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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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A Maximally Permissive Robustness Analysis for Automated Manufacturing systems Allowing Multiple Server Failures

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IEEE Transactions on Automation Science and engineering 2025年 22卷 12485-12499页

作者： Li, Junyan Hu, Hesuan Xidian University School of Electro-Mechanical Engineering Shaanxi Xi’an710071 China Nanyang Technological University School of Computer Science and Engineering College of Engineering Jurong West 639798 Singapore Xi’an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Shaanx Xi’an710054 China

As one of the essential issues when designing optimal control policies for automated manufacturing systems (AMSs) with unreliable resources, maximally permissive behavior should be ensured for the controlled system. This paper is devoted to the maximally permissive robustness analysis for AMSs allowing multiple server failures. First, all subnet systems are categorized by their usage of unreliable resources so as to provide more general definitions of the robust, strongly robust, and weakly robust markings. Second, we provide algorithms to compute reduced reachability graphs (R2Gs) and strongly connected components (SC2s), respectively. Then, the necessary and sufficient conditions to verify the robustness of markings are obtained by analyzing the transitions involved in each component in an SC2and the relation between markings in the disconnected marking subset and SC2. Finally, the effectiveness and efficiency of the theoretical results are validated via examples. © 2004-2012 IEEE.

关键词： Smart manufacturing

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Work in Progress: Considering Human Factors in Collaborative Decision Making for Secure Architecture Design 12th

Work in Progress: Considering Human Factors in Collaborati...

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12th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2022

作者： Hamid, Brahim Jaskolka, Jason University of Toulouse UT2 Toulouse France Systems and Computer Engineering Carleton University OttawaON Canada

ISBN: (纸本)9783031830716

Developing large and complex software systems requires the collaboration of teams of developers, architects, managers, and more. These teams often work together during the design phase to make architectural design decisions that can impact many different system qualities including security. Thus, it is important to consider the human factors including levels of expertise and experience, as well as different attitudes and behaviors towards securing the system that impact the decision-making process of the team members individually and as a whole. This paper presents a work in progress that describes a conceptual framework to consider human factors in collaborative decision-making for secure architecture design. The framework is based on a combination of model-driven engineering techniques and human science approaches. We describe a design scenario with which we illustrate the usage of the proposed framework from the development team perspective. This work aims to enhance the understanding of how collaborative decisions are made and to provide better traceability of decisions impacting system security to gain confidence in the decisions taken by a team of diverse members. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Architectural design

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Proposal of a Document Layout Analysis Method Adopting Pre-processing Based on Document Image Classification 16th

Proposal of a Document Layout Analysis Method Adopting Pre...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Kakinoki, Kanta Katayama, Tetsuro Department of Computer Science and Systems Engineering Faculty of Engineering University of Miyazaki 1-1 Gakuen-Kibanadai Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615346

To extract important information from the document images, document layout analysis research has been carried out. Previous research analyzes document layouts only for specific document formats. This paper proposes a document layout analysis method that adopts pre-processing based on document image classification to analyze document layouts for various document formats. From the application examples, we have confirmed that the proposed method can correctly analyze document layout by using training models trained for each document format. Furthermore, to evaluate the usefulness of the proposed method, we have compared the F score of the proposed method for document layout analysis using a single training model covering various document formats and using a training model generated for each document format. As a result, we have confirmed that our proposed method is useful in document layout analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： document image classification document layout analysis rule-based correction

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Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models 1

Exploring Author Style in Nakba Short Stories: A Comparative...

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1st International Workshop on Nakba Narratives as Language Resources, Nakba-NLP 2025

作者： Hamed, Osama Zaidkilani, Nadeem Department of Computer Systems Engineering Palestine Technical University-Kadoorie Palestine Department of Computer Engineering and Security Mathematics University Rovira i Virgili Spain Department of Engineering and Technology Al-Zaytona University of Science and Technology Palestine

ISBN: (纸本)9798891762169

Measuring semantic similarity and analyzing authorial style are fundamental tasks in Natural Language Processing (NLP), with applications in text classification, cultural analysis, and literary studies. This paper investigates the semantic similarity and stylistic features of Nakba short stories, a key component of Palestinian literature, using transformer-based models, AraBERT, BERT, and RoBERTa. The models effectively capture nuanced linguistic structures, cultural contexts, and stylistic variations in Arabic narratives, outperforming the traditional TF-IDF baseline. By comparing stories of similar length, we minimize biases and ensure a fair evaluation of both semantic and stylistic relationships. Experimental results indicate that RoBERTa achieves slightly higher performance, highlighting its ability to distinguish subtle stylistic patterns. This study demonstrates the potential of AI-driven tools to provide more in-depth insights into Arabic literature, and contributes to the systematic analysis of both semantic and stylistic elements in Nakba narratives. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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CNN-Based Multi-Output and Multi-Task Regression for Supershape Reconstruction from 3D Point Clouds

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第5期49卷 37-48页

作者： Remmach, Hassnae Razak, Siti Fatimah Abdul Ullah, Arif Yogarayan, Sumendra Sayeed, Md Shohel Mrhari, Amine Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh40000 Morocco Faculty of Information Science and Technology Multimedia University Ayer Keroh75450 Malaysia Research in Computer Science Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco

In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learning capabilities is proposed for three-dimensional object reconstruction from 3D point cloud. Our approach is grounded in the original Point Net architecture, which addresses the difficulties associated with convolution when applied to point clouds. Firstly, this paper is modified using a Multi-Output Regressor to accurately recreate Super forms from 3D point clouds. Using this method, we first extract features from the 3D point cloud using Point Net. After that, a Multi-Output Regressor receives these data and uses them to anticipate the Super shape parameters needed to reconstruct the shape. Taking in the data, the Multi-Output Regressor retrieved characteristics from Point Net and simultaneously predicts several outcomes. Second, a Multi-Task Regressor is used to modify the Point Net. The network gains from the capacity to transfer knowledge from one task to another, improving the model's overall performance. The model would forecast the ten parameters needed to create the shape in the case of rebuilding Super shapes. The test findings were better than expected;they are intriguing in terms of prediction accuracy and cost, and they update the result by 80%, which is a good accomplishment for the study. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 3D reconstruction

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Random Staircase Generator Matrix Codes: Coding Theorem, Performance Analysis and Code Design

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IEEE Transactions on Information Theory 2025年第5期71卷 3497-3509页

作者： Wang, Qianfan Wang, Yiwen Wang, Yixin Liang, Jifan Ma, Xiao School of Computer Science China Sun Yat-sen University Engineering and Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China Sun Yat-sen University School of Systems Science and Engineering Guangzhou510006 China

In this paper, we present a class of codes, referred to as random staircase generator matrix codes (SGMCs), which have staircase-like generator matrices. In the infinite-length region, we prove that the random SGMC is capacity-achieving over binary-input output-symmetric (BIOS) channels. In the finite-length region, we propose the generalized representative ordered statistics decoding with local constraints (LC-ROSD) algorithm for the SGMCs. The most distinguished feature of the SGMCs with LC-ROSD is that the staircase-like matrices enable parallel implementation of the Gaussian elimination (GE), avoiding the serial GE of conventional OSD and supporting a potential low decoding latency, as implied from simulations. To analyze the performance of random SGMCs in the finite-length region, we derive the ensemble weight spectrum and invoke the conventional union bound. We also derive a partially random coding union (RCU) bound, which is tighter than the conventional one and is used as a criterion to design the SGMCs. Staircase-like generator matrices allow us to derive a series of (tighter and tighter) lower bounds based on the second-order Bonferroni inequality with the incremental number of codewords. The numerical results show that the decoding performance can match well with the proposed partially RCU bound for different code rates and different profiles. The numerical results also show that the tailored SGMCs with the LC-ROSD algorithm can approach the finite-length performance bound, outperforming the 5G low-density parity-check (LDPC) codes, 5G polar codes, and Reed-Muller (RM) codes. © 2025 IEEE. All rights reserved.

关键词： Decoding

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Arabic Topic Classification Corpus of the Nakba Short Stories 1

Arabic Topic Classification Corpus of the Nakba Short Storie...

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1st International Workshop on Nakba Narratives as Language Resources, Nakba-NLP 2025

ISBN: (纸本)9798891762169

In this paper, we enrich Arabic Natural Language Processing (NLP) resources by introducing the "Nakba Topic Classification Corpus (NTCC)," a novel annotated Arabic corpus derived from narratives about the Nakba. The NTCC comprises approximately 470 sentences extracted from eight short stories and captures the thematic depth of the Nakba narratives, providing insights into both historical and personal dimensions. The corpus was annotated in a two-step process. One third of the dataset was manually annotated, achieving an IAA of 87% (later resolved to 100%), while the rest was annotated using a rule-based system based on thematic patterns. This approach ensures consistency and reproducibility, enhancing the corpus’s reliability for NLP research. The NTCC contributes to the preservation of the Palestinian cultural heritage while addressing key challenges in Arabic NLP, such as data scarcity and linguistic complexity. By like topic modeling and classification tasks, the NTCC offers a valuable resource for advancing Arabic NLP research and fostering a deeper understanding of the Nakba narratives. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Deep neural network for emojis-based sentiment classification

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International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Balaji, Prasanalakshmi Alsid, Linda Elzubir Gasm Sagar, Kalpna Prakasam, Shanmugapriya Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science and Business Systems Rajalakshmi Engineering College Thandalam India

Sentiment classification using emojis on social media has become increasingly crucial in recent years. Social media commonly uses emojis to convey feelings, emotions, and moods. Hence, in this article, a Jellyfish Algorithm based on a Deep Convolution Neural Network (JA-DCNN) is developed for the sentiment classification of the emojis. Initially, the emoji images are collected from the open-source system. The proposed sentiment classification process uses a Jellyfish Algorithm (JA) optimized Deep Convolution Neural Network (DCNN). In DCNN, the hyperparameters are selected with the help of JA to improve accuracy and efficiency. The proposed method aims to improve the training features, maintain the resolution, reduce memory consumption, and improve the system’s accuracy. The proposed method decreases the computation cost and memory consumption, which empowers the system’s performance in emojis-based sentiment classification. The proposed method is implemented in MATLAB, and performance metrics of similarity and quality measurements evaluate performances. To validate the performance of the proposed methodology, it is compared with conventional methods such as MobilenetV2, ResNet18, and ResNet50, which have been proven to obtain better performance metrics. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Classification Deep convolution neural network Emojis Jellyfish algorithm Sentiment analysis

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MIDDLE: A Mobility-Driven Device-Edge-Cloud Federated Learning Framework

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IEEE Transactions on Mobile Computing 2025年第06期24卷 4589-4606页

作者： Zhang, Songli Zheng, Zhenzhe Wu, Fan Li, Bingshuai Shao, Yunfeng Chen, Guihai Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems China Huawei Technologies Huawei Noah's Ark Lab China

Federated learning (FL) can be implemented in large-scale wireless networks in a hierarchical way, introducing edge servers as relays between the cloud server and devices. These devices are dispersed within multiple clusters coordinated by edges. However, the devices are typically mobile users with unpredictable trajectories, and the impact of their mobility on the model training process is not well-studied. In this work, we propose a new MobIlity-Driven feDerated LEarning framework, namely MIDDLE. MIDDLE addresses unbalanced model updates by capitalizing on model aggregation opportunities on mobile devices due to their mobility across edges. It consists of two components: on-device model aggregation, which aggregates models from different edges carried by mobile devices as they move across edges, and in-edge device selection, adjusting the current edge optimization direction through careful device selection. Theoretical analysis emphasizes that on-device model aggregation can reduce bias in model updating on edges and the cloud, thereby accelerating the FL model convergence. Building on this analysis, we introduce on-device global control averaging, modifying the training process on mobile devices and extending MIDDLE into MIDDLE+. Extensive experimental results validate that MIDDLE and MIDDLE+ can reduce the time steps to reach the target accuracy by 19.44% and 20.37% at least, respectively. © 2002-2012 IEEE.

关键词： Mobile edge computing

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