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Semiconductor and Electronic Technology (ISSET), International Symposium on

作者： Qiang Li Xinqiang Ma Meng Yan School of Big Data and Software Engineering Chongqing University Chongqing China School of Mathematics and Artificial Intelligence Chongqing University of Arts and Sciences Chongqing China

ISBN: (数字)9798350390285

ISBN: (纸本)9798350390292

The integrated circuit packaging testing technology is highly demanding, and the task is arduous. The existing sorting efficiency of integrated circuit packaging testing is low, and it is difficult to detect unqualified products for rapid centralized processing. There is an urgent need for upgrading and improvement. This paper proposes to fine-tune the large language model and establish a large language model monitoring system for integrated circuit packaging and inspection, embedding it in a CNC system, thereby providing support for the intelligent transformation and upgrading of integrated circuit production and processing.

关键词： Semiconductor device modeling Production systems Large language models Knowledge graphs Packaging Integrated circuit packaging Integrated circuit modeling Monitoring Testing Sorting

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Multi-Stage Transfer Learning Evolutionary Algorithm for Dynamic Multiobjective Optimization

Multi-Stage Transfer Learning Evolutionary Algorithm for Dyn...

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Congress on Evolutionary Computation

作者： Qianhui Wang Qingling Zhu Junkai Ji College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen PR China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Recently, the application of transfer learning within dynamic multiobjective evolutionary algorithms (DMOEAs) has shown significant potential to solve dynamic multiobjective optimization problems (DMOPs). This approach utilizes the transfer learning technique which reuses information from previous environments to accelerate the search process in the new environment. However, the risk of negative transfer can lead to an erroneous search direction and inefficient use of computational resources. To address this issue, this paper proposes a novel multistage transfer learning DMOEA, named MSTL. The algorithm is designed to enhance the convergence and diversity of the population through the combination of the pre-transfer learning stage and the transfer learning and validation stage when environmental changes occur. In the pre-transfer learning stage, a suitable target domain, source domain, and validation classifier sample sets are generated guided by historical information. In the transfer learning and validation stage, the TrAdaboost technique to transfer knowledge from the target to the source domain, with the results verified by two validation classifiers to mitigate negative transfer. Experimental results demonstrate that our proposed algorithm outperforms four competing algorithms in terms of diversity and convergence on a widely recognized benchmark suite.

关键词： Accuracy Heuristic algorithms Transfer learning Sociology Evolutionary computation Benchmark testing Classification algorithms

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DMSDRec: Dynamic Structure-aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation

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IEEE Transactions on Services Computing 2025年

作者： Li, Yue Zeng, Jun Tang, Haoran Wen, Junhao Gao, Min Zhou, Wei Chongqing University School of big data and software engineering Chongqing40044 China Hong Kong Polytechnic University Department of Computing Hung Hom Kowloon Hong Kong

The rise of smart devices has accelerated location-based services, creating vast trajectory data for Point-of-Interest (POI) recommendations. However, user omissions or privacy concerns often result in incomplete trajectory data, compromising sequential patterns and spatiotemporal relationships. Existing solutions using graph structures, self-supervised learning (SSL), or spatiotemporal contexts still face two limitations: (1) graph-based and SSL methods produce suboptimal trajectory representations due to respective inherent constraints;(2) noise interference persists when modeling distorted spatiotemporal signals. To mitigate these issues, we propose a Dynamic Structure-aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation (DMSDRec). Specifically, we introduce a dynamic structure-aware improved graph masked autoencoder that adaptively and dynamically distills global transitional information for self-supervised augmentation. It naturally avoids the noise introduced by existing SSL methods' dependency on manual views augmentation. Meanwhile, the masked reconstruction task synergistically enhances trajectory representations by capturing deeper cross-sequence dependencies. Additionally, we propose an effective latent-space spatiotemporal diffusion denoising method. First, we employ graph structures to model spatiotemporal relationships, utilizing higher-order structural information to alleviate the linear spatiotemporal relationship deviations caused by incomplete trajectories. Building on this, we implement diffusion models in the latent space to systematically identify and remove noise from spatiotemporal representations. Through experimental results on two real-world datasets, we demonstrate the superiority of our proposed DMSDRec in terms of recommendation accuracy and robustness. © 2008-2012 IEEE.

关键词： Dynamic Structure-awareness Graph Masked Autoencoder Next POI Recommendation Spatiotemporal Diffusion

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Fuzzy Neural Network Sliding-Mode Control with Actor-Critic for a Class of Robot Systems 10

Fuzzy Neural Network Sliding-Mode Control with Actor-Critic ...

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10th International Conference on Orange Technology, ICOT 2022

作者： Hung, Lon-Chen Wei, Chun-Cheng Chen, Hung-Jen Yang, Chih-Fu Cheng, Wei-Min Lunghwa University of Science and Technology Department of Electronic Engineering Taiwan School of Software and Big Data Changzhou College of Information Technology China Lunghwa University of Science and Technology Department of Computer Information and Network Engineering China

ISBN: (纸本)9781665454193

In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties. © 2022 IEEE.

关键词： Sliding mode control

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Liu Min Li Chu He Xu Tian Junbin Mao Min Zeng Jin Liu Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications.

关键词： Performance evaluation Sensitivity Spaceborne radar Millimeter wave radar Depression data models Reliability Monitoring Signal resolution Diseases

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Fuzzy Deduplication Scheme Supporting Pre-verification of Label Consistency 17th

Fuzzy Deduplication Scheme Supporting Pre-verification of L...

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17th International Conference on Provable and Practical Security, ProvSec 2023

作者： Tang, Zehui Zeng, Shengke Li, Tao Cheng, Shuai Zheng, Haoyu School of Computer and Software Engineering Xihua University Chengdu610039 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Computer Science Qufu Normal University Rizhao276825 China

ISBN: (纸本)9783031455124

Efficiently and securely removing encrypted redundant data with cross-user in the cloud is challenging. Convergent Encryption (CE) is difficult to resist dictionary attacks for its deterministic tag. Server-aided mechanism is against such attacks while it may exist collusion. Focus on multimedia data, this paper proposes an efficient and secure fuzzy deduplication system without any additional servers. We also propose a notion of pre-verification of label consistency to compensate for the irreparable post-verification loss. Compared with other fuzzy deduplication schemes, our work has apparent advantages in deduplication efficiency and security based on a natural data set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cryptography

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The research of human interaction recognition based on fusion features of key frame feature library

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International Journal of Information and Communication Technology 2021年第1期18卷 57-69页

作者： Zhang, Haiyan Gao, Shangbing Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China Nanjing Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

Some issues such as computational complexity and low recognition accuracy still exist in human interaction recognition. In order to solve the problem, the paper has proposed innovative and effective method based on fixed features of key frame feature library. Firstly, GIST feature and HOG feature were extracted from the pre-processed videos. Secondly, training videos with different kinds of actions were clustered by the K-means algorithm respectively to get key frame feature of each action for constructing key frame feature library. And similarity measure was utilised to calculate the frequency of different key frames in every interactive video, and statistical histogram representation of interactive videos were obtained. Finally, the decision level fusion was achieved by using SVM classifier based on histogram intersection kernel to obtain impressive results on UT-interaction dataset. The correct recognition rate of the proposed algorithm is 85%, which indicates that the proposed algorithm is simple and effective than others. © 2021 Inderscience Enterprises Ltd.

关键词： K-means clustering

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Tensor Nuclear Norm Based Matrix Regression Based Projections for Feature Extraction of Hyperspectral Images 26

Tensor Nuclear Norm Based Matrix Regression Based Projection...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Qiu, Hong Jin, Heng Wang, Renfang Liu, Xiufeng Gao, Guang Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Shanghai Ocean University College of Information Shanghai200120 China Technical University of Denmark Department of Technology Management and Economics Denmark Co. Ltd. Ningbo China

ISBN: (纸本)9798350331684

With high spectral resolution, hyperspectral image(HSI) data will result in the Hughes phenomenon, which brings a huge challenge to hyperspectral image classification(HIC). Feature extraction can be applied to address this problem. But several traditional methods often ignore the spatial structure information of HSI data. In this paper, we propose a tensor nuclear norm based matrix regression based projections(TNMRP) for feature extraction of hyperspectral images. Firstly, TNMRP preprocesses the data by a filling method. Then, it automatically builds the graph of block-tensor samples and uses the optimal sparse coding coefficients to obtain the weight matrix. Finally, based on tensor representation, TNMRP calculates the optimal projection matrix. Experiments of classification on Indian Pines and Pavia University databases demonstrate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Matrix algebra

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Nfgcl: A Negative-Sampling-Free Graph Contrastive Learning Framework for Recommendation

SSRN

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SSRN 2024年

作者： Xiao, Yuxi Ma, Rui Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China School of Computer & Network Security Chengdu University of Technology Sichuan610059 China

Recently, Contrastive Learning (CL) has seen rapid growth in graph-based recommender systems. CL-based Collaborative Filtering (CF) methods typically integrate the primary recommendation task with auxiliary CL tasks, and their notable performance can be analyzed using metrics such as uniformity and alignment in contrastive representation learning. However, most CL-based CF methods create different views of the same sample through graph augmentation in CL tasks and rely on negative sampling to distinguish user preferences. These processes are not only time-consuming but also risk learning incorrect signals. In this paper, we propose a novel Negative-sampling-Free Graph Contrastive Learning (NFGCL) framework for recommendation to balance uniformity and alignment without negative sampling. Specifically, we use in-batch positive user-item pairs to construct a preference matrix where each element represents the cosine similarity between a user and an item. We then develop a novel contrastive objective to make the preference matrix close to an identity matrix. This approach draws positive pairs closer and distances negative pairs without negative sampling, effectively improving alignment while ensuring uniformity, thereby enhancing performance. To better capture positive preferences, we design a representation-level augmentation method that integrates the output of the first-layer Graph Convolutional Networks (GCN) into the final embeddings, which improves the alignment between positive pairs. Meanwhile, we employ the uniformity loss to further regulate the uniformity of user/item representation distribution. We conduct extensive experiments on three public datasets to evaluate the effectiveness of our proposed method. The results demonstrate that our framework outperforms baseline methods and effectively balances uniformity and alignment. © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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