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IEEE International Conference on Systems, Man and Cybernetics

作者： Yingjia Xiang Xuanyu Lu Wei Zhou Junhao Wen School of Big Data and Software Engineering Chongqing University Chongqing China School of Computing China University of Geosciences Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

User behavior sequence modeling represents users' interests and preferences, which has a crucial impact on click-through rate (CTR) prediction models and recommendation systems. In practical scenarios, multiple types of interactive behaviors usually constitute users' complex interests and preferences. However, existing CTR prediction models based on multi-type interactive behavior modeling have two limitations: (1) they cannot handle noise in users' global interests, resulting in the inability to model accurate global interest preferences; (2) they struggle to depict users' local interest patterns and cannot model the complex patterns embedded in multi-behavior sequences. To overcome these limitations, an Adaptive Local and Global Interests Network For Click-Through Rate Prediction (ALGIN) is proposed, which effectively models global and local interests within multi-behavior interaction sequences through a unified framework. Firstly, ALGIN employs a Fourier Global Interest Modeling module to eliminate noise in the behavioral sequence; secondly, ALGIN designs a Multi-Scale Local Interest Extraction module to capture behavioral patterns within multi-type interactive sequences; finally, an Item-driven Interest Selector is used to integrate users' local interests with their global interests. Experiments on multiple public datasets demonstrate that the ALGIN model outperforms the current state-of-the-art (SOTA), and ablation experiments confirm the effectiveness of each module within the model.

关键词： Adaptation models Adaptive systems Fourier transforms Accuracy Convolution Noise Predictive models Recommender systems

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A Survey on Ai Enabled Secure Social Industrial Internet of Things in Agri-Food Supply Chain

SSRN

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SSRN 2025年

作者： Halder, Sajal Mamun, Quazi Mahboubi, Arash Walsh, Patrick Islam, M.D. Zahidul Islam, M.D. Rafiqul School of Computing Mathematics and Engineering Charles Sturt University NSW Australia Data61 CSIRO Melbourne Australia

The integration of Artificial Intelligence (AI) with secure Social Industrial Internet of Things (SIIoT) in the agri-food supply chain represents a significant step forward in tackling the challenges of efficiency, security, and sustainability. AI technologies such as machine learning, predictive analytics, and natural language processing are harnessed to analyze vast datasets generated by interconnected IIoT devices, providing real-time insights and facilitating well-informed decision-making. These insights help in forecasting crop yields, monitoring environmental conditions, optimizing resource usage, and preventing supply chain disruptions. Secure communication protocols and blockchain technologies are implemented to safeguard data privacy and integrity, ensuring that sensitive information is protected from cyber threats. However, there are limited research works that focus the AI, IoT and security issues in each step of smart farming. Most of the existing works focused on related technologies AI, IoT and security and focused on their applications but were not classified step-wise technologies use. In this paper, we demonstrate how these various technologies play crucial roles in enhancing efficiency, transparency, and security in each step of the agri-food industry. We present two real case studies and show how these technologies are involved in each step of the agri-food industry (e.g., smart farming and cold storage supply chain). Furthermore, we present a structured taxonomy that clarifies the interconnectedness of these technologies within the agri-food industry based on recent research works. Finally, we have discussed some challenges and potential future research directions with SIIoT in the agri-food industry. © 2025, The Authors. All rights reserved.

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Towards Efficient and Diverse Generative Model for Unconditional Human Motion Synthesis 24

Towards Efficient and Diverse Generative Model for Unconditi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yu, Hua Liu, Weiming Bai, Jiapeng Gui, Xu Hou, Yaqing Ong, Yewsoon Zhang, Qiang Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education School of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science Zhejiang University Hangzhou China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China College of Computing and Data Science Nanyang Technological University Singapore Singapore Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Recent generative methods have revolutionized the way of human motion synthesis, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields. However, there are still challenges in unconditionally generating highly diverse human motions from a given distribution. To enhance the diversity of synthesized human motions, previous methods usually employ deep neural networks (DNNs) to train a transport map that transforms Gaussian noise distribution into real human motion distribution. According to Figalli's regularity theory, the optimal transport map computed by DNNs frequently exhibits discontinuities. This is due to the inherent limitation of DNNs in representing only continuous maps. Consequently, the generated human motions tend to heavily concentrate on densely populated regions of the data distribution, resulting in mode collapse or mode mixture. To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis. First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex optimization to match the noise distribution with the latent space distribution of human motions through the Optimal Transport (OT) map. Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion. Through qualitative and quantitative experiments, MOOT achieves state-of-the-art results surpassing the latest methods, validating its superiority in unconditional human motion generation. © 2024 ACM.

关键词： Generative adversarial networks

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A modelling analysis to predict the traffic using K-NN algorithm

A modelling analysis to predict the traffic using K-NN algor...

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Narmatha, R. Prakash, M. Vennila, V. Tamil Nadu Dharmapuri India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

In this modern world due to Road traffic, many people are unable to reach their destination at the correct time. For example, if a person needed to reach the hospital in critical condition due to road traffic, they are unable to reach the hospital and got stuck in traffic which will lead to death. We lost a lot of people because of this road traffic. So, Road Safety is the most important part of our daily life. Conjointly, this prediction accomplished enhanced the safety and security of women empowerment which acted as a security guard for people especially women, protecting themselves in every circumstance. Here, statistical analysis of road traffic networks of selective locations is implemented to prognosticate road traffic. Alert and divert the people who were identified in the traffic zone and found the location of the people who were identified in the unsafe zone. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Hospitals

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An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai 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 Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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Peak Evaluation for Liquid Chromatography-Mass Spectrometry data Using Neural Network on Statistical Quality Metrics 18

Peak Evaluation for Liquid Chromatography-Mass Spectrometry ...

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18th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2024

作者： Dong, Liheng Dai, Juehong Guan, Teo Bee Guo, Lei Wu, Caisheng Xu, Jingjing School of Computing and Data Science Xiamen University Malaysia Sepang43600 Malaysia Xiamen University Department of Electronic Science Xiamen361005 China Interdisciplinary Institute for Medical Engineering Fuzhou University Fuzhou350108 China School of Pharmaceutical Sciences Xiamen University Xiamen361005 China

ISBN: (纸本)9798331505639

Peak picking is a crucial step in the data analysis workflow of Liquid Chromatography-Mass Spectrometry (LCMS) in untargeted metabolomics. The process is challenging due to interference from background signals such as noise and matrix effects. To address this challenge, we first collected 5,447 ion peaks and manually scored them after alignment. Next, we calculated 12 quality metrics for each ion peak. Finally, we developed a Multilayer Perceptron (MLP) model to perform regression analysis, as well as binary and ternary classification, making it adaptable to various application scenarios. The results demonstrate that the MLP-based peak evaluation method is effective in accurately predicting peak quality. Researchers can quickly initiate their work by setting just one or two score thresholds based on their specific needs. This approach retains the flexibility to handle different datasets without the need to adjust thresholds for each peak quality metric, representing a step towards more flexible and efficient peak picking. © 2024 IEEE.

关键词： Regression analysis

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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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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Monte Carlo methods Supervised learning Knowledge graphs Signal processing Cognition Question answering (information retrieval) Problem-solving Mirrors Speech processing Optimization

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Learning Self-Supervised Vision Transformers from Scratch for Aerial Person Re- Identification 13

Learning Self-Supervised Vision Transformers from Scratch fo...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Peng, Houfu Xu, Lili Lu, Xing Xia, Daoxun Guizhou Normal University Guizhou Key Laboratory of Information and Computing Science Guiyang China School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

In recent years, person re-identification (Re-ID) as a widely studied computer vision task, has reached a saturation state under closed-world setting, which encourages researchers to further explore more realistic scenarios. Among them, person Re- Idin aerial imagery is proposed and improved due to its unique practical importance in public security. However, since the aerial person images are taken by unmanned aerial vehicles (UAV s), influenced by camera height and angle of view, there are more serious problems such as weak appearance feature and occlusion than ground person images. Most of the current state-of-the-art person Re-ID methods on closed-world datasets are based on local convolution neural network, and hardly works well when applying them to aerial person Re- Idtasks directly. In this paper, we improve the emerging vision transformer (ViT) and apply it to the person Re- Idin aerial imagery. It should be noted that a large amount of data is required to be pretrained for ViTs to achieve competitive performance. Considering the limitations of data, computing power and flexibility in practical scenarios, we improve the pre-training process based on self-supervised learning, and achieve training ViTs from scratch with limited data. Specifically, in pre-training stage, the self-supervised paradigm based on parameter instance discrimination is applied to capture feature alignment and instance similarity, which alleviates the data-hungry of ViTs caused by the lack of inductive bias. Extensive comparative evaluation experiments are conducted on the aerial Re- Iddataset. Our method achieves a Rank-1 accuracy of 65.29% and a mean average precision (mAP) of 57.31%, which proves its effectiveness in aerial person Re-ID tasks. © 2023 IEEE.

关键词： Antennas

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

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Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis

arXiv

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arXiv 2024年

作者： Li, Fengjie Jiang, Jiajun Sun, Jiajun Zhang, Hongyu College of Intelligence and Computing Tianjin University China School of Big Data and Software Engineering Chongqing University China

Automated Program Repair (APR) has garnered significant attention due to its potential to streamline the bug repair process for human developers. Recently, LLM-based APR methods have shown promise in repairing real-world bugs. However, existing APR methods often utilize patches generated by LLMs without further optimization, resulting in reduced effectiveness due to the lack of program-specific knowledge. Furthermore, the evaluations of these APR methods have typically been conducted under the assumption of perfect fault localization, which may not accurately reflect their real-world effectiveness. To address these limitations, this paper introduces an innovative APR approach called GIANTREPAIR. Our approach leverages the insight that LLM-generated patches, although not necessarily correct, offer valuable guidance for the patch generation process. Based on this insight, GIANTREPAIR first constructs patch skeletons from LLM-generated patches to confine the patch space, and then generates high-quality patches tailored to specific programs through context-aware patch generation by instantiating the skeletons. To evaluate the performance of our approach, we conduct two large-scale experiments. The results demonstrate that GIANTREPAIR not only effectively repairs more bugs (an average of 27.78% on Defects4J v1.2 and 23.40% on Defects4J v2.0) than using LLM-generated patches directly, but also outperforms state-of-the-art APR methods by repairing at least 42 and 7 more bugs under perfect and automated fault localization scenarios, respectively. © 2024, CC BY.

关键词： Repair

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