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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Dongxiao He Lianze Shan Jitao Zhao Hengrui Zhang Zhen Wang Weixiong Zhang College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science University of Illinois at Chicago Chicago IL School of Cybersecurity Northwestern Polytechnical University Xi'an China Department of Computing Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong

ISBN: (纸本)9798331314385

Graph Contrastive Learning (GCL) has emerged as a powerful approach for generating graph representations without the need for manual annotation. Most advanced GCL methods fall into three main frameworks: node discrimination, group discrim ination, and bootstrapping schemes, all of which achieve comparable performance. However, the underlying mechanisms and factors that contribute to their effectiveness are not yet fully understood. In this paper, we revisit these frameworks and reveal a common mechanism—representation scattering—that significantly enhances their performance. Our discovery highlights an essential feature of GCL and unifies these seemingly disparate methods under the concept of representation scattering. To leverage this insight, we introduce Scattering Graph Representation Learning (SGRL), a novel framework that incorporates a new representation scattering mechanism designed to enhance representation diversity through a center-away strategy. Additionally, consider the interconnected nature of graphs, we develop a topology-based constraint mechanism that integrates graph structural properties with representation scattering to prevent excessive scattering. We extensively evaluate SGRL across various downstream tasks on benchmark datasets, demonstrating its efficacy and superiority over existing GCL methods. Our findings underscore the significance of representation scattering in GCL and provide a structured frame-work for harnessing this mechanism to advance graph representation learning. The code of SGRL is at https://***/hedongxiao-tju/SGRL.

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Developing Conversational Speech Systems for Robots to Detect Speech Biomarkers of Cognition in People Living with Dementia

arXiv

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arXiv 2025年

作者： Perumandla, Rohith Bae, Young-Ho Izaguirre, Diego Hwang, Esther Murphy, Andrew Hsu, Long-Jing Sabanovic, Selma Bennett, Casey C. Department of Computing & Digital Media DePaul University ChicagoIL United States Department of Data Science Hanyang University Seoul Korea Republic of AI Technology Center POSCO DX Pohang Korea Republic of School of Informatics Computing & Engineering Indiana University BloomingtonIN United States

This study presents the development and testing of a conversational speech system designed for robots to detect speech biomarkers indicative of cognitive impairments in people living with dementia (PLwD). The system integrates a backend Python WebSocket server and a central core module with a large language model (LLM) fine-tuned for dementia to process user input and generate robotic conversation responses in real-time in less than 1.5 seconds. The frontend user interface, a Progressive Web App (PWA), displays information and biomarker score graphs on a smartphone in real-time to human users (PLwD, caregivers, clinicians). Six speech biomarkers based on the existing literature - Altered Grammar, Pragmatic Impairments, Anomia, Disrupted Turn-Taking, Slurred Pronunciation, and Prosody Changes - were developed for the robot conversation system using two datasets, one that included conversations of PLwD with a human clinician (DementiaBank dataset) and one that included conversations of PLwD with a robot (Indiana dataset). We also created a composite speech biomarker that combined all six individual biomarkers into a single score. The speech system's performance was first evaluated on the DementiaBank dataset showing moderate correlation with MMSE scores, with the composite biomarker score outperforming individual biomarkers. Analysis of the Indiana dataset revealed higher and more variable biomarker scores, suggesting potential differences due to study populations (e.g. severity of dementia) and the conversational scenario (human-robot conversations are different from human-human). The findings underscore the need for further research on the impact of conversational scenarios on speech biomarkers and the potential clinical applications of robotic speech systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Human robot interaction

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Speaking from Experience: Co-designing E-textile Projects with Older Adult Fiber Crafters 23

Speaking from Experience: Co-designing E-textile Projects wi...

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17th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2023

作者： Jelen, Ben Lazar, Amanda Harrington, Christina Pradhan, Alisha Siek, Katie A. Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN United States College of Information Studies University of Maryland College ParkMD United States HCI Institute Carnegie Mellon University College ParkMD United States School of Informatics Computing Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9781450399777

Researchers support race, gender, and age diverse groups of people to create with maker electronics. These groups include older adults, who are often overlooked as not interested or capable of learning new technologies due to ageist stereotypes. One approach, often involving e-textiles, leverages crafting as a bridge to broaden participation in making. We investigated ways to broaden participation in maker electronics for older adults by remotely co-designing e-textile projects with 6 older adult crafters over the course of 5 workshop sessions for a total of 45 hours. We developed a deeper understanding of their practices, identifying a Planner-Improviser Spectrum for how they approached their craft, and created medium fidelity prototypes. Our design implications draw on our participants' crafting experience and their experience in the workshop to highlight what e-textile toolkit designers can learn from skilled older adult crafters, such as selecting familiar materials, supporting aesthetic goals, and making electronics more attainable. © 2023 ACM.

关键词： Older adults electronic toolkits maker electronics remote workshop co-design crafting crafters crafting technology.

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Learning-Based Directional Improvement Prediction for Dynamic Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Ye, Yulong Liu, Songbai Zhou, Junwei Lin, Qiuzhen Jiang, Min Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China School of Informatics Xiamen University Fujian China Department of Computing The Hong Kong Polytechnic University Hong Kong China

In recent years, dynamic multiobjective evolutionary algorithms (DMOEAs) using the prediction strategy have shown promising performance for solving dynamic multiobjective optimization problems (DMOPs), as they can predict environmental changing trends in advance. However, most of them follow a regular change pattern and thus their performance is compromised when solving DMOPs with irregular change patterns (e.g., nonlinear correlations). To alleviate this challenge, this article proposes a DMOEA with a learnable prediction for tackling DMOPs. Specifically, a neural network is designed to effectively capture diverse change patterns of the environment. Based on the change patterns learned, a directional improvement prediction (DIP) is developed to guide the evolutionary search toward promising directions in the decision space. In this way, a superior initial population with good convergence and diversity is predicted by DIP, which can be more effective for solving various DMOPs. Comprehensive empirical studies show that the proposed DIP is effective and the proposed algorithm has some advantages over five competitive DMOEAs when solving three commonly used benchmarks and one real-world problem. IEEE

关键词： Neural networks

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DNPM: A Neural Parametric Model for the Synthesis of Facial Geometric Details

DNPM: A Neural Parametric Model for the Synthesis of Facial ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Haitao Cao Baoping Cheng Qiran Pu Haocheng Zhang Bin Luo Yixiang Zhuang Juncong Lin Liyan Chen Xuan Cheng School of Informatics Xiamen University China Department of Electronic Engineering Tsinghua University China China Mobile (Hangzhou) Information Technology Co. Ltd. China School of Computing and Data Sciences Xiamen University Malaysia Malaysia

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Parametric 3D models have enabled a wide variety of computer vision and graphics tasks, such as modeling human faces, bodies and hands. In 3D face modeling, 3DMM is the most widely used parametric model, but can’t generate fine geometric details solely from identity and expression inputs. To tackle this limitation, we propose a neural parametric model named DNPM for the facial geometric details, which utilizes deep neural network to extract latent codes from facial displacement maps encoding details and wrinkles. Built upon DNPM, a novel 3DMM named Detailed3DMM is proposed, which augments traditional 3DMMs by including the synthesis of facial details only from the identity and expression inputs. Moreover, we show that DNPM and Detailed3DMM can facilitate two downstream applications: speech-driven detailed 3D facial animation and 3D face reconstruction from a degraded image. Extensive experiments have shown the usefulness of DNPM and Detailed3DMM, and the progressiveness of two proposed applications.

关键词： Solid modeling Computer vision Three-dimensional displays Codes Computational modeling Encoding Parametric statistics

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Kalman-based Adaptive Moment Estimation Optimisation Algorithm to Enhance GPT in LLMs for Medical Sentiment Analysis of Patient Health-related Feedback

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

作者： Chen, Xingchi Ma, Wenxin Li, Dazhou Zhu, Fa Routray, Sidheswar Guduri, Manisha Margala, Martin The Pengcheng Laboratory Shenzhen518000 China The College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China The College of Information Science and Technology College of Artificial Intelligence Nanjing Forestry University Nanjing China The Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India The School of Computing and Informatics University of Louisiana LafayetteLA70503 United States

The progress in Natural Language Processing (NLP) using Large Language Models (LLMs) has greatly improved medical sentiment analysis of patient feedback extraction from health-related question and answer. However, using LLMs to analyze such data often requires significant training data and computational resources, resulting in considerable increases in training costs and durations, which is one of the primary issues in applying LLMs to real-world healthcare scenarios. To tackle these challenges, a novel optimization algorithm named KAdamEnGPT4LLM, based on Kalman filters and Adaptive Moment Estimation, is proposed to enhance training efficiency and reduce training costs of LLMs for analyzing patient feedback sentiment. Furthermore, the optimization algorithm KAdam-EnGPT4LLM is employed in training the LLM model GPT4ALL for medical sentiment analysis, resulting in the development of GPT4ALLMediSentAly-KAdam, which leds to faster convergence and more stable training specifically for medical questions and answer in the context of healthcare. The results show that our GPT4ALLMediSentAly-KAdam with the optimization algorithm KAdamEnGPT4LLM achieved better performance that include the best Accuracy, Recall, F1-score, and Runtime for both datasets, outperforming traditional fine-tuned LLMs such as the classic GPT4ALL, Ada, Babbage, Curie, and Duvinci. © 2013 IEEE.

关键词： Adaptive filters

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Development of 2D Indoor Monitoring and Positioning System (IMPS) Using Received Signal Strength Indicator (RSSI) Assisted by Internet of Things (IoT) 11th

Development of 2D Indoor Monitoring and Positioning System (...

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Wahab, Nur Haliza Abdul Ariffin, Sharifah Hafizah Syed Latiff, Liza Radzi, Raja Zahilah Raja Mohd Wessam, H. Aaina Saleh, N. School of Computing Faculty of Engineering Universiti Teknologi Malaysia Skudai Malaysia School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Skudai Malaysia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Skudai Malaysia School of Mechanical Engineering Faculty of Engineering Universiti Teknologi Malaysia Skudai Malaysia

ISBN: (纸本)9789811681288

As humans, nearly eighty percent of our day-to-day activities are conducted in an indoor environment, making indoor tracking and monitoring extremely necessary. The needs of having an Indoor Positioning System (IPS) has become crucial as the development is critically challenged due to the fact that satellite signals via Global Positioning System (GPS) cannot penetrate through a building. Variety techniques and approaches on IPS have been proposed but the most desirable approach used is the Received Signal Strength Indicator (RSSI) due to existing infrastructure already in place to provide a low-cost implementation. Internet of Things (IoT) itself has brought drastic changes and opened new opportunities for growth and innovation in technology. An integration between RSSI and IoT is the perfect combination of the development of IPS to allow an increased accuracy. This paper proposes an Indoor Monitoring and Positioning System (IMPS) using RSSI algorithm and triangulation technique integrated with IoT. The experimental results show that the proposed IMPS has achieved an average accuracy of 0.5 m for 2D. Future work has suggested an improvement in the IMPS with AR mapping integrated with Machine Learning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and Engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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Fruit Quality Assessment with Densely Connected Convolutional Neural Network 12

Fruit Quality Assessment with Densely Connected Convolutiona...

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12th International Conference on Electrical and Computer Engineering, ICECE 2022

作者： Morshed, Md. Samin Ahmed, Sabbir Ahmed, Tasnim Islam, Muhammad Usama Ashikur Rahman, A.B.M. Islamic University of Technology Department of Computer Science and Engineering Gazipur1704 Bangladesh University of Louisiana at Lafayette School of Computing and Informatics LafayetteLA United States King Fahd University of Petroleum & Minerals Department of Ics Dhahran Saudi Arabia

ISBN: (纸本)9798350398793

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications. © 2022 IEEE.

关键词： Fruits

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A Logical Investigation of Stock Market Prediction and Analysis using Supervised Machine Learning Algorithm

A Logical Investigation of Stock Market Prediction and Analy...

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Networking and Communications (ICNWC), International Conference on

作者： R. Dhanalakshmi V. Vasantha Kumar Saleem Basha N. Vijayaraghavan School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of CSE KCG College of Technology Chennai India Computing and informatics department Mazoon University college Muscat oman

In the Field of computer science, artificial intelligence (AI) is a broad field, which is concerned with structuring smart products and machines able to perform tasks which require the intellectual capability of humans. Artificial Intelligence is in need for Stock trading’s future. Robotic advisers examine millions of pieces of data using artificial intelligence and carry out the analysis at the best possible price. Market forecasts are more precise and effective thanks to the analysts. For providing higher returns, the trading firms efficiently mitigate the risk using Artificial Intelligence Techniques. Our proposed Stock Market system is a Web Application which uses the Random Forest Algorithm. The Random Forest Algorithm comes under Supervised Learning. With the system, one can forecast the stock prices for the subsequent days.

关键词： Computer science Machine learning algorithms Social networking (online) Supervised learning Forestry Prediction algorithms Stock markets

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