Article
Computer Science, Artificial Intelligence
Ayushu Zhang, Gang Li, Xiao-Ping Zhang, You He
Summary: In this paper, we propose a new TrMTT model based on deep learning to solve the state estimation problem of strong maneuvering targets. The TrMTT model uses a new residual mapping between the observation trajectory and the real trajectory to estimate the target states, and extracts and fuses features through encoder and decoder branches, providing more correlation information for learning the transition law of the target states.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Ao Li, Cong Feng, Yuan Cheng, Yingtao Zhang, Hailu Yang
Summary: This paper proposes a new method for incomplete multiview subspace clustering. By using multiple kernel completion, low-redundant representation learning, and weighted tensor low-rank constraint, intact and compact subspaces can be obtained. Instead of the traditional pairwise subspace fusion, the proposed method fuses multiview subspaces with a weighted tensor low-rank constraint, which explores higher-order relationships among views and assigns appropriate weights to each view. Extensive experiments demonstrate the effectiveness of the proposed method.
INFORMATION FUSION
(2024)
Article
Computer Science, Theory & Methods
Carlo Mazzocca, Nicola Romandini, Rebecca Montanari, Paolo Bellavista
Summary: With the proliferation of IoT devices, edge cloud computing has become a promising paradigm to bring cloud capabilities closer to data sources. Federated Learning (FL) is emerging as a distributed ML approach that enables models to be trained on remote devices using their local data. However, traditional FL solutions still face challenges, and researchers are starting to propose leveraging blockchain technologies to address them.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Artificial Intelligence
Juncai Guo, Jin Liu, Xiao Liu, Li Li
Summary: Code summarization is crucial for software maintenance, aiming to generate concise natural-language descriptions summarizing the functionality of source code automatically. This paper proposes HetCoS to extract the syntactic and sequential features of source code by exploring its inherent heterogeneity for code summarization. Experimental results demonstrate the superiority of our approach over sixteen state-of-the-art baselines.
INFORMATION FUSION
(2024)
Article
Computer Science, Theory & Methods
Shuai Liu, Tao Ju
Summary: This paper proposes an asynchronous parallel optimization method APapo to address challenges in parallel optimization of large-scale DNN models. The method achieves fine-grained task segmentation, maximizes computing resource utilization, and improves training speed while maintaining accuracy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Artificial Intelligence
Baiyang Chen, Yongxiang Li, Dezhong Peng, Hongmei Chen, Zhong Yuan
Summary: This paper proposes a novel information fusion model based on multi-scale fuzzy granules and fuzzy rough set theory, along with an unsupervised outlier detection algorithm. Experimental results demonstrate that the proposed method performs comparably or better than leading outlier detection methods.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Sabina Umirzakova, Shabir Ahmad, Latif U. Khan, Taegkeun Whangbo
Summary: The integration of deep learning models and the IoT is revolutionizing healthcare and improving patient care. However, the utilization of low-resolution images generated by IoT devices introduces biases in deep learning models, impacting clinical decision-making. This survey highlights the need for accurate image restoration in medical imaging and emphasizes the role of developing precise super-resolution methods to enhance the quality of medical images and improve the performance of deep learning models in healthcare applications.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Changzhong Wang, Yan Wang, Tingquan Deng, Weiping Ding
Summary: This paper proposes a missing multi-label learning model based on the fusion of two-level nonlinear mappings, which can better depict the true distribution of multi-label data and has good robustness.
INFORMATION FUSION
(2024)
Article
Computer Science, Theory & Methods
Xinkun Tao, Pengfei Sun, Zhuang Xiao, Chengcheng Fu, Xiaoyun Feng, Qingyuan Wang
Summary: This paper focuses on the energy optimization problem of traction substations and addresses the difficulty of considering time-varying parameters and environmental characteristics of freight train in mechanism modeling. A new optimization method called Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches is proposed, which uses a data-driven model to solve for the train's speed curve, traction substation power, and contact network voltage. Experimental analysis validates the high accuracy of the proposed method in reducing energy consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Theory & Methods
Arya Tanmay Gupta, Sandeep S. Kulkarni
Summary: Lattice-linear systems allow nodes to execute asynchronously. The eventually lattice-linear algorithms introduced in this study guarantee system transitions to optimal states within specified moves, leading to improved performance compared to existing literature. Experimental results further support the benefits of lattice-linearity.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(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)
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
M. J. Uray
Summary: This paper analyzes the computational costs of various operations and algorithms in algebraic number fields using exact arithmetic. It provides running time and output size calculations for operations in the number field, and extends the algorithms to handle operations in a specific type of number field. The paper also gives a polynomial upper bound on the running time when computations are performed exactly.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Theory & Methods
Francesca Cioffi, Davide Franco, Carmine Sessa
Summary: This article demonstrates how to obtain further information on the direct summands of the derived pushforward by applying the decomposition theorem to a suitable resolution of singularities. It also includes Poincaré polynomial expressions and an algorithm for computing the unknown terms in these expressions.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(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
Martijn de Vos, Georgy Ishmaev, Johan Pouwelse
Summary: DESCAN is a decentralized and censorship-resistant indexing and search engine for Web3 that allows storage and retrieval of Web3 transactions. It utilizes custom rules for indexing transactions and stores the generated triplets in a distributed transaction graph, enabling decentralized search. Four modifications are proposed to improve the system's robustness by enhancing the Skip Graph data structure. Experimental results demonstrate that DESCAN can tolerate adversarial and unresponsive peers without disruption. Searches in DESCAN are completed quickly, even as the network grows, and storage and network costs are evenly distributed among peers.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Artificial Intelligence
Juan Baz, Mikel Ferrero-Jaurrieta, Irene Diaz, Susana Montes, Gleb Beliakov, Humberto Bustince
Summary: This paper studies the aggregation of multiple predictors in time series forecasting and introduces a new pre-aggregation extension operator. By examining the behavior and performance of the operator from a probabilistic perspective, its effectiveness is validated in practical examples.
INFORMATION FUSION
(2024)
