会议名称：
14th International Conference on Semantics, Knowledge and Grids (SKG)
会议时间：
SEP 12-14, 2018
会议地点：
Guangzhou, PEOPLES R CHINA
会议主办单位：
[Chen, Xianglong;Ouyang, Chunping;Liu, Yongbin;Luo, Lingyun;Yang, Xiaohua] Univ South China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.
会议论文集名称：
International Conference on Semantics Knowledge and Grids
关键词：
Text classification;Deep learning;CNN;RNN
摘要：
Deep learning has shown its effectiveness in many tasks such as text classification and computer vision. Most text classification tasks are concentrated in the use of convolution neural network and recurrent neural network to obtain text feature representation. In some researches, Attention mechanism is usually adopted to improve classification accuracy. According to the target of task 6 in NLP&CC2018, a hybrid deep learning model which combined BiGRU, CNN and Attention mechanism was proposed to improve text classification. The experimental results show that the Fl-score of the proposed model successfully excels the task's baseline model. Besides, this hybrid Deep Learning model gets higher Precision, Recall and Fl-score comparing with some other popular Deep Learning models, and the improvement of on Fl-score is 5.4% than the single CNN model.
摘要：
It has long been known that there are software applications for which it is difficult to detect subtle errors, faults, defects, or anomalies because there is no reliable "test oracle" to indicate what the correct output should be for arbitrary input. The absence of a test oracle clearly presents a challenge in testing the software applications of scientific computing from the domain of nuclear power plant. Metamorphic testing has been shown to be a simple yet effective technique in addressing the quality assurance of these "non-testable programs." In this paper, we introduce Metamorphic testing method to address the oracle problem as mentioned above. We identify a metamorphic relation for a real-world scientific computing programs which do not have test oracles, and demonstrate the effectiveness of metamorphic testing in identifying the error.
期刊：
PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON NUCLEAR ENGINEERING, 2017, VOL 2,2017年2
通讯作者：
Li Meng
作者机构：
[Li Meng; Yang Xiao-Hua] CNNC Key Lab High Trusted Comp, Hengyang 421001, Hunan, Peoples R China.;[Li Yu-Yan; Wang Li-Jun] Univ South China, Sch Elect Engn, Hengyang 421001, Hunan, Peoples R China.
通讯机构：
[Li Meng] C;CNNC Key Lab High Trusted Comp, Hengyang 421001, Hunan, Peoples R China.
会议名称：
25th International Conference on Nuclear Engineering
会议时间：
JUL 02-06, 2017
会议地点：
Shanghai, PEOPLES R CHINA
会议主办单位：
[Li Meng;Yang Xiao-Hua] CNNC Key Lab High Trusted Comp, Hengyang 421001, Hunan, Peoples R China.^[Wang Li-Jun;Li Yu-Yan] Univ South China, Sch Elect Engn, Hengyang 421001, Hunan, Peoples R China.
摘要：
In view of the characteristics of the physical code Nestor the focus is on the correctness of calculation for which the test adequacy criterion has been established. This is based on structural coverage and the input domain. According to such test adequacy criterion, testing strategies have been applied on the entire testing process. They consist of unit static, unit dynamic, integration, system and regression test strategy. Each strategy is composed of test target, test range, technology and method, entry criterion, completion criterion, test focus and priority. After compared with 11 basic benchmarks from nuclear power plants and calculation result of benchmark programs, the ELEMENT program result is correct and credible; the relative error of result is less than three percent. The ELEMENT testing is adequacy. Its test cases covers fuel grid element types, fuel types, non-combustible grid element types, and control rod computational models. Furthermore, it puts forward a research direction in the future.
摘要：
To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.
作者机构：
[阳小华; 闫仕宇; 刘朝晖] School of Computer Science and Technology, University of South China, Hengyang, 421001, China;[刘华; 于涛; 刘朝晖; 谢金森; 李萌; 阳小华; 闫仕宇] CNNC Key Laboratory on High Trusted Computing, Hengyang, 421001, China
通讯机构：
School of Computer Science and Technology, University of South China, Hengyang, China
