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arXiv 2025年

作者： Rahimi, Maryam Yaghoobzadeh, Yadollah Daliri, Mohammad Reza Biomedical Engineering Department School of Electrical Engineering Iran University of Science and Technology Tehran Iran Electrical and Computer Engineering Department University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

Large language models (LLMs) not only exhibit human-like performance but also share computational principles with the brain’s language processing mechanisms. While prior research has focused on mapping LLMs’ internal representations to neural activity, we propose a novel approach using explainable AI (XAI) to strengthen this link. Applying attribution methods, we quantify the influence of preceding words on LLMs’ next-word predictions and use these explanations to predict fMRI data from participants listening to narratives. We find that attribution methods robustly predict brain activity across the language network, revealing a hierarchical pattern: explanations from early layers align with the brain’s initial language processing stages, while later layers correspond to more advanced stages. Additionally, layers with greater influence on next-word prediction—reflected in higher attribution scores—demonstrate stronger brain alignment. These results underscore XAI’s potential for exploring the neural basis of language and suggest brain alignment for assessing the biological plausibility of explanation methods. © 2025, CC BY-SA.

关键词： Modeling languages

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Work-in-Progress Paper: Creating and Validating the Conceptual Assessment for Sedimentology courses 54

Work-in-Progress Paper: Creating and Validating the Conceptu...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Stepanova, Anna Anwar, Saira Laya, Juan Carlos Zarikian, Carlos A. Alvarez Hammond, Tracy Texas A&m University Department of Computer Science & Engineering College StationTX United States Texas A&m University Department of Multidisciplinary Engineering College StationTX United States Texas A&m University Department of Geology & Geophysics College StationTX United States Texas A&m University International Ocean Discovery Program College StationTX United States

ISBN: (纸本)9798350351507

Contribution: In this work-in-progress paper we describe the process of creating and validating a conceptual assessment in the field of sedimentology for undergraduate geoscience courses. The mechanism can aid future geoscience educators and researchers in the process of academic assessment development aligned with learning objectives in these courses. Background: Prior literature review supports the benefits of using active learning tools in STEM (science, Technology, engineering, and Mathematics) courses. This paper is part of a larger project to develop and incorporate research-based active learning software in sedimentology and other geoscience courses to improve grade point average (GPA) and time to graduation for Hispanic students at Texas A&M University. To evaluate the novel tool, we designed and validated the conceptual assessment instrument presented in this work. Research Question: What is the process to develop and validate a conceptual assessment for sedimentology? Methodology: This paper follows quantitative analysis and the assessment triangle approach and focuses on cognition, observation, and interpretation to design and evaluate the conceptual assessment. In the cognition element of the triangle, we explain the mechanism for creating the assessment instrument using students' learning objectives. The observation element explains the mechanism of data collection and the instrument revision. The interpretation element explains the results of the validation process using item response theory and reliability measures. We collected the conceptual assessment data from 17 participants enrolled in two courses where sedimentology topics are taught. Participants were geology majors in one of the courses and engineering majors in the other. Findings: The team developed a conceptual assessment that included eight multiple-choice (MCQ) and four open-ended response questions. The results of the design process described the conceptualization of questions and their

关键词： Sedimentology

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Elevating Robust Multi-Talker ASR by Decoupling Speaker Separation and Speech Recognition

arXiv

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arXiv 2025年

作者： Yang, Yufeng Taherian, Hassan Kalkhorani, Vahid Ahmadi Wang, DeLiang Department of Computer Science and Engineering The Ohio State University 2015 Neil Avenue ColumbusOH43210 United States Center for Cognitive and Brain Sciences The Ohio State University 1835 Neil Ave ColumbusOH43210 United States

Despite the tremendous success of automatic speech recognition (ASR) with the introduction of deep learning, its performance is still unsatisfactory in many real-world multi-talker scenarios. Speaker separation excels in separating individual talkers but, as a frontend, it introduces processing artifacts that degrade the ASR backend trained on clean speech. As a result, mainstream robust ASR systems train the backend on noisy speech to avoid processing artifacts. In this work, we propose to decouple the training of the speaker separation frontend and the ASR backend, with the latter trained on clean speech only. Our decoupled system achieves 5.1% word error rates (WER) on the Libri2Mix dev/test sets, significantly outperforming other multi-talker ASR baselines. Its effectiveness is also demonstrated with the state-of-the-art 7.60%/5.74% WERs on 1-ch and 6-ch SMS-WSJ. Furthermore, on recorded LibriCSS, we achieve the speaker-attributed WER of 2.92%. These state-of-the-art results suggest that decoupling speaker separation and recognition is an effective approach to elevate robust multi-talker ASR. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech recognition

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Learner Action Patterns in the Problem-Solving Process Related to program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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SlideMLP: A Pure Multi-layer Perceptrons Method For Medical Image Segmentation

SlideMLP: A Pure Multi-layer Perceptrons Method For Medical ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chaoqi Han Bingcai Chen Chanjuan Liu Qian Ning Victor C.M. Leung Shouzhen Jiao Qing Liu School of Computer Science and Technology Dalian University of Technology Dalian China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian China College of Electronics and Information Engineering Sichuan University Chengdu China Dept. of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Convolutional Neural Networks and Attention-based Transformer have emerged as the preferred models for medical image processing. Recently, specific network architectures relying solely on multilayer perceptrons (MLPs) have gained popularity and demonstrated excellent results in various computer vision tasks. In particular, CycleMLP has demonstrated good performance in dense prediction tasks owing to its adaptability to image size and linear computational complexity. However, the basic operator of CycleMLP has a fixed sampling location for any feature map and samples very few target organs in medical images characterized by an extreme imbalance between foreground and background. Therefore, effectively extracting the features of target organs becomes challenging. In this paper, we propose a new MLP-like module, SlideMLP, by considering the sparsity of target organs in medical images. This module extracts a set of offsets from the input feature maps and utilizes these offsets to re-select the sampling points. This approach effectively enhances the sampling rate of target organs while retaining the advantages of CycleMLP. Additionally, we constructed a U-shaped network with a pure MLP using this module and assessed its robustness using two datasets with different modalities. Comparative results with state-of-the-art methods demonstrate that the method proposed in this paper can achieve a substantial Dice Similarity Coe cient (DSC) while utilizing fewer parameters.

关键词： Image segmentation Neural networks Network architecture Multilayer perceptrons Feature extraction Transformers Robustness

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

Parallel Approximations for High-Dimensional Multivariate No...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

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Machine Learning with Physics Knowledge for Prediction: A Survey

arXiv

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arXiv 2024年

作者： Watson, Joe Song, Chen Weeger, Oliver Gruner, Theo Le, An T. Hansel, Kay Hendawy, Ahmed Arenz, Oleg Trojak, Will Cranmer, Miles D’Eramo, Carlo Bülow, Fabian Goyal, Tanmay Peters, Jan Hoffman, Martin W. Department of Computer Science Technical University of Darmstadt Germany Germany Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Centre for Cognitive Science Technical University of Darmstadt Germany ABB Corporate Research Center Mannheim Germany Department of Mechanical Engineering Technical University of Darmstadt Germany IBM Research UKI United Kingdom Data Intensive Science University of Cambridge United Kingdom

This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Kumar Isha Jain Durga Sritha Dongla Shivangi Rai Vani Rastogi Eva Kaushik Rohit Kaushik School of Engineering & Applied Science The George Washington University Washington D.C USA Computer Science & Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Computer Science & Engineering Department Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh Data Scientist DXC Technology Noida India CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.

关键词： Deep learning Pathology Accuracy Melanoma computer architecture Benchmark testing Skin Convolutional neural networks Lesions Medical diagnosis

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LSTM-based Eye-Movement Trajectory Analysis for Reading Behavior Classification

LSTM-based Eye-Movement Trajectory Analysis for Reading Beha...

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2022 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2022

作者： Hassan, Ahmad Fan, Wei Hu, Xiaoyu Wang, Wenhao Li, Hanxi School of Computer and Information Engineering Jiangxi Normal University 330022 China Research Center of Jiangxi Social Psychological Service System Construction 330022 China Jiangxi Key Laboratory of Psychology and Cognitive Science 330022 China School of Psychology Jiangxi Normal University 330022 China

ISBN: (纸本)9781510654846

Studying eye movement trajectory through eye tracker can provide insights into classification mechanism of memory and reasoning. For the given text or images, memory and reasoning based observations are useful in multiple key areas of psychology, cognition and related fields. For our project, the EyeLink 1000 Plus is used to inspect and capture fixation, saccades, blinks etc. in eye tracking protocols of each participant during trials. Based on fixation sequence of each participant our trained deep recurrent neural network architecture–LSTM classifies whether a participant is performing the given text-based task by memorizing it or by inference. To set trial sentences, syllogism– deductive reasoning type is followed. Sixty university students (mature readers) participated in both memory and reasoning-based trials. Participants were then divided into two equal groups and of which one group was instructed to perform memory task first while the second group was instructed to perform reasoning based task first and then their order of performing the given task was changed. From sixty-one different sentences, sixty randomly selected sentences were presented to each participant. Sequential signals from fixation of each participant were then processed to get the results. Our trained LSTM model’s high accuracy to classify the memory and the reasoning based reading of participants ensures the significance of our work which will provide a solid base for future works on eye movements to build intelligent techniques in the field of A.I backed psychology, healthcare and neuro-marketing. Our trained model has the potential for (a) achieving very high accuracy for memory and reasoning classification, (b) for data learning, it saves enormous time, (c) Economical and comfortable for trials to record data via EyeLink 1000 plus (d) and finally, possible future work opportunities are enlisted. © 2022 SPIE.

关键词： Eye movements

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