https://scholarworks.gnu.ac.kr/handle/sw.gnu/660 2026-03-14T06:10:23Z https://scholarworks.gnu.ac.kr/handle/sw.gnu/59625 Title: Student Dropout Prediction for University with High Precision and Recall Authors: Kim, Sangyun; Choi, Euteum; Jun, Yong-Kee; Lee, Seongjin Abstract: Application to student counseling and reducing the dropout rate in universities.Since a high dropout rate for university students is a significant risk to local communities and countries, a dropout prediction model using machine learning is an active research domain to prevent students from dropping out. However, it is challenging to fulfill the needs of consulting institutes and the office of academic affairs. To the consulting institute, the accuracy in the prediction is of the utmost importance; to the offices of academic affairs and other offices, the reason for dropping out is essential. This paper proposes a Student Dropout Prediction (SDP) system, a hybrid model to predict the students who are about to drop out of the university. The model tries to increase the dropout precision and the dropout recall rate in predicting the dropouts. We then analyzed the reason for dropping out by compressing the feature set with PCA and applying K-means clustering to the compressed feature set. The SDP system showed a precision value of 0.963, which is 0.093 higher than the highest-precision model of the existing works. The dropout recall and F1 scores, 0.766 and 0.808, respectively, were also better than those of gradient boosting by 0.117 and 0.011, making them the highest among the existing works; Then, we classified the reasons for dropping out into four categories: "Employed", "Did Not Register", "Personal Issue", and "Admitted to Other University." The dropout precision of "Admitted to Other University" was the highest, at 0.672. In post-verification, the SDP system increased counseling efficiency by accurately predicting dropouts with high dropout precision in the "High-Risk" group while including more dropouts in total dropouts. In addition, by predicting the reasons for dropouts and presenting guidelines to each department, the students could receive personalized counseling. 2023-05-01T00:00:00Z https://scholarworks.gnu.ac.kr/handle/sw.gnu/1325 Title: A Fine-Grained Secure Service Provisioning Platform for Hypervisor Systems Authors: Seo, Junho; Lee, Seonah; Kim, Ki-Il; Kim, Kyong Hoon Abstract: As computing technology has been recently widely adopted, most computing devices provide security-related services as basic requirements, which is an important research issue for sustainability of computing devices. The rapid increase of software components makes it difficult to detect or prevent vulnerabilities in the large-size software. One of the prominent approaches for ensuring secure service is the isolation of service which allows the related code and data to be executed only in a particular area. In this paper, we provide a secure service provisioning platform for hypervisor systems. The main contribution of the proposed framework is to enhance the previous secure service provisioning platform in order to solve the non-preemption problem of secure services. Thus, the proposed framework improves the security isolation service in hypervisors and can be used for fine-grained secure service in secure embedded systems. 2022-05-01T00:00:00Z https://scholarworks.gnu.ac.kr/handle/sw.gnu/1718 Title: On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software Authors: Choi, Eu-teum; Kim, Tae-hyung; Jun, Yong-Kee; Lee, Seongjin; Han, Mingyun Abstract: Airborne health management systems prevent functional failure caused by errors or faults in airborne software. The on-the-fly repairing of atomicity violations in ARINC 653 concurrent software is critical for guaranteeing the correctness of software execution. This paper introduces RAV (Repairing Atomicity Violation), which efficiently treats atomicity violations. RAV diagnoses an error on the fly by utilizing the training results of software and treats to control access to the shared variable of the thread where the error has occurred. The evaluation of RAV measured the time overhead by applying methods found in previous works and RAV to five synthesis programs containing an atomicity violation. 2022-02-01T00:00:00Z https://scholarworks.gnu.ac.kr/handle/sw.gnu/1714 Title: Architectural Process for Flight Control Software of Unmanned Aerial Vehicle with Module-Level Portability Authors: Jargalsaikhan, TSogbayar; Lee, Keonpyo; Jun, Yong-Kee; Lee, Seongjin Abstract: To apply UAVs (Unmanned Aerial Vehicle) into different fields, including research and industry, and expand it quickly, reliable but modular software is required. The existing flight control software (FCS) of the UAV consists of various types of modules categorized into different layers, and it is responsible for coordinating, monitoring, and controlling the vehicle during its flight. This paper proposes mpFCS, a structure of UAV flight control software, which provides portability to its modules and is easy to expand. The mpFCS consists of four segments and several modules within the segments. mpFCS provides portability for each module within the segment. Existing software does not provide portability for its modules because of the tight coupling resulting from its different and private interfaces. The mpFCS uses interfaces of the standard airborne software architecture to transfer data between its modules. Moreover, the structure provides portability for its modules to run in the standard airborne software environment. In order to verify the mpFCS, we tested the mpFCS with the conformance test suite of the airborne software that provides the testing environment for the interfaces and modules of the software. The mpFCS passed the test. Test results show that all modules of the mpFCS are portable. Additionally, portable modules can be interoperable with other software, and the structure is expandable with new modules that use standard airborne software interfaces. 2022-02-01T00:00:00Z