: Graph contrastive learning (GCL) aims to generate supervision information by transforming graph data itself, and it has become a focus of graph research recently. However, most GCL methods are unsupervised and struggle with balancing multi-view graph information. To address this, we propose a semi-supervised multi-view graph contrastive learning (SMGCL) framework for graph classification. The framework captures comparative relations between label-independent and label-dependent node pairs across different views and incorporates a label augmentation module and a shared decoder module to enhance discriminative representations and extract underlying relationships between representations and graph topology. Experimental results demonstrate the superiority of our proposed framework for graph classification tasks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yu Xie, Shengze Lv, Yuhua Qian, Chao Wen, Jiye Liang
Summary: Researchers propose a novel active and semi-supervised graph neural network framework, which can effectively perform graph classification tasks using a small number of labeled and unlabeled graph examples, and achieves competitive performance on benchmark graph datasets.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Artificial Intelligence
Guangfeng Lin, Xiaobing Kang, Kaiyang Liao, Fan Zhao, Yajun Chen
Summary: Graph learning dynamically captures data distribution structure based on graph convolutional networks. The quality of learning the graph structure directly impacts semi-supervised classification using GCN. Existing methods combine computational layers and losses into GCN to explore global and local graphs, which have different roles in semi-supervised classification.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Chenglong Zhang, Bingbing Jiang, Zidong Wang, Jie Yang, Yangfeng Lu, Xingyu Wu, Weiguo Sheng
Summary: In this paper, an efficient multi-view feature selection method (EMSFS) is proposed to address the issues in multi-view semi-supervised feature selection. EMSFS combines graph learning, label propagation, and multi-view feature selection within a unified framework. The method can adaptively learn a graph and exploit the similarity structure to enhance the reliability of the graph. It also achieves high computational efficiency.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Wei Ju, Xiao Luo, Zeyu Ma, Junwei Yang, Minghua Deng, Ming Zhang
Summary: This paper proposes a Graph Harmonic Neural Network (GHNN) that combines the advantages of graph convolutional networks and graph kernels to fully utilize unlabeled data, overcoming the scarcity of labeled data in semi-supervised scenarios.
Article
Computer Science, Information Systems
Xiao Shen, Haofeng Zhang, Lunbo Li, Wankou Yang, Li Liu
Summary: This study proposes a novel multi-view graph cross-modal hashing (MGCH) method for generating hash codes in a semi-supervised manner. Unlike conventional graph-based hashing methods, MGCH only employs multi-view graphs as learning assistance and demonstrates superiority in cross-modal hashing tasks.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Bo Jiang, Si Chen, Beibei Wang, Bin Luo
Summary: The problem of multiple graph learning involves learning consistent representation by exploiting the complementary information of multiple graphs. This paper proposes a novel learning framework, called Multiple Graph Learning Neural Networks (MGLNN), which aims to learn an optimal graph structure from multiple graph structures and integrate multiple graph learning and Graph Neural Networks' representation. Experimental results demonstrate that MGLNN outperforms other methods on semi-supervised classification tasks.
Article
Computer Science, Artificial Intelligence
Aiping Huang, Zheng Wang, Yannan Zheng, Tiesong Zhao, Chia-Wen Lin
Summary: This study proposes an embedding regularizer learning scheme for multi-view semi-supervised classification. By integrating diversity, sparsity, and consensus, the framework effectively handles limited labeled multi-view data and demonstrates its effectiveness and superiority through extensive experimental results.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Najmeh Ziraki, Fadi Dornaika, Alireza Bosaghzadeh
Summary: This article introduces a Multiple-View Consistent Graph construction and Label propagation algorithm that simultaneously constructs a consistent graph based on several descriptors and performs label propagation over unlabeled samples. Experimental results show that the proposed method outperforms other methods on face and handwritten digit databases.
Article
Computer Science, Artificial Intelligence
N. Ziraki, A. Bosaghzadeh, F. Dornaika, Z. Ibrahim, N. Barrena
Summary: Graphs play a crucial role in the performance of graph-based semi-supervised learning methods, and their construction should be carefully considered. This letter focuses on graph-based semi-supervised learning with multiple views for the data, addressing the missing concept of data smoothness in graph construction. By merging data smoothness and label smoothness, and performing label fitness and projection matrix calculation, this approach effectively improves the efficiency and performance of semi-supervised classification compared to single feature methods and other fusion algorithms. Experimental results with image databases demonstrate the superiority of this approach.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Ruigang Zheng, Weifu Chen, Guocan Feng
Summary: Inspections on current graph neural networks suggest re-evaluating the computational aspect of final aggregation to focus on intra-class relations and produce smoother predictions. By incorporating metric learning and entropy losses, the proposed algorithm effectively reduces inter-class edge weights and improves classification accuracy.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Bin Zhang, Qianyao Qiang, Fei Wang, Feiping Nie
Summary: This study combines an anchor-based approach with multi-view semi-supervised learning to propose a new method called fast multi-view SSL (FMSSL). By learning a graph model, FMSSL addresses the challenges in multi-view SSL, improving performance while reducing computational complexity. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Fei Wu, Xiao-Yuan Jing, Pengfei Wei, Chao Lan, Yimu Ji, Guo-Ping Jiang, Qinghua Huang
Summary: Semi-supervised multi-view learning (SML) is a hot research topic that has gained attention in recent years, particularly in the domain of webpage classification. This paper proposes a novel approach called semi-supervised multi-view graph convolutional networks (SMGCN) for improving the performance of SML. The approach learns optimal graph structures and fuses multi-view representations to achieve state-of-the-art classification performance in webpage classification.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Zijia Zhang, Yaoming Cai, Wenyin Gong
Summary: This paper presents a novel semi-supervised learning framework, Graph Convolutional Extreme Learning Machines (GCELM), for handling graph data in non-Euclidean domains. The proposed methods achieve significantly better results than previous methods on 36 benchmark datasets, thanks to the use of random graph convolution and a voting ensemble strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Hao Li, Yongli Wang, Yanchao Li, Gang Xiao, Peng Hu, Ruxin Zhao, Bo Li
Summary: Batch mode active learning (BMAL) aims to train reliable learning models by efficiently requesting ground truth annotations for beneficial unlabeled points. However, current BMAL methods may have suboptimal batch acquisition due to fixed weights for sampling criteria. This work proposes an Adaptive Criteria Weights batch selection algorithm (ACW) to dynamically adjust the importance of criteria for semi-supervised learning, demonstrating superiority over existing BMAL approaches.
INFORMATION SCIENCES
(2021)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)