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A Triangular Stable Node network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing Data processing Vectors Space exploration Resource management Speech processing Mutual information

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A Novel Collaborative Edge Caching via User Preference Awareness and Adaptive Clustering in MENs

A Novel Collaborative Edge Caching via User Preference Aware...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Pengfei Gu Huiling Shi Hao Hao Mingfa Li Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

In this paper, we propose a collaborative caching placement scheme leveraging user preference awareness and adaptive clustering (UPAAC), which jointly considers the heterogeneity of user preferences and user activity to reduce user access latency and improve the quality of service. Specifically, we first utilize Bidirectional Long Short-Term Memory (Bi-LSTM) to capture dynamic user behavior to predict user preferences, and then density-based spatial clustering of applications with noise (DBSCAN) is used to cluster users based on the predicted user preferences for more efficient cache placement. Furthermore, the cache placement optimal problem is defined as the latency reduction maximization problem under the constraint of the cache capacity and latency threshold, and a genetic algorithm (GA) is used to optimize the problem and design the cache placement strategy. The simulation outcomes indicate that the suggested approach outperforms the conventional caching methods by significantly decreasing content access latency and enhancing the cache hit ratio.

关键词： Noise Collaboration Quality of service Prediction algorithms Smart systems Probability distribution Timing History Long short term memory Genetic algorithms

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An Efficient Token Mixer Model for Sheet Metal Defect Detection

An Efficient Token Mixer Model for Sheet Metal Defect Detect...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huijuan Hao Sijian Zhu Changle Yi Yu Chen Hongge Zhao Yue Feng Rong Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Defects such as scratches, patches, and cracks frequently occur during sheet metal production. However, the low detection accuracy and slow processing speed of industrial defect detection models significantly impede enterprise production efficiency. The aforementioned issues primarily manifest in three aspects. Firstly, the model complexity and computational overhead are substantial. Secondly, detecting small local defects poses a significant challenge. Thirdly, extracting global features, such as elongated scratches, proves to be difficult. To address these challenges, this paper introduces a novel network architecture called SATRNet. Firstly, within the model backbone, the STR module is devised. Through incorporation of the sparse self-attention method and the CNNs parallel vision Transformer model in the shallow layers, this module significantly enhances the model's capability to extract global features. Secondly, the SCATR module is designed in this paper. By substituting self-attention with the designed SCA soft attention as the token mixer, the module aims to enhance detection accuracy while reducing the number of parameters, thereby fundamentally addressing the problem of model complexity. Finally, this paper presents the GCD bottleneck convolution module. This module combines shallow and deep features, enabling the fusion of more information beneficial for detection, thereby achieving improved efficacy in capturing minute targets. Experiments demonstrate that SATRNet surpasses existing advanced models in detection accuracy on public datasets.

关键词： Accuracy Computational modeling Metals Production network architecture Feature extraction Transformers Complexity theory Mixers Defect detection

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Patent Recommendation Methods Based on Transformer Encoders and Regularization Strategies

Patent Recommendation Methods Based on Transformer Encoders ...

引用

Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Wan Zheng Qingyan Ding Na Li Yu Pan Xuecheng Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation service has become a major demand of all walks of life in the society. Patent data collection and analysis can not only help enterprises grasp the development status of the industry, but also provide decision support for them in the research and development process of science and technology services. In view of the problem of inaccurate patent recommendation caused by the inconsistency between the search text and the patent text length and the low accuracy of traditional patent recommendation, a word and sentence dual processing model based on Transformer encoder and regularization strategy is proposed to complete the patent recommendation task from the level of sentence granularity and word granularity. The sentence granularity feature extraction module uses the transformer encoder superimposed multi-head attention method to obtain the feature information between the sentence vector groups, so that the sentence vector fuses the feature information of each sentence, and solves the problem of low recommendation accuracy caused by the inconsistent length of the patent text and the search text; The word granularity feature extraction module uses the twin network model to extract the feature information of the sentence granularity vector using the Transformer encoder using the double splicing operation, improve the model performance and model accuracy; Introduce a strategy based on Dropout and symmetric JS divergence regularization, improve the model generalization ability and accuracy. Experiments have proved that it outperforms SBERT, BERT, MGMSN and SVM models in terms of performance.

关键词： Patents Accuracy Fuses Splicing Feature extraction Transformers Search problems Vectors Data models Data mining

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Improvement in the Transferability of Target Adversarial Examples Based on Data Augmentation

Improvement in the Transferability of Target Adversarial Exa...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Kun Wang Xing Jin Fuqiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Despite the high performance of deep neural networks in image classification, they are very sensitive to those adversarial examples with small perturbations, which is likely to cause classification errors. Currently, there are various adversarial attack methods developed, but the adversarial samples generated using most methods are still disadvantaged by the poor target ransferability of cross-model in black-box scenarios. To address this issue, a new method called Adaptive Variation Method (AVM) is proposed in this paper to improve the transferability of targeted adversarial examples in black-box scenarios. AVM relies on Adjustment of diversified inputs(ADI) method to achieve data enhancement on the input image and Maximum absolute difference (MD) method to calculate the loss for adversarial perturbation. Specifically, the Resized-Diverse-Inputs (RDI) method is improved in this paper to propose a new diversified input method, namely the Adjustment of diversified inputs (ADI) method. By means of repeated scaling and random filling performed on the input image, the image becomes relatively rough, with its input diversity and random flexibility improved. In addition, the Maximum absolute difference (MD) method is proposed in this paper to enhance the advantage of adversarial samples by minimizing the maximum absolute difference between the adversarially perturbed global image and local image. According to the experimental results, this method improves the success rate in the presence of transfer-based targeted attacks.

关键词： Deep learning Perturbation methods Data enhancement Closed box Data augmentation Robustness Filling Cybernetics Overfitting Image classification

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A Blockchain-based PHR Sharing Scheme with Attribute Privacy Protection

A Blockchain-based PHR Sharing Scheme with Attribute Privacy...

引用

IEEE International Conference on Trust, security and Privacy in computing and Communications (TrustCom)

作者： Chaohe Lu Zhongyuan Yu Guijuan Wang Anming Dong Xiang Tian School of Computer Science Qufu Normal University Rizhao China School of Information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.

关键词： Access control Privacy Smart contracts Blockchains Encryption InterPlanetary File System Medical diagnosis Protection Secure storage Medical diagnostic imaging

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OCF-HP: An Offline Compaction Framework Based on Hotspot Prediction for Data Lakehouse

OCF-HP: An Offline Compaction Framework Based on Hotspot Pre...

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Electronic technology and Information Science (ICETIS), International Conference on

作者： Bowen Sun Xiaohan Yang Ying Guo Zhihao Zhao Jing Chen Hu Zhang Jibin Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

With the rapid advancement of information technologies, the global data volume is increasing at an unprecedented speed. Data Lakehouse has become an effective solution for real-time aggregation of multimodal heterogeneous data and supporting multiple query modes. It is worth noting that due to the column-based storage commonly employed in Data Lakehouse and the resulting mechanisms such as Copy-on-Write and Merge-On-Read, data query speed, data freshness, and storage costs have become huge challenges. In this paper, an Offline Compaction Framework based on Hotspot Prediction (OCF-HP) was designed to address the above challenges. Specifically, we analyzed the Timeline data in Hudi which is the mainstream Data Lakehouse framework, then predicted the hotspot periods of data writing based on the improved LSTM model using such data, and realized automatic trigger of Hudi offline compaction tasks in reasonable non-hot periods. The experiment results show that OCF-HP provides Hudi with a more optimized offline compaction solution so that data lakehouse managers could achieve better query speed and data freshness with reducing storage costs in Hudi's read optimized mode.

关键词： Analytical models Costs Data integrity Predictive models Writing Data models Compaction

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Bidding Management Platform for Cloud-Chain Convergence

Bidding Management Platform for Cloud-Chain Convergence

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International Conference on Computer Supported Cooperative Work in Design

作者： Yanzhi Zhang Xiaoming Wu Xiangzhi Liu Huomin Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Strengthening the supervision of bidding business is an important measure to optimise the business environment of bidding and ensure fair competition among enterprises. The existing blockchain-based bidding platform has already achieved the uploading of simple business, but with the development of the bidding industry, single-modal data storage is not enough to support the credible traceability of business processes. Therefore, to address the problems of multimodal data uploading, storage space limitation and performance in the bidding field, and combined with the node complexity of the bidding scenario, we design and propose a bidding management platform for cloud-chain convergence(BMPC 3 ), in which ChainMaker serves as the implementation platform. In this alliance chain, we meet different business logic requirements by designing specific smart contracts and storage structures. According to different business data types, the ledger storage method is optimised to meet the on-chain storage requirements of multimodal data such as images, videos, PDFs, etc. Meanwhile, we explore the blockchain architecture and system configuration, and propose an optimisation scheme, which effectively improves the system transaction throughput. The final experiment shows that the transaction throughput of the system can reach 3300TPS, which can meet the application requirements.

关键词： Industries Federated learning Smart contracts Memory Throughput Logic Optimization

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A Patent Fast Recommendation Algorithm Based on User Requirements Combined with an Inverted Table

A Patent Fast Recommendation Algorithm Based on User Require...

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Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation has always been an essential service for the innovation and development of all walks of life. The matching of demand and patent information can enable enterprises to quickly obtain the innovation and development trend of the industry. The current patent recommendation method has the problem of slow recommendation speed and insufficient comprehensive recommendation results. According to the existing problems, a patent fast recommendation algorithm based on user needs combined with inverted table is proposed. The algorithm using dual track recommended way for accurate recommendation and extensive recommendation, in the process of recommendation built by patent data inversion table and document list auxiliary recommendation to improve recommendation speed and accuracy, when accurate recommendation when using improved requirements based on GPT-2 compression model recommended after demand text compression, when widely recommended using TF-IDF mechanism for demand text of each paragraph after keyword extraction, finally generate widely recommendation list and accurate recommendation list. This recommendation method can not only realize the fast and accurate recommendation of patents, but also provide users with more innovative ideas for extensive recommendation. The experiments show that the proposed recommendation algorithm has higher speed and accuracy than the original recommendation algorithm.

关键词： Patents Vocabulary Technological innovation Accuracy Semantics Predictive models Writing Prediction algorithms Data mining Robotics and automation

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Cross Auto-Encoder for Inscription Character Inpainting

Cross Auto-Encoder for Inscription Character Inpainting

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International Joint Conference on Neural networks (IJCNN)

作者： Long Zhao Zonglong Yuan Yuhao Lou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Character inpainting, a challenging task, has seen slow development. Existing networks focus on either enhancing the encoding process, involving feature extraction or improving the decoding process, which revolves around character reconstruction. Some also work on refining the loss function for training the model. However, none of these models enhances both the encoding and decoding processes in character inpainting. In response to this gap, our paper leverages insights from the field of image inpainting, specifically the multi-stage inpainting network, to introduce the Cross Auto-Encoder. The Cross Auto-Encoder comprises parallel encoders and decoders that simultaneously enhance both the encoding and decoding processes in character inpainting. The two parallel encoders employ dilated convolution and branching convolution channel attention modules to extract character features concurrently. Meanwhile, the parallel decoder incorporates a self-attention mechanism, utilizing different loss functions for training, sharing parameters, and decoding the reconstructed character. Our extensive experiments on the expanded inscription dataset demonstrate that the Cross AutoEncoder surpasses other existing character inpainting models in the context of inscriptions. Furthermore, we conducted experiments on the handwritten Chinese character recognition dataset, yielding results superior to those achieved by current character inpainting models. In conclusion, the Cross Auto-Encoder stands out as a novel and effective approach, successfully addressing the shortcomings of existing character inpainting models by enhancing both encoding and decoding processes.

关键词： Training Convolution Refining Feature extraction Encoding Decoding Complexity theory

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