Visible to the public Biblio

Filters: Keyword is cleaning  [Clear All Filters]
2021-02-22
Alzahrani, A., Feki, J..  2020.  Toward a Natural Language-Based Approach for the Specification of Decisional-Users Requirements. 2020 3rd International Conference on Computer Applications Information Security (ICCAIS). :1–6.
The number of organizations adopting the Data Warehouse (DW) technology along with data analytics in order to improve the effectiveness of their decision-making processes is permanently increasing. Despite the efforts invested, the DW design remains a great challenge research domain. More accurately, the design quality of the DW depends on several aspects; among them, the requirement-gathering phase is a critical and complex task. In this context, we propose a Natural language (NL) NL-template based design approach, which is twofold; firstly, it facilitates the involvement of decision-makers in the early step of the DW design; indeed, using NL is a good and natural means to encourage the decision-makers to express their requirements as query-like English sentences. Secondly, our approach aims to generate a DW multidimensional schema from a set of gathered requirements (as OLAP: On-Line-Analytical-Processing queries, written according to the NL suggested templates). This approach articulates around: (i) two NL-templates for specifying multidimensional components, and (ii) a set of five heuristic rules for extracting the multidimensional concepts from requirements. Really, we are developing a software prototype that accepts the decision-makers' requirements then automatically identifies the multidimensional components of the DW model.
2020-12-17
Gao, X., Fu, X..  2020.  Miniature Water Surface Garbage Cleaning Robot. 2020 International Conference on Computer Engineering and Application (ICCEA). :806—810.

In light of the problem for garbage cleaning in small water area, an intelligent miniature water surface garbage cleaning robot with unmanned driving and convenient operation is designed. Based on STC12C5A60S2 as the main controller in the design, power module, transmission module and cleaning module are controlled together to realize the function of cleaning and transporting garbage, intelligent remote control of miniature water surface garbage cleaning robot is realized by the WiFi module. Then the prototype is developed and tested, which will verify the rationality of the design. Compared with the traditional manual driving water surface cleaning devices, the designed robot realizes the intelligent control of unmanned driving, and achieves the purpose of saving human resources and reducing labor intensity, and the system operates security and stability, which has certain practical value.

2020-02-10
Zhang, Jiemin, Mao, Jian, Liu, Jinming, Tang, Zhi, Gu, Zhiling, Liu, Yongmei.  2019.  Cloud-based Multi-core Architecture against DNS Attacks. 2019 14th International Conference on Computer Science Education (ICCSE). :391–393.
The domain name resolution system provides support service for website visits as the basic service of the Internet. With the increase of DNS attacks, it has brought copious challenges to network security. The paper studies on the key defense technologies against DNS attacks based on the DNS principle. The multi-core customized to the DNS is adopted to analyze hardware kernel, while AI algorithms being realized for malicious flow cleaning and intelligent routing running on the cloud system established specifically for DNS. The designed DNS intelligent cloud system can provide high-efficiency domain name resolution in practice, while ensuring the network security.
2019-12-09
Alemán, Concepción Sánchez, Pissinou, Niki, Alemany, Sheila, Boroojeni, Kianoosh, Miller, Jerry, Ding, Ziqian.  2018.  Context-Aware Data Cleaning for Mobile Wireless Sensor Networks: A Diversified Trust Approach. 2018 International Conference on Computing, Networking and Communications (ICNC). :226–230.

In mobile wireless sensor networks (MWSN), data imprecision is a common problem. Decision making in real time applications may be greatly affected by a minor error. Even though there are many existing techniques that take advantage of the spatio-temporal characteristics exhibited in mobile environments, few measure the trustworthiness of sensor data accuracy. We propose a unique online context-aware data cleaning method that measures trustworthiness by employing an initial candidate reduction through the analysis of trust parameters used in financial markets theory. Sensors with similar trajectory behaviors are assigned trust scores estimated through the calculation of “betas” for finding the most accurate data to trust. Instead of devoting all the trust into a single candidate sensor's data to perform the cleaning, a Diversified Trust Portfolio (DTP) is generated based on the selected set of spatially autocorrelated candidate sensors. Our results show that samples cleaned by the proposed method exhibit lower percent error when compared to two well-known and effective data cleaning algorithms in tested outdoor and indoor scenarios.

2017-03-07
Park, Jonggyu, Kang, Dong Hyun, Eom, Young Ik.  2016.  File Defragmentation Scheme for a Log-Structured File System. Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems. :19:1–19:7.

In recent years, many researchers have focused on log-structured file systems (LFS), because it gracefully enhances the random write performance and efficiently resolves the consistency issue. However, the write policy of LFS can cause a file fragmentation problem, which degrades sequential read performance of the file system. In this paper, we analyze the relationship between file fragmentation and the sequential read performance, considering the characteristics of underlying storage devices. We also propose a novel file defragmentation scheme on LFS to effectively address the file fragmentation problem. Our scheme reorders the valid data blocks belonging to a victim segment based on the inode numbers during the cleaning process of LFS. In our experiments, our scheme eliminates file fragmentation by up to 98.5% when compared with traditional LFS.