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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Wei Guo Hangjun Che Man-Fai Leung Nankun Mu Xiangguang Dai Yuming Feng School of Electronic and Information Engineering Southwest University Chongqing China School of Electronic Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China School of Computing and Information Science Faculty of Science and Engineering Anglia Ruskin University Cambridge UK Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China

We propose a latent multi-view subspace clustering model based on Laplacian regularized representation. To emphasize the information of the representation matrix at the local level and reflect the grouping effect of clustering, a Laplacian regularization is imposed on the representation matrix. Additionally, To boost the efficiency o f c lustering, we apply nonnegative constraints on the representation matrix. A unified framework is designed to solve the proposed model by using ALM and LADMAP methods. The proposed model has excellent performance, as demonstrated by a number of experimental findings.

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Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups 18th

Visitors Vis: Interactive Mining of Suspected Medical Insura...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Dong, Rixin Liu, Hanlin Guo, Xu Zhou, Jiantao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9789819996360

As medical insurance continues to grow in size, the losses caused by medical insurance fraud cannot be underestimated. Current data mining and predictive techniques have been applied to analyze and explore the health insurance fraud population. However, previous studies only provide summary results and do not show the details of comparative analysis during the visit, resulting in the inability of auditors to quickly identify anomalous behavior. In this paper, we propose a visual analytics system for interactive medical insurance fraud detection to support the exploration and interpretation of different access processes. We propose a weighted MinDL to improve the accuracy of visit pattern classification in the time-series modeling process, and design a data analysis model and visual analysis view based on medical insurance fraud characteristics to reveal and explore the characteristics of medical insurance fraud groups. We collaborate with related organizations to design and implement an interactive visualization for medical insurance fraud group detection using real Medicare data and expert interviews The system is effective and practical in detecting and analyzing medical insurance fraud syndicates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health insurance

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An Uncovered Neurons Information-Based Fuzzing Method for DNN

An Uncovered Neurons Information-Based Fuzzing Method for DN...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing Software

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Research on Generating Xi'an Drum Music Based on Generative Adversarial Network

Research on Generating Xi'an Drum Music Based on Generative ...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Tianmian Liang Peng Li Yumei Cao Xiaojun Wu Huifang Zhao Chenye Qi Ministry of Culture and Tourism Key Laboratory of Intelligent Computing and Service Technology for Folk Song Xi’an China School of Computer Science Shaanxi Normal University Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an China Xi'an Key Laboratory of Cultural Tourism Resources Development and Utilization Xi’an China School of Music Shaanxi Normal University Xi'an China

Currently most AI music generator platforms mainly focus on creating modern music, without focusing on the creation of folk music. And most music generation neural network models use recursive neural networks and their variants, resulting in excessively repetitive music information, greatly reducing the fun of music. When using only generative adversarial networks (GANs) to generate music, it is easy to experience unstable training, vanishing gradients, and mode collapse. Failure to consider time dependence can reduce the authenticity of generated music. In order to solve the problem that most of the conventional deep learning GAN music generation network models do not consider music theory rules in music generation and Xi'an drum music faces the scarcity of tracks and difficulties in composition, we proposed a GAN network music generative model based on music theory rules and characteristics of Chinese Folk Songs. When generating music, the generator CNN and the regulator CNN train together, and generate music melodies under the constraints of the voice column CNN module. The generated results are sent to the discriminator CNN, which submits feedback to the generator CNN for competition, to compensate for the shortcomings of previous technology and ultimately generate better melodies, thereby improving the authenticity, sweetness, and rhythm of the generated music.

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Construction and Application of the SMART Model for Adaptive Industrial Data Collection Based on Knowledge Graphs

Construction and Application of the SMART Model for Adaptive...

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IEEE International Conference on Big Data

作者： Wendan Cheng Zhen Zhang Heng Qian Qiuyue Wang Guanqun Su Lingge Meng 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shu Ju (Shandong) Intelligent Technology Co. Ltd. Jinan China Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd. Jinan China Shandong Qingniao IIoT Co. Ltd. Jinan China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Industrial data collection is the foundation for implementing enterprise digitization, which is of great significance to the development of intelligent manufacturing. However, Industrial Data Collection Standards (IDCS) are primarily published in paper or PDF formats, which makes it challenging to associate and reuse knowledge. This brings difficulties to data collection and management of heterogeneous devices, thus affecting the adaptive collection of industrial data. For this reason, this paper introduces SMART into industrial data collection and proposes the construction and application of a knowledge graph-based SMART Model. First, ontology semantic reasoning and natural language processing techniques are utilized to develop an ontology model for industrial data collection and extract fine-grained IDCS knowledge. Then, the knowledge is integrated according to the standard primitive structure and conceptual reasoning rules by constructing a standard association model to form the Industrial Data Collection Standards Knowledge Graph (IDCS-KG). Finally, the SMART competence level is quantitatively assessed based on the completion degree of each operation within the SMART Model. An experimental case study demonstrates that the SMART Model can collect intelligent adaptive industrial data through the reasoning and analysis of equipment adaptation protocols and data quality management strategies.

关键词： Adaptation models Analytical models Protocols Knowledge graphs Data collection Ontologies Data models Cognition Natural language processing Standards

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An Automatic Music Generation Method Based on Rscln_Transformer Network

SSRN

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SSRN 2022年

作者： Zhang, Yumei Lv, Xiaojiao Li, Qi Wu, Xiaojun Su, Yuping Yang, Honghong School of Computer Science Shaanxi Normal University Xi’an China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an China Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi’an China

With the development of artificial intelligence and deep learning, a large number of music generation methods have been proposed. Recently, Transformer has been widely used in music generation. However, the structural complexity of music puts forward higher requirements for music generation. In this paper, we propose a new automatic music generation network which consists of a Recursive Skip Connection with Layer Normalization (RSCLN) model, a Transformer-XL model and a multi-head attention mechanism. Our method not only alleviate the gradient disappearance problem in the model training, but also increase the ability of the model to capture the correlation of music information before and after, so as to generate music works closer to the original music style. Effectiveness of the RSCLN_Transformer-XL music automatic generation method is verified through music similarity evaluation experiments using music structure similarity and listening test. The experimental results show that the RSCLN_Transformer-XL music automatic generation model can generate better music than the Transformer-XL model. © 2022, The Authors. All rights reserved.

关键词： Music

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Leveraging the Dual Capabilities of LLM: LLM-Enhanced Text Mapping Model for Personality Detection 39

Leveraging the Dual Capabilities of LLM: LLM-Enhanced Text M...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bi, Weihong Kou, Feifei Shi, Lei Li, Yawen Li, Haisheng Chen, Jinpeng Xu, Mingying School of Computer Science National Pilot School of Software Engineering BUPT Beijing 100876 China Key Laboratory of Trustworthy Distributed Computing and Service BUPT Ministry of Education Beijing 100876 China State Key Laboratory of Media Convergence and Communication CUC Beijing 100024 China State Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China School of Economics and Management BUPT Beijing 100876 China Beijing Technology and Business University Beijing 100048 China Xiangjiang Laboratory Changsha 410205 China North China University of Technology Beijing 100144 China

ISBN: (纸本)157735897X

Personality detection aims to deduce a user’s personality from their published posts. The goal of this task is to map posts to specific personality types. Existing methods encode post information to obtain user vectors, which are then mapped to personality labels. However, existing methods face two main issues: first, only using small models makes it hard to accurately extract semantic features from multiple long documents. Second, the relationship between user vectors and personality labels is not fully considered. To address the issue of poor user representation, we utilize the text embedding capabilities of LLM. To solve the problem of insufficient consideration of the relationship between user vectors and personality labels, we leverage the text generation capabilities of LLM. Therefore, we propose the LLM-Enhanced Text Mapping Model (ETM) for Personality Detection. The model applies LLM’s text embedding capability to enhance user vector representations. Additionally, it uses LLM’s text generation capability to create multi-perspective interpretations of the labels, which are then used within a contrastive learning framework to strengthen the mapping of these vectors to personality labels. Experimental results show that our model achieves state-of-the-art performance on benchmark datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mapping

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A quaternary analogue of Tang-Ding codes

arXiv

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arXiv 2023年

作者： Shi, Minjia Tao, Sihui Kim, Jon-Lark Solé, Patrick The Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Department of Mathematics Sogang University Seoul Korea Republic of Aix Marseille Univ CNRS Centrale Marseille I2M Marseille France

In a recent paper, Tang and Ding introduced a class of binary cyclic codes of rate close to one half with a designed lower bound on their minimum distance. The definition involves the base 2 expansion of the integers in their defining set. In this paper we propose an analogue for quaternary codes. In addition, the performances of the subfield subcode and of the trace code (two binary cyclic codes) are investigated. Copyright © 2023, The Authors. All rights reserved.

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Blockchain-Based Multi-Cloud Data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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International Conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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