scholarly journals Image Network Teaching Resource Retrieval Algorithm Based on Deep Hash Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guotao Zhao ◽  
Jie Ding
Keyword(s):  
Retrieval Algorithm ◽  
Data Detection ◽  
Analysis Method ◽  
Teaching Resources ◽  
Moment Invariant ◽  
Network Teaching ◽  
Detection Model ◽  
Teaching Resource ◽  
Hash Algorithm ◽  
Image Network

In order to improve the retrieval ability of multiview attribute coded image network teaching resources, a retrieval algorithm of image network teaching resources based on depth hash algorithm is proposed. The pixel big data detection model of the multiview attribute coding image network teaching resources is constructed, the pixel information collected by the multiview attribute coding image network teaching resources is reconstructed, the fuzzy information feature components of the multiview attribute coding image are extracted, and the edge contour distribution image is combined. The distributed fusion result of the edge contour of the view image of the network teaching resources realizes the construction of the view feature parameter set. The gray moment invariant feature analysis method is used to realize information coding, the depth hash algorithm is used to realize the retrieval of multiview attribute coded image network teaching resources, and the information recombination is realized according to the hash coding result of multiview attribute coded image network teaching resources, thus improving the fusion. The simulation results show that this method has higher precision, better retrieval precision, and higher level of resource fusion for multiview coded image network teaching resource retrieval.

scholarly journals Research on Teaching Resource Recommendation Algorithm Based on Deep Learning and Cognitive Diagnosis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Fei Zhou
Keyword(s):  
Joint Probability ◽  
Matrix Decomposition ◽  
Cognitive Diagnosis ◽  
Learning Needs ◽  
Test Question ◽  
Recommendation Algorithm ◽  
Teaching Resources ◽  
Network Teaching ◽  
Teaching Resource ◽  
History Of

With the increasing abundance of network teaching resources, the recommendation technology based on network is becoming more and more mature. There are differences in the effect of recommendation, which leads to great differences in the effect of recommendation algorithms for teaching resources. The existing teaching resource recommendation algorithm either takes insufficient consideration of the students’ personality characteristics, cannot well distinguish the students’ users through the students’ personality, and pushes the same teaching resources or considers the student user personality not sufficient and cannot well meet the individualized learning needs of students. Therefore, in view of the above problem, combining TDINA model by the user for the students to build cognitive diagnosis model, we put forward a model based on convolution (CUPMF) joint probability matrix decomposition method of teaching resources to recommend the method combined with the history of the students answer, cognitive ability, knowledge to master the situation, and forgetting effect factors. At the same time, CNN is used to deeply excavate the test question resources in the teaching resources, and the nonlinear transformation of the test question resources output by CNN is carried out to integrate them into the joint probability matrix decomposition model to predict students’ performance on the resources. Finally, the students’ knowledge mastery matrix obtained by TDINA model is combined to recommend corresponding teaching resources to students, so as to improve learning efficiency and help students improve their performance.

The Implementation of Network Teaching Resources Sharing Center Based on Google Cloud

2014 ◽  
Vol 989-994 ◽  
pp. 5580-5583
Author(s):  
Hui E Chen
Keyword(s):  
Information Technology ◽  
Resource Sharing ◽  
School Quality ◽  
Technology In Education ◽  
Teaching Resources ◽  
Network Teaching ◽  
Geographical Environment ◽  
Cloud Network ◽  
Resources Sharing

The traditional network mode of resource sharing Easily lead to "information island", The link between information not unimpeded,With the development of information technology in education, The Google cloud network will break caused by geographical environment restricted resource barrier, Let the school quality curriculum and teaching resources sharing,Fully guarantee the fairness of education.

Development and Application of Information Technology in University Basketball Network Courses

2014 ◽  
Vol 1078 ◽  
pp. 345-348
Author(s):  
Ke Fei Wang
Keyword(s):  
Computer Network ◽  
Sports Injuries ◽  
Theory And Practice ◽  
Online Forums ◽  
Network Teaching ◽  
Module Design ◽  
Teaching Resource ◽  
System Maintenance ◽  
Network Platform ◽  
Resource Library

Based on the university basketball teaching theory and practice,combined with module design theory.Using literature studies,surveys,interviews,computer network design and software development,testing feedback methods designed modern colleges.basketball network teaching courses.The system includes basketball theoretical teaching resource library,basketball skills teaching resource library, basketball learning online forums, basketball sports injuries advisory system and system maintenance updates five modules. Expounded the issue of the system design, key technologies, modules and functional design,running instances and so on. Research and development of the system provided a good network platform for the college basketball training,so make the existing basketball teaching and training network, made the quality of teaching to improve and enhance.

Simulation on Optimized Intrusion Detection of Multi-Layer, Distributed and Large Differences Database

2014 ◽  
Vol 556-562 ◽  
pp. 2886-2889
Author(s):  
Nuo Wang ◽  
Yan Li ◽  
Li Min Yuan
Keyword(s):  
Intrusion Detection ◽  
Detection Algorithm ◽  
Data Detection ◽  
Detection Accuracy ◽  
Hierarchical Processing ◽  
Detection Model ◽  
Categorical Attributes ◽  
Point Data ◽  
Point To Point ◽  
Database Intrusion Detection

Different from the traditional single databases, there is a big difference between different layers’ data of multi-level database. The differentiation of categorical attributes is small. Traditional database intrusion detection process is simply to consider the point to point data detection between the layers, without considering the similarity between the layers and ignoring the optimization for detected properties of the applied classification between the levels, resulting in lower detection accuracy. In order to avoid the above-mentioned defects of the conventional algorithm, this paper propos an intrusion detection model of multi-layered network by introducing the coarse-to-fine concept. The intrusion feature of computer database is extracted to be used as the basis for intrusion detection of database. The particle swarm distinguish tree is established to make the hierarchical processing for nodes. Through the probability operation of database intrusion detection in different layers, intrusion detection of multi-layer, distributed and large differences database can be achieved. Experimental results show that the use of the intrusion detection algorithm for multi-layer, distributed and large differences database, can increase the security of the database, ensure the safe operation of the database.

The Study on Educational Resources Based on the Recommended Services

2014 ◽  
Vol 687-691 ◽  
pp. 2411-2414
Author(s):  
Wen Lu Li
Keyword(s):  
Online Teaching ◽  
Feature Representation ◽  
Educational Resources ◽  
Recommendation Algorithm ◽  
Teaching Resources ◽  
Teaching System ◽  
Network Teaching ◽  
Referral Service ◽  
Recommended Services ◽  
Vast Network

In the network teaching system, with the increasing teaching resources, student resources are increasingly difficult to find interesting, so join teaching resource referral service in online teaching system allows students freed from the vast network of information resources, greatly saving time and effort spent on students in search of information. Under these conditions and needs, educational resources referral service technology has been progressively developed. This article is recommended for Educational Resources Services referral service model, resource feature representation and recommendation algorithm based on machine learning and other key technologies were discussed and studied.

Integration of Network Resources Based on Unified Identity Authentication

2014 ◽  
Vol 623 ◽  
pp. 258-261
Author(s):  
Pei Ying Fan ◽  
Yang Qiu
Keyword(s):  
Governance Structure ◽  
Advanced Technology ◽  
Network Resources ◽  
Teaching Resources ◽  
Network Teaching ◽  
Main Research ◽  
Article Analysis ◽  
The Difference ◽  
Set Up ◽  
Unified Authentication

This article analysis the current situation of the virtual construction in university, which information non-sharing, poor safety and difficult management. Aiming at data diversification and the difference between platform, develop tools and system, making every network teaching resources system as an “information islet”, thus cause the disperse and independence of resources and data, integrate each resources system effectively by looking for a service governance structure. We proposed management system and method for private universities of long-term development. The main research is to set up the unified authentication system which can complete unified authentication and authority and administer. Using the virtual server technology, Information Office will set up a virtual campus portal platform with unified planning and concentration deployment, which is simple operation, advanced technology, safe and reliable, to realize unified management and guarantee the information resources interoperability and consistency.

The Design and Implementation of Resources Sharing System Based on the Cloud Computing

2014 ◽  
Vol 543-547 ◽  
pp. 3316-3319
Author(s):  
Zi Wan
Keyword(s):  
Cloud Computing ◽  
Computer Technology ◽  
Resource Sharing ◽  
Research Approach ◽  
Network Technology ◽  
Network Resources ◽  
Teaching Resources ◽  
Teaching Resource ◽  
Development Technology ◽  
Resources Sharing

Accompanying the development of the computer technology and network technology, the sharing of teaching resources is achieved by exploiting the network resources in most schools. The traditional network model for the sharing of teaching resources is likely to result in information island, that result in links among the islands not smooth, it is also difficult to achieve the needs of users. So research approach of the teaching resource sharing system based on cloud computing is given in this paper. The design of this approach combines cloud computing and web development technology, makes full use of the characteristics of cloud computing, and reaches the purpose of the elimination of the information island.

scholarly journals Advantages and Challenges of Cooperative Learning in Two Different Cultures

2021 ◽  
Vol 12 (1) ◽  
pp. 3
Author(s):  
Mohammad Reza Keramati ◽  
Robyn M. Gillies
Keyword(s):  
Cooperative Learning ◽  
Teaching And Learning ◽  
Limited Information ◽  
Analysis Method ◽  
Approaches To Teaching ◽  
Teaching Resources ◽  
Safe Environment ◽  
University Instructors ◽  
Traditional Approaches ◽  
Different Cultures

Cooperative learning (CL) is a teaching and learning pedagogy that has been used widely in school but there is limited information on instructors’ perceptions of CL nor observations of how CL is implemented in higher education settings. In this study, we used an interpretative-qualitative approach to investigate the advantages and challenges of embedding CL in instructors’ teaching in Iran and Australia. Data were collected through interviews and observations and thematic analysis method was used to analyse the data. Results showed that despite challenges such as the lack of familiarity with CL and how it can be implemented in university curricula, issues associated with assessment, and time constraints, CL created an interactive, pleasant, and safe environment for deep learning in both countries. The findings showed that there were challenges in Iran such as a tendency to use traditional approaches to teaching, insufficient understanding of how to establish teamwork, and a lack of up-to-date teaching resources. In Australia, changing courses, working with external students, catering for individual differences, and building positive relationships were some of the challenges of implementing CL. We believe that these challenges can be overcome if university instructors are prepared to address them.

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