A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.
Journal of the Korea Academia-Industrial cooperation Society
/
v.19
no.11
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pp.550-560
/
2018
Many countries have provided support for research and development and implemented policies for Advanced Driver Assistance Systems (ADAS) for enhancing the safety of vehicles. With such efforts, the toll of casualties due to traffic accidents has decreased gradually. Korea has exhibited the lowest toll of casualties due to traffic accidents and is ranked 32nd in mortality among the 35 OECD members. Traffic accidents typically fall into three categories depending on the cause of the accident: vehicle to vehicle (V2V), vehicle to pedestrian (V2P), and vehicle independent. Most accidents are caused by drivers' mistakes in recognition, judgment, or operation. ADAS has been proposed to prevent and reduce accidents from such human errors. Moreover, the global automobile industry has recently been developing various safety measures, but on-road tests are still limited and contain various risks. Therefore, this study investigated the international standards for evaluation tests with regard to the assessment techniques in braking capability to cope with the limitations of on-road tests. A theoretical formula for braking force and a control algorithm are proposed, which were validated by comparing the results with those from an on-road test. These results verified the braking force depending on the functions of ADAS. The risks of on-road tests can be reduced because the proposed theoretical formula allows a prediction of the tendencies.
Purpose: The purpose of this study is to prevent recidivism by recognizing the seriousness of recidivism against sexual offenders under the age of 13 and providing customized social adaptation services based on risk. Method: The study evaluate the efficiency of existing models and proposed model systems, and compare and review what features and operational differences exist from existing models. Result: The proposed model will collect data from related agencies on sexual violence offenders with a high risk of recidivism and classify them into three risk groups through risk algorithms to provide social adaptation services for each risk group. In addition, by monitoring primary social support matching data, storing and re-analyzing the results data to rematch social support services, the model differs from the existing model in preventing recidivism of sexual violence offenders from a long-term perspective. Conclusion: The proposed model of this study is meaningful in that it proposed the basic data of a response system to prevent recidivism from a long-term perspective of sexual offenders with the highest risk of recidivism by collecting and analyzing data on sexual offenders.
Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.
Types of innovation can be categorized into simplification, information, automation, and intelligence. Intelligence is the highest level of innovation, and RPA can be seen as one of intelligence. Robotic Process Automation(RPA), a software robot with artificial intelligence, is an example of intelligence that is suited for simple, repetitive, large-scale transaction processing tasks. The RPA, which is already in operation in many companies in Korea, shows what needs to be done to naturally focus on the core tasks in a situation where the need for a strong organizational culture is increasing and the emphasis is on voluntary leadership, strong teamwork and execution, and a professional working culture. The introduction was considered naturally according to the need to find. Robotic Process Automation, or RPA, is a technology that replaces human tasks with the goal of quickly and efficiently handling structural tasks. RPA is implemented through software robots that mimic humans using software such as ERP systems or productivity tools. RPA robots are software installed on a computer and are called robots by the principle of operation. RPA is integrated throughout the IT system through the front end, unlike traditional software that communicates with other IT systems through the back end. In practice, this means that software robots use IT systems in the same way as humans, repeat the correct steps, and respond to events on the computer screen instead of communicating with the system's application programming interface(API). Designing software that mimics humans to communicate with other software can be less intuitive, but there are many advantages to this approach. First, you can integrate RPA with virtually any software you use, regardless of your openness to third-party applications. Many enterprise IT systems are proprietary because they do not have many common APIs, and their ability to communicate with other systems is severely limited, but RPA solves this problem. Second, RPA can be implemented in a very short time. Traditional software development methods, such as enterprise software integration, are relatively time consuming, but RPAs can be implemented in a relatively short period of two to four weeks. Third, automated processes through software robots can be easily modified by system users. While traditional approaches require advanced coding techniques to drastically modify how they work, RPA can be instructed by modifying relatively simple logical statements, or by modifying screen captures or graphical process charts of human-run processes. This makes RPA very versatile and flexible. This RPA is a good example of the application of digital to intelligence(D2I).
WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.
Buddhism was introduced in the Korea Peninsula 1600 years ago, and now there are over 10 million believers in Korea. The systematic Management of Temple Records has a spiritual and cultural value in a rapidly changing modern society. This study proposes a better management system of Buddhist temple records for the Jogye Order of Korean Buddhism. this system Not only supports transparency of religious affairs, but presents a way for a more effective management. in this study, I conducted a study on the national legislation for the preservation of buddhist temples and the local rules of religious affairs from the Jogye Order. Through this, I analyzed the problems of Buddhist records management. in the long term, to improve these problems, I purpose the establishment of temple archives be maintained by parish head offices. This study presents a retention schedule for this systematic establishment system. I present charts for the standard Buddhist records management that manage the total process systematically from the production of records to its discard. Also I present a general plan to prevent random defamation of Buddhist temple documents and impose a duty for preservation. I intend for this plan to be subject to discussion and tailored to the particular needs of temple reads. In creating these charts standard of Buddhist temple records management, I analyzed operating examples of foreign religious institutions and examined their retention periods. I also examined the retention periods and classification system from the Jogye Order. Then I presented ways for this management system to operate through computer programs. There is a need to establish a large scale management system to arrange the records of buddhist documents. We must enforce the duty of conserving records through the proposed management system. We need the system to manage even the local parish temple records through the proposed management system and the operation of the proposed archive system. This study presents research to from the basic of the preservation and the passing of traditional records to future generations. I also discovered the historical cultural and social value that these records contain. Systematically confirmed Buddhist temple records management will pave the way that these tangible and intangible cultural records handed down from history can be the cultural heritages. establishing a temple records management system will pave the way for these cultural records to be handed down to future generations as cultural heritages.
Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.
Statement of problem: Changes of the marginal bone around dental implants have significance not only for the functional maintenance but also for the esthetic success of the implant. It was proposed that bone-retention elements such as microthreads at the coronal part of implant might help maintain the marginal bone level. Purpose: This study was designed to evaluate the effect of microthread configuration within the marginal coronal portion of the implant fixture at the marginal bone changes after loading around two different external hex implants. Material and methods: Twenty-four patients were included and randomly assigned to treatment with $Br{{\aa}}nemark$ system implants (Group 1, rough-surfaced implants, n=20) and Oneplant system implants (Group 2, rough-surfaced neck with microthreads, n=20). Clinical and radiographic examinations were conducted at baseline (implant loading) and 1 year postloading. Data analysis was performed by the SAS statistical package version 9.1.3 (SAS Institute, Cary, NC, USA) and the final model was calculated by the MIXED procedure (three-level ANCOVA) for marginal bone change of each test group at baseline and 1 year follow-up. Results: Comparing to baseline, significant differences were noted in marginal bone level changes for the 2 groups at 1 year follow-up (P<0.05). Group 1 had a mean crestal bone level changes of $0.83{\pm}0.31mm$; Group 2 had a mean crestal bone level changes of $0.44{\pm}0.36mm$. Rough-surfaced with microthreads implants showed significantly less marginal bone loss than rough surfaced neck without microthread implants. Conclusion: A rough surface with microthreads at the implant was beneficial design to maintain the marginal bone level against functional loading.
Rodents and many other mammals have two chemosensory systems that mediate responses to pheromones, the main and accessory olfactory system, MOS and AOS, respectively. The chemosensory neurons associated with the MOS are located in the main olfactory epithelium, while those associated with the AOS are located in the vomeronasal organ(VNO). Pheromonal odorants access the lumen of the VNO via canals in the roof of the mouth, and are largely thought to be nonvolatile. The main pheromone receptor proteins consist of two superfamilies, V1Rs and V2Rs, that are structurally distinct and unrelated to the olfactory receptors expressed in the main olfactory epithelium. These two type of receptors are seven transmembrane domain G-protein coupled proteins(V1R with $G_{{\alpha}i2}$, V2R with $G_{0\;{\alpha}}$). V2Rs are co-expressed with nonclassical MHC Ib genes(M10 and other 8 M1 family proteins). Other important molecular component of VNO neuron is a TrpC2, a cation channel protein of transient receptor potential(TRP) family and thought to have a crucial role in signal transduction. There are four types of pheromones in mammalian chemical communication - primers, signalers, modulators and releasers. Responses to these chemosignals can vary substantially within and between individuals. This variability can stem from the modulating effects of steroid hormones and/or non-steroid factors such as neurotransmitters on olfactory processing. Such modulation frequently augments or facilitates the effects that prevailing social and environmental conditions have on the reproductive axis. The best example is the pregnancy block effect(Bruce effect), caused by testosterone-dependent major urinary proteins(MUPs) in male mouse urine. Intriguingly, mouse GnRH neurons receive pheromone signals from both odor and pheromone relays in the brain and may also receive common odor signals. Though it is quite controversial, recent studies reveal a complex interplay between reproduction and other functions in which GnRH neurons appear to integrate information from multiple sources and modulate a variety of brain functions.
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