Evolution Characteristics of Government-Industry-University Cooperative Innovation Network of Electronic Information Industry in Liaoning Province, China

It is important to optimize the cooperative innovation network for the improvement of economic competence and innovative power. Based on the patent information services platform, we obtain invention patent data for the electronic information industry in Liaoning from 1985 to 2015. This paper analyzes the cooperative innovation network structure, its spatiotemporal evolution and the triple helix relationship of government-industry-university(GIU) by using the social network analysis method and the triple helix theory as well as UCINet, ArcGIS and NetDraw. The empirical results show that: 1) the number of the subjects of the electronic information industry GIU cooperative innovation network in Liaoning demonstrates a gradual increase from 1985 to 2015, with the same trend in concentration. In terms of its subject and its centrality, the universities have a higher position, and the industries have a lower position,while the status of the government is still unclear. 2) The cooperative innovation network presents a core-periphery structure, and the polarization effect of innovation subjects tends to be obvious. There is certain distance-decay regularity in the cooperative innovation network, and a strong geographical proximity to cooperative innovation. 3) The compactness shows a downward trend as a whole. In terms of the extent of participation, the industries are better than the government but worse than the universities. This means that the cooperative innovation network of GIU in the electronic information industry in Liaoning is in the initial stage of formation.

A Practical Study of Big Data Technology in Computer Network Information Security Processing

In recent years,China has witnessed continuous development and progress in its scientific and technological landscape,with widespread utilization of computer networks.Concurrently,issues related to computer network information security,such as information leakage and virus invasions,have become increasingly prominent.Consequently,there is a pressing need for the implementation of effective network security measures.This paper aims to provide a comprehensive summary and analysis of the challenges associated with computer network information security processing.It delves into the core concepts and characteristics of big data technology,exploring its potential as a solution.The study further scrutinizes the application strategy of big data technology in addressing the aforementioned security issues within computer networks.The insights presented in this paper are intended to serve as a valuable reference for individuals involved in the relevant fields,offering guidance on effective approaches to enhance computer network information security through the application of big data technology.

CAS COMPUTER NETWORK INFORMATION CENTER

The Computer Network Information Center(CNIC) is a newly-established CAS institute,which is engaged in the research and development of network and scientific databases.At present the Center is in charge of two key projects:a scientific database project and a national computing and networking facility of China (NCFC).

Research on the Application of Computer Network Technology in Electronic Information Engineering

Computer network technology has a very important role in electronic information engineering.The application of this technology not only enriches the traditional information engineering functions,but also effectively guarantees the efficiency and quality.At present,due to the wide variety of external information that Chinese citizens are exposed to,in addition to the variegated and mixed content,it is easy for information to be leaked or stolen.In order to avoid similar situations,it is necessary for people to pay more attention to network system security,so as to ensure that the quality of life of citizens can be guaranteed.In view of that,this paper discusses and analyzes the application of computer network technology in electronic information engineering.

Application Strategy of Computer Network Technology in Electronic Information Engineering

This paper discussed the application strategy of the computer network technology in the electronic information engineering. The use of computer network management technology, designed to maximize the effectiveness of computer network operations, while ensuring network management security. With the advent of the digital age, the computer network has been basically popular, so the computer network management technology research value and application value is more prominent become a digital information technology research one of the hot spots.. Good antivirus software can be easily installed in a few minutes to each NT server in the organization, and can be downloaded and distributed to all the target machine, set by the network administrator and management, it will work with the operating system and other security measures are tightly integrated to become part of the network security management, and automatically provide the best network virus defense measures. In the future, more integration of the methodologies will be considered and discussed.

Modern Electronic Information Industry to Promote Economic and Social Information—An Interview with Electronics Industry Minister Hu Qili

Spurred by the world information tide, China has organized a series of information projects, called the "Three Gold" projects. Recently I had an interview with Mr. Hu Qili, Minister of Electronics Industry, about the establishment of China’s modern Electronic Information Industry. Mr. Hu told me that information is the mark of development of a modern society and electronics is the major means of carrying information. Establishing

A Dairy Industry Information Cooperative Service System Based on a Production Process Ontology

Agricultural information cooperative services （AICS） are now becoming an important aspect of agriculture informatization. This study offers an ontological conception of the agricultural production process, establishing operational divisions corresponding to both the stages and the fundamental information requirements of the dairy industry production process in China, yielding a process ontology for the dairy industry as well as a business chain model. A framework for a dairy industry information cooperative service system was established, with service functions realized in a prototype system. The resulting agricultural process ontology built on the basis of biological characteristics has advantages as a classification standard for agricultural information; whereas the business chain model based on this agricultural process ontology allows for an effective distribution of agricultural information cooperative services. This study outlines a concept and demonstrates a prototype of a cooperative service capable of integrating diverse online agricultural information relevant to the dairy industry in China.

City Network Evolution Characteristics of Smart Industry: Evidence from Yangtze River Delta, China

The dual-path model of industrial evolution and spatial progression has been widely acknowledged and incorporated into the strategic planning to promote the development of urban industries and regional collaborations.However,current research on inter-enter-prise city networks mainly focuses on the single sector of flows on all enterprise branches,such as product value chains and production factors,but neglects that of particular industry department.Built upon the new economic geography and city networks theory,this paper develops a methodological framework that focuses on the analysis of city network evolution characteristics of smart industry.Particu-larly,a conceptual model of smart industry enterprise-industry-city is proposed and then applied to a case study of smart industry in the Yangtze River Delta Region,China.Using enterprise supplier-customer data,a city network of smart industry is constructed and sub-sequently analyzed with the proposed model.Findings indicate that the smart industry network in Yangtze River Delta Region exhibits a hierarchical structure and the expansion of the network presents a small-world network characteristic.The study not only makes a meth-odological contribution for revealing the industrial and spatial evolution path of the current smart industry,but also provides empirical support for the formulation of new economic development policies focused on smart industries,demonstrating the role of city clusters as carriers of regional synergistic development.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Research on Fine-Grained Recognition Method for Sensitive Information in Social Networks Based on CLIP

With the emergence and development of social networks,people can stay in touch with friends,family,and colleagues more quickly and conveniently,regardless of their location.This ubiquitous digital internet environment has also led to large-scale disclosure of personal privacy.Due to the complexity and subtlety of sensitive information,traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data,thus weakening the deep connections between text and images.In this context,this paper adopts the CLIP model as a modality discriminator.By using comparative learning between sensitive image descriptions and images,the similarity between the images and the sensitive descriptions is obtained to determine whether the images contain sensitive information.This provides the basis for identifying sensitive information using different modalities.Specifically,if the original data does not contain sensitive information,only single-modality text-sensitive information identification is performed;if the original data contains sensitive information,multimodality sensitive information identification is conducted.This approach allows for differentiated processing of each piece of data,thereby achieving more accurate sensitive information identification.The aforementioned modality discriminator can address the limitations of existing sensitive information identification technologies,making the identification of sensitive information from the original data more appropriate and precise.

Automatic Extraction of Medical Latent Variables from ECG Signals Utilizing a Mutual Information-Based Technique and Capsular Neural Networks for Arrhythmia Detection

From a medical perspective,the 12 leads of the heart in an electrocardiogram(ECG)signal have functional dependencies with each other.Therefore,all these leads report different aspects of an arrhythmia.Their differences lie in the level of highlighting and displaying information about that arrhythmia.For example,although all leads show traces of atrial excitation,this function is more evident in lead II than in any other lead.In this article,a new model was proposed using ECG functional and structural dependencies between heart leads.In the prescreening stage,the ECG signals are segmented from the QRS point so that further analyzes can be performed on these segments in a more detailed manner.The mutual information indices were used to assess the relationship between leads.In order to calculate mutual information,the correlation between the 12 ECG leads has been calculated.The output of this step is a matrix containing all mutual information.Furthermore,to calculate the structural information of ECG signals,a capsule neural network was implemented to aid physicians in the automatic classification of cardiac arrhythmias.The architecture of this capsule neural network has been modified to perform the classification task.In the experimental results section,the proposed model was used to classify arrhythmias in ECG signals from the Chapman dataset.Numerical evaluations showed that this model has a precision of 97.02%,recall of 96.13%,F1-score of 96.57%and accuracy of 97.38%,indicating acceptable performance compared to other state-of-the-art methods.The proposed method shows an average accuracy of 2%superiority over similar works.

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

Wireless Information and Power Transfer in Underwater Acoustic Sensor Networks

Wireless information and power transfer(WIPT) enables simultaneously communications and sustainable power supplement without the erection of power supply lines and the replacement operation of the batteries for the terminals. The application of WIPT to the underwater acoustic sensor networks(UWASNs) not only retains the long range communication capabilities, but also provides an auxiliary and convenient energy supplement way for the terminal sensors, and thus is a promising scheme to solve the energy-limited problem for the UWASNs. In this paper, we propose the integration of WIPT into the UWASNs and provide an overview on various enabling techniques for the WIPT based UWASNs(WIPT-UWASNs) as well as pointing out future research challenges and opportunities for WIPT-UWASNs.

Research on College Network Information Security Protection in the Digital Economy Era

In the era of the digital economy,the informatization degree of various industries is getting deeper and deeper,and network information security has also come into people’s eyes.Colleges and universities are in the position of training applied talents,because of the needs of teaching and education,as well as the requirements of teaching reform,the information construction of colleges and universities has been gradually improved,but the problem of network information security is also worth causing people to ponder.The low security of the network environment will cause college network information security leaks,and even hackers will attack the official website of the university and leak the personal information of teachers and students.To solve such problems,this paper studies the protection of college network information security against the background of the digital economy era.This paper first analyzes the significance of network information security protection,then points out the current and moral problems,and finally puts forward specific countermeasures,hoping to create a safe learning environment for teachers and students for reference.

Research on Heterogeneous Information Network Link Prediction Based on Representation Learning

A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

Gold Projects of China's Electronic Information Industry

The news that China’s electronicinformation industry would carryout the Three Gold Projects received astrong response around the world,especially from the transnationalcompanies engaged intelecommunications, computers andinformation networks. IBM, Unisys, Visa,Siemens, Intel, AT＆T, Bell, Dell, NTT,NEC, 3COM and Fujitsu came to Chinato hold talks with potential clients andshowed their products with a view totaking a share in this big market. With the emergence of the country’selectronic in formation industry in the early1980s, the national electronic computerand large integrated circuit leading

Six Measures to Help SMEs out——An Interview with the Vice Minister of Industry and Information Technology

With the global economic turmoil and the changes in Chinese economic pol- icies in 2008,small,and medium-sized enterprises(SMEs)have all felt the early-coming of a"cold season".They have been undergoing extreme dif- ficulties in doing businesses due to a series of factors including

Economic Information Industry in China

Human history has experienced agricultural society and industrial society and is now entering the information society. The major economic developed countries of the world have drawn up and implemented their information plans. An information revolution is being carried out around the world. How to set up China’s information industry and economic information network is of