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Deep Learning for Detecting Mental Disorder from User Content on Social Media

SN Computer Science 2025年第2期6卷 1-29页

作者： Tounsi, Malek Ameur, Hanen Jamoussi, Salma Multimedia Information Systems and Advanced Computing Laboratory University of Sfax Technopole of Sfax Sfax Tunisia

The prevalence of mental health disorders is rising, with many individuals expressing their thoughts and feelings through social media posts. This presents an opportunity for early detection of mental conditions using natural language processing (NLP). This paper addresses the lack of effective tools for detecting mental disorders from Arabic social media content. We propose Ara-MentalBERT, a novel deep learning fusion model that combines AraBERT and MentalBERT using a deep joint autoencoder (DJAE). The DJAE model aims to integrate Arabic language understanding with specialized mental health domain knowledge by fusing Arabic and mental health embedding representations of text. Our proposed method involves three key steps: (1) fine-tuning pre-trained AraBERT and MentalBERT models, (2) employing a deep joint autoencoder for Ara-Mental text representation learning, and (3) utilizing a mental health disorder classifier with the fused Ara-Mental representations. We evaluate our model on two diverse datasets: a Reddit dataset and a Twitter dataset. Results demonstrate significant improvements over existing methods, achieving an accuracy of 81.12% on the Reddit dataset for combined titles and posts and 86.12% accuracy on the Arabic tweets. These findings highlight the potential of our model to enhance mental health detection in Arabic-speaking communities, offering a powerful tool for identifying at-risk individuals more effectively. Our proposed method makes valuable progress at the intersection of NLP, mental health, and deep learning, paving the way for Arabic social media monitoring to facilitate early intervention and improve mental healthcare. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ara-Mental representation Ara-MentalBERT AraBERT Deep joint autoencoder Deep learning Mental health disorders MentalBERT

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Hybrid EEG-fNIRS Detection of MCI Subtypes Based on Transformer Network

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SN Computer Science 2025年第4期6卷 1-13页

作者： Bouaziz, Bassem Chaabene, Siwar Mahdi, Walid Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) University of Sfax Sfax 3023 Tunisia

Preventing the progression of mild cognitive impairment (MCI) to dementia is of paramount importance in the field of healthcare. Furthermore, detecting the subtle indications of MCI in its early stages is difficult but necessary. MCI can appear as either amnestic (aMCI) or non-amnestic (naMCI), confounding the diagnosis even more. However, with the development of improved neuroimaging techniques, computational algorithms that analyze data acquired during clinical tests might provide a solution. Among them, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) offer invaluable insights into an individual’s cognitive state. In this paper, we present a novel approach for distinguishing individuals diagnosed with MCI, specifically aMCI and naMCI, from healthy controls (HC). We employ a hybrid fusion of EEG and fNIRS signals, acquired during a cognitive task. To address this complex classification problem, we propose a new method based on the transformer network for MCI detection. Our experimental results demonstrate a remarkable classification accuracy of 99.78%, highlighting the significant improvement in MCI detection capabilities achieved through the integration of multimodal neuroimaging data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cognitive task EEG FNIRS HC vs aMCI vs naMCI MCI detection Multi-modality Transformer network

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A Forced Decoding-based Approach for Enhancing Low-resource ASR

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Neural Processing Letters 2025年第3期57卷 1-10页

作者： Liu, Yunpeng Yang, Xukui Qu, Dan School of Information Systems Engineering University of Information Engineering Henan Zhengzhou450001 China The Laboratory for Advanced Computing and Intelligence Engineering Jiangsu Wuxi214001 China

Multilingual automatic speech recognition represents a crucial research direction in tackling the challenges associated with low-resource scenarios. To effectively incorporate language-specific information in the joint training of a model across multiple languages and leverage linguistic similarities to enhance performance on the target low-resource language, this paper introduces a language similarity evaluation approach based on forced decoding. Specifically, when the target language is specified, the speeches of the source language are decoded into transcription in the target language, and the normalized posterior is utilized as the foundation for evaluating language similarity. Comprehensive experiments and analyses conducted on six low-resource languages reveal that the proposed approach achieves an average word error rate relative reduction of 21.74, 7.68, and 3.45% compared to three widely used benchmark methods, respectively, thereby validating the effectiveness of our approach. © The Author(s) 2025.

关键词： Decoding

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Exploring Edge-Driven Collaborative Fine-Tuning Toward Customized AIGC Services

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IEEE Network 2025年第3期39卷 293-301页

作者： Zhang, Qinnan Zong, Tianqi Xiong, Zehui Sun, Pangbo Zhu, Jianming Qiu, Wangjie Zheng, Zhiming Poor, H. Vincent Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Zhongguancun Laboratory Beijing100191 China Singapore University of Technology and Design Information Systems Technology and Design Tampines 487372 Singapore Qingcheng.AI Ltd. Beijing100084 China Central University of Finance and Economics School of Information Beijing100081 China Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

With the popularization of artificial intelligence-generated content (AIGC), the explosion of end users is spurring an unprecedented diversity in preferences. To cope with this challenge, the AIGC paradigm is shifting from cloud-driven pre-trained large models to edge-driven personalized customization models. The former are large-scale models with more than 100 billion parameters trained based on massive data, while the latter introduce heterogeneous user preferences or professional knowledge to fine-tune the former. However, such fine-tuning incurs costly resource consumption and privacy disclosure. In this paper, we offer a holistic perspective on the AIGC fine-tuning framework spanning from end user to edge to cloud. Specifically, we first investigate how to deploy an edge-driven collaborative fine-tuning task through federated learning. Then, we discuss a verifiable model consensus protocol and fairness incentive design for edge servers to participate in a collaborative learning task. In addition, a case study focuses on the medical scenario, where we develop a practical X-ray diagnostic demo through collaborative fine-tuning of a multimodal pre-training model, and the diagnostic dialogue and performance results are compared. Finally, potential research directions are identified to advance the edge-driven customized AIGC services. © 1986-2012 IEEE.

关键词： Phase change materials

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Quantum Direct Steganography Scheme Based on Modified Generator Projection Directions of Steane Code over a Single-Type Pauli Channel

arXiv

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arXiv 2025年

作者： Hao, Chaolong Ma, Quangong Qu, Dan Shi, Dawei School of Information Systems Engineering Information Engineering University 62 Science Avenue Zhengzhou450001 China Laboratory for Advanced Computing and Intelligence Engineering Jiangsu Wuxi214000 China

In quantum mechanics, measurements of a quantum state in various directions yield distinct outcomes, a principle that forms the foundation of quantum communication theory. This paper expands upon this concept by introducing a method to modify generator projection directions (MGPD) within quantum stabilizer codes. Employing the Steane code ((7, 1, 3) code), as a fundamental carrier, we develop a novel scheme for direct quantum steganography across a single-type Pauli channel. The infeasibility of eavesdropping decoding under MGPD is proven. We detail the steganographic encoding and decoding schemes, corresponding quantum circuits, and eavesdropping detection principles. We also use a’Sudoku’-style strategy to balance steganographic error probabilities and provide the complete steganography protocol. Relative to existing studies, the MGPD method achieves embedding rates approaching and attaining the upper limit of the information capacity for the (n, k, d) = (7, 1, 3) code within a noise probability range of approximately 1/(n + 1) = 12.5%. It also reduces the consumption of auxiliary keys from O(log (N)) to O(1), while enabling eavesdropping detection and steganography of arbitrary quantum states. We investigate its potential applications in quantum communication and assess its benefits in the context of secret information transmission and eavesdropping detection in noisy channels. Although the MGPD method incorporates certain idealized assumptions and limitations, it provides novel perspectives on the concealment of quantum information. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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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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Pronounced advance on typhoon track forecast with global convection-permitting model

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Science Bulletin 2025年第9期70卷 1424-1428页

作者： Gu, Jun Zhao, Chun Li, Gudongze Feng, Jiawang Xu, Mingyue Du, Qiuyan Xia, Zihan Li, Yubin Chen, Guanghua Hao, Xiaoyu Chen, Junshi An, Hong Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software University of Science and Technology of China Hefei230026 China Laoshan Laboratory Qingdao266237 China CAS Center for Excellence in Comparative Planetology University of Science and Technology of China Hefei230026 China School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

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Triangle Matters! TopDyG: Topology-aware Transformer for Link Prediction on Dynamic Graphs 34

Triangle Matters! TopDyG: Topology-aware Transformer for Lin...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Xin Cai, Fei Zheng, Jianming Pan, Zhiqiang Chen, Wanyu Chen, Honghui Chen, Chonghao National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China College of Electronic Countermeasures National University of Defense Technology Hefei China

ISBN: (纸本)9798400712746

Dynamic graph link prediction is widely utilized in the complex web of the real world, such as social networks, citation networks, recommendation systems, etc. Recent Transformer-based link prediction methods on dynamic graphs not only fail to model the fine-grained structures such as triangles with the vanilla Transformers in the graph serialization process, but also amplify the imbalanced distribution of graphs because of their over-estimation of high-degree nodes. To tackle these issues, we propose a Topology-aware Transformer on Dynamic Graph (TopDyG) for link prediction, consisting of a topology injected Transformer (Ti-Transformer) and a mutual information learning (Mi-Learning). The Ti-Transformer explores the explicit structure of serialized graphs, capturing the topological features. The Mi-Learning mines the relationship between nodes by modeling the mutual information with a prior knowledge, alleviating the over-estimation of high-degree nodes when applying the Transformer-based models for the dynamic graph link prediction task. Extensive experiments on four public datasets containing both transductive and inductive settings present the superiority of our proposal. In particular, TopDyG presents an improvement of 43.27% and 28.75% against the state-of-the-art baselines in terms of NDCG and Jaccard, respectively. The advantages are especially obvious on the high-density graphs. © 2025 Copyright held by the owner/author(s).

关键词： Dynamic graphs Link prediction Topology Transformer

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