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8th International Conference on Computer and Communication Systems, ICCCS 2023

作者： Zheng, Peng Chen, Yanhao Kumar, Burra Venkata Durga School of Computing & Data Science Xiamen University Malaysia Selangor Sepang Malaysia

ISBN: (纸本)9781665456128

Urban traffic congestion is becoming a severe problem. A reinforcement learning-based intelligent traffic signal control system can alleviate traffic congestion problems. However, when reinforcement learning faces complex multi-agent decision problems, it is difficult for single-agent reinforcement learning algorithms to realize the complex relationships among multiple agents. Therefore, integrating reinforcement learning with multi-agent techniques in multiple ways is becoming inevitable. Based on reinforcement learning, multi-agents obtain optimal regional strategies through continuous interaction and improvement. This paper proposes a new approach to regional intelligent traffic signal control combining multi-agent techniques and deep reinforcement learning. The method effectively reduces the average trip waiting time (ATWT) and the total waiting queue length (TWQL) of vehicles in the region while performing better on the reward function compared to the deep Q network (DQN) algorithm. This approach improves traffic efficiency and increases resource utilization efficiency while reducing congestion time, road utilization, and resource waste. © 2023 IEEE.

关键词： Reinforcement learning

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Face Detection and Classification of Sleep State of Humans by Deep Learning Mechanisms

Face Detection and Classification of Sleep State of Humans b...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Zhaoqi, Zho Kumar, Burra Venkata Durga Xiamen University Malaysia School of Computing and Data Science DE Selangor Malaysia

ISBN: (纸本)9798350322309

1/3 of lifetime of a person is spent on the sleep, and thus there is no doubt that sleep is one of the most important things for every person. However, due to various causes like working overtime or pressure or life, modern people tend to be in short of sleep but unaware of this fact. This project will try to solve this problem with the CNN model based on deep learning mechanism. The project focuses on the classification of sleep state, and the sleep state can be classified as adequate sleep, not adequate sleep and badly need sleep. The person who badly need sleep is required to go to bed at once in case that he or she slips into a coma suddenly with risk of losing life. To test the dataset, this paper requires the help from others who are willing to fill in the form of questionnaire and their personal pictures. This project get inspiration from previous framework and explore the factors that influence the accuracy of classification, including known factors and new factors. The goal of the project is to provide a more convenient way for people to do the classification of sleep state in their daily life. © 2023 IEEE.

关键词： Face recognition

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Road Network Representation Learning with the Third Law of Geography 38

Road Network Representation Learning with the Third Law of G...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Haicang Huang, Weiming Chen, Yile He, Tiantian Cong, Gao Ong, Yew-Soon College of Computing and Data Science Nanyang Technological University Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier Al Research Agency for Science Technology and Research Singapore

Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemphasis on the distance effect as outlined in the First Law of Geography. In response, we propose to endow road network representation with the principles of the recent Third Law of Geography. To this end, we propose a novel graph contrastive learning framework that employs geographic configuration-aware graph augmentation and spectral negative sampling, ensuring that road segments with similar geographic configurations yield similar representations, and vice versa, aligning with the principles stated in the Third Law. The framework further fuses the Third Law with the First Law through a dual contrastive learning objective to effectively balance the implications of both laws. We evaluate our framework on two real-world datasets across three downstream tasks. The results show that the integration of the Third Law significantly improves the performance of road segment representations in downstream tasks. Our code is available at https://***/Haicang/Garner. © 2024 Neural information processing systems foundation. All rights reserved.

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Counseling Responses for Mental Health Forum Questions with Early Maladaptive Schema Prediction 27

Counseling Responses for Mental Health Forum Questions with ...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Gollapalli, Sujatha Das Ang, Beng Heng Du, Mingzhe Ng, See-Kiong Institute of Data Science National University of Singapore Singapore Integrative Sciences and Engineering Programme National University of Singapore Singapore College of Computing and Data Science Nanyang Technological University China

ISBN: (纸本)9781643685489

State-of-the-art Large Language Models (LLMs) have shown remarkable capabilities for general Question Answering (QA) tasks. However, their practical use for answering mental health questions has been limited due to the missing link between LLM-generated answer responses and well-established theories and guiding principles from Psychology and Counseling. We present a first step in this direction with STeer, an AI-based method that supports Schema Therapy-enabled responses for mental health questions on community QA forums. STeer uses Early Maladaptive Schemas (EMSs), a fundamental concept from Schema Therapy that characterizes "self-defeating, unhealthy patterns of thought and behavior" in individuals, to effectively prompt state-of-the-art LLMs to generate specific, theoretically-grounded, empathetic counseling responses to mental health questions. We present EMSRank, a novel method based on the Personalized PageRank algorithm, to automatically predict the EMSs from mental health forum question texts. We show that EMSRank is computationally scalable and can be further combined with textual entailment to obtain high precision, explainable EMS labels for mental health forum questions. To address the current lack of annotated datasets, we also leveraged on EMSRank to create a first-of-its-kind, large dataset of about 23K EMS-annotated mental health questions from three diverse, currently operating, peer-support community forums for mental health. With the global rise in mental health issues, our work is a timely step towards enabling the use of AI-based assistive tools for counseling support on mental health community forums. © 2024 The Authors.

关键词： Electronic health record

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of Computer science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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Evaluation of Deep-learning based Lane Detection under Low-light Environments

Evaluation of Deep-learning based Lane Detection under Low-l...

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2022 International Conference on Computer Vision, Application, and Algorithm, CVAA 2022

作者： Wu, Yubing School of Computing and Data Science Xiamen University Malaysia Sepang43900 Malaysia

ISBN: (纸本)9781510664036

Lane detection is an important technology in autonomous driving. Currently, the mainstream lane detection methods are based on deep learning. However, most of deep learning-based lane detection models are trained on images captured on sunny days with enough light. It is concerned about their performance when being used to detect lane lines in low-light environments. In this study, images of lane lines captured in the evening and on rainy days are used to evaluate the performance of a representative LaneNet model. The result shows that the model performed badly when detecting lane lines on rainy days or in the evening. When there is enough lamplight in the evening, the performance of the model is better with part of lane lines being detected, but it still cannot detect correctly as it does on sunny days. Two potential future directions are also proposed in this study. © 2023 SPIE.

关键词： Deep learning

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A Keypoint-Based Object Detection Algorithm 7

A Keypoint-Based Object Detection Algorithm

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Hou, Xiangdan Chen, Quansong Liu, Hongpu School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Hebei Province Key Laboratory of Big Data Computing Tianjin China

ISBN: (纸本)9798350370782

An improved algorithm is proposed for the omission and re-detection problems in the point cloud object detection method CenterPoint. The algorithm firstly adds Focal sparse convolution module to the feature extraction network to increase the proportion of important information in the features;then redefines the generation function of the detection head heatmap, and the supervisory signal generated by the elliptical Gaussian kernel is more closely matched to the real shape of the object to inhibit the generation of negative samples;finally, the overall regression of the bounding box information is applied to the training, so that the back propagation loss gain of the single dimensional information is regulated by the associated information. The improved algorithm is trained and evaluated on the KITTI 3D object detection dataset, and the overall mean Average Precision (mAP) is improved by 3.29% compared with the base model CenterPoint. The experimental results show that the improved algorithm effectively suppresses the redetection and omission problems of CenterPoint. © 2024 IEEE.

关键词： CenterPoint Focal sparse convolution Gaussian kernel object detection overall regression

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of Computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Hop-level Direct Preference Optimization for Knowledge Graph Reasoning with Trees

Hop-level Direct Preference Optimization for Knowledge Graph...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shen, Tiesunlong Wang, Jin Zhang, Xuejie Cambria, Erik School of Information Science and Engineering Yunnan University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Recent advancements in knowledge graph question answering (KGQA) have shown promise, yet existing methods often fail to align with human reasoning patterns. This study proposes HD-PORT (hop-level direct preference optimization for knowledge graph reasoning with trees), a novel approach that combines Monte Carlo Tree Search (MCTS) with hop-level direct preference optimization (HDPO) for KGQA tasks. MCTS simulates human-like reasoning by exploring multiple inference paths in knowledge graphs, generating interpretable reasoning chains and rich hop-level preferences. HDPO then leverages these preferences for model optimization, addressing the limitations of traditional supervised learning and existing DPO methods that focus on overall solution preferences. By optimizing preferences at each reasoning step, HD-PORT more closely mirrors human problem-solving strategies. Experimental results on benchmark datasets demonstrate that HD-PORT significantly outperforms state-of-the-art methods in both accuracy and interpretability, particularly for complex, multi-hop reasoning tasks. © 2025 IEEE.

关键词： Graph signal processing Knowledge graph Large language model Reasoning

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