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A real-time data acquisition method of industrial production line based on OPC technology

International Journal of Internet Manufacturing and Services 2023年第1期9卷 16-31页

作者： Xu, Airong He, Panpan Meng, Kaiyuan Xue, Jijun Wang, Chao College of Mechanical Engineering Xi’an Shiyou University Xi’an710065 China Software Research Institute College of Computer Science Xi’an Shiyou University Xi’an710065 China

In view of the low timeliness and accuracy of traditional production line data acquisition methods, this study proposed a real-time data acquisition method of industrial production line based on OPC technology. Considering the heterogeneity of industrial production line data sources, a data transmission scheme is generated based on OPC technology and multi-point communication with Modbus protocol after data preprocessing by piecewise linear interpolation method. Then, the improved decision tree algorithm is used to design the data processing mode. Finally, the current state of industrial production line data is judged by comparing the threshold value, and the real-time data collection of industrial production line is completed after the collection interval is determined. Experimental results show that the acquisition accuracy of this method is up to 96.8%, the maximum acquisition time is only 12.5 s, and the highest packet loss rate is only 1.89%, indicating that this method improves the acquisition quality. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Data acquisition

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Adaptive Mitigation of Blackhole Attacks in Blockchain-Enhanced software Defined Networks

Adaptive Mitigation of Blackhole Attacks in Blockchain-Enhan...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Uludag, Mehmed K. Karakus, Murat Guler, Evrim Uludag, Suleyman University of Michigan Department of Computer Science and Engineering Ann Arbor United States Ankara University Department of Software Engineering Ankara06830 Turkey Bartin University Department of Computer Engineering Bartin74110 Turkey University of Michigan Department of Computer Science FlintMI48502 United States

ISBN: (纸本)9798350367942

software-Defined Networking (SDN) and Blockchain (BC) are transformative technologies reshaping network management and security, utilizing their synergies. SDN's centralized control enhances flexibility and efficiency but introduces vulnerabilities due to its single point of control. With its decentralized and immutable ledger, BC offers a solution by distributing control and providing a tamper-proof audit trail. This paper integrates these technologies to address blackhole attacks - a critical vulnerability where compromised SDN controllers disrupt network performance. We propose Blockchain-Enhanced SDN for Adaptive Path Finding (BeS4APF) algorithm against blackhole attacks in the multidomain SDNs. The algorithm maintains a high Packet Delivery Ratio (PDR) by dynamically adjusting network paths in response to node failures. The BeS4APF algorithm presented effectively maintains a high PDR by dynamically adjusting paths in response to node failures. The methodology involves monitoring the network, detecting compromised nodes, and recalculating optimal paths using Dijkstra's algorithm and node-disjoint path selection. Experiments with synthetic networks of varying sizes (from 60 to 120 domains) demonstrate that the algorithm successfully handles domain-compromising node attacks, maintaining high PDR even with increasing stochastic disruptions. Our preliminary results show that, while PDR slightly deviates and recovers in smaller networks, it stabilizes towards almost 100% in larger networks after initial adjustments. This work advances the integration of SDN and BC, offering a robust approach to securing modern networks against evolving stochastic attack scenarios and threats. © 2024 IEEE.

关键词： Stochastic systems

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Utilizing Evidential Reasoning (ER) Approach for software Components Selection

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SN computer science 2025年第3期6卷 1-13页

作者： Ahmad, Nabot Al-Qerem, Ahmad Omar, Firas Jebreen, Issam Department of Software Engineering Al Zaytoonah University of Jordan Amman Jordan Computer Science Department Zarqa University Zarqa Jordan Business Intelligence Department Al Zaytoonah University of Jordan Amman Jordan Software Engineering Department Middle East University Amman Lebanon

software component selection is one of the most challenging problems for software developers to meet customer needs. Despite various methods researchers propose to aid in component selection, a gap exists in addressing the complexities of decision-making under uncertainty. This study utilizes the Evidential Reasoning (ER) approach, which effectively handles multi-criteria decision-making (MCDM) scenarios to evaluate software components for an online bookstore. The methodology involves assessing quality attributes using the ER approach incorporating evaluation grades, weights, and belief degrees to manage information uncertainty. The results demonstrate that the ER approach provides a robust framework for software component selection leading to informed decisions. This approach offers significant advantages in scenarios where traditional MCDM methods are insufficient, thereby enhancing the reliability of component selection in software engineering. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making Evidential reasoning Multi-criteria software component

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Blockchain-based conditional privacy-preserving authentication scheme for vehicular ad hoc networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6273-6286页

作者： Gong, Cheng Xiong, Ling He, Xingyu Niu, Xianhua College of Computer and Software Engineering Xihua University Sichuan Chengdu610099 China National Key Laboratory of Science and Technology Communications University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Security and privacy are two challenging issues in Vehicular Ad-hoc Networks (VANETs), which have attracted widespread concern from industry and academia. However, the existing security schemes for VANETs environment have various problems, such as the unlinkability and the cross region communication,etc. To overcome these weaknesses, this paper designs a blockchain-based conditional privacy-preserving authentication scheme (BCPAS) for VANETs. The proposed scheme has the following two advantages: (1) Pseudonym ID is employed in authentication phase to ensure the real identity of vehicle is unlinkable. (2) Blockchain technology is adopted to make cross-region authentication more efficient. Compared with the most recent scheme, the computational efficiency of our scheme has improved 50% with 3 service management nodes, and 87% with 15 service management nodes in the Authentication and Member Secrets Generate Phase. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Optimal Bounds for Distinct Quartics 51

Optimal Bounds for Distinct Quartics

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Charalampopoulos, Panagiotis Gawrychowski, Pawel Ghazawi, Samah School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom Institute of Computer Science University of Wroclaw Poland Department of Computer Science University of Haifa Israel Department of Software Engineering Braude College of Engineering Karmiel Israel

ISBN: (纸本)9783959773225

A fundamental concept related to strings is that of repetitions. It has been extensively studied in many versions, from both purely combinatorial and algorithmic angles. One of the most basic questions is how many distinct squares, i.e., distinct strings of the form UU, a string of length n can contain as fragments. It turns out that this is always O(n), and the bound cannot be improved to sublinear in n [Fraenkel and Simpson, JCTA 1998]. Several similar questions about repetitions in strings have been considered, and by now we seem to have a good understanding of their repetitive structure. For higher-dimensional strings, the basic concept of periodicity has been successfully extended and applied to design efficient algorithms – it is inherently more complex than for regular strings. Extending the notion of repetitions and understanding the repetitive structure of higher-dimensional strings is however far from complete. Quartics were introduced by Apostolico and Brimkov [TCS 2000] as analogues of squares in two dimensions. Charalampopoulos, Radoszewski, Rytter, Waleń, and Zuba [ESA 2020] proved that the number of distinct quartics in an n × n 2D string is O(n2 log2 n) and that they can be computed in O(n2 log2 n) time. Gawrychowski, Ghazawi, and Landau [SPIRE 2021] constructed an infinite family of n × n 2D strings with Ω(n2 log n) distinct quartics. This brings the challenge of determining asymptotically tight bounds. Here, we settle both the combinatorial and the algorithmic aspects of this question: the number of distinct quartics in an n × n 2D string is O(n2 log n) and they can be computed in the worst-case optimal O(n2 log n) time. As expected, our solution heavily exploits the periodic structure implied by occurrences of quartics. However, the two-dimensional nature of the problem introduces some technical challenges. Somewhat surprisingly, we overcome the final challenge for the combinatorial bound using a result of Marcus and Tardos [JCTA 2004] for permuta

关键词： Optimization

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TOST: School Bus Monitoring and Management System for Schools and Parents

TOST: School Bus Monitoring and Management System for School...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Thakur, Fahad Aqeel Hafiz, Muhammad Alani, Maath Saadoon, Muntadher Kian Chiew, Chiew Thiam University of Malaya Faculty of Computer Science & Information Technology Kuala Lumpur Malaysia Hillool Software Engineering Department Queensland Australia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia

ISBN: (纸本)9798350302684

School bus contributes positively to reducing the number of cars on the road, thereby reducing the environmental impact of cars. It also dramatically relieves working parents, who do not have to pick up and drop off their children separately in their private vehicles. However, the irregularity of the bus arrival times often leads to an extended waiting time for students at the bus stops, potentially compromising the students' safety. The communication barrier between schools, parents and bus drivers is also a significant challenge, mainly when a delay occurs during the transit of the students. This paper presents TOST (to-school-together), a school bus monitoring and management system for schools and parents. The school administrators, parents, and bus drivers are connected in one comprehensive school bus system. The proposed system provides realtime updates on the bus status, allowing peace of mind and easing the burden of parents, school administrators, and bus drivers. The design of TOST is based on feedback from the system stakeholders collected via a survey. We draw a complete understanding of the existing issues by analyzing the feedback. Finally, we have implemented the system using state-of-the-art technologies to address the identified issues and fulfil the system requirements. © 2023 IEEE.

关键词： Buses

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A Transformer-Based Language Model for Sentiment Classification and Cross-Linguistic Generalization: Empowering Low-Resource African Languages

A Transformer-Based Language Model for Sentiment Classificat...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Raychawdhary, Nilanjana Hughes, Nathaniel Bhattacharya, Sutanu Dozier, Gerry Seals, Cheryl D. Auburn University Computer Science and Software Engineering AuburnAL United States Auburn University at Montgomery Computer Science and Computer Information Systems MontgomeryAL United States

ISBN: (纸本)9798350322347

Sentiment analysis is a widely studied area of natural language processing (NLP), however, a lot of attention has been given to languages with higher resources, like English. By concentrating on low-resource African languages including Hausa, Nigerian Pidgin, Igbo, Yoruba, Swahili, and Kinyarwanda for which there is a lack of curated datasets for sophisticated AI applications. This research intends to close the gap by examining the viability of current NLP strategies and creating new ones to meet this challenge. Sentiment analysis enables us to comprehend the feelings and viewpoints expressed in textual information. A transformer-based language model is presented in this research for low-resource African languages in order to classify sentiment and generalize across languages. We fine-tune the XLM-RoBERTa model on Hausa, Nigerian Pidgin, Igbo, and Yoruba, and then assess its performance in these languages as well as Swahili and Kinyarwanda, which the model had not previously seen. Our findings show that, even in languages with few linguistic resources, the model is effective at performing sentiment analysis tasks for known and unknown languages. This research helps close the digital language gap by extending natural language processing capabilities to under-resourced African languages. © 2023 IEEE.

关键词： Sentiment analysis

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SSA: a novel method for Single-cell and Spatial transcriptomics Alignment 16th

SSA: a novel method for Single-cell and Spatial transcriptom...

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16th International Conference on Bioinformatics and Computational Biology, BICOB 2024

作者： Tran, Bang Tran, Dao Nguyen, Tin Department of Engineering & Computer Science California State University 6000 J St SacramentoCA95819 United States Department of Computer Science and Software Engineering 345 W Magnolia Ave AuburnAL36849 United States

Single-cell RNA sequencing (scRNA-seq) provides expression profiles of individual cells but fails to preserve crucial spatial information. On the other hand, Spatial Transcriptomics technologies are able to analyze specific regions within tissue sections, but lack of the capability to examine in single-cell resolution. To overcome these issues, we present Single-cell and Spatial transcriptomics Alignment (SSA), a novel technique that employs an optimal transport algorithm to assign individual cells from a scRNA-seq atlas to their spatial locations in actual tissue based on their expression profiles. SSA has demonstrated superior performance compared to existing methods SpaOTsc, Tangram, Seurat and DistMap using 10 semi-simulated datasets generated from a high-resolution spatial transcriptomics human breast cancer dataset with 100,064 cells. This advancement provides a refined tool for researchers to delve deeper in understanding of the relationship between cellular spatial organization and gene expression. © 2024, EasyChair. All rights reserved.

关键词： Tissue

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