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MHFNet: A Multimodal Hybrid-embedding Fusion Network for Automatic Sleep Staging

IEEE Journal of Biomedical and Health informatics 2025年第5期29卷 3387-3397页

作者： Liu, Ruhan Li, Jiajia Wen, Yang Huang, Xian Sheng, Bin Feng, David Dagan Zhang, Ping Central South University Furong Laboratory Hunan Changsha China Xiangya Hospital Central South University Department of Dermatology Hunan Changsha China Shanghai Jiao Tong University Shanghai School of Chemistry and Chemical Engineering National Center for Translational Medicine China College of Electronics and Information Engineering Shenzhen China The Third Xiangya Hospital of Central South University Department of Neurology China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China The University of Sydney School of Computer Science SydneyNSW2006 Australia Ohio State University Department of Computer Science and Engineering ColumbusOH43210 United States Ohio State University Department of Biomedical Informatics ColumbusOH43210 United States

Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global temporal information. Second, the correlation for special waves in different signals is rarely used in sleep staging modeling. Third, the logic of scoring rules based on adjacent epochs is not considered in developing sleep staging models. This paper introduces a multimodal hybrid-embedding fusion network (MHFNet), which aims to tackle these challenges in automating sleep stage scoring. MHFNet comprises multi-stream Xception blocks to extract wave characteristics, a hybrid time-embedding module to combine local and global temporal information, a dual-path gate transformer to fuse and enhance attention features, and a refined output header to reconstruct sleep scoring. We perform experiments using three publicly available datasets (SleepEDF-ST, SleepEDF-SC, and SHHS). Experimental results indicate the superiority of MHFNet over baseline approaches in cross-validation. Moreover, at the individual level, MHFNet yielded an average R 2 score improvement of 9% in the testing dataset compared to state-of-the-art models, paving the way for its applications in real-world sleep medicine. © 2013 IEEE.

关键词： Sleep research

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LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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Does Adding a Modality Really Make Positive Impacts in Incomplete Multi-Modal Brain Tumor Segmentation?

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2194-2205页

作者： Qiu, Yansheng Jiang, Kui Yao, Hongdou Wang, Zheng Satoh, Shin'ichi Wuhan University School of Computer Science Wuhan430072 China Shenzhen518000 China National Institute of Informatics Tokyo101-8430 Japan The University of Tokyo Graduate School of Information Science and Technology Department of Information and Communication Engineering Tokyo113-8654 Japan

Previous incomplete multi-modal brain tumor segmentation technologies, while effective in integrating diverse modalities, commonly deliver under-expected performance gains. The reason lies in that the new modality may cause confused predictions due to uncertain and inconsistent patterns and quality in some positions, where the direct fusion consequently raises the negative gain for the final decision. In this paper, considering the potentially negative impacts within a modality, we propose multi-modal Positive-Negative impact region Double Calibration pipeline, called PNDC, to mitigate misinformation transfer of modality fusion. Concretely, PNDC involves two elaborate pipelines, Reverse Audit and Forward Checksum. The former is to identify negative regions impacts of each modality. The latter calibrates whether the fusion prediction is reliable in these regions by integrating the positive impacts regions of each modality. Finally, the negative impacts region from each modality and miss-match reliable fusion predictions are utilized to enhance the learning of individual modalities and fusion process. It is noted that PNDC adopts the standard training strategy without specific architectural choices and does not introduce any learning parameters, and thus can be easily plugged into existing network training for incomplete multi-modal brain tumor segmentation. Extensive experiments confirm that our PNDC greatly alleviates the performance degradation of current state-of-the-art incomplete medical multi-modal methods, arising from overlooking the positive/negative impacts regions of the modality. The code is released at PNDC. © 2025 IEEE.

关键词： Multicarrier modulation

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Applied Federated Model Personalization in the Industrial Domain: A Comparative Study

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3192-3210页

作者： Siniosoglou, Ilias Argyriou, Vasileios Fragulis, George Fouliras, Panagiotis Papadopoulos, Georgios Th Lytos, Anastasios Sarigiannidis, Panagiotis University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece Kingston University Department of Networks and Digital Media LondonKT1 2EE United Kingdom University of Macedonia Department of Applied Informatics Thessaloniki546 36 Greece University of Athens Department of Informatics and Telematics Harokopio Athens157 72 Greece Sidroco Holdings Ltd. Research & Development Department Nicosia1082 Cyprus MetaMind Innovations P.C. Research & Development Department Kozani50100 Greece

The time-consuming nature of training and deploying complicated Machine and Deep Learning (DL) models for a variety of applications continues to pose significant challenges in the field of Machine Learning (ML). These challenges are heightened in the federated domain, where optimizing models for individual nodes is particularly difficult. Many methods have been developed to tackle this problem, aiming to reduce training expenses and time while maintaining efficient optimisation. Three suggested strategies to tackle this challenge include Active Learning, Knowledge Distillation, and Local Memorization. These methods enable the adaptive finetuning of the leveraged AI models allowing for model personalization with local data, thereby improving the effectiveness of current models. The present study delves into the fundamental principles of these three approaches and proposes an advanced Federated Learning System that utilises different Personalization methods towards improving the accuracy of AI models and enhancing user experience in real-time NG-IoT applications, investigating the efficacy of these techniques in the local and federated domain. The results of the original and optimised models are then compared in both local and federated contexts using a comparison analysis. The analysis reveals promising results for optimizing and personalizing models using the proposed techniques. © 2020 IEEE.

关键词： Adversarial machine learning

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Self-Service Framework for Hospital Management System

Self-Service Framework for Hospital Management System

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Lubis, Muharman Lubis, Bastian Naufal, Fadhil Izzati, Berlian Maulidya Lubis, Arif Ridho Ramadhani, Rafian Fakhrurroja, Hanif Telkom University Industrial Engineering West Java Bandung Indonesia Department of Anesthesiology and Reanimation Universitas Sumatra Utara Sumatra Utara Indonesia Department of Computer Engineering and Informatics Politeknik Negri Medan Medan Indonesia

ISBN: (纸本)9789819795581

Self-service technology framework was developed for implementation at Tarakan Hospital, Jakarta, an institution under the administration of the DKI Jakarta provincial government that previously lacked such autonomous services. Leveraging the Healthcare Innovation Framework, which is dedicated to fostering innovations within the healthcare sector, this research proposes the introduction of a Tele-Assistance application mock-up at Tarakan Hospital Jakarta. This application is designed to offer Tele-Assistance for outpatients and Tele-Inpatient services for those admitted to the hospital. The adoption of this service model is anticipated to eliminate the need for physical queues and registration form fill-outs, facilitated by the integration of patients’ National Identification Numbers (NIK) with the Tele-Assistance application. Furthermore, the application will enable patients to check room availability, thereby reducing face-to-face interactions. Consequently, the creation of a service blueprint is essential for visualizing the interactions between patients and the self-service system, ensuring a seamless integration of this innovative solution into the hospital's operational framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Healthcare innovation Patient engagement Self-service technology Service blueprint Tele-assistance

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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of computer Science 2025年 1-31页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

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Science China Technological Sciences 2025年第5期68卷 294-296页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Foshan Institute of Intelligent Equipment Technology Foshan Huashu Robot Co. Ltd. Center on Frontiers of Computing Studies (CFCS) School of Computer SciencePeking University Beijing Academy of Artificial Intelligence Department of Informatics Universit?t Hamburg

Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.

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Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification 3rd

Normative Modeling with Focal Loss and Adversarial Autoenc...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Zhao, Songlin Zhou, Rong Zhang, Yu Chen, Yong He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Bioengineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783031820069

In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer’s Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at https://***/soz223/FAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Multi-Modal Assessment Framework for Comparison of Specialized Deep Learning and General-Purpose Large Language Models

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IEEE Transactions on Big Data 2025年第03期11卷 1001-1012页

作者： Nadeem, Mohammad Sohail, Shahab Saquib Madsen, Dag Oivind Alzahrani, Ahmed Abrahim Ser, Javier Del Muhammad, Khan Aligarh Muslim University Department of Computer Science Aligarh India VIT Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India University of South-Eastern Norway Norway King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia TECNALIA Derio48160 Spain Department of Communications Engineering Bilbao48013 Spain Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Recent years have witnessed tremendous advancements in Al tools (e.g., ChatGPT, GPT 4, and Bard), driven by the growing power, reasoning, and efficiency of Large Language Models (LLMs). LLMs have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. Despite their proficiency in general queries, specialized tasks such as metaphor understanding and fake news detection often require finely tuned models, posing a comparison challenge with specialized Deep Learning (DL). We propose an assessment framework to compare task-specific intelligence with general-purpose LLMs on suicide and depression tendency identification. For this purpose, we trained two DL models on a suicide and depression detection dataset, followed by testing their performance on a test set. Afterward, the same test dataset is used to evaluate the performance of four LLMs (GPT 3.5, GPT 4, Google Bard, and MS Bing) using four classification metrics. The BERT-based DL model performed the best among all, with a testing accuracy of 94.61%, while GPT 4 was the runner-up with accuracy 92.5%. Results demonstrate that LLMs do not outperform the specialized DL models but are able to achieve comparable performance, making them a decent option for downstream tasks without specialized training. However, LLMs outperformed specialized models on the reduced dataset. © 2015 IEEE.

关键词： Contrastive Learning

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Partition Strategies for Vertex-Centric Historical Graph Systems 25

Partition Strategies for Vertex-Centric Historical Graph Sys...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Alexandros Spitalas Georgios Tsolas Kostas Tsichlas Computer Engineering and Informatics Department University of Patras Rio Achaia Greece

ISBN: (纸本)9798400706295

This paper explores partitioning strategies for vertex-centric historical graph systems within distributed environments, focusing on efficient data management and query execution. Historical graphs, which capture the dynamic evolution of vertices and edges over time, present unique challenges for storage and computation due to their constantly changing nature. We investigate two partitioning approaches for offline and online environments. Both offline and online algorithms aim to minimize an appropriately defined notion of edge cuts within the historical graph setting; the former algorithm is based on BFS, while the latter is based on a greedy partitioning approach. Both algorithms are compared qualitatively and quantitatively to hash-based methods that are mainly used in this setting. They are evaluated on real-world datasets, using metrics such as weighted edge cut score ratio and load balance ratio. Our experiments reveal that our online method consistently reduces edge cuts with minimal overhead, making it suitable for real-time distributed processing systems for historical graphs. Similarly, the offline algorithm achieves much better results for weighted edge cuts, but it requires severely more computational resources to achieve this performance. Additionally, the study highlights how varying the number of workers and partitioning thresholds impacts system performance across different datasets. All our experiments have been carried out in a simulation environment. The results provide valuable insights into optimizing partition strategies for historical graphs, paving the way for more efficient graph analytics in distributed systems.

关键词： partitioning strategies

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