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arXiv 2024年

作者： Hu, Zeyu Xiao, Yuzhi Huang, Tao Huo, Xuanrong College of Computer Qinghai Normal University Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining810008 China Key Laboratory of Tibetan Information Processing Ministry of Education Xining810008 China

Sequential recommendation is one of the important branches of recommender system, aiming to achieve personalized recommended items for the future through the analysis and prediction of users’ ordered historical interactive behaviors. However, along with the growth of the user volume and the increasingly rich behavioral information, how to understand and disentangle the user’s interactive multi-intention effectively also poses challenges to behavior prediction and sequential recommendation. In light of these challenges, we propose a Contrastive Learning sequential recommendation method based on Multi-Intention Disentanglement (MIDCL). In our work, intentions are recognized as dynamic and diverse, and user behaviors are often driven by current multi-intentions, which means that the model needs to not only mine the most relevant implicit intention for each user, but also impair the influence from irrelevant intentions. Therefore, we choose Variational Auto-Encoder (VAE) to realize the disentanglement of users’ multi-intentions. We propose two types of contrastive learning paradigms for finding the most relevant user’s interactive intention, and maximizing the mutual information of positive sample pairs, respectively. Experimental results show that MIDCL not only has significant superiority over most existing baseline methods, but also brings a more interpretable case to the research about intention-based prediction and recommendation. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Learning Features by Minimizing the Interframe Differences 19th

Learning Features by Minimizing the Interframe Differences

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19th Chinese Conference on Image and Graphics Technologies and applications, IGTA 2024

作者： Zhao, Dong Zhang, Dan School of Computer Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China

ISBN: (纸本)9789819799183

In this paper, we present a novel yet intuitive unsupervised feature learning approach, referred to as Minimizing Interframe Differences (MID). The idea is the following: as long as the unsupervised features successfully encode the essential information about the visual structures of the frames, the differences between the before and after frames can be minimally encoded. MID is implemented with a difference encoding module and a frame reconstruction module. The former tries to minimize the difference between frames, while the latter first maps the difference encoding to optical flow and then realizes frame reconstruction based on the learned optical flow. All the modules are jointly learned in an end-to-end way through the reconstruction loss. We verify the feasibility of MID in the image datasets by replacing the natural transformation of videos to artificially parameterized transformation for images. Experimental results show that MID is able to predict optical flow accurately based on the minimum difference coding and learn a powerful visual representation that greatly enhances other visual tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Combining Cache and Refresh to Optimize SSD Read Performance Scheme 15th

Combining Cache and Refresh to Optimize SSD Read Performance...

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15th International Symposium on Advanced Parallel processing Technologies, APPT 2023

作者： Chen, Jinli Li, Peixuan Xie, Ping College of Computer of Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China The Key Laboratory of Internet of Things of Qinghai Province Xining810016 China Academy of Plateau Science and Sustainability Xining810016 China

ISBN: (纸本)9789819978717

In the era of continuous advances in flash technology, the storage density of NAND flash memory is increasing, but the availability of data is declining. In order to improve data availability, low-density parity check codes (LDPC), which have been highly corrected in recent years, are used in flash memory. However, although LDPC can solve the problem of low data availability, it also brings the problem of long decoding time. Moreover, the LDPC decoding delay time is related to its decoding level, and the higher the LDPC decoding level, the longer the delay time. Long decoding delays can have an impact on the read performance of the flash. Therefore, in order to improve the speed of reading data in flash memory, this paper proposes a scheme combining cache and flushing, the main idea of which is to use the cache to reduce LDPC decoding time, and at the same time refresh the pages with high latency replaced by the cache, so that the pages with high latency can be restored to the state of low latency pages Experimental results show that this scheme can significantly reduce LDPC decoding delay and improve data availability with less overhead, and optimize the read performance of flash memory. The experimental results show that compared with the original strategy, the average response time is reduced by 24%, and the average IOPS value is increased by 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decoding

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Color image authentication and encryption algorithm based on dual random phase encoding and dual chaotic system 25

Color image authentication and encryption algorithm based on...

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Proceedings of the 2025 2nd International Conference on Generative Artificial Intelligence and information Security

作者： Tingting Liu Rongbin Li Jun Yin The College of Computer Qinghai Normal University Xining Qinghai China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining Qinghai China

ISBN: (纸本)9798400713453

Initially, an improved piecewise Logistic chaotic map is proposed, which is combined with a novel four-dimensional hyperchaotic system to form a dual-chaos system. By introducing dual random phase encoding and leveraging the dual-chaotic system along with Arnold transformation, the authentication image is encrypted. The authentication and encrypted images are combined, and the dual-chaos system along with DNA dynamic encoding and computation methods are employed to perform encryption and decryption on the combined image, thereby achieving both image encryption and authentication. In the performance analysis of the encrypted images, the algorithm proposed in this paper not only provides authentication functionality but also demonstrates strong security, with advantages such as high decryption quality and fast encryption speed. Furthermore, its authentication feature makes it suitable for most encryption scenarios.

关键词： Chaotic System

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Research on Feature Selection Methods Based on Hypergraph Theory in Multi-scale Decision information System 24

Research on Feature Selection Methods Based on Hypergraph Th...

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3rd International Conference on Artificial Intelligence and intelligent information processing, AIIIP 2024

作者： Zheng, Ziwei Geng, Shengling Zhou, Mingquan School of Computer Science Qinghai Normal University Qinghai Xining China Academy of Plateau Science and Sustainability People’s Government of Qinghai Province Beijing Normal University Qinghai Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400707308

Feature selection is critical for enhancing data processing efficiency and decision accuracy. In multi-scale decision information systems, a novel hypergraph-based feature selection method is proposed to address the challenge of selecting the optimal scale combination from multi-scale features. An adjacency relationship matrix is defined to induce a hypergraph, which visualizes the relationships among multi-scale features and transforms the feature selection problem into a minimal vertex cover problem within the hypergraph. This approach facilitates the selection of the optimal scale feature subset. Additionally, the algorithm incorporates a priority queue to manage the sorting of features, significantly reducing time complexity and improving solution efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Data handling

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Image restoration employing Cross-ViT combined generative adversarial networks 4

Image restoration employing Cross-ViT combined generative ad...

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4th International Conference on Image processing and intelligent Control, IPIC 2024

作者： Liu, Hanxiao Li, Lin School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Academy of Plateau Science and Sustainability Xining China National Qinghai-Tibet Plateau Scientific Data Center Qinghai Sub-center Xining China

ISBN: (纸本)9781510682313

We propose Cross-ViT GAN, a novel image restoration model that addresses the limitations of CNNs by integrating Cross-ViT into GANs. The Cross-ViT GAN includes both generators and discriminators. In the generator, we replace the original normalization layer with a self-modulation layer, allowing the intermediate feature map to adapt to the input noise vector during backpropagation, thus enhancing generated image quality. The discriminator incorporates Cross-ViT, dividing the input image into large and small branches and using cross-attention to exchange features between different scales, enhancing image features for better recognition. We use three datasets, CelebA, Palces2 and CIFAR-10, to evaluate the performance of the model. The experimental results show that the evaluation metrics PSNR:26.48, SSIM:0.923, IS:4.11, and FID:1.65 of the Cross-ViT GAN model in the CelebA dataset outperform those of other comparative models, which proves the effectiveness of the Cross-ViT GAN model. © 2024 SPIE.

关键词： Gallium nitride

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Hypernetwork Representation Learning With the Transformation Strategy 22

Hypernetwork Representation Learning With the Transformation...

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6th International Conference on High Performance Compilation, Computing and Communications, HP3C 2022

作者： Zhu, Yu Zhao, Haixing Huang, Jianqiang Wang, Xiaoying Qinghai University Department of Computer Technology and Applications Xining810000 China Qinghai Normal University State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810000 China

ISBN: (纸本)9781450396295

In real life, there are many cases that cannot be described by the network abstracted as the graph, but can be described perfectly by the hypernetwork abstracted as the hypergraph. Different from the network, the hypernetwork structure is more complex and poses a great challenge to the existing network representation learning methods. Therefore, in order to overcome the challenge of the hypernetwork structure, a hypernetwork representation learning method with the transformation strategy is proposed. Firstly, as three types of transformation strategies from the hypergraph to the graph, line graph, incidence graph and 2-section graph are combined into three types of integral graphs with the hyperedge information, namely incidence graph + 2-section graph, line graph + incidence graph and line graph + incidence graph + 2-section graph. Secondly, a shallow neural network algorithm is trained respectively on five types of networks abstracted as incidence graph, 2-section graph, incidence graph + 2-section graph, line graph + incidence graph and line graph +incidence graph + 2-section graph to obtain node representation vectors. Finally, the evaluation experiment is conducted on four different types of hypernetwork datasets. The experimental results demonstrate that the node classification performance of 2-section graph is better than that of other graphs, and the link prediction performance of incidence graph + 2-section graph is better than that of other graphs. © 2022 ACM.

关键词： Graph theory

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CS-MAC: A Concurrent Scheduling MAC Protocol for Underwater Acoustic Networks

CS-MAC: A Concurrent Scheduling MAC Protocol for Underwater ...

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2022 International Conference on Neural Networks, information, and Communication Engineering, NNICE 2022

作者： Shang, Yi Du, Xiujuan College of Computer The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Xining810008 China Academy of Plateau Science and Sustainability Xining810008 China

ISBN: (纸本)9781510655171

Aiming at the characteristics of underwater acoustic networks such as long delay, low channel utilization and low throughput, as well as the existence of temporal-spatial uncertainties and hidden terminals problem, this paper proposes a concurrent scheduling MAC protocol. The protocol exchanges information such as ID, location, level of the receiving and sending nodes, and the scheduling time of data reception by sending handshake control frames. so as to realize the concurrent transmission of data from multiple sending nodes to one receiving node, which not only avoids data collision to a great extent, but also effectively solves the problems of temporal-spatial uncertainties, hidden terminals and and high energy consumption and other problems in Underwater Acoustic Networks, and improve the fairness of node channel access. Network performance such as channel utilization and network throughput. The simulation results shows that the network throughput of CS-MAC protocol is better than ALOHA, R-MAC, and slotted- FAMA, etc. CS-MAC protocol outperforms ALOHA and LSPB-MAC protocols in terms of packet delivery rate and end-to-end delay. © 2022 SPIE.

关键词： Scheduling

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Multi-scale Heterogeneous Graph Contrastive Learning*

Multi-scale Heterogeneous Graph Contrastive Learning*

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IEEE International Conference on Big Data

作者： Mingyuan Li Lei Meng Zhonglin Ye Haixing Zhao Yuzhi Xiao Shujuan Cao College of Computer Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

In recent years, heterogeneous graph neural networks have become the mainstream approach for handling heterogeneous graph data. However, due to the sparsity of labels, most existing methods for heterogeneous graph neural networks typically employ a semi-supervised learning approach, which has certain limitations in practical applications. To address this issue, we propose a self-supervised heterogeneous graph representation learning method, namely Multi-scale Heterogeneous Graph Contrastive Learning (MHGCL). This approach decodes encoded information from two perspectives: meta-paths and network patterns, in a multi-scale fashion. It uses a loss function that maximizes the similarity between positive pairs at different scales and minimizes the similarity between negative pairs. This encourages related nodes and edges to be close to each other in the embedding space, while unrelated nodes and edges are pushed farther apart. Experimental results demonstrate that MHGCL comprehensively captures semantic information at different scales between nodes. It exhibits excellent performance in node classification tasks, validating its effectiveness in heterogeneous graph node embedding learning.

关键词：

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A Smart Cuckoo Filter with Disjoint-set to Detect the Endless Loop

A Smart Cuckoo Filter with Disjoint-set to Detect the Endles...

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2022 Global Conference on Robotics, Artificial Intelligence and information Technology, GCRAIT 2022

作者： Hua, Wendi Xie, Ping School of Computer Qinghai Normal University Key Laboratory of Internet of Things of Qinghai Province Qinghai China State Key Laboratory of Tibetan Intelligent Information Processing and Application Institute of Plateau Science and Sustainability Xining China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

Cuckoo filter is a new type of approximate membership query data structure that stores the fingerprints of set elements. Because of its higher performance than bloom filter, cuckoo filter has been developed in recent years and gradually replaced the bloom filter in many fields. However, the endless loop problem in cuckoo hashing makes element insertion inefficient and unstable, which hinders cuckoo filter development. To solve this problem, we propose a new variant of cuckoo filter called smart cuckoo filter. Smart cuckoo filter uses a variable bucket structure to save the element insertion information at each position. It keeps buckets aligned to memory, thereby reducing the space waste and inefficiency of data access. Smart cuckoo filter uses the disjoint-set to determine whether the element will fall into the endless loop before the element is inserted. Also, the insertion is friendly to the concurrency of the entire filter. The experiments demonstrate that the element insertion speed of smart cuckoo filter in the endless loops is better than that of the traditional cuckoo filter, which can reach 2.2x in some cases. © 2022 IEEE.

关键词： Data structures

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