KIPS Transactions on Software and Data Engineering
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v.11
no.7
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pp.273-282
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2022
Testing approaches for configurable software product lines differs significantly from a single software testing, as it requires consideration of common parts used by all member products of a product line and variable parts shared by some or a single product. Test coverage is a measure of the adequacy of testing performed. Test coverage measurements are important to evaluate the adequacy of testing at the software product line level, as there can be hundreds of member products produced from configurable software product lines. This paper proposes a method for measuring code coverage at the product line level in configurable software product lines. The proposed method tests the member products of a product line after hierarchizing member products based on the inclusion relationship of the selected features, and quantifies SPL(Software Product Line) test coverage by synthesizing the test coverage of each product. As a result of applying the proposed method to 11 configurable software product line cases, we confirmed that the proposed method could quantitatively visualize how thoroughly the SPL testing was performed to help verify the adequacy of the SPL testing. In addition, we could check whether the newly performed testing for a member product covers the newly added code parts of a feature.
Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.
Jung-Wook, Park;Li, Zhuang;Jeong Seok, Yoon;Chan-Hee, Park;Changlun, Sun;Changsoo, Lee
Tunnel and Underground Space
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v.32
no.6
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pp.568-585
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2022
In the present study, we proposed a numerical method for simulating thermally induced fracture slip using a grain-based distinct element model (GBDEM). As a part of DECOVALEX-2023, the thermo-mechanical loading test on a saw-cut rock fracture conducted at the Korea Institute of Civil Engineering and Building Technology was simulated. In the numerical model, the rock sample including a saw-cut fracture was represented as a group of random Voronoi polyhedra. Then, the coupled thermo-mechanical behavior of grains and their interfaces was calculated using 3DEC. The key concerns focused on the temperature evolution, thermally induced principal stress increment, and fracture normal and shear displacements under thermo-mechanical loading. The comparisons between laboratory experimental results and the numerical results revealed that the numerical model reasonably captured the heat transfer and heat loss characteristics of the rock specimen, the horizontal stress increment due to constrained displacement, and the progressive shear failure of the fracture. However, the onset of the fracture slip and the magnitudes of stress increment and fracture displacement showed discrepancies between the numerical and experimental results. We expect the numerical model to be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated in further study.
KIPS Transactions on Software and Data Engineering
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v.13
no.1
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pp.35-49
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2024
Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.
As the use of ICT became a part of daily social life, online community has emerged as a new type of social organization. Online community is a virtual space which enables many people to participate and contribute together to collective knowledge. Anonymity in online communities can encourage active social participation by people with various social constraints, however, anonymity can also lead to serious social pathology. As a result, it is necessary to study on what is fundamentally influencing human behavior and how people's behavior is controlled in anonymous online community. This study focuses on human identity and investigate the factors affecting human behavior control in anonymous online environment by examining various aspects of identity in online discussion community. This study empirically verifies the causal relationship between factors, including social & technical anonymities, various identity dimensions, intrinsic motivation to participate in the community, group norm conformity, and quality of discussion. It also analyzes the difference between groups by the level of anonymity, gender, age, community usage period, and discussion topic. Based on the findings, this research provides theoretical and practical implications for online community management strategies and a better culture on Internet discussion.
Ho Lee;Juhyeon Lee;Junho Bae;Woosik Shin;Hee-Woong Kim
Knowledge Management Research
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v.25
no.1
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pp.135-154
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2024
As the digital transformation accelerates, the demand for professionals with competencies in various digital technologies such as artificial intelligence, big data is increasing in the industry. In response, the government is developing various educational programs to nurture talent in these emerging technology fields. However, the lack of a clear definition of competencies, which is the foundation of curriculum development and operation, has posed challenges in effectively designing digital technology education programs. This study systematically reviews the definitions and characteristics of competencies presented in prior research based on a literature review. Subsequently, in-depth interviews were conducted with 30 experts in emerging technology fields to derive a definition of competencies suitable for technology education programs. This research defines competencies for the development of technology education programs as 'a set of one or more knowledge and skills required to perform effectively at the expected level of a given task.' Additionally, the study identifies the elements of competencies, including knowledge and skills, as well as the principles of competency construction. The definition and characteristics of competencies provided in this study can be utilized to create more systematic and effective educational programs in emerging technology fields and bridge the gap between education and industry practice.
Journal of the Korea Society of Computer and Information
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v.16
no.2
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pp.249-256
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2011
Finding a new set of features representing textured images is one of the most important studies in textured image analysis. This is because it is impossible to construct a perfect set of features representing every textured image, and it is inevitable to choose some relevant features which are efficient to on-going image processing jobs. This paper intends to find relevant features which are efficient to textured image segmentation. In this regards, this paper presents a different method for the segmentation of textured images based on the Gabor filter. Gabor filter is known to be a very efficient and effective tool which represents human visual system for texture analysis. Filtering a real-valued input image by the Gabor filter results in complex-valued output data defined in the spatial frequency domain. This complex value, as usual, gives the module and the phase. This paper focused its attention on the phase information, rather than the module information. In fact, the module information is considered very useful at region analysis in texture, while the phase information was considered almost of no use. But this paper shows that the phase information can also be fully useful and effective at region analysis in texture, once a good method introduced. We now propose "phase derivated method", which is an efficient and effective way to compute the useful phase information directly from the filtered value. This new method reduces effectively computing burden and widen applicable textured images.
The present study was conducted to (1) explore the perceptions of Korean men and women about what is an important relationship for them and how do each gender group construe relational self, and (2) develop the scale to assess the factors of relational self and verify construct validity of the scale. 40 college students and 60 adults participated in survey and FGI (Focused Group Interview) respectively, and content analysis of their responses yielded 2 categories with 39 characteristics of relational self. The one category was named 'instrumentality' which was important to men and the other was named 'expressivity' which was important to women. The list of 39 items was administered to a nationwide sample of 1503 Korean adults to assess their construal of relational self through the 6-point Likert scale. Principal axis factor analysis showed that the two categories were unidimensional with high reliability. As a result of factor analysis on each category, a total of 9 factors were extracted. Specifically, the instrumentality consisted of factors such as utilitarianism, independence, initiativeness, self-assurance, and competence. And the factors of expressivity were empathy, passiveness, dependency, consideration. The tests of mean difference revealed that men had higher scores in most of the instrumental factors, while women had higher scores in most of the expressive factors. But there was no sex difference in the interdependent self-construal scale(Cross, 2000) which has been frequently used for measuring relational self. This is related to the Korean's collective cultural characteristics, and it was concluded that the relationship with others is very important to both Korean men and women, but the meaning and expectation of the relationship as well as the method for its preservation are different to each sex group. In addition, the correlation analyses indicated that the feminity score was positively correlated with the expressiveness while the masculinity score was positively correlated with instrumentality. This result implicated the differences of relational self among Korean people were related to the socialization process of each sex, i.e., sex role identity. Finally, limitations of this study and the directions for future research were discussed.
Cancer is a complex disease heterogeneously composed of various types of cells including cancer stem-like cells responsible for relapse and chemoresistance in the tumor microenvironment. The conventional two-dimensional cell culture-based platform has critical limitations for representing the heterogeneity of cancer cells in the three-dimensional tumor niche in vivo. To overcome this insufficiency, three-dimensional cell culture methods in a scaffold-dependent or -free physical environment have been developed. In this study, we improved and simplified the HCT-8 colon cancer cell-based spheroid culture protocol and evaluated the relationship between cancer stemness and responses of chemosensitivity to 5- Fluorouracil (5-FU), a representative anticancer agent against colon cancer. Supplementation with defined growth factors in the medium and the culture dish of the regular surface with low attachment were required for the formation of constant-sized spheroids containing $CD44^+$ and $CD133^+$ colon cancer stem cells. The chemo-sensitivities of $CD44^+$ cancer stem cells in the spheroids were much lower than those of $CD44^-$ non-stem-like cancer cells, indicating that the chemoresistance to 5-FU is due to the stemness of colon cancer cells. Taken together, the inflammation and oncogenic gut environment-sensitive HCT-8 cell-based colon cancer spheroid culture and comparative evaluation using the simplified model would be an efficient and applicable way to estimate colon cancer stemness and pharmaceutical response to anticancer drugs in the realistic tumor niche.
Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.
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