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SSRN 2023年

作者： Zhou, Yangtao Chu, Hua Li, Qingshan Li, Jianan Zhang, Shuai Zhu, Feifei Hu, Jingzhao Wang, Luqiao Yang, Wanqiang School of Computer Science and Technology XiDian University Xi’an710071 China Intelligent Financial Software Engineering New Technology Joint Laboratory XiDian University Xi’an710071 China Shanghai Fairyland Software Corp. Ltd. Shanghai200233 China

Next Basket Recommendation (NBR) is an essential task within the realm of recommendation systems, dedicated to the anticipation of user preferences in the next moment based on the analysis of users’ historical sequences of engaged baskets. Current NBR models utilize unique identities (IDs) information to represent distinct users and items, and focus on capturing the dynamic preferences of users through sequential encoding techniques such as recurrent neural networks and hierarchical time decay modeling, which have dominated the NBR field for over a decade. However, these models exhibit two significant limitations, resulting in suboptimal representations for both users and items. Firstly, the dependence on unique IDs information for the derivation of user and item representations, ignores the rich semantic relations that interweave the items. Secondly, the majority of NBR models remain bound to modeling an individual user’s historical basket sequence, thereby neglecting the broader vista of global collaborative relations among users and items. To address these limitations, we introduce a dual-tower model with semantic perception and timespan-coupled hypergraph for the next basket recommendation. It is carefully designed to integrate the semantic and collaborative relations into both the user and item representations. Specifically, to capture rich semantic relations effectively, we propose a hierarchical semantic attention mechanism with a large language model to integrate multi-aspect textual semantic features of items for basket representation learning. Simultaneously, to capture global collaborative relations explicitly, we design a timespan-coupled hypergraph convolutional network to efficiently model high-order structural connectivity on hypergraph among users and items. Comprehensive experiments on two public datasets demonstrate that our proposed model significantly outperforms the mainstream NBR models on two classical evaluation metrics, Recall and NDCG. © 20

关键词： Semantics

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Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems

arXiv

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arXiv 2025年

作者： Dong, Li Jiang, Feibo Peng, Yubo School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Intelligent Software and Engineering Nanjing University Suzhou215163 China

Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. To address these issues, we present an Attention-based UAV Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (ATOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UAVs. TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UAV trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Antennas

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Research on Fault Diagnosis Method Based on Structural Causal Model in Tennessee Eastman Process 7th

Research on Fault Diagnosis Method Based on Structural Causa...

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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. This makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. Therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. The global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. The verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Lightweight Small Target Detection Algorithm Based on YOLOv5 2

Lightweight Small Target Detection Algorithm Based on YOLOv5

引用

2nd International Conference on Applied Statistics, Computational Mathematics, and software engineering, ASCMSE 2023

作者： Song, Nan Wang, Ying Xin, Zhiyi Xin, Huili Liu, Tianxu Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng475004 China Software Engineering Intelligent Information Processing Innovation Base-Subject Innovation Base of Henan Higher Universities Henan University Kaifeng475004 China Henan Provincial Communications and Transportation Dispatch and Command Center Zhengzhou450000 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

To address the problems of current small target detection algorithms such as slow detection speed, high memory consumption, and not well applied to mobile devices, an improved lightweight small target detection algorithm MTGYOLOv5 is proposed based on YOLOv5s algorithm. firstly, MobileNet V3 is used instead of the backbone network to reduce the number of network parameters and increase the speed of operation. Secondly, the small target detection layer is added to the original network structure to enhance the detection capability of small targets. Finally, Ghost module is introduced to replace the commonly used convolutional kernel to ensure detection accuracy and further lightweighting. The improved algorithm was tested on the processed TinyPerson dataset. The experimental results show that the overall average accuracy of mAP of YOLOv5s algorithm is improved by 4.5%, the parameters are reduced by 1/2, the FPS is improved from 123.54 to 187.62, and the speed is enhenced by 51.87%. The algorithm effectively improves the detection speed while ensuring the detection accuracy. © 2023 SPIE.

关键词： Signal detection

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Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

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

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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Timo: In-memory temporal query processing for big temporal data

Timo: In-memory temporal query processing for big temporal d...

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作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

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intelligent Operational Adjustment of Copper Flotation Process Based on CBR-GRNN

Intelligent Operational Adjustment of Copper Flotation Proce...

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Chinese Control and Decision Conference, CCDC

作者： Hao Yan Xinchun Jia Zhenji Feng Tao Song Fuli Wang School of Automation and Software Engineering Shanxi University Taiyuan China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The mechanism of the copper flotation process is complex and highly uncertain, making it difficult to establish a precise mechanism model. For a long time, the operational adjustment of the flotation process has mainly relied on manual labor, which makes it difficult to ensure mineral quality and economic benefits for the enterprise. In order to achieve automation of the flotation process operation, this article proposes an intelligent operational adjustment method based on case-based reasoning and the generalized regression neural network (CBR-GRNN). In the offline stage, the working conditions are divided based on PCA and K-means method, and the case bases are established for each type of different working conditions. In the online stage, we first determine the category of the target case and search in the corresponding case base to obtain the most similar operational adjustment solution. Due to the fact that the most similar case may not fully match the target case, further compensation is necessary. Therefore, this paper proposes a new difference-driven case revision scheme using GRNN. The final solution for reuse is the operational adjustment value compensated by GRNN. Finally, the experimental results in an actual copper flotation process have demonstrated that the proposed method can effectively perform operational adjustment. Furthermore, the superiority of the proposed method is further demonstrated by comparing it with the other two techniques.

关键词： Employee welfare Economics Automation Uncertainty Neural networks Process control Minerals

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Overview Paper A Comprehensive Survey of Digital Image Steganography and Steganalysis

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APSIPA Transactions on Signal and Information Processing 2024年第1期13卷

作者： Luo, Weiqi Wei, Kangkang Li, Qiushi Ye, Miaoxin Tan, Shunquan Tang, Weixuan Huang, Jiwu School of Computer Science and Engineering Sun Yat-sen University China School of Software Nanchang University China Shenzhen Key Laboratory of Media Security Shenzhen University China Institute of Artificial Intelligence Guangzhou University China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University China

In the realm of digital communications, steganography and steganalysis have become a solution for securely exchanging covert information. This survey initiates with an exploration of the widely used passive-warden scenario model, analyzing its significance, key performance indicators, relevant databases, and clarifying some commonly misunderstood fundamental concepts associated with this model. Subsequently, the paper comprehensively examines the evolution and current state of digital image steganography and steganalysis, highlighting the transition from traditional handcrafted based methods to sophisticated deep learning based techniques developed over the past two decades. It offers thorough descriptions and evaluations of typical methods in both steganography and steganalysis, with a particular emphasis on deep learning-based techniques that have emerged in recent years. Furthermore, the survey identifies significant challenges currently faced in translating theoretical research into practical applications. By integrating these insights, the survey not only charts the historical development and technological advancements in steganography and steganalysis but also establishes a proactive agenda for future research aimed at enhancing security in covert communications. © 2024 W. Luo, K. Wei, Q. Li, M. Ye, S. Tan, W. Tang and J. Huang.

关键词： Steganalysis steganography

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Hybrid Variational Autoencoder for Collaborative Filtering 25

Hybrid Variational Autoencoder for Collaborative Filtering

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Jinxin Xiao, Yingyuan Zhu, Ke Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance. © 2022 IEEE.

关键词： Collaborative filtering

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Enhance Sketch Recognition’s Explainability via Semantic Component-Level Parsing

arXiv

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arXiv 2023年

作者： Zhu, Guangming Wang, Siyuan Wu, Tianci Zhang, Liang School of Computer Science and Technology Xidian University China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province China Xi’an Key Laboratory of Intelligent Software Engineering China

Free-hand sketches are appealing for humans as a universal tool to depict the visual world. Humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus that which types of semantic components constitute each sketch category. For example, an airplane should at least have a fuselage and wings. Based on this analysis, a semantic component-level memory module is constructed and embedded in the proposed structured sketch recognition network in this paper. The memory keys representing semantic components of each sketch category can be self-learned and enhance the recognition network’s explainability. Our proposed networks can deal with different situations of sketch recognition, i.e., with or without semantic components labels of strokes. Experiments on the SPG and SketchIME datasets demonstrate the memory module’s flexibility and the recognition network’s explainability. The code and data are available at https://***/GuangmingZhu/SketchESC. © 2023, CC BY.

关键词： Semantics

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