Epoch-based Application Collaboration for Sensing Data Analysis

The application collaboration was addressed to provide energy-efficient data services for distributed sensing applications to collaboratively interacting to achieve a desired global objective not detectable by any single cluster. An epoch-based transaction model was proposed by using the concept of sphere of control （SoC）, and a collaborative sensing application can be dynamically formed as a nested architecture composed of time-synchronized applications. By loosening the rigid constraints of ACID to adapt the requirements of sensor networks, the submission, rollback and consistency rules ware educed and a two-phrase submission protocol was designed. Finally, it was illustrated that the model is capable of providing an adaptive formal template for sensing application collaboration. Our performance evaluations show that by applying the two-phrase submission protocol, we can significantly improve the number of reported answers and response time, raise resource utilization, and reduce the energy cansumption and data loss.

The Application of Classroom Collaboration and Extracurricular Collaborative Activities to Non-English Majors

This paper talks about the research of application of collaborative learning to non-English majors in Dalian University, First of all, the paper gives a brief review of the theoretical background of collaborative learning. Next, it states the reasons why collaborative learning is ineffective in China and points out the necessity to apply the approach to non-English majors in China. Then, exemplifications of the application of classroom collaboration and extracurricular collaborative activities to non-English majors in our university are given. Finally, research findings are presented.

A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods.

Collaborative Applications Based on the Internet

The basic concepts of collaborative applications and some relevant aspects about network communications are introduced. With the examples of some collaborative applications developed by us, several technical problems about collaborative applications, such as loss of data packets, unknown IP address and timedelay, are discussed in detail. And the methods of solving these problems are also provided.

A New Generation of Intelligent Mapping and Remote Sensing Scientific Test Satellite Luojia-301

With the continuous improvement of the performance and the increasing variety of optical mapping and remote sensing satellites,they have become an important support for obtaining global accurate surveying and mapping remote sensing information.At present,optical mapping and remote sensing satellites already have sub-meter spatial resolution capabilities,but there is a serious lag problem in mapping and remote sensing information services.It is urgent to develop intelligent mapping and remote sensing satellites to promote the transformation and upgrading to real-time intelligent services.Firstly,based on the three imaging systems of the optical mapping and remote sensing satellites and their realization methods and application characteristics,this paper analyzes the applicable system of the intelligent mapping and remote sensing satellites.Further,according to the application requirements of real-time,intelligence,and popularization,puts forward the design concept of integrated intelligent remote sensing satellite integrating communication,navigation,and remote sensing and focuses on the service mode and integrated function composition of intelligent remote sensing satellite.Then expounds on the performance and characteristics of the Luojia-301 satellite,a new generation of intelligent surveying and mapping remote sensing scientific test satellite.And finally summarizes and prospects the development and mission of intelligent mapping remote sensing satellites.Luojia-301 satellite integrates remote sensing and communication functions.It explores an efficient and intelligent service mode of mapping and remote sensing information from data acquisition to the application terminal and provides a real service verification platform for on-orbit processing and real-time transmission of remote sensing data based on space-ground internet,which is of great significance to the construction of China’s spatial information network.