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arXiv 2025年

作者： Tsagkas, Nikolaos Sochopoulos, Andreas Danier, Duolikun Lu, Chris Xiaoxuan Mac Aodha, Oisin School of Informatics University of Edinburgh United Kingdom Department of Computer Science University College London United Kingdom

The integration of pre-trained visual representations (PVRs) into visuo-motor robot learning has emerged as a promising alternative to training visual encoders from scratch. However, PVRs face critical challenges in the context of policy learning, including temporal entanglement and an inability to generalise even in the presence of minor scene perturbations. These limitations hinder performance in tasks requiring temporal awareness and robustness to scene changes. This work identifies these shortcomings and proposes solutions to address them. First, we augment PVR features with temporal perception and a sense of task completion, effectively disentangling them in time. Second, we introduce a module that learns to selectively attend to task-relevant local features, enhancing robustness when evaluated on out-of-distribution scenes. Our experiments demonstrate significant performance improvements, particularly in PVRs trained with masking objectives, and validate the effectiveness of our enhancements in addressing PVR-specific limitations. Project page: ***/pvrobo © 2025, CC BY-SA.

关键词： Robot learning

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Dialogue-to-Video Retrieval via Multi-Grained Attention Network

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IEEE Access 2025年 13卷 82302-82311页

作者： Yu, Yi Zhang, Jiwei Tang, Suhua Li, Wei Hiroshima University Graduate School of Advanced Science and Engineering Higashihiroshima739-0046 Japan KDDI Research Inc. Human-Centered AI Laboratories Fujimino356-8502 Japan Wakayama University Department of Systems Engineering Wakayama640-8441 Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Chofu182-8585 Japan Fudan University School of Computer Science Shanghai200433 China

Dialogue-to-video retrieval is an interesting while challenging task, which exploits an AI agent to retrieve the video that matches and aligns with the conversational context between users. In particular, given a history of dialogue exchanges, the agent is expected to identify the most fitting video content that complements the ongoing conversation. The computational complexity posed by the processing of videos within deep neural networks encourages us to adapt CLIP, a cutting-edge large multi-modal models (LLMs), into the dialogue-video domain. On this basis, we propose a multi-grained attention network (MGAT), integrating query-scoring, dual-softmax and query-bank normalization techniques. We design a multi-grained attention module to optimize the inadequate modeling of conversation semantics in existing pre-trained models, dynamically assign weights during conversation feature extraction, and introduce conversation context features in multimodal alignment. Most importantly, fine-grained similarity by per-round query-frame scoring and coarse-grained similarity by high-level semantics of all rounds of dialogues for each video are respectively calculated, which are further learned via multi-grained attention mechanism. This approach effectively transfers CLIP's text-image multimodal knowledge into the dialogue-video retrieval system, alleviating the need for resource-intensive and costly fine-tuning dialogue-video procedures. Extensive experiments on multiple datasets demonstrate that our method outperforms state-of-the-art approaches. © 2013 IEEE.

关键词： Deep neural networks

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Digital twins: cornerstone to circular economy and sustainability goals

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Environment, Development and Sustainability 2025年 1-42页

作者： Ali, Zain Anwar Zain, Mahreen Hasan, Raza Pathan, Muhammad Salman AlSalman, Hussain Almisned, Faisal Abdulaziz Maynooth International Engineering College Maynooth University Co. Kildare Maynooth W23 V5XH Ireland Electronic Engineering Department Sir Syed University of Engineering & Technology Karachi Pakistan College of Economics and Management Nanjing University of Aeronautics and Astronautics 29 Jiangsu Avenue Nanjing China Department of Science and Engineering Southampton Solent University Southampton SO14 0YN United Kingdom School of Computing Dublin City University Dublin Ireland Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Accurate information is a prerequisite for circulation and greater longevity of materials and products, enabling the economically and environmentally successful transition of circular economy (CE) in the aspiration towards sustainable development. However, a lack of appropriate digital tools is noticed in supplying relevant information and supporting recyclability, reusability, and reducibility processes. This study focuses on a building block of evolving technologies anointed the digital twin (DT). It aims to conceptualize DT as a cornerstone for the CE paradigm, positively influencing sustainability. It also endeavors to scrutinize remarkable implementations of DT-driven CE, accomplishing sustainable development goals (SDGs) in Ireland. An iterative methodology based on four steps is adopted to review an explosion of relevant, recent literature published in 2019–2024 and multiple case studies of Irish construction and manufacturing sectors. By analyzing the literature, this study discovers the evolution and taxonomy of DT, other technologies essential for its deployment, its proactive role in implementing CE and SDGs, and some challenges. Key findings of case studies reveal the current trajectory of employing DT with other digital products, upholding the CE practices within the bounds of the SDGs in Irish companies. In conclusion, DT is a conceivable means for advancing CE solutions and attaining SDGs towards social, economic, and environmental sustainability. The implications of this study are both theoretically and practically significant, encouraging industrialists and academia to adopt DT. It will help scholars gain a thorough understanding and guide industrialists to downsize their carbon footprint and waste, accomplishing SDGs. It will inspire managers with DT-driven information for decision-making and facilitate government and policymakers to collaborate in designing essential policies. © The Author(s) 2025.

关键词： Circular Economy (CE) Digital Twin (DT) Ireland R-principles Sustainability Sustainable Development Goals (SDGs)

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Tibyan corpus: balanced and comprehensive error coverage corpus using ChatGPT for Arabic grammatical error correction

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PeerJ computer science 2025年 11卷 e2724-e2724页

作者： Alrehili, Ahlam Alhothali, Areej Department of Computer Sciences Faculty of Computing and Information Technology King Abdul Aziz University Jeddah Saudi Arabia Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

Natural language processing (NLP) augments text data to overcome sample size constraints. Scarce and low-quality data present particular challenges when learning from these domains. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. Moreover, data-augmentation techniques are commonly used in languages with rich data resources to address problems such as exposure bias. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources for grammatical error correction (GEC) despite being one of the most popular among Arabs and non-Arabs because of its close connection to Islam. Therefore, this study aims to develop an Arabic corpus called "Tibyan" for grammatical error correction using ChatGPT. ChatGPT is used as a data augmenter tool based on a pair of Arabic sentences containing grammatical errors matched with a sentence free of errors extracted from Arabic books, called guide sentences. Multiple steps were involved in establishing our corpus, including collecting and pre-processing a pair of Arabic texts from various sources, such as books and open-access corpora. We then used ChatGPT to generate a parallel corpus based on the text collected previously, as a guide for generating sentences with multiple types of errors. By engaging linguistic experts to review and validate the automatically generated sentences, we ensured they were correct and error-free. The corpus was validated and refined iteratively based on feedback provided by linguistic experts to improve its accuracy. Finally, we used the Arabic Error Type Annotation tool (ARETA) to analyze the types of errors in the Tibyan corpus. Our corpus contained 49% of errors, including seven types: orthography, morphology, syntax, semantics, punctuation, merge, and split. The Tibyan corpus contains approximately 600 K tokens. © 2025 Alrehili and Alhothali

关键词： Semantics

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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A Review Analysis on Smart Blind Stick Using IoT 4

A Review Analysis on Smart Blind Stick Using IoT

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Asha, P.K. Shanmugha Priya, R.K. Nagaraj, D. Aswini, S. Wilson, G Preethi Vinoth Kumar, K. New Horizon College of Engineering Department of Chemistry Bengaluru India Jai Shriram Engineering College Avinashipalayam Department of Information Technology Tirupur India Dhanalakshmi College of Engineering Department of Cse Chennai India Bangalore Technological Institute Department of Cse Bengaluru India Jerusalem College of Engineering Department of Computer Science & Business Systems Chennai India New Horizon College of Engineering Department of Eee Bengaluru India

ISBN: (纸本)9798331523923

Millions of individuals worldwide struggle with blindness and other forms of visual impairment. 940 million individuals will suffer some degree of vision loss by 2015, according to the WHO (World Health Organization). 246 million people experience fleeting apparitions, while 39 million people are blind. Most people with bad vision are older than 50 and live in industrialized cultures. The main challenge that blind individuals encounter is mobility. They are cut off from their environment. Since they are oblivious to what is going on around them, they require assistance. Technological advancements have been crucial in increasing the mobility and freedom of those who are blind. One such innovation is the Smart Blind Stick, a high-tech assistance gadget made to improve the safety and navigation of the blind. The goal of this review article is to examine the many facets of Smart Blind Stick technology, including its functionality, benefits, drawbacks, and overall influence on users' life. This review aims to shed light on the current status of Smart Blind Stick technology and its potential future advances by reviewing existing literature and advancements. © 2025 IEEE.

关键词： Obstacle detectors

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Optimizing Heart-Kidney Interaction for Cancer Detection Through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Othman, Zalinda Yahya, Yazrina Zakree Ahmad Nazri, Mohd Leng Goh, Say Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran96351-14578 Iran Universiti Kebangsaan Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Kebangsaan Malaysia Center for Software Technology and Management Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Malaysia Sabah Kampus Antarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States RayanMiniFab Tehran University Science and Technology Park Tehran*** Iran

A decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization by analyzing the available data. The heart-kidney (HK) model proposed in this paper, as a DSS, simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA’s local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm’s effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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ClusterLDA: Clustering-Based Topic Extraction for Online News

ClusterLDA: Clustering-Based Topic Extraction for Online New...

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Women in Data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Juan Kenichi Sutan Jumanah Alshehri Rafaa Aljurbua Zoran Obradovic Department of Computer and Information Sciences Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA USA Department of Management Information Systems College of Business Administration Imam Abdulrahman bin Faisal University Dammam Saudi Arabia Department of Computer Science College of Computer Qassim University Buraydah Saudi Arabia

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Topic extraction from large text datasets is a critical task for understanding and analyzing thematic patterns across documents. This study explores three methods for improving topic modeling: standard Latent Dirichlet Allocation (LDA), a clustering-based approach combined with LDA, and an experimental method enhanced with Wikipedia-supplemented clustering. We demonstrate that applying clustering prior to LDA significantly improves topic extraction by aligning the modeling process with the natural structure of the data, allowing for the identification of smaller, yet meaningful, topics that are often overshadowed in standard LDA due to thematic imbalances. Additionally, we applied the improved topic modeling approach to construct an entity-topic network that contextualizes entities' sentiments within the topics they were mentioned in, providing a nuanced view of the dataset. Although our hypothesis that Wikipedia-enhanced clustering would further improve topic extraction was not supported, as it introduced noise and worsened performance, ClusterLDA approach proved effective in enhancing the granularity of topic extraction and addressing the limitations of standard LDA. These findings highlight the potential of clustering to support more coherent and semantically relevant topic extraction, offering a foundation for future advancements in topic modeling and sentiment analysis.

关键词： Text mining Electronic publishing Sentiment analysis Noise Encyclopedias Data models Internet Online services Resource management Standards

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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