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2021 ACM International Conference on Intelligent Computing and its Emerging Applications, ICEA 2021

作者： Wang, Chunxiao Zhao, Zhigang Zhang, Jian Huo, Jidong Qilu University of Technology Shandong Academy of Sciences Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781450391603

In order to support the dynamic perception neural network of underground space and the intelligent brain of the city, according to the characteristics of multi-source, multi-category, multidimensional and multi-quantity of geological data, this paper studies the large data storage system which integrates multi-source acquisition and converged storage and intelligent processing to solve the problems of wide range, long time, multidimensional source and diverse processing of underground space information. This system promotes the combination of sensor network, big data and other technologies with the urban underground space perception industry, realizes the digitalization and intellectualization of various underground space information, improves the planning, risk assessment and disaster prediction of underground space, and provides support for the comprehensive development and utilization of underground space. © 2021 ACM.

关键词： Big data

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Causal and Local Correlations Based Network for Multivariate Time Series Classification

arXiv

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arXiv 2024年

作者： Du, Mingsen Wei, Yanxuan Zheng, Xiangwei Ji, Cun School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. To address this issue, the causal and local correlations based network (CaLoNet) is proposed in this study for multivariate time series classification. First, pairwise spatial correlations between dimensions are modeled using causality modeling to obtain the graph structure. Then, a relationship extraction network is used to fuse local correlations to obtain long-term dependency features. Finally, the graph structure and long-term dependency features are integrated into the graph neural network. Experiments on the UEA datasets show that CaLoNet can obtain competitive performance compared with state-of-the-art methods. © 2024, CC BY.

关键词： Graph neural networks

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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Data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

arXiv

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arXiv 2023年

作者： Xu, Fangzhi Lin, Qika Han, Jiawei Zhao, Tianzhe Liu, Jun Cambria, Erik The School of Computer Science and Technology Xi’an Jiao-tong University Shaanxi Xi’an710049 China The Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering National Engineering Lab for Big Data Analytics Shaanxi Xi’an710049 China The School of Computer Science and Engineering Nanyang Technological University Singapore

Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) have emerged as a noteworthy innovation in natural language processing (NLP). However, the question of whether LLMs can effectively address the task of logical reasoning, which requires gradual cognitive inference similar to human intelligence, remains unanswered. To this end, we aim to bridge this gap and provide comprehensive evaluations in this paper. Firstly, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings. Considering the comprehensiveness of evaluations, we include 3 early-era representative LLMs and 4 trending LLMs. Secondly, different from previous evaluations relying only on simple metrics (e.g., accuracy), we propose fine-level evaluations in objective and subjective manners, covering both answers and explanations, including answer correctness, explain correctness, explain completeness and explain redundancy. Additionally, to uncover the logical flaws of LLMs, problematic cases will be attributed to five error types from two dimensions, i.e., evidence selection process and reasoning process. Thirdly, to avoid the influences of knowledge bias and concentrate purely on benchmarking the logical reasoning capability of LLMs, we propose a new dataset with neutral content. Based on the in-depth evaluations, this paper finally forms a general evaluation scheme of logical reasoning capability from six dimensions (i.e., Correct, Rigorous, Self-aware, Active, Oriented and No hallucination). It reflects the pros and cons of LLMs and gives guiding directions for future works. © 2023, CC BY.

关键词： Natural language processing systems

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MGRR-SQL: Multi-Granularity Relevant Retrieval-Based in-Context Learning Approach for Text-to-SQL Generation

MGRR-SQL: Multi-Granularity Relevant Retrieval-Based in-Cont...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Yiming Zhan Zhao Li Tong Chen Shiwei Wu Yang Li 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 Shandong Evay Info Technology Co. Ltd. Jinan China School of Cyberspace Security (School of Cryptology) Hainan University Haikou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In-Context learning serves as a vital component for enhancing Text-to-SQL task performance. However, current in-context learning methodologies for Text-to-SQL applications fail to sufficiently bridge the significant cross-domain correlation gap that exists between few-shot examples and the intended query target. Our approach overcomes these challenges through a novel relevant retrieval-based in-context learning method, which operates across three distinct granularity levels: (1) semantic similarity retrieval for analogous questions, (2) query kernel entity matching for retrieving strongly relevant questions and (3) character-level similarity analysis for fine-grained question retrieval. Additionally, we introduce a novel Object-Relational Mapping (ORM)-based database prompt and a post cell value linking method that effectively describes the schema while avoiding error propagation common in traditional schema linking approaches. Our experimental evaluations conducted on the benchmark datasets BIRD and Spider reveal that our approach delivers strong performance, comparable to state-of-the-art methods.

关键词： Learning systems Structured Query Language Databases Large language models Computational modeling Semantics Benchmark testing Birds Business intelligence Standards

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NQNR: News Recommendation Method Based on News Quality-Aware Modeling

NQNR: News Recommendation Method Based on News Quality-Aware...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Baojie Xu Zhenyu Yang Yan Huang Wenyue Hu Zhibo Zhang The 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Personalized News Recommendation (PNR) can enhance user experience by alleviating information overload. Traditional news recommendation methods consider all clicking behaviors as user interests, resulting in biased user modeling that fails to accurately capture user interests. In addition, although there are methods to reduce the impact of low-quality news at the representation level by simply filtering it through the attention mechanism. However, this only implicitly models the news in the interaction sequence without specifically considering the quality of each news, and thus has very limited effect in identifying noise. To address these issues, this paper proposes News Recommendation method based on News Quality-aware modeling (NQNR). We attempt to explicitly model the news in the click sequence and candidate ranking one by one to visually assess the quality of each news. Specifically, we design a detection module to detect whether the input news is low-quality news. Then, by reducing the influence of low-quality news in user modeling and candidate ranking, user interests are modeled more accurately, while recommendations of such news are reduced for users. In addition, to capture the similarity of vectors more accurately, we also design a similarity computation method based on the multiple attention mechanism in the detection module. Experiments on a large real-world Microsoft News Dataset (MIND) show that our model significantly outperforms previous models. Our code is posted at the following URL: https://***/xxbbjj/NQNR-.

关键词： Uniform resource locators Attention mechanisms Codes Accuracy Filtering Computational modeling Noise Vectors User experience Cybernetics

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xitong Ma Yuansen Kuang Lin Yuan Cheng Tian Yijie Zeng Song 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 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation; 2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily; 3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models.

关键词： Knowledge engineering Visualization Terminology Semantics MIMICs Memory management Radiology Feature extraction Data models Biomedical imaging

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Yuansen Kuang Xitong Ma Jing Zhao Guangchen Wang Yijie Zeng Song 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 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices; previous models suffer from local detail loss of positional embedding in 3D images; previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models.

关键词： Solid modeling Image segmentation Adaptation models Three-dimensional displays Correlation Image coding Self-supervised learning Generators Biomedical imaging Tumors

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A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Chenglong Shi Haibin Geng Wenfeng Jiang Jinbao Li Chao Wu Song 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 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models.

关键词： Correlation Message passing Semantics Collaboration Logic gates Graph neural networks Topology History Data mining Cybernetics

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Reformation and Exploration of Practical Teaching in New Generation of Information technology Related Majors Based on Online Practice Teaching Platform 2

Reformation and Exploration of Practical Teaching in New Gen...

引用

2nd International Conference on Information science and Education, ICISE-IE 2021

作者： Zhang, Hu Guo, Ying Yang, Meihong Chen, Jing Wang, Jibin Wang, Jun Qilu University of Technology Shandong Academy of Sciences Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665438292

Under the background of the integration of science and education, to meet the needs of a new generation of information technology-related majors innovative practice teaching demand for diversified experimental environments and experimental teaching resources, an online practice teaching platform based on Kubernetes was planned and constructed, and a platform is provided for teachers to convert scientific research achievements into teaching materials relying on the online practice teaching platform. It also provides students with large-scale distributed experimental resources allocated according to their needs. The online practice teaching platform adopts a distributed architecture deployment, and uses advanced container orchestration technology to construct a student's experimental environment. Through the use of the online practical teaching platform, the level of practical teaching management has been improved, and the scientific, humanized and intelligent management of practical teaching has been realized. © 2021 IEEE

关键词： Students

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