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Promoting sustainable logistics in the electronics industry: Circular intuitionistic fuzzy framework for evaluating smart robotics technologies

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Expert systems with Applications 2025年 287卷

作者： Athar Farid, Hafiz Muhammad Dabic-Miletic, Svetlana Jameel, Toqeer Simic, Vladimir Riaz, Muhammad Pamucar, Dragan Department of Computer Science University of Huddersfield Huddersfield United Kingdom University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade11010 Serbia Department of Mathematics SIMATS Engineering Saveetha Institute of Mechanical and Technical Sciences Tamilnadu Chennai India Department of Mathematics University of the Punjab Lahore54590 Pakistan Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department Sarıyer İstanbul34396 Turkey

The integration of robotic smart technologies into manufacturing is essential for enhancing efficiency, reducing errors, and improving overall production quality. However, decision-making in this domain is often challenged by uncertainty and imprecise information. This study utilises circular intuitionistic fuzzy sets (CIFS) to tackle the challenge of ambiguity in expert evaluations. CIFS can capture both the membership and non-membership degrees of an evaluated element, while ensuring that these values remain within a circular boundary. The evaluation process initiates with the circular intuitionistic fuzzy (CIF) logarithmic percentage change-driven objective weighting (LOPCOW) method, which utilises logarithmic transforms to performance scores to determine the significance of each criterion. The framework proposes the CIF ranking comparison (RANCOM) method to ascertain subjective criterion weights. The CIF measurement of alternatives and ranking according to compromise solution (MARCOS) method is then suggested to rank the robotic smart technology alternatives based on multiple performance criteria. A case study is conducted to demonstrate the effectiveness of the proposed framework. The case study demonstrates that robotic smart technologies play a pivotal role in enhancing sustainability and efficiency in the electronics industry, but also present challenges such as high costs as well as potential ecology and social concerns. Sensitivity analysis and comparative evaluations confirm the stability and efficiency of the model, reinforcing its applicability in intelligent manufacturing. The findings help managers choose the best sustainable robotic alternative based on smart technology engagement. Fundamental guidelines for determining the most acceptable alternative for smart production and logistics activities are provided. © 2025 Elsevier Ltd

关键词： Industrial electronics

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Relational Message Passing for Fully Inductive Knowledge Graph Completion

Relational Message Passing for Fully Inductive Knowledge Gra...

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International Conference on Data Engineering

作者： Yuxia Geng Jiaoyan Chen Jeff Z. Pan Mingyang Chen Song Jiang Wen Zhang Huajun Chen College of Computer Science and Technology Zhejiang University Hangzhou China Donghai Laboratory Zhoushan China Department of Computer Science The University of Manchester Manchester United Kingdom School of Informatics The University of Edinburgh Edinburgh United Kingdom NAIE PDU Huawei Technologies Co. Ltd. Xi’an China AZFT Joint Lab for Knowledge Engine Zhejiang University Hangzhou China

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but they (i) usually can only predict triples involving unseen entities alone, failing to address more realistic fully inductive situations with both unseen entities and unseen relations, and (ii) often conduct message passing over the entities with the relation patterns not fully utilized. In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. It passes messages directly between relations to make full use of the relation patterns for subgraph reasoning with new techniques on graph transformation, graph pruning, relation-aware neighborhood attention, addressing empty subgraphs, etc., and can utilize the relation semantics defined in the KG’s ontological schema. Extensive evaluation on multiple benchmarks has shown the effectiveness of RMPI’s techniques and its better performance compared with the existing methods that support fully inductive KGC. RMPI is also comparable to the state-of-the-art partially inductive KGC methods with very promising results achieved. Our codes, data and some supplementary experiment results are available at https://***/zjukg/RMPI.

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+TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease

arXiv

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arXiv 2024年

作者： Li, Zuotian Liu, Xiang Tang, Ziyang Zhang, Pengyue Jin, Nanxin Eadon, Michael T. Song, Qianqian Chen, Yingjie Su, Jing Department of Biostatistics and Health Data Science Indiana University School of Medicine Indiana United States Department of Computer Graphics Technology Purdue University Indiana United States Department of Computer and Information Technology Purdue University Indiana United States Department of Medicine Indiana University School of Medicine Indiana United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Gerontology and Geriatric Medicine Wake Forest School of Medicine North Carolina United States

Objective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMR) for personalized precision management of chronic disease progression. Methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DEPOT is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results: The TrajVis clinical information system is composed of four panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases. © 2024, CC BY-NC-ND.

关键词： Diseases

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From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation systems: Survey, Taxonomy and Performance Evaluation

arXiv

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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Intelligent Distributed Computing and Systems Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi United Arab Emirates

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation *** Codes I.2, I.2.7, I.4, I.4.9 © 2024, CC BY.

关键词： Neural machine translation

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The Role of Internet of Things (IoT) to Fight against Covid-19

The Role of Internet of Things (IoT) to Fight against Covid-...

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Proceedings of the International Conference on Data science, Machine Learning and Artificial Intelligence

作者： Md Alimul Haque Deepa Sonal Shameemul Haque Kailash Kumar Moidur Rahman Department of Computer Science Veer Kunwar Singh University Ara Bihar India Department of Computer Science Patna Women's College Patna Bihar India Al - Hafeez College Ara Bihar India College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia College of Computing and Information Technology Jazan University Jizan Kingdom of Saudi Arabia

ISBN: (纸本)9781450387637

The Internet of Things (IoT) has become a new topic of research in various scientific and corporate fields, particularly in healthcare, in recent decades. The IoT revolution transforms new healthcare services by the inclusion of scientific, political and environmental prospects. It progresses from traditional to specialized healthcare systems, making it easier to identify, treat and manage patients. COVID - 19 has developed into a pandemic that spread around the globe. Day and night, scientists and engineers are working to produce a vaccine, develop additional research equipment and improve surveillance. Using contactless equipment is one of the critical factors to reduce the spread. To prevent the transmission of the coronavirus IoT can be used. That is an interconnection between the Internet and electronic devices. Devices are sensuous and recordable as well as can watch and respond. In this article, we discussed Covid-19 literature, monitoring techniques and proposed an IoT model to help mitigate the transmission of Covid-19.

关键词： COVID-19 Healthcare Internet of Things (IoT) Monitoring

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年第6期14卷 1733-1737页

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis

arXiv

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arXiv 2023年

作者： Dong, Yanjie Wang, Luya Chi, Yuanfang Wang, Jia Zhang, Haijun Yu, Fei Richard Leung, Victor C.M. Hu, Xiping The Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China The College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China The Department of Systems and Computer Engineering Carleton University Ottawa Canada The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks. © 2023, CC BY.

关键词： Momentum

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Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems

arXiv

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arXiv 2023年

作者： Ji, Xiang Wang, Huazheng Chen, Minshuo Zhao, Tuo Wang, Mengdi Department of Electrical and Computer Engineering School of Engineering and Applied Science Princeton University PrincetonNJ08544 United States School of Electrical Engineering and Computer Science College of Engineering Oregon State University Oregon CorvallisOR97331 United States H. Milton Stewart School of Industrial and Systems Engineering College of Engineering Georgia Institute of Technology AtlantaGA30332 United States

For a real-world decision-making problem, the reward function often needs to be engineered or learned. A popular approach is to utilize human feedback to learn a reward function for training. The most straightforward way to do so is to ask humans to provide ratings for state-action pairs on an absolute scale and take these ratings as reward samples directly. Another popular way is to ask humans to rank a small set of state-action pairs by preference and learn a reward function from these preference data. Recently, preference-based methods have demonstrated substantial success in empirical applications such as InstructGPT. In this work, we develop a theoretical comparison between these human feedback approaches in offline contextual bandits and show how human bias and uncertainty in feedback modelings can affect the theoretical guarantees of these approaches. Through this, our results seek to provide a theoretical explanation for the empirical successes of preference-based methods from a modeling perspective. © 2023, CC BY.

关键词： Decision making

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A causal analysis of harm 22

A causal analysis of harm

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Sander Beckers Hana Chockler Joseph Y. Halpern Cluster of Excellence in Machine Learning University of Tübingen and Munich Center for Mathematical Philosophy LMU causaLens and Department of Informatics King's College London Computer Science Department Cornell University

ISBN: (纸本)9781713871088

As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework that addresses when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and "replaced by more well-behaved notions". As harm is generally something that is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality [13]. The key features of our definition are that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems.

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Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey

arXiv

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arXiv 2021年

作者： Chen, Jiaoyan Geng, Yuxia Chen, Zhuo Pan, Jeff Z. He, Yuan Zhang, Wen Horrocks, Ian Chen, Huajun Department of Computer Science University of Oxford United Kingdom College of Computer Science and Technology Zhejiang University China School of Informatics The University of Edinburgh United Kingdom

Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision. As sufficient labeled training data are not always ready due to e.g., continuously emerging prediction targets and costly sample annotation in real world applications, machine learning with sample shortage is now being widely investigated. Among all these studies, many prefer to utilize auxiliary information including those in the form of Knowledge Graph (KG) to reduce the reliance on labeled samples. In this survey, we have comprehensively reviewed over 90 papers about KG-aware research for two major sample shortage settings - zero-shot learning (ZSL) where some classes to be predicted have no labeled samples, and few-shot learning (FSL) where some classes to be predicted have only a small number of labeled samples that are available. We first introduce KGs used in ZSL and FSL as well as their construction methods, and then systematically categorize and summarize KG-aware ZSL and FSL methods, dividing them into different paradigms such as the mapping-based, the data augmentation, the propagation-based and the optimization-based. We next present different applications, including not only KG augmented prediction tasks such as image classification, question answering, text classification and knowledge extraction, but also KG completion tasks, and some typical evaluation resources for each task. We eventually discuss some challenges and open problems from different perspectives. Copyright © 2021, The Authors. All rights reserved.

关键词： Zero-shot learning

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