• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.7
• /
• pp.3511-3532
• /
• 2019

Coverage-guided fuzzing is an efficient solution that has been widely used in software testing. By guiding fuzzers through the coverage information, seeds that generate new paths will be retained to continually increase the coverage. However, we observed that most samples follow the same few high-frequency paths. The seeds that exercise a high-frequency path are saved for the subsequent mutation process until the user terminates the test process, which directly affects the efficiency with which the low-frequency paths are tested. In this paper, we propose a fuzzing solution, ER-Fuzz, that truncates the recording of a high-frequency path to influence coverage. It utilizes a deep learning-based classifier to locate the high and low-frequency path transfer points; then, it instruments at the transfer position to promote the probability low-frequency transfer paths while eliminating subsequent variations of the high-frequency path seeds. We implemented a prototype of ER-Fuzz based on the popular fuzzer AFL and evaluated it on several applications. The experimental results show that ER-Fuzz improves the coverage of the original AFL method to different degrees. In terms of the number of crash discoveries, in the best case, ER-Fuzz found 115% more unique crashes than did AFL. In total, seven new bugs were found and new CVEs were assigned.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.551-552
• /
• 2021

인공지능 기술의 발달로 현대사회 사람들은 일상생활에 편리함을 제공받고 업무의 효율성과 생산성이 향상되었다. 대한민국 보육교사들은 수많은 업무로 인해 근무시간 대비 휴식시간과 점심시간이 턱없이 부족하다. 본 논문에서는 보육교사가 일일이 아동들의 사진을 분류하는 업무에 편의성을 제공하여 보다 많은 휴식시간을 보장받고 활용할 수 있도록 전이학습을 통한 아동 이미지 자동 분류 시스템에 대해 기술하고자 한다. 이 시스템을 통해 분류된 아동들의 사진을 매년 제작하는 유아 포토북 제작에도 활용할 수 있을 것으로 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.35-36
• /
• 2021

딥러닝을 이용한 이미지 처리에서 데이터 셋이 반드시 필요하다. 월패드는 널리 보급되는 다양한 성능을 포함한 IoT가전으로 그 기능의 사용을 돕기 위해서는 해당 월패드에 해당하는 매뉴얼을 제공해야 하고 이를 위해 딥러닝을 이용한 월패드 분류를 이용 할 수 있다. 하지만 월패드 중 일부 모델은 화면의 전반사가 매우 심해 기존의 작은 데이터 셋으로는 딥러닝을 이용한 이미지 분류 성능이 좋지 못하다. 본 논문은 이를 해결하기 위해 추가적으로 데이터 셋을 구축하고 이를 이용해 대규모 데이터로 사전 학습된 VGG16, VGG19, ResNet50, MobileNet 등을 이용해 전이학습을 통해 월패드를 분류한다.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.4
• /
• pp.136-141
• /
• 2023

The continuous shrinking of transistors in integrated circuits leads to difficulties in improving performance, resulting in the emerging transistors such as nanosheet field-effect transistors. In this paper, we propose a TCAD-machine learning framework of nanosheet FETs to model the current-voltage characteristics. Sentaurus TCAD simulations of nanosheet FETs are performed to obtain a large amount of device data. A machine learning model of I-V characteristics is trained using the multi-layer perceptron from these TCAD data. The weights and biases obtained from multi-layer perceptron are implemented in a PSPICE netlist to verify the accuracy of I-V and the DC transfer characteristics of a CMOS inverter. It is found that the proposed machine learning model is applicable to the prediction of nanosheet field-effect transistors device and circuit performance.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.324-330
• /
• 2021

Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investigated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.3
• /
• pp.481-489
• /
• 2020

Safety and security during the handling of hazardous materials is a great concern for anyone in the field. One driving point in the security field is the ability to detect the source of the danger and take action against it as quickly as possible. Via the usage of a fully convolutional network, it is possible to create the label map of an input image, indicating what object is occupying the specific area of the image. This research employs the usage of U-net, which was constructed in biomedical field segmentation to segment cells, instead of the original FCN. One of the challenges that this research faces is the availability of ground truth with precise labeling for the dataset. Testing the network after training resulted in some images where the network pronounces even better detail than the expected label map. With better detailed label map, the network might be able to produce better segmentation is something to be studied in further research.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.706-708
• /
• 2023

In this study, we embark on a journey to uncover the essence of emotions by exploring the depths of transfer learning on three pre-trained transformer models. Our quest to classify five emotions culminates in discovering the KLUE (Korean Language Understanding Evaluation)-BERT (Bidirectional Encoder Representations from Transformers) model, which is the most exceptional among its peers. Our analysis of F1 scores attests to its superior learning and generalization abilities on the experimental data. To delve deeper into the mystery behind its success, we employ the powerful SHAP (Shapley Additive Explanations) method to unravel the intricacies of the KLUE-BERT model. The findings of our investigation are presented with a mesmerizing text plot visualization, which serves as a window into the model's soul. This approach enables us to grasp the impact of individual tokens on emotion classification and provides irrefutable, visually appealing evidence to support the predictions of the KLUE-BERT model.

• The Journal of Korean Association of Computer Education
• /
• v.19 no.6
• /
• pp.55-68
• /
• 2016

Informatics curriculum revised 2015 proposed the use of block type and text type of programming language by organizing problem solving and the programming unit in a spiral. The purpose of this study is to find out whether the algorithms helps programming learning and whether there is a positive transition effect in block type programming learning to text type programming trailing learning. For 15 elementary school students was conducted block type and text type programming learning. As a result of the research, it is confirmed that writing the algorithm in a limited way can interfere with the learner's expression of thinking, but the block type programming learning has a positive transition to the text type programming learning. This study is meaningful that it suggested a plan for the programming education which is sequential from elementary school.

• Journal of East Asia Management
• /
• v.2 no.1
• /
• pp.93-103
• /
• 2021

Struggle refers to the process of overcoming various difficulties for a goal. The spirit of struggle is a positive attitude and reaction reflected in the process of struggle. Cultivating the spirit of struggle of college students is the call of the new era. In essence, the cultivation of the spirit of struggle is a process of learning, which is in line with Bandura's Observation Learning Theory(Bandura, 1977):Attention, Maintenance, Reproduction and Motivation. The cultivation of College Students' spirit of struggle in the new era is also a learning process of enriched experience. It is necessary to cultivate the spirit of struggle into the soul of college students and make it become a habit of students. Moreover, it is crucial to carry out adaptive transformation of Bandura's observation learning theory. By studying the mechanism of the spirit of struggle of college students, taking innovation and entrepreneurship education as a means, and aiming at cultivating the connotation of President Xi's thought on socialism with Chinese characteristics for a new era, this paper constructs the AIST model for cultivating the spirit of struggle of college students in the new era. This model includes online learning acceptance platform(Acceptance), classroom experience stimulation platform(Inspiration), iterative training solidified platform (Solidification), and competition practice transfer platform(Transfer). The purpose of this model is to provide a practical way for universities to fulfill the fundamental task of moral education and cultivate qualified socialist builders and successors. The number of students using the online learning acceptance platform ranked the first among that of the similar courses in China; The classroom experience stimulation platform and the iterative training solidified platform support each other, with an effective rate of 97%; The competition practice transfer platform has realized the continuous growth of the number of awards won in competitions for three years. The direction of future efforts is to establish the external mechanism of the spirit of struggle, to ensure the effectiveness of classroom experience and iterative training, to cultivate teachers with coaching skills, and to accurately measure the transformation point of external and endogenous motivation.

• Annual Conference on Human and Language Technology
• /
• 2020.10a
• /
• pp.223-226
• /
• 2020

엔터티 링킹을 위해서는 엔터티 링킹을 수행 할 후보 엔터티의 정보를 얻어내는 것이 필요하다. 하지만, 엔터티 정보를 획득하기 어려운 경우, 엔터티 링킹을 수행 할 수 없다. 이 논문에서는 이를 해결하기 위해 데이터셋으로부터 엔터티의 가상 엔터티 설명문을 작성하고, 이를 통해 엔터티 링킹을 수행함으로써 엔터티 정보가 없는 환경에서도 2.58%p밖에 성능 하락이 일어나지 않음을 보인다.