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arXiv 2023年

作者： Deng, Yiqin Zhang, Haixia Chen, Xianhao Fang, Yuguang The Shandong Key Laboratory of Wireless Communication Technologies Shandong Jinan250061 China The School of Control Science and Engineering Shandong University Shandong Jinan250061 China The Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong The Department of Computer Science City University of Hong Kong Hong Kong

Multi-access edge computing (MEC) is a promising technology to enhance the quality of service, particularly for low-latency services, by enabling computing offloading to edge servers (ESs) in close proximity. To avoid network congestion, collaborative edge computing has become an emerging paradigm to enable different ESs to collaboratively share their data and computation resources. However, most papers in collaborative edge computing only allow one-hop offloading, which may limit computing resource sharing due to either poor channel conditions or computing workload at ESs one-hop away. By allowing ESs multi-hop away to also share the computing workload, a multihop MEC enables more ESs to share their computing resources. Inspired by this observation, in this paper, we propose to leverage omnipresent vehicles in a city to form a data transportation network for task delivery in a multi-hop fashion. Here, we propose a general multi-hop task offloading framework for vehicle-assisted MEC where tasks from users can be offloaded to powerful ESs via potentially multi-hop transmissions. Under the proposed framework, we develop a reinforcement learning based task offloading approach to address the curse of dimensionality problem due to vehicular mobility and channel variability, with the goal to maximize the aggregated service throughput under constraints on end-to-end latency, spectrum, and computing resources. Numerical results demonstrate that the proposed algorithm achieves excellent performance with low complexity and outperforms existing benchmark schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Large-Scale Data Generation Using SWLBM on the Sunway TaihuLight Supercomputer and Subsequent Data Mining with Physics-Informed Neural Networks 12th

Large-Scale Data Generation Using SWLBM on the Sunway TaihuL...

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12th CCF Conference on BigData, BigData 2024

作者： Chu, Xuesen Guo, Wei Wu, Tianqi Cai, Shengze Yang, Guangwen China Ship Scientific Research Center Wuxi214082 China Taihu Lake Laboratory of Deep Sea Technology and Science Wuxi214082 China Department of Computer Science and Technology Tsinghua University Beijing100084 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China National Supercomputing Center in Wuxi Wuxi214072 China

ISBN: (纸本)9789819610235

Generating large-scale datasets on supercomputers is a critical component of modern research, enabling sophisticated analysis and mining applications. In the context of SUBOFF model studies for advanced submarine development, we emphasize the significance of supercomputer-generated big data and its subsequent application. Utilizing the Sunway TaihuLight supercomputer and the SWLBM software, we employ the lattice Boltzmann method (LBM) to generate a comprehensive dataset of flows over a SUBOFF model. This dataset is then used to train a physics-informed neural network (PINN) model, which aims to reconstruct flow fields from sparse velocity measurements. The PINN is designed to perform super-resolution velocity reconstruction and concurrent pressure field inference. Our results show that the reconstructed flow fields, including pressure, are in good agreement with the full-resolution LBM references, highlighting the promise of this approach for complex flow motion reconstruction. This research demonstrates the potential of supercomputer-generated big data in enhancing simulations and experiments in the study of SUBOFF and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computational fluid dynamics

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Overview of bioinformatic tools to study viral infections

Overview of bioinformatic tools to study viral infections

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Verdiana Zulian Anna Rosa Garbuglia Giulia Fiscon Paola Paci Virology Laboratory National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Institute for Systems Analysis and Computer Science "Antonio Ruberti" National Research Council Rome Italy

microRNAs play an important role in post-transcriptional gene regulation. Recently, viral microRNAs have been discovered in several viruses, including Hepatitis B virus. This brief work explores bioinformatics tools for viral/host miRNA research and provides insights into the roles of miRNAs in HBV infection, offering an overview of this field, in order to facilitate the selection of the most suitable bioinformatics tools according to individual needs and research goals.

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Switching Multiplicative Watermark Design Against Covert Attacks

arXiv

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arXiv 2025年

作者： Gallo, Alexander J. Anand, Sribalaji C. Teixeira, Andre M.H. Ferrari, Riccardo M.G. Department of Electronics Information and Bioengineering Politecnico di Milano Milano Italy School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden Department of Information Technology Uppsala University PO Box 337 UppsalaSE-75105 Sweden Delft Center for Systems and Control Mechanical Engineering TU Delft Delft Netherlands

Active techniques have been introduced to give better detectability performance for cyber-attack diagnosis in cyber-physical systems (CPS). In this paper, switching multiplicative watermarking is considered, whereby we propose an optimal design strategy to define switching filter parameters. Optimality is evaluated exploiting the so-called output-to-output gain of the closed loop system, including some supposed attack dynamics. A worst-case scenario of a matched covert attack is assumed, presuming that an attacker with full knowledge of the closed-loop system injects a stealthy attack of bounded energy. Our algorithm, given watermark filter parameters at some time instant, provides optimal next-step parameters. Analysis of the algorithm is given, demonstrating its features, and demonstrating that through initialization of certain parameters outside of the algorithm, the parameters of the multiplicative watermarking can be randomized. Simulation shows how, by adopting our method for parameter design, the attacker’s impact on performance diminishes. © 2025, CC BY-NC-ND.

关键词： Networked control systems

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DOMAIN RANDOMIZATION VIA ENTROPY MAXIMIZATION

arXiv

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arXiv 2023年

作者： Tiboni, Gabriele Klink, Pascal Peters, Jan Tommasi, Tatiana D’Eramo, Carlo Chalvatzaki, Georgia Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Technische Universität Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Center for Mind Brain and Behavior Uni. Marburg and JLU Giessen Germany

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hinges on the choice of the sampling distribution of the dynamics parameters, since high variability is crucial to regularize the agent’s behavior but notoriously leads to overly conservative policies when randomizing excessively. In this paper, we propose a novel approach to address sim-to-real transfer, which automatically shapes dynamics distributions during training in simulation without requiring real-world data. We introduce DOmain RAndomization via Entropy MaximizatiON (DORAEMON), a constrained optimization problem that directly maximizes the entropy of the training distribution while retaining generalization capabilities. In achieving this, DORAEMON gradually increases the diversity of sampled dynamics parameters as long as the probability of success of the current policy is sufficiently high. We empirically validate the consistent benefits of DORAEMON in obtaining highly adaptive and generalizable policies, i.e. solving the task at hand across the widest range of dynamics parameters, as opposed to representative baselines from the DR literature. Notably, we also demonstrate the Sim2Real applicability of DORAEMON through its successful zero-shot transfer in a robotic manipulation setup under unknown real-world parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Deep Learning Models for Intelligent Healthcare: Implementation and Challenges 7th

Deep Learning Models for Intelligent Healthcare: Implementat...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Rehman, Sadaqat ur Tu, Shanshan Shah, Zubair Ahmad, Jawad Waqas, Muhammad Rehman, Obaid ur Kouba, Anis Abbasi, Qammer H. Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Namal Institute Mianwali42250 Pakistan Department of Computer Science and Engineering Hammad Bin Khalifa University Doha Qatar School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering Sarhad University of Science and IT Peshawar25000 Pakistan Department of Computer Science Prince Sultan University Riyadh Saudi Arabia James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783030786083

The rapid developments of artificial intelligent (AI) is being transformed for its extensive use-cases, people-centered intelligent systems focusing on care delivery, research encounter complex problems related to improve the overall infrastructure and management of intelligent delivery service;for instance, bringing transformation in healthcare sector for monitoring patients with chronic disease. Most of these systems are driven by state-of-the-art learning algorithms i.e., Convolution Neural Network. The CNN algorithm is considered to be one of the most prominent architectures of DL. Recently, due to enormous growth in the amount of annotated data and the development of CNN hardware accelerator, further, boost the research on CNN and accomplished benchmark enactment on different applications. This paper presents cutting-edge applications of CNN for an intelligent healthcare system. We provide useful findings of different CNN features such as optimization, fast computation, design, activation function, and loss function. To our knowledge, this is the first comprehensive work to address the recent trends in the architecture of CNN, which offers insight to the underlying problems and provides the potential solutions for any given intelligent healthcare applications. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiang Yang Shuxin Zhang Suyu Dong Long Xu Weihe Dong Xiaokun Li Pengzhong Sun Feng Jiang Xianyu Zhang Gongning Luo College of Computer and Control Engineering Northeast Forestry University Harbin China School of Medicine and Health Harbin Institute of Technology Harbin China School of Computer Science and Technology Heilongjiang University Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Breast Surgery Harbin Medical University Cancer Hospital Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level.

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An Exact Algorithm for Finite Metric Space Embedding into a Euclidean Space When the Dimension of the Space Is Not Known 21st

An Exact Algorithm for Finite Metric Space Embedding into a ...

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21st International Conference on Computational science, ICCS 2021

作者： Skubalska-Rafajlowicz, Ewa Rafajlowicz, Wojciech Department of Computer Engineering Wroclaw University of Science and Technology Wyb. Wyspianskiego 27 Wroclaw50 370 Poland Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wyb. Wyspianskiego 27 Wroclaw50 370 Poland

ISBN: (纸本)9783030779603

We present a O(n3) algorithm for solving the Distance Geometry Problem for a complete graph (a simple undirected graph in which every pair of distinct vertices is connected by a unique edge) consisting of n+ 1 vertices and non-negatively weighted edges. It is known that when the solution of the problem exists, the dimension of the Euclidean embedding is at most n. The algorithm provides the smallest possible dimension of the Euclidean space for which the exact embedding of the graph exists. Alternatively, when the distance matrix under consideration is non-Euclidean, the algorithm determines a subset of graph vertices whose mutual distances form the Euclidean matrix. The proposed algorithm is an exact algorithm. If the distance matrix is a Euclidean matrix, the algorithm provides a geometrically unambiguous solution for the location of the graph vertices. The presented embedding method was illustrated using examples of the metric traveling salesman problem that allowed us in some cases to obtain high dimensional partial immersions. © 2021, Springer Nature Switzerland AG.

关键词： Undirected graphs

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Unified Temporal Erasable Itemset Mining with a Lower-Bound Strategy

Unified Temporal Erasable Itemset Mining with a Lower-Bound ...

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IEEE International Conference on Big Data

作者： Tzung-Pei Hong Jia-Xiang Li Yu-Chuan Tsai Wei-Ming Huang Department of Computer Science and Information Engineering National University of Kaohsiung Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan Department of Electrical and Control China Steel Inc Kaohsiung Taiwan

ISBN: (纸本)9781665480468

Erasable itemset mining has been a valuable mining problem for manufacturers. It can extract less profitable materials from a product dataset and provide managers with good decision-making and a trade-off between cost and profit. However, the traditional erasable itemset mining methods seldom consider the time factor. For time-sensitive industries such as agro-processors, the time range is important in determining which materials are less profitable. Hong et al. first proposed the concept of temporal erasable itemset mining and seven lifespan options. They also proposed a unified temporal erasable (UTE) mining algorithm for getting incomplete temporal erasable itemsets. Howerever, the UTE algorithm does not satisfy the property of downward closure. In this work, we propose an improved algorithm to improve the performance of the UTE algorithm based on a lower-bound strategy and satisfying the property of downward closure. The proposed algorithm uses a hash table to store information that will be reused during the mining process to avoid scanning a dataset multiple times. The designed lower-bound strategy can preserve the downward closure property, narrowing the search space of candidate itemsets. In numerical experiments, we compare the performance using several metrics between the proposed method and the previous work. From the results of experiments, our proposed method outperforms the existing method on various metrics, such as execution time and the number of candidate erasable itemsets.

关键词： Measurement Industries Costs Itemsets Databases Decision making Big Data

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Incremental Fuzzy Utility Mining with Tree Structure

Incremental Fuzzy Utility Mining with Tree Structure

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IEEE International Conference on Big Data

作者： Tzung-Pei Hong Wei-Teng Hung Wei-Ming Huang Yu-Chuan Tsai Department of Computer Science and Information Engineering National University of Kaohsiung Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Electrical and Control China Steel Inc Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9781665480468

High-utility-itemset mining, extended from frequent-itemset mining, considers external utilities of items in quantitative databases to obtain the itemsets with high utility. To easily understand those patterns with high utility values, fuzzy utility mining adopts fuzzy sets to increase the readability of the derived utility itemsets. However, real-world databases are usually dynamic. New transactions may be intermittently added, and the corresponding mined patterns must be updated to keep correct knowledge. In this paper, we propose an incremental method based on our previously proposed batch-processing fuzzy utility tree-based mining algorithm. The proposed approach adopts the fast-update (FUP) strategy. It considers newly coming data to readjust the head table and the tree structure, which generate desired itemsets in two phases. The experimental results reveal that the proposed algorithm outperforms the tree-based batch mining method.

关键词： Fuzzy sets Databases Itemsets Heuristic algorithms Big Data Data mining

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