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Exploring online communication in Asperger's syndrome: A combined approach with large language models and time series analysis

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Information Processing and Management 2025年第5期62卷

作者： Zhang, Xupin Jiang, Tian Zhou, Guanghao Zheng, Yanyu Luo, Jiebo School of Economics and Management East China Normal University Shanghai China Department of Counseling & Human Development University of Rochester RochesterNY United States School of Data Science & Engineering East China Normal University Shanghai China Department of Computer Science University of Rochester RochesterNY United States

Individuals with Asperger's syndrome have difficulty understanding and expressing emotions, which affects their social interactions. Online communities, such as Wrong Planet, provide a supportive space for these individuals to express their feelings. This study analyzes 496,924 texts from 761 self-identified individuals with Asperger's Syndrome to explore the unique social, linguistic, and emotional characteristics of their communication. Using large language models (LLMs) such as LLama2 and combined with LIWC features using XGBoost, we achieved a Normalized Mean Square Error (NMSE) of 0.0273. Our results show that external factors such as monetary and family concerns have a significant impact on emotional fluctuations. In addition, individuals with Asperger's syndrome often use more colloquial language, express negative emotions directly, and use fewer prepositions, articles, and numerals. These findings help to understand the communication patterns and emotional expressions of individuals with Asperger's syndrome and provide a basis for developing personalized interventions and strategies. © 2025

关键词： Emotion Recognition

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Multimodal Brain Tumor Segmentation Using a Dual-Branch Vision Transformer Integrated with Region-Attention Fusion Network

Multimodal Brain Tumor Segmentation Using a Dual-Branch Visi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sunita Yadwad Rajeswari C D. Nirmal Raj Pravin Dange Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Balaji Institute of Management and Human Resource Development (BIMHRD) Pune India Symbiosis Institute of Management Studies Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Accurate segmentation of brain tumors is a critical task in medical imaging, aiding diagnosis, treatment planning, and prognosis. This paper introduces a novel framework for brain tumor segmentation that leverages a Dual-Branch Vision Transformer (DB-ViT) integrated with a Region-Attention Fusion Network (RAFN) to address the limitations of conventional convolutional neural networks. The DB-ViT processes multimodal MRI sequences, including T1, T1c, T2, and FLAIR, to extract global contextual features while preserving modality-specific information. Simultaneously, the RAFN enhances segmentation precision by refining local features and focusing on critical regions of interest through an attention mechanism. The proposed method effectively combines global and local feature representations, ensuring accurate delineation of tumor boundaries. Extensive experiments were conducted on the BraTS dataset, demonstrating the superiority of our approach over existing methods such as U-Net, Attention U-Net, and TransUNet. Quantitative evaluation metrics, including Dice Score and Intersection over Union (IoU), indicate significant improvements in segmentation accuracy. The framework also achieves computational efficiency and robustness across diverse tumor morphologies. Visual results further validate its ability to enhance boundary delineation, particularly in heterogeneous tumor regions. The proposed methodology represents a significant advancement in automated medical image analysis, offering potential for real-time clinical applications. Future work aims to explore broader clinical validation and real-time deployment of this framework.

关键词： Measurement Image segmentation computer vision Accuracy Attention mechanisms Refining Brain tumors Transformers Real-time systems Planning

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Enhancing Image Search Engine Accuracy Using Psychological Intention Diagrams

Enhancing Image Search Engine Accuracy Using Psychological I...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Aswathi KP Sunita Yadwad D. Nirmal Raj Shailesh Rastogi Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer science and Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of Management and Human Resource Development (BIMHRD) Pune India Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

As the Internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies.

关键词： Visualization Accuracy Digital images Semantics Image retrieval Psychology Machine learning Search engines Internet Standards

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MODELING OF ENHANCED FRACTAL CHEETAH OPTIMIZER WITH DEEP LEARNING FOR INTEGRATED ARABIC DIALECT IDENTIFICATION

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Fractals 2025年第2期33卷

作者： Alahmari, Saad Al-Shathry, Najla I. Eltahir, Majdy M. Alruwais, Nuha Sayed, Ahmed Abdelbagi, Sitelbanat Department of Computer Science Applied College Northern Border University Arar91431 Saudi Arabia Department of Language Preparation Arabic Language Teaching Institute Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Applied College Mahayil King Khalid University Abha Saudi Arabia Department of Computer Science and Engineering College of Applied Studies and Community Services King Saud University Saudi Arabia P.O. Box 22459 Riyadh11495 Saudi Arabia Research Center Future University in Egypt New Cairo11835 Egypt Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia

The Arabic language has different variants that can roughly be classified into three major types, namely Modern Standard Arabic (MSA), Dialectal Arabic (DA), and Classical Arabic (CA). There are slight variations between CA and MSA regarding pronunciation, syntax, and terminology. On the other hand, DA is quite different from MSA and CA in that it reflects the country of origin, or at least the geographic location of the speaker if the mobility factor is considered. Deep learning demonstrated its effectiveness in DA detection tasks, leveraging neural network models such as transformer models, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Researchers have established models that automatically discriminate between Arabic dialects by processing contextual information, linguistic features, and phonetic patterns embedded in large datasets. Using Deep Learning (DL) in Arabic dialect detection contributes in various complex systems to the development of natural language processing. It assists in addressing the problems related to a wide variety of Arabic dialects, which facilitate applications including machine translation, speech recognition, and sentimental analysis tailored to regional linguistic variation. This study suggests an Enhanced Fractal Cheetah Optimization Algorithm with Deep Learning for Integrated Arabic Dialect Identification (ECOADL-IADI) technique. The ECOADL-IADI technique mainly intends to classify the variations of the Arabic language into multiple classes. At the preliminary level, the ECOADL-IADI technique performs preprocessing of Arabic text. Next, the BERT word embedding process is carried out. The ECOADL-IADI technique applies a Bidirectional Recurrent Neural Network (BiRNN) model for Arabic dialect identification. Lastly, the ECOA-based hyperparameter selection process improves the classifier outcomes of the BiRNN model. A wide range of experiments were conducted to examine the detection results of the ECODL-IADI t

关键词： Recurrent neural networks

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Scalable Subspace Clustering via Purity Kernel Tensor Learning

Scalable Subspace Clustering via Purity Kernel Tensor Learni...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rachit Adhvaryu S Rajes Kannan Sanjay C. P Pravin Dange Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Balaji Institute of Management and Human Resource Development (BIMHRD) Sri Balaji University Pune India Symbiosis Institute of Management Studies Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Subspace clustering is a fundamental task in machine learning, particularly for high-dimensional datasets where the goal is to group data points that lie in distinct subspaces. Traditional methods, such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR), have demonstrated good performance but often suffer from high computational costs, particularly when applied to large-scale data. To address these limitations, this paper introduces a novel technique called Purity Kernel Tensor Learning (PKTL). PKTL combines the power of tensor decomposition with a purity kernel function to improve both the efficiency and effectiveness of subspace clustering. By utilizing tensor-based representations, the method captures higher-order relationships in the data, allowing for a more robust clustering process. The purity kernel function measures the coherence within the clusters, enhancing the identification of subspaces in complex datasets. The proposed approach not only reduces computational complexity but also improves the clustering accuracy compared to existing methods. Experiments on synthetic and real-world datasets, including ORL, Yale, and MNIST, show that PKTL outperforms conventional subspace clustering algorithms, both in terms of clustering accuracy and scalability. The results demonstrate the potential of PKTL in handling large datasets while maintaining high performance, making it a promising approach for scalable subspace clustering in real-world applications.

关键词： Tensors Accuracy Scalability Noise Clustering algorithms Machine learning Robustness Computational efficiency Noise measurement Kernel

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User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions 25

User-Driven Value Alignment: Understanding Users' Perception...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Xianzhe Fan Qing Xiao Xuhui Zhou Jiaxin Pei Maarten Sap Zhicong Lu Hong Shen Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Language Technologies Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Stanford University Stanford California USA Language Technologies Institute Carnegie Mellon Unviersity Pittsburgh Pennsylvania USA Department of Computer Science George Mason University Fairfax Virginia USA

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On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Bucur, Ana-Maria Moldovan, Andreea-Codrina Parvatikar, Krutika Zampieri, Marcos Khudabukhsh, Ashiqur R. Dinu, Liviu P. University of Bucharest Interdisciplinary School of Doctoral Studies Bucharest050663 Romania Universitat Politècnica de València PRHLT Research Center València46020 Spain Rochester Institute of Technology RochesterNY14623 United States George Mason University Fairfax22030 United States University of Bucharest Human Language Technology Research Center Department of Computer Science Faculty of Mathematics and Computer Science Bucharest050663 Romania

Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a review on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This review contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics. © 2013 IEEE.

关键词： COVID-19

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human Decision-making is Susceptible to AI-driven Manipulation

arXiv

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arXiv 2025年

作者： Sabour, Sahand Liu, June M. Liu, Siyang Yao, Chris Z. Cui, Shiyao Zhang, Xuanming Zhang, Wen Cao, Yaru Bhat, Advait Guan, Jian Wu, Wei Mihalcea, Rada Althoff, Tim Lee, Tatia M.C. Huang, Minlie The CoAI Group DCST Institute for Artificial Intelligence Tsinghua University Beijing China State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Hong Kong Laboratory of Neuropsychology and Human Neuroscience The University of Hong Kong Hong Kong The LIT Group Department of Computer Science and Engineering University of Michigan Ann Arbor United States Department of Psychology University of International Relations Beijing China Department of Chinese Language and Literature Northwest Minzu University Lanzhou China Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States ANT Group China

Artificial Intelligence (AI) systems are increasingly intertwined with daily life, assisting users in executing various tasks and providing guidance on decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users’ cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized controlled trial with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) employing established psychological tactics to reach its hidden objectives. To ensure participant well-being, this study involved hypothetical scenarios, pre-screening for at-risk individuals, and comprehensive post-study debriefing. By analyzing participants’ decision patterns and shifts in their preference ratings post-interaction, we found significant susceptibility to AI-driven manipulation. Particularly, across both decision-making domains, participants interacting with the manipulative agents shifted toward harmful options at substantially higher rates (financial, MA: 62.3%, SEMA: 59.6%;emotional, MA: 42.3%, SEMA: 41.5%) compared to the NA group (financial, 35.8%;emotional, 12.8%). Notably, our results reveal that even simple manipulative objectives (MA) can be as effective as employing established psychological strategies (SEMA) in swaying human decision-making. These findings suggest that AI-driven manipulation could become widely spread, as it does not require sophisticated tactics and expertise to influence users. By revealing the potential for covert AI influence, this study highlights a critical vulnerability in human-AI interactions, emphasiz

关键词： Finance

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STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics

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Nature Communications 2025年第1期16卷 1-17页

作者： Su, Haohao Wu, Yuesong Chen, Bin Cui, Yuehua Department of Statistics and Probability Michigan State University East Lansing 48824 MI United States Department of Pharmacology and Toxicology Michigan State University East Lansing 48824 MI United States Department of Computer Science and Engineering Michigan State University East Lansing 48824 MI United States Department of Pediatrics and Human Development Michigan State University Grand Rapids 49503 MI United States

One of the major challenges in spatial transcriptomics is to detect spatially variable genes (SVGs), whose expression patterns are non-random across tissue locations. Many SVGs correlate with cell type compositions, introducing the concept of cell type-specific SVGs (ctSVGs). Existing ctSVG detection methods treat cell type-specific spatial effects as fixed effects, leading to tissue spatial rotation-dependent results. Moreover, SVGs may exhibit random spatial patterns within cell types, meaning an SVG is not always a ctSVG, and vice versa, further complicating detection. We propose STANCE, a unified statistical model for both SVGs and ctSVGs detection under a linear mixed-effect model framework that integrates gene expression, spatial location, and cell type composition information. STANCE ensures tissue rotation-invariant results, with a two-stage approach: initial SVG/ctSVG detection followed by ctSVG-specific testing. We demonstrate its performance through extensive simulations and analyses of public datasets. Downstream analyses reveal STANCE’s potential in spatial transcriptomics analysis. © The Author(s) 2025.

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Association of risk factors with mental illness in a rural community: Insights from machine learning models

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BJPsych Open 2025年第3期11卷 e96-e96页

作者： Al-Mamun, Firoj Mamun, Mohammed A. Hasan, Md Emran Almerab, Moneerah Mohammad Islam, Johurul Muhit, Mohammad CHINTA Research Bangladesh Savar Dhaka Bangladesh Department of Public Health and Informatics Jahangirnagar University Savar Dhaka Bangladesh Department of Public Health University of South Asia Dhaka Bangladesh School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Psychology College of Education and Human Development Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia CSF Global Banani Dhaka Bangladesh

Background Mental health conditions, particularly depression and anxiety, are highly prevalent and impose substantial health burdens globally. Despite advancements in machine learning, there is limited application of these methods in predicting common mental illnesses within community populations in low-resource settings. Aims This study aims to examine the prevalence and associated risk factors of common mental illnesses collectively (depression and anxiety) in a rural Bangladeshi community using machine learning models. Method This cross-sectional study surveyed 490 adults aged 18-59 in a rural Bangladeshi community. Depression and anxiety were assessed using the Patient Health Questionnaire (PHQ-2) and Generalised Anxiety Disorder (GAD-2) scales. Machine learning models, including Categorical Boosting, the support vector machine, the random forest and XGBoost (eXtreme Gradient Boosting), were trained on 80% of the data-set and tested on 20% to evaluate predictive accuracy, precision, F1 score, log-loss and area under the receiver operating characteristic curve (AUC-ROC). Results Some 20.4% of participants experienced at least one common mental illness. Feature importance analysis identified house type, age group and educational status as the most significant predictors. SHAP (Shapley Additive exPlanations) values highlighted their influence on model outputs, and the XGBoost gain metric confirmed the importance of marital status and house type, with gains of 0.76 and 0.73, respectively. XGBoost delivered the best performance, achieving an F1 score of 71.01%, precision of 71.58%, accuracy of 71.15% and the lowest log-loss value of 0.56. The random forest had an accuracy of 78.21% and an AUC-ROC of 0.90. Conclusions The findings of this study suggest targeted interventions addressing housing and social determinants could improve mental health outcomes in similar rural settings. Further studies should consider longitudinal data to explore causal relationships. © The

关键词： anxiety depression machine learning Mental health rural community

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