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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Jianfeng Yuan Na Zhang Bo Yang Xin Zhao Xu Wu Lin Wang Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China Shandong Provincial Key Laboratory of Preparation and Measurement of Building Materials University of Jinan Jinan China

Cement, as an important material, is widely applied in the world. The macroscopic properties of cement are strongly related to its microstructure. To acquire cement microstructure images, scanning electron microscopy (SEM) is a popular scanning technique for obtaining high-quality backscattered electrons (BSE) images of cement microstructure. Nevertheless, this technique generally cannot acquire large-scale microstructural images due to the limited scanning area. Therefore, this paper proposes a BSE texture synthesis framework based on convolutional neural network (CNN) technology. In this framework, the generator can generate BSE images. The framework also optimizes the generator by perceptual loss. Ultimately, experiments demonstrate that our method can generate arbitrary size BSE textures similar to a given BSE texture.

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Research on Blockchain-based Access Control Technology for Medical Data 4

Research on Blockchain-based Access Control Technology for M...

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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Li, Yan Zhang, Guofeng Feng, Bin Yang, Shihao Zhao, Chuan Wang, Peng Shen, Cong Zhang, Yanan School of Information Science and Engineering University of Jinan Shandong Jinan250022 China School of Information Science and Technology Taishan University Shandong Taian271000 China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Shandong Jinan250022 China College of Electronic Information Shandong Xiandai University Shandong Jinan250104 China

ISBN: (纸本)9798331528324

Medical data sharing can provide secure, trust-worthy and high-quality health care services to a wide range of patients and healthcare providers, but the data sharing process faces the risk of identity and information security leakage. This study begins by examining access control technologies for health data sharing, emphasizing the analysis of blockchain-based solutions for securing various types of medical data. It reviews and assesses the current state of these technologies across four dimensions-attributes, signatures, roles, and multi-smart contracts-and further develops a blockchain-based system for controlling access to medical data. Finally, it summarizes and analyzes the challenges faced by medical data sharing, and provides an outlook on the future development of the technology. This study is of great sig- nificance in guiding the deep integration of blockchain technology and medical digitization, and guaranteeing the secu-rity of medical data throughout its life cycle. © 2024 IEEE.

关键词： Electronic health record

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Cross-chain Architecture of Blockchain Integrating Notary Mechanism and Relay-chain Technology 4

Cross-chain Architecture of Blockchain Integrating Notary Me...

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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Yang, ShiHao Zhang, GuoFeng Feng, Bin Li, Yan Zhao, Chuan Wang, Peng Shen, Cong Zhang, YaNan School of Information Science and Engineering University of Jinan Shandong Jinan250022 China School of Information Science and Technology Taishan University Shandong Taian271000 China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Shandong Jinan250022 China College of Electronic Information Shandong Xiandai University Shandong Jinan250104 China

ISBN: (纸本)9798331528324

Due to the independent nature of Blockchain systems in different organizations, data sharing and asset transfer between these Blockchain systems face extreme challenges. The phenomenon of data silos is gradually emerging, and Blockchain cross chain technology provides important technical support for bridging data silos, achieving interoperability and data sharing between Blockchains. The focus of this article is on the systematic study and comparison of four widely used cross-chain technologies and platforms, and designs a Blockchain cross-chain architecture that integrates notary and relay chain technologies based on the analysis results. Compared with using a single notary mechanism or a single relay cross-chain technology, it has better scalability, stronger customization, and higher security. This work has a certain promoting effect on the development of cross chain technology. © 2024 IEEE.

关键词： Blockchain

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Motion-Centric Retrieval of 3d Human Skeleton and 2d Human Image Sequences

SSRN

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SSRN 2024年

作者： Wang, Ying Zhang, Lu Peng, Jingliang Lv, Na Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China School of Information Science and Engineering University of Jinan Jinan250022 China

In this work, we propose a novel unified framework for cross-modal retrieval of 3D human skeletons and 2D human image sequences (videos), driven by deep motion analysis. To the best of our knowledge, this is the first published framework of its kind. Specifically, we design a novel neural network (NN) structure consisting of multiple parallel branches, each extracting feature vectors for one modality, and multiple logistic regression units, each predicting the match score between feature vectors of two modalities. Each branch is constructed on the model of Bi-directional Encoder Representations of Transformers (BERT). During retrieval, inter-modality matching is based on logistic regression over the feature vectors. We experimentally demonstrate that our proposed framework comprehensively supports both single-modal classification and cross-modal retrieval, and achieves state-of-the-art performance in both aspects. © 2024, The Authors. All rights reserved.

关键词： Logistic regression

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Prediction and Analysis of Infectious Disease Visit Data based on DPC 10th

Prediction and Analysis of Infectious Disease Visit Data Bas...

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10th International Conference on Signal and Information Processing, network and Computers, ICSINC 2022

作者： Zhang, Rui Song, Yuhui Du, Tao School of Information Engineering Shandong Management University No. 3500 Dingxiang Road Changqing District Shandong Jinan250357 China School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9789811999673

Since the outbreak of the COVID-19 in early 2020, the prevention and control of infectious diseases has been raised to a higher level. However, tuberculosis still ranks in the forefront of the incidence rate of various infectious diseases in China. The tuberculosis epidemic has also brought great economic pressure and negative social impact to the society every year. Therefore, we have always been very concerned about how to effectively prevent and control the spread of tuberculosis. However, the diagnostic data of tuberculosis are often high-dimensional, huge, messy and difficult to be used effectively. How to extract knowledge from the data to help medical staff find the incidence trend of tuberculosis to assist decision-making has become a practical topic. In this paper, after clarifying and standardizing the original data, the density peak clustering (DPC) algorithm is used for deep mining. The knowledge is extracted through clustering analysis and visualization. Finally, analysis results can intuitively illustrate the effectiveness and practical research significance of this work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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A DPC based Recommendation Algorithm for Internship Positions 10th

A DPC Based Recommendation Algorithm for Internship Position...

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10th International Conference on Signal and Information Processing, network and Computers, ICSINC 2022

作者： Zhang, Rui Bi, Lingyun Du, Tao School of Information Engineering Shandong Management University No.3500 Dingxiang Road Shandong Changqing District Jinan250357 China School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9789811999673

In the graduation internship activities, the choice of centralized internship based on school-enterprise cooperation units is limited, and the independent internship method will inevitably lead to the problem of blind job selection due to students' lack of social experience. In this paper, we mine historical practice data and propose a recommendation algorithm based on binomial association rules and density peak clustering analysis named DPC-RA. Through binomial association rules, students' internship results in different units are associated with the classification data of unit names, and the internship unit name data is numerically realized. DPC (density peak clustering) algorithm is used to cluster and mine internship data, and obtain multiple clusters. This method can provide accurate personalized work recommendations and improve the quality of internship. Experiments show that this method is effective. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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Consciousness Neural network for Path Tracking Control of Floating Objects at Sea

Consciousness Neural Network for Path Tracking Control of Fl...

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2022 International Conference on Machine Learning, Cloud computing and intelligent Mining, MLCCIM 2022

作者： Zhao, Minghao Zhang, Tianchi Zhang, Jing School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan China School of Information Science and Engineering Chongqing Jiaotong University Chongqing China

ISBN: (数字)9781665498586

ISBN: (纸本)9781665498586

The problem of tracking and controlling drifting objects on the sea is often faced with many problems, such as complex control environment, low stability of controller and a large number of algorithm calculation. In order to realize the accurate control of drifting objects on the sea, a course controller based on consciousness neural network technology is proposed. First of all, combined with Nash equilibrium guidance, based on the trajectory characteristics and control requirements of drifting objects, the tracking problem of drifting objects is modeled, and its state space, action space and reward function are designed. Then, the conscious neural network algorithm is used as the implementation of the controller, and the off-line learning method is used to train the controller. Finally, the trained controller is compared with the original controller BP-PID controller is compared to analyze the control effect. The simulation results show that the designed controller based on consciousness neural network can converge rapidly from the training process to meet the control requirements. Compared with BP-PID controller, the trained neural network has the advantages of fast tracking and small error. The research results can provide a reference for the path tracking control of drifting objects on the sea. © 2022 IEEE.

关键词： Controllers

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Community Evolution Tracking based on Core Node Extension and Edge Variation Discerning 17th

Community Evolution Tracking Based on Core Node Extension a...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Zhuang, Qifeng Yu, Zhiyong Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Communities exist anywhere in various complex networks, and community evolution tracking is one of the most well-liked areas of inquiry in the study of dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matching the community in tracking evolution events, making it difficult to trace the whole evolution of communities because of community matching errors. In addition, the majority of evolution tracking algorithms do not adequately account for the potential scenarios in community evolution, resulting in erroneous detection of evolution events. In this research, a community evolution tracking algorithm based on edge variation discerning and core node extension is proposed. First, we detect communities based on the core node extension strategy, which avoids the problem of community matching errors. Second, we track community evolution based on the edge variation discerning strategy, which fully considers various situations that may occur during the community evolution process. According to the outputs of our experiments, our system can effectively track the evolution of communities in synthetic dynamic networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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INSIDER: A framework for assessing confidence in psychological scales based on multi-modal physiological signal fusion

INSIDER: A framework for assessing confidence in psychologic...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Youmian Wang Bin Hu Zhenxiang Chen Xiaoqing Jiang Wenjuan Liu Peicheng Wang Shandong Provincial Key Laboratory of NetWork Based Intelligent Computing University of Jinan Jinan China

The majority of polygraph questions used in psychometric objectivity assessments have inadequate applicability, low validity, and lengthy assessment times. In order to improve the Trusted Multi-View Classification (TMC) and produce more objective scale confidence results, this paper proposes a framework for assessing confidence in psychological scales based on multi-modal physiological signal fusion (INSIDER). This framework uses physiological signals (i.e. ECG, Eye Movement, and ECG) to identify lying response behaviors. By using the CNN (Temporal&Spatial) deep learning framework, in the three modalities, the EEG has the best accuracy (92.67%) in detecting lying answer behaviors; the TMC-based hybrid fusion method had both high accuracy (96.32%) and high noise immunity; and high accuracy (93.59%) can be attained by using LR classifier in the confidence assessment of the combined scale. Experimental resluts demonstrated that it can replace polygraph questions. When choosing the product, INSIDER takes into account the necessity for portability, which might serve as a model for the development of future mental health scale assessment systems.

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Fu-W:A Hyperspectral Image Classification Algorithm Combining Mini Graph Convolutional networks and Convolutional neural network 23

Fu-W:A Hyperspectral Image Classification Algorithm Combinin...

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6th International Conference on Big Data Technologies, ICBDT 2023

作者： Shi, Jiechuan Liu, Kun Yuan, Hao Wang, Can Yang, Bo School of Information Science and Engineering University of Jinan Jinan250022 China China High-resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9798400707667

Convolutional Neural network (CNN) is a widely used neural network in deep learning, and Graph Convolutional network (GCN) is one of the most effective semi -supervised methods. It spread node information in a conversion way. In this article, we have studied the differences between CNN and GCN in the classification of high -spectrum image. Because the traditional GCN algorithm needs to build an adjacent matrix on all data, the calculation cost is very high, especially in large -scale remote sensing problems. MinigCNS uses a small -batch learning method to solve the problem of CCN calculation costs, and then it has not solved the problem of low efficiency of single model classification. This article studies the advantages of minigcns and CNN, and proposes a weighted fusion network FU-W, which weighs the minigcns and CNN weighted integration to break the bottleneck of single model performance. We experimented with the fusion algorithm on the two high -spectrum data sets, and its overall accuracy can reach 88.8%in Indian Pines. The experiment proves the superiority of the fusion strategy for a single CNN or GCN model. © 2023 ACM.

关键词： Convolutional neural networks

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