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the Influence of Family Involvement on Enterprise Innovation Based on big data Analysis

The Influence of Family Involvement on Enterprise Innovation...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Yingkai Tang Yelin Ren Business School Sichuan University Chengdu China

For family enterprises, evergreen is their long-term goal of unremitting pursuit, and the development of technological innovation activities for family firms with the goal of evergreen is of great significance. Starting from three dimensions of family ownership participation, family management participation and family inheritance participation, this paper extracts data from China Stock Market & Accounting Research database (CSMAR) database and Chinese Research data Services Platform (CNRDS), using python for text extraction and analysis of financial statements, based on the data of 506 listed companies in China from 2015 to 2019 screened from tens of thousands of companies, this paper draws a conclusion that family participation will restrain the technological innovation of enterprises. At last, the article puts forward some suggestions, such as diluting equity, hiring external professional managers, and forming a good corporate culture, to alleviate the negative impact of family inheritance and promote the long-term development of enterprises.

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Research on Telecom Customer Satisfaction Prediction Strategy

Research on Telecom Customer Satisfaction Prediction Strateg...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Haosong Gou Ling Su Gaoyi Zhang Haicong Li Zhiheng Li China Mobile Communications Group Sichuan Co. Ltd. University of Electronic Science and Technology of China Chengdu China China Mobile Communications Group Sichuan Co. Ltd. Chengdu China

Customer satisfaction is critical for telecom operators to gain competitive advantages. the customer satisfaction index is used to evaluate the customers' satisfaction with the services, such as network experience and after-sales service. this paper aims to train an XGBoost and CatBoost-based model to predict customer satisfaction by using network key performance indicators, customer personal information, and customer communication behavior information to help operators predict the satisfaction score to enhance customer satisfaction. Finally, we trained a better model by selecting key factors, model training, validating, and testing. the final experiment shows our trained model achieves much better performance than the usual linear regression model.

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Research on Complex Task Scheduling Algorithm in Crowdsourcing

Research on Complex Task Scheduling Algorithm in Crowdsourci...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Minglan Fu Jiekun Li Lvqiang Chen Debao Chen School of Computer Science and Technology Huaibei Normal University Huaibei China

the crowdsourcing reflects the new concept of "pay for work" on the internet. However, the self-interest of users and the rapid growth of user number pose new challenges to the complex task allocation in crowdsourcing. In response to these challenges, two aspects of the work have been done. First, a benefit distribution algorithm based on a new evaluation index was proposed, which can solve the problem that the task scheduling optimization algorithm will reach the nonoptimal Nash equilibrium to some extent. Second, an allocation algorithm for complex tasks was proposed based on the best response strategy and benefit distribution results to improve the stability of task allocation under the premise of ensuring a higher system revenue. this algorithm solves the problem that the existing algorithms for crowdsourcing platform does not consider the users' self-interest and right values. the simulation results verified the effectiveness of the proposed algorithms.

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A Generative Adversarial Network with Attention Mechanism for Time Series Forecasting

A Generative Adversarial Network with Attention Mechanism fo...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Min Su Shengdong Du Jie Hu Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

Time series forecasting has widespread applications in everyday life, e.g. in the domains of weather forecasting, transportation planning, and electricity demand prediction. Due to the high dimensions, uncertainties, and changes of data in time series, accurately predicting future trends of time series remains a challenge. In this paper, we propose a new time series forecasting model, the Generative Adversarial Network Model with Attention Mechanism (GANAM), which uses a global residual network to enhance the attention mechanism module and acts as a generator to learn the deep temporal features and then uses the discriminator of the generative adversarial network for improving the forecasting capability. In addition, the classical time series model can lead to an accumulation of errors in the multi-step prediction process. the proposed model also can effectively reduce this problem by utilizing the generative adversarial network with an attention mechanism. the experiments on various real-world datasets have demonstrated the effectiveness of our model.

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Online Public Opinion Evolution Research Based on Knowledge Graph

Online Public Opinion Evolution Research Based on Knowledge ...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Mincheng Chen Shaojie Hu Siqi Cai Zhejiang Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Hangzhou China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

In order to excavate the law of the dissemination of network public opinion in social hot events and provide reference for the response and governance of network public opinion events, a online public opinion analysis method based on knowledge graph is proposed. We construct the public opinion theme graph by its participation in subjects, post content, forwarding data and remark data to present a network structure that reflects the characteristics of forwarding-remark relationship; to construct the public opinion evolution graph to distinguish the dissemination characteristics of different stages; to study the participating subjects by social network analysis to explore the role of each subject in the development of public opinion events in multiple dimensions. Finally, through empirical analysis, it has been demonstrated that this method is more accurate, objective, and comprehensive in analyzing online public opinion, providing timely insights into the trend of public opinion evolution.

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Research on Sentiment Classification of MOOC User Comments Based on Machine Learning

Research on Sentiment Classification of MOOC User Comments B...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Tianle Cai Yuwei Zhu Yingchun Liu College of Educational Science and Technology Zhejiang University of Technology Hangzhou China

the increasing use of information technology in education has led to the emergence of online teaching methods, such as MOOC, that plays a vital role in promoting the reform and development of basic education. MOOC primarily uses micro-lessons to present learning content, and students can collaborate, transfer and share information by exchanging questions in the comment section. MOOC comments reflect the learners' evaluation and attitude towards the course. However, comment text is unstructured data, so obtaining accurate information about learners' emotional feedback from comment text is challenging for both users and platforms, and improving MOOC lessons using feedback from comments proves difficult. In this study, we used the "Chinese University MOOC" platform as a case study, utilizing artificial labeling to create experimental datasets. We compared the sentiment classification of four conventional machine learning models, explored error cases, and proposed a model optimization scheme. the results serve as a reference for future research on sentiment classification of spoken text data.

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Tri-Branch CNN for Age-Related Macular Degeneration Categorization with Incomplete Multi-Modality Ophthalmology Images

Tri-Branch CNN for Age-Related Macular Degeneration Categori...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Qinshuo Wang Qing Guo Xianchang Liu Ruichun Tang Faculty of Information Science and Engineering Ocean University of China Qingdao China

the problems of incomplete data and insufficient feature utilization are commonly existing in disease diagnosis with multi-modality ophthalmology images. In this paper, we firstly develop a three-constraint generative adversarial network (TCGAN) to synthesize the missing ophthalmology images, so that the differences between the feature maps of the synthesized images and the corresponding real images are as small as possible to preserve the information relevant to the diagnosis. then, we propose a CFP-OCT multi-modality network (COMNet), in which two branches are used to extract features from CFP images and OCT images, and a fusion branch is composed of four multi-modality fusion (MMF) modules, which adopts an attention mechanism based fusion method to generate delicate fusion features at each stages of the network. Experimental results showed that using TCGAN to synthesize missing images for data augmentation is effective, while COMNet achieved better diagnosis performance.

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Introduction of Information Security Fatigue Scale for Teleworking and its Effectiveness

Introduction of Information Security Fatigue Scale for Telew...

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big data, cloud computing, data science & Engineering (bcd)

作者： Hiroki Koyama Shigeaki Tanimoto Atsushi Shimoda Seiko Taki Atsushi Kanai Faculty of Social System Science Chiba Institute of Technology Chiba Japan Faculty of Management and Information Studies Japan International University Ibaraki Japan Faculty of Information and Transformation Science Chiba Institute of Technology Chiba Japan Faculty of Science and Engineering Hosei University Tokyo Japan

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In recent years, government-led “work style reform” has been promoted, and the teleworking style of work has been attracting attention. In particular, the spread of COVID-19 infection in 2020 has led to the rapid spread of telework. the spread of teleworking has led to a shift to paperless and web conferencing, and the risk of information security has increased due to the location-independent work style. In particular, unlike the face-to-face environment of an office, the telework environment poses psychological risks such as loneliness due to lack of communication. this paper clarifies information security fatigue in a telework environment based on previous research on security fatigue in a face-to-face environment that also considers such psychological aspects, and proposes countermeasures against it. First, we designed a questionnaire that took into account the telework environment based on the questionnaire survey of a previous study, and conducted a questionnaire survey using a web-based questionnaire. Considering the impact of COVID-19, a questionnaire survey was conducted twice, once in December 2020 at the early stage of COVID-19 expansion and once in January 2023 after COVID-19 establishment, both times targeting approximately 500 Japanese teleworkers. the results of these surveys showed that the security fatigue scale in the telework environment was similar to the information security fatigue results in the face-to-face environment of the previous study, with approximately 20% in the ideal state. In other words, security fatigue was found to be the same in a telework environment as in a face-to-face environment. Furthermore, the results of the analysis of the open-ended responses to the questionnaire survey showed similar trends to those indicated in previous studies. As a result of these findings, it was clarified that countermeasures such as “implementation of education to arouse awareness of the concerned parties” and “interviews regarding information s

关键词： Surveys COVID-19 Education Information security Psychology Web conferencing Fatigue Market research Remote working Interviews

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Chinese-Mongolian Machine Translation Combining Sentence Structure Information

Chinese-Mongolian Machine Translation Combining Sentence Str...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Aijun Zhang Weijian Hu Mingxing Luo Lingfang Li Institute of Information Engineer Inner Mongolia University of Science & Technology Baotou China Institute of Information Science & Technology Southwest Jiaotong University Chengdu China Institute of Information Science & Technology Inner Mongolia University of Science & Technology Southwest Jiaotong University Chengdu China

Neural machine translation have achieved state-of-the-art accuracy on widespread used language pairs, but falls short on low-resource or complex-morphological language pairs. Mongolian is a typical agglutinative language which of complex morphological changes lead to the misunderstand of machine translation. In this work, we improve the quality of Chinese-Mongolia translation by combining double syntactic structure information. Convolutional neural network follows with interest is clause structure in the sequence, and extract the clause features through window sliding. the dependency structure of sentences affects the distribution of attention between words, we construct the dependency score to upgrade the attention mechanism for deeper encoding. We show the efficacy of our model not only in Chinese-Mongolian but also English-German, and the results prove that the fusion of double syntactic structure information helps machine translation.

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A data Augmentation Method for Wound Infection Prediction

A Data Augmentation Method for Wound Infection Prediction

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Tianhai Zhao Jianhua Gu Qianru Zhang School of Computer Science Northwestern Polytechnical University Xi’an China School of Software Northwestern Polytechnical University Xi’an China

Wound infection is a common clinical symptom, which not only brings economic and psychological burden to patients, but also increases the difficulty of wound treatment. It is feasible to predict the bacterial infection of wounds based on the current rapidly developing machine learning technologies and wound microenvironment indicator data. However, due to the confidentiality of clinical data, the complexity of medical experiments and other reasons, it is usually impossible to obtain a large amount of standard clinical data. therefore, based on a small amount of wound micro-environment data collected in relevant experiments of Tangdu Hospital of Air Force Military Medical University, we propose a multivariate time series data augmentation method based on DTW to improve the performance of machine learning methods on the task of wound infection prediction. through experiments, we verified the effectiveness of the method, and the prediction accuracy of NN-DTW was improved by 20.61%. Furthermore, we also conducted experimental verification on some datasets of the UCR/UEA archives, and the results show that the method is also effective for those time series datasets.

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