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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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Factors affecting the growth of online agricultural communities for knowledge sharing 34

Factors affecting the growth of online agricultural communit...

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34th Australasian Conference on Information Systems, ACIS 2023

作者： Pathirage, Samya Ginige, Athula School of Computing Data and Mathematical Sciences Western Sydney University Sydney Australia

Sustainable agricultural practices are becoming important in the face of growing global hunger. Localised agriculture community knowledge is essential to guide these practices, but some barriers must be overcome to harness the full potential. Online communities can overcome some knowledge-sharing barriers that physical communities cannot. However, there is a lack of studies to understand factors related to online-agricultural communities’ growth for knowledge sharing. We investigated five Facebook-based agricultural communities over 18 months, found growth patterns from member counts, and Cross-tabulated them with content characteristics. Results highlighted that distinct separation into knowledge creators and consumers stifles growth. Flourishing communities were marked by encouraging interaction for knowledge sharing, fostering social interaction, avoiding authoritative distinctions, promoting members to be prosumers, upholding explicit community norms and purposes, disseminating clear and relevant information, and possessing identifiable, self-explanatory names. Copyright © 2023 [Samya Pathirage, Athula Ginige].

关键词： Agriculture

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A HIGH ORDER SCHEME FOR FRACTIONAL DIFFERENTIAL EQUATIONS WITH THE CAPUTO-HADAMARD DERIVATIVE

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Journal of Computational Mathematics 2025年第3期43卷 615-640页

作者： Xingyang Ye Junying Cao Chuanju Xu School of Science Jimei UniversityXiamen 361021China School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen UniversityXiamen 361005China

In this paper,we consider numerical solutions of the fractional diffusion equation with theαorder time fractional derivative defined in the Caputo-Hadamard sense.A high order time-stepping scheme is constructed,analyzed,and numerically *** contribution of the paper is twofold:1)regularity of the solution to the underlying equation is investigated,2)a rigorous stability and convergence analysis for the proposed scheme is performed,which shows that the proposed scheme is 3+αorder *** numerical examples are provided to verify the theoretical statement.

关键词： Caputo-Hadamard derivative Fractional differential equations High order scheme Stability and convergence analysis

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data-Faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables 38

Data-Faithful Feature Attribution: Mitigating Unobservable C...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Sun, Qiheng Xia, Haocheng Liu, Jinfei Zhejiang University China Institute of Blockchain and Data Security China Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

The state-of-the-art feature attribution methods often neglect the influence of unobservable confounders, posing a risk of misinterpretation, especially when it is crucial for the interpretation to remain faithful to the data. To counteract this, we propose a new approach, data-faithful feature attribution, which trains a confounder-free model using instrumental variables. The cluttered effects of unobservable confounders in a model trained as such are decoupled from input features, thereby aligning the output of the model with the contribution of input features to the target feature in the data generation. Furthermore, feature attribution results produced by our method are more robust when focusing on attributions from the perspective of data generation. Our experiments on both synthetic and real-world datasets demonstrate the effectiveness of our approaches. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing Protein Language Model With Feature Integration for Anticancer Peptide Prediction

Enhancing Protein Language Model With Feature Integration fo...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Binte Sayuti, Tiara Natasha Cheng, Shen Ajith, Santhisenan Bin Abdul Samad, Abdul Hadi Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Health Informatics Lab Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350351552

In the fight against cancer, anticancer peptides (ACP) hold promising therapeutic potential due to their selective cytotoxicity and lower side effects compared to traditional treatments. However, identifying novel ACP is challenged by high costs and labor-intensive processes. Protein language models (PLMs), such as ESM-2 and ProtBERT, have revolutionized peptide prediction by leveraging vast datasets to capture complex biological patterns through pre-training. However, they often struggle to accurately model specific biochemical interactions. To address this limitation, we integrated four sequence-based features: amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid group pairs (CKS), and k-mer sparse matrix (k-mer) through a cross-attention mechanism. These features infuse biochemical insights that PLM alone may overlook, enabling a more detailed prediction of anti-cancer properties. This integration enhances biochemical insights, improving prediction accuracy by 15.8% for ProtBERT and 2.9% for ESM-2, with ESM-2 achieving the highest accuracy at 77.8%. SHapley Additive exPlanations (SHAP) analysis confirms the importance of these features, demonstrating that incorporating amino acid features into PLMs enhances ACP prediction. © 2024 IEEE.

关键词： Prediction models

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 IEEE.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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Conditional Prototypical Optimal Transport for Enhanced Clue Identification in Multiple Choice Question Answering 37th

Conditional Prototypical Optimal Transport for Enhanced Clu...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Yang, Wangli Yang, Jie Li, Wanqing Guo, Yi School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Data and Mathematical Sciences Western Sydney University Penrith Australia

ISBN: (纸本)9789819603473

This paper introduces the Conditional Prototypical Optimal Transport (CPOT) algorithm for clue identification in Multiple Choice Question Answering (MCQA) tasks. Existing clue-based methods suffer from inefficiencies, often relying on pseudo-labels or external resources, which introduce noise and additional computational demands. By contrast, the proposed CPOT method formulates clue identification as a sentence-oriented prototyping task, then further identifies sentences closest to prototype centroids as clues. Additionally, by leveraging the question and options as contextual guides, CPOT extends traditional Optimal Transport (OT) theory with constraints for unique assignment and uniform distribution across prototypes. This approach ensures semantically similar features converge within their prototypes and also maintains diversity among identified clues, enhancing answer accuracy. Empirical studies on several competitive benchmarks consistently demonstrate the superiority of our proposed method over different traditional approaches, with a substantial average improvement of 1.1—3.5 absolute percentage points in answering accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clue Identification Conditional Prototypical Optimal Transport Multiple Choice Question Answering

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Secure Resource Allocation via Constrained Deep Reinforcement Learning 6th

Secure Resource Allocation via Constrained Deep Reinforceme...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Aerial Image-Based Semantic Segmentation for Forest Fire Identification

Aerial Image-Based Semantic Segmentation for Forest Fire Ide...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Salma, Nadilla Asyifa Sulistiyo, Mahmud Dwi Risnandar Sthevanie, Febryanti Wulandari, Gia Septiana Telkom University School of Computing Bandung Indonesia Telkom University Research Center for Data and Information Sciences Brin School of Computing Bandung Indonesia

ISBN: (纸本)9798350327472

Fire is a serious disaster because it can endanger the safety of living things and cause great losses. Detection of forest fires using sensor-based devices or through analysis of weather data becomes less efficient because it is not real-time, its scope depends on sensor deployment, and sometimes it misses small fire spreads that are potentially dangerous. The key idea in this paper is to utilize a semantic segmentation approach to precisely detect forest fires and thus prevent the spread of larger fires. The proposed method uses aerial images since it is a practical way to tackle problems in fire monitoring and detection. Since similar research is still limited, let alone necessary to be implemented in countries potentially susceptible to forest fires, this study demonstrates deep learning-based semantic segmentation methods as preliminary research for identifying forest fires. We analyzed the implementation of the well-known FCN and U-Net models with some backbone options and used the FLAME dataset to train the models and evaluate them quantitatively. Based on the experimental results, FCN with VGG-16 backbone became the model with the highest Mean IoU value of 0.825. We also tested the best model on several aerial images arbitrarily taken from the internet to see the qualitative performance and analyze its possibility of being applied to situations outside the research dataset. © 2023 IEEE.

关键词： Semantic Segmentation

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Media Power Measuring via Emotional Contagion

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Journal of Social computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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