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arXiv 2025年

作者： Shahin, Nada Ismail, Leila Lab Department of Computer Science and Software Engineering College of Information Technology UAE University Al-Ain United Arab Emirates Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research UAE University Al-Ain United Arab Emirates

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community. © 2025, CC BY.

关键词： Neural machine translation

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

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Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle 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 Independent Researcher

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.

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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Got-Cqa: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answering

SSRN

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SSRN 2025年

作者： Zhang, Lingling Huang, Muye Wang, Qianying Wang, Yaxian Wu, Wenjun He, Ziqi Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China SPKLSTN Lab Department of Computer Science Xi’an Jiaotong University Shaanxi 710049 China Lenovo Research Beijing100094 China

Chart Question Answering (CQA) aims at answering questions based on the visual chart content, which plays an important role in chart summarization, business data analysis, and data report generation. CQA is a challenging multi-modal task because of the strong context dependence and complex reasoning requirement. The former refers to answering this question strictly based on the analysis of the visual content or internal data of the given chart, while the latter emphasizes the various logical and numerical reasoning involved in answer prediction process. In this paper, we pay more attention on the complex reasoning in CQA task, and propose a novel Graph-of-Thought (GoT) guided compositional reasoning model called GoT-CQA to overcome this problem. At first, we transform the chart-oriented question into a directed acyclic GoT composed of multiple operator nodes, including localization, numerical and logical operator. It reflects the human brain's solution process to this question intuitively. After that, we design an efficient auto-compositional reasoning framework guided by the GoT, to excute the multi-step reasoning operations in various types of questions. Comprehensive experiments on ChartQA and PlotQA-D datasets show that GoT-CQA achieves outstanding performance, especially in complex human-written and reasoning questions, comparing with the latest popular baselines. © 2025, The Authors. All rights reserved.

关键词： Question answering

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QGHNN: A quantum graph Hamiltonian neural network

arXiv

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arXiv 2025年

作者： Wang, Wenxuan School of Computer Science and Engineering Central South Univerisity Changsha410083 China Department of Computer Science City University of Hong Kong 999077 Hong Kong Key Lab of MIMS College of Computer Science and Technology Guangxi Normal University Guilin541004 China

Representing and learning from graphs is essential for developing effective machine learning models tailored to non-Euclidean data. While Graph Neural Networks (GNNs) strive to address the challenges posed by complex, high-dimensional graph data, Quantum Neural Networks (QNNs) present a compelling alternative due to their potential for quantum parallelism. However, much of the current QNN research tends to overlook the vital connection between quantum state encoding and graph structures, which limits the full exploitation of quantum computational advantages. To address these challenges, this paper introduces a quantum graph Hamiltonian neural network (QGHNN) to enhance graph representation and learning on noisy intermediate-scale quantum computers. Concretely, a quantum graph Hamiltonian learning method (QGHL) is first created by mapping graphs to the Hamiltonian of the topological quantum system. Then, QGHNN based on QGHL is presented, which trains parameters by minimizing the loss function and uses the gradient descent method to learn the graph. Experiments on the PennyLane quantum platform reveal that QGHNN outperforms all assessment metrics, achieving the lowest mean squared error of 0.004 and the maximum cosine similarity of 99.8%, which shows that QGHNN not only excels in representing and learning graph information, but it also has high robustness ability. QGHNN can reduce the impact of quantum noise and has significant potential application in future research of quantum knowledge graphs and recommendation systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum computers

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Auxiliary-Variable Adaptive Control Barrier Functions

arXiv

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arXiv 2025年

作者： Liu, Shuo Xiao, Wei Belta, Calin Department of Mechanical Engineering Boston University BrooklineMA02215 United States Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology CambridgeMA United States Department of Electrical and Computer Engineering Department of Computer Science University of Maryland College ParkMD United States

This paper addresses the challenge of ensuring safety and feasibility in control systems using Control Barrier Functions (CBFs). Existing CBF-based Quadratic Programs (CBF-QPs) often encounter feasibility issues due to mixed relative degree constraints, input nullification problems, and the presence of tight or time-varying control bounds, which can lead to infeasible solutions and compromised safety. To address these challenges, we propose Auxiliary-Variable Adaptive Control Barrier Functions (AVCBFs), a novel framework that introduces auxiliary variables in auxiliary functions to dynamically adjust CBF constraints without the need of excessive additional constraints. The AVCBF method ensures that all components of the control input explicitly appear in the desired-order safety constraint, thereby improving feasibility while maintaining safety guarantees. Additionally, we introduce an automatic tuning method that iteratively adjusts AVCBF hyperparameters to ensure feasibility and safety with less conservatism. We demonstrate the effectiveness of the proposed approach in adaptive cruise control and obstacle avoidance scenarios, showing that AVCBFs outperform existing CBF methods by reducing infeasibility and enhancing adaptive safety control under tight or time-varying control bounds. Copyright © 2025, The Authors. All rights reserved.

关键词： Self adjusting control systems

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DFE: Deep Flow Embedding for Robust Network Traffic Classification

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IEEE Transactions on Network science and Engineering 2025年第3期12卷 1597-1612页

作者： Wang, Zhijiong Zhang, Anguo Li, Hung Chun Yin, Yadong Chen, Wei Lam, Chan Tong Mak, Peng Un Vai, Mang I. Gao, Yueming Pun, Sio Hang Zhuhai UM Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory Zhuhai519031 China Lingyange Semiconductor Inc. Zhuhai519030 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics 999078 China Fuzhou University School of Physics and Information Engineering Fuzhou350108 China Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Human Phenome Institute Shanghai200437 China Fudan University Human Phenome Institute Shanghai200437 China Macau Polytechnic University Faculty of Applied Sciences 999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China Fuzhou University Key Lab of Medical Instrumentation and Pharmaceutical Technology of Fujian Province Fuzhou350108 China

People's increasing demand for high-quality network services has prompted the continuous attention and development of network traffic classification (NTC). In recent years, deep flow inspection (DFI) is considered to be the most effective and promising method to solve the NTC. However, DFI still cannot effectively address the problem of changes in flow characteristics of complex packet flows and the discovery of new traffic categories. In this paper, we propose a metric learning based deep learning solution with feature compressor, named deep flow embedding (DFE). The feature compressor is used to compress the feature information transmitted layer by layer in DL backbone while maintaining the computational accuracy, so that the backbone can remove as much noise, redundancy, and other irrelevant information from the input data as possible, and achieve more robust feature extraction of network traffic flow. The deep learning (DL) backbone generates an embedding vector for each network packet flow. Then the embedding vector is compared with the vector template preset for each traffic type in the template library to determine the category of the packet flow. Experimental results verify that our method is more effective than the traditional DFI methods in overcoming the problems of flow characteristics variation and new category discovery. © 2013 IEEE.

关键词： Network embeddings

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Multi-domain CSI-based Indoor Localization with Deep Attention Networks for MIMO JCAS system 5

Multi-domain CSI-based Indoor Localization with Deep Attenti...

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5th IEEE International Symposium on Joint Communications and Sensing, JC and S 2025

作者： Mukherjee, Anirban Susarla, Praneeth Katragunta, Pravallika Bulusu, S. S. Krishna Chaitanya Silven, Olli Juntti, Markku Jayagopi, Dinesh Babu Lopez, Miguel Bordallo International Institute of Information Technology Bangalore Multimodal Perception Lab Bangalore560100 India University of Oulu Center for Machine Vision and Signal Analysis Oulu90570 Finland Carleton University Department of Computer Science Ottawa Canada University of Oulu Centre for Wireless Communications Oulu90570 Finland

ISBN: (纸本)9798331531652

Indoor localization is essential for future 6G systems that combine communication and sensing. Accurate positioning is vital for applications like augmented reality and autonomous robotics. In this context, we propose a multi-domain channel state information (CSI)-based localization approach using deep attention networks (DAN) in a massive multiple input multiple output-based (maMIMO) system. We introduce the extraction of features from CSI information in multiple domains, including time, frequency, and Doppler, and design uni-domain and multidomain feature sets. We implement the proposed DAN approach leveraging attention mechanisms to integrate and effectively process the multi-domain CSI data. We evaluate the performance of our model using a publicly available maMIMO dataset and compare it with baseline convolutional neural network (CNN) models. Our results indicate that the DAN-based approach enhances localization performance more than uni-domain, multidomain CNN models and also existing multi-domain-based CNN benchmarks. These findings highlight the benefits of using multidomain features, especially from the Doppler domain along with attention mechanisms for reliable indoor localization. © 2025 IEEE.

关键词： 5G mobile communication systems

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Artificial Intelligence as Automated technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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A Role of Wearable Health technology in Smart Cities 3rd

A Role of Wearable Health Technology in Smart Cities

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis

arXiv

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arXiv 2025年

作者： Li, Yanfei Yin, Teng Shang, Wenyi Liu, Jingyu Wang, Xi Zhao, Kaiyang Machine Intelligence Lab College of Computer Science and Technology Sichuan University Chengdu China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Neurosurgery West China Hospital of Sichuan University Chengdu China

Missing modalities pose a major issue in Alzheimer’s Disease (AD) diagnosis, as many subjects lack full imaging data due to cost and clinical constraints. While multi-modal learning leverages complementary information, most existing methods train only on complete data, ignoring the large proportion of incomplete samples in real-world datasets like ADNI. This reduces the effective training set and limits the full use of valuable medical data. While some methods incorporate incomplete samples, they fail to effectively address inter-modal feature alignment and knowledge transfer challenges under high missing rates. To address this, we propose a Prototype-Guided Adaptive Distillation (PGAD) framework that directly incorporates incomplete multi-modal data into training. PGAD enhances missing modality representations through prototype matching and balances learning with a dynamic sampling strategy. We validate PGAD on the ADNI dataset with varying missing rates (20%, 50%, and 70%) and demonstrate that it significantly outperforms state-of-the-art approaches. Ablation studies confirm the effectiveness of prototype matching and adaptive sampling, highlighting the potential of our framework for robust and scalable AD diagnosis in real-world clinical settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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