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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...

引用

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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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

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 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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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

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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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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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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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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SOD-YOLO: A New Small Object Traffic Sign Recognition network

SOD-YOLO: A New Small Object Traffic Sign Recognition Networ...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Xunjia Yin Xiaoming Wu Xiangzhi Liu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security 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

Traffic sign recognition is a challenging task for unmanned systems, especially the problem of detecting small targets. During traffic sign recognition (TSR), traffic sign targets are small, represented by a small number of pixels in the image, and lack sufficient detail, making it difficult to detect them using a detector. To solve these problems a new traffic sign target detection algorithm is proposed, which introduces a new convolutional module SPD-Conv to capture the feature information of small target traffic signs more efficiently and replaces the C3 module in the backbone network with a C2f module, which greatly reduces the parameters and computation of the original network and achieves a lighter weight to make the model easier to deploy. A new weighted bi-directional feature pyramid network is used to replace the original feature pyramid network. In addition, a coordinate lightweight attention module is introduced for the small target feature loss problem, and finally, a new loss function is proposed to improve the convergence speed of the model while improving the small target detection accuracy. Extensive experimental results on CCTSDB and tt-100k datasets show that our method is more versatile and superior for traffic sign small target detection compared to several state-of-the-art methods.

关键词： YOLO Adaptation models Accuracy Target recognition Computational modeling Detectors Feature extraction

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CB-YOLO: A Small Object Detection Algorithm for Industrial Scenarios

CB-YOLO: A Small Object Detection Algorithm for Industrial S...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hao Chen Xiaoming Wu Zhanzhi Su Ying Zhao Yunfeng Dong Xiangzhi Liu 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

In certain specific industrial scenarios, smoking and cellphone usage are strictly prohibited behaviors. In these scenarios, it is crucial to rapidly and accurately detect smoking and cell phone usage, and promptly issue warnings, to ensure industrial safety. The detection of prohibited behaviors using computer vision has gained attention from researchers with the development of artificial intelligence. However, detecting small objects like cigarettes and cell phones in complex backgrounds and varying angles poses a challenge due to their changing shapes. In this paper, we propose a detection model called CB-YOLO, which utilizes YOLOv7 as the baseline model, for detecting smoking and mobile phone usage behavior. Firstly, we propose a new channel space pyramid network called CSPPF that pools features at different scales and introduces scale focusing on improving the perceptual ability of the network to better handle targets at various scales and locations. Secondly, we propose an enhanced feature pyramid called AWBFPN. This introduces additional learnable weight parameters to improve the model's ability to fuse multi-scale features effectively. Finally, we have also proposed a new loss function called Size-IoU. The experimental results demonstrate that our algorithm outperforms the baseline model. Specifically, it achieves a 3.9% improvement in Precision, a 2.1% improvement in Recall, a 6.4% improvement in mAP@0.5, and a 2% improvement in mAP@0.5:0.95 on the Phone_check dataset. Similarly, on the Smoking dataset, our algorithm achieves a 3.3% improvement in Precision, a 1.9% improvement in Recall, a 1.7% improvement in mAP@0.5, and a 0.3% improvement in mAP@0.5:0.95.

关键词： Fuses Shape Cellular phones Focusing Object detection Robustness Mobile handsets Safety Iterative methods Cybernetics

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StarMFND: Star Topology Multi-Domain Fake News Detection

StarMFND: Star Topology Multi-Domain Fake News Detection

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Data Science and information Technology (DSIT), International Conference on

作者： Pengfei Gu Yibing Chen Wanlong Bing Yahui Liu Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection.

关键词： Economics Social networking (online) Computational modeling Stars Feature extraction Data models Topology Internet Fake news information technology

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu 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 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)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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