• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.827-839
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• 2012

A cell phone is very close to the user and therefore should be considered in digital forensic investigation. Recently, the proportion of smartphone owners is increasing dramatically. Unlike the feature phone, users can utilize various mobile application in smartphone because it has high-performance operating system (e.g., Android, iOS). As acquisition and analysis of user data in smartphone are more important in digital forensic purposes, smartphone forensics has been studied actively. There are two way to do smartphone forensics. The first way is to extract user's data using the backup and debugging function of smartphones. The second way is to get root permission, and acquire the image of flash memory. And then, it is possible to reconstruct the filesystem, such as YAFFS, EXT, RFS, HFS+ and analyze it. However, this methods are not suitable to recovery and analyze deleted data from smartphones. This paper introduces analysis techniques for fragmented flash memory pages in smartphones. Especially, this paper demonstrates analysis techniques on the image that reconstruction of filesystem is impossible because the spare area of flash memory pages does not exist and the pages in unallocated area of filesystem.

• Journal of the Korean Society for information Management
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• v.38 no.1
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• pp.243-262
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• 2021

The present study aims to identify the factors that affect the continuous use and discontinuance of the hospital information seeking applications(hospital apps thereafter) by employing the post acceptance model. The surveys were conducted with people who used the hospital apps from October 11 to 18, 2019. Researchers collected 125 valid data and analyzed them by using the structural equation model. The study found that the satisfaction and confirmation of expectation for the hospital apps users had significant effects on intention for continuous use and perceived usefulness, respectively. However, the perceived usefulness did not have a significant effect on the intention for continue use. The present study has identified the variables that influence the continuous use of these innovative technologies. The findings of the study confirmed the post acceptance model by observing the adoption and use of the hospital apps and extended the literature of the post acceptance model by discussing the unique characteristics of the hospital apps that satisfy the urgent help-seekers under emergency situations or the information needs emphasizing promptness. In addition, based on the benefits and limitations of hospital apps reported by consumers, the study provided practical implications for designing more user-friendly apps to hospital app developers or managers.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.69-78
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• 2022

As the use of Internet of Things (IoT) devices has expanded, digital forensics coverage of the National Police Agency has expanded to smart home areas. Accordingly, most of the existing studies conducted to acquire smart home platform data were mainly conducted to analyze local data of mobile devices and analyze network perspectives. However, meaningful data for evidence analysis is mainly stored on cloud storage on smart home platforms. Therefore, in this paper, we study how to acquire stored in the cloud in a Hey Home Air environment by extracting accessToken of user accounts through a cookie database of browsers such as Microsoft Edge, Google Chrome, Mozilia Firefox, and Opera, which are recorded on a PC when users use the Hey Home app-based "Hey Home Square" service. In this paper, the it was configured with smart temperature and humidity sensors, smart door sensors, and smart motion sensors, and artifacts such as temperature and humidity data by date and place, device list used, and motion detection records were collected. Information such as temperature and humidity at the time of the incident can be seen from the results of the artifact analysis and can be used in the forensic investigation process. In addition, the cloud data acquisition method using OpenAPI proposed in this paper excludes the possibility of modulation during the data collection process and uses the API method, so it follows the principle of integrity and reproducibility, which are the principles of digital forensics.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.283-290
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• 2022

In this study, an IoT inspection system that can be linked with a server was developed using a gas timer and ESP-01 Wi-Fi module installed on a gas valve in the home. The server environment of the gas leak IoT inspection system was installed with APM (Apache, PHP, MySQL) to collect gas pressure data by generation so that leakage checks could be performed. In order to control the gas leak IoT inspection system, the app inventory was used to manage the gas leak check value in real time. In addition, user convenience has been enhanced so that membership management, WiFi settings, and leakage check values can be checked through mobile apps. In order to manage subscribers by region, the user list was checked by logging in in in the administrator mode so that the information on whether or not the leak test was conducted and the results could be provided. In addition, when the user presses the gas leak check button, the pressure is automatically checked, and the measured value is stored in the server, and when a gas leak occurs, the leakage check is performed after alarm and repair so that it can be used if normal. In addition, in order to prevent overlapping membership, membership management can be performed based on MAC addresses.

• Journal of Veterinary Clinics
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• v.41 no.2
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• pp.71-78
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• 2024

Changes in elimination behavior, including urination and defecation, are common clinical signs of numerous disorders in cats. Therefore, this study attempted to automatically measure the elimination behavior of cats using the litter box and develop an early warning system for the guardian in case of abnormalities. To construct an early warning system for abnormal changes through cat elimination behavior, it consisted of a litter box, an automatic sensor for data collection and data wifi transmission, a server for data analysis, and a mobile phone app for result transmission and early warning. To establish the reference interval (RI), the elimination behavior was monitored for more than 2 weeks using a motion sensor within a litter box in 37 healthy cats and 19 diseased cats. The data were expressed as daily total visits, daily total stay duration, average stay duration per elimination, weekly total visits, and weekly total stay duration. Healthy cats showed median daily total visits of 3 times/day (RI 1.0-7.0) and daily total stay duration of 192 s/day (RI 8.0-452.0). For weekly data, the median total visits were 20 times/week (RI 3.0-35.25) and the median total stay duration was 1,147 s/week (RI 80.0-2,249.5). The average stay duration per elimination was 59 s/elimination (RI 4.67-132.0). Diseased cats showed more frequent elimination behavior than healthy cats (p < 0.001). Otherwise, for each elimination, diseased cats had shorter stay durations than healthy cats (p < 0.001). This study established the RIs of elimination behavior parameters (frequency and duration) in healthy cats. The present study might help guardians and veterinarians detect changes in elimination behaviors in diseased cats at an early stage.

• Information Systems Review
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• v.23 no.1
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• pp.93-113
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• 2021

Service orientation and customer orientation are recognized as important success factors in service companies. However, these constructs are evaluated through self-diagnosis within the service company based on service delivery experience. For this reason, Fintech companies that provide financial services based on non-face-to-face channels such as mobile APP have limitations in evaluating their service orientation and customer orientation. Therefore, in this study, the perceived customer orientation is conceptualized so that service orientation and customer orientation can be evaluated through customer evaluation. In addition, the antecedents and consequences of the perceived customer orientation based on the technology acceptance model were demonstrated. As a result, it was confirmed the mediating effect of perceived customer orientation in the relationship between perceived ease of use and usefulness and customer's continuous use intention and word of mouth intention. This study laid the foundation for the Fintech companies that provide all financial services throughout non-face-to-face to measure their service orientation and customer orientation through customer evaluation and utilize them in establishing service operation strategies.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.30-43
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• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.

• Korean Journal of Environment and Ecology
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• v.37 no.3
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• pp.221-231
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• 2023

Great tit (Parus major) is a bioindicator species that can measure environmental changes in urban ecosystems and plays an important role in maintaining health as a representative insectivorous bird. Researchers have utilized artificial nest box surveys to understand the reproductive ecology of the Paridae family of birds, including the Great tits, but it is difficult to conduct a macroscopic study due to spatial and temporal limitations. This study designed and applied a citizen-participatory monitoring of artificial nest boxes project to transcend the limitations of expert-centered monitoring methods. The Suwon Front Yard Bird Monitoring Team installed artificial nest boxes in green spaces in Suwon, Gyeonggi Province and observed the reproductive ecology of the Paridae family through the participation of voluntary citizen surveyors. Participants were recruited through an online survey from February 9 to February 22, 2021, and they directly performed from installation to observation of artificial next boxes from February 23 to August 31, 2021. Online education was provided to the volunteers for the entire monitoring process to lower the entry barrier for non-expert citizen surveyors and collect consistent data, and observation records were collected through a mobile app. A total of 98 citizen surveyors participated in the citizen-participatory monitoring of artificial nest boxes project, and 175 (84.95%) of the 256 distributed artificial nest boxes were installed in green spaces in Suwon City. Among the installed artificial nest boxes, the results of the citizen science project were confirmed for 173 (83.98%), excluding two boxes with position coordinate generation errors. A total of 987 artificial nest box observation records were collected from citizen surveyors, with a minimum of one time, a maximum of 26 times, and an average of 5.71±4.37 times. The number of observations of artificial birdhouses per month was 70 times (7.09%) in February, 444 times (44.98%) in March, 284 times (28.77%) in April, 133 times (13.48%) in May, 46 times (4.66%) in June, 6 times (0.61%) in July, and 4 times (0.41%) in August. Birds using the artificial nest boxes were observed in 57 (32.95%) of the 173 installed artificial nest boxes, and they included Great tit (Parus major) using 12 boxes (21.05%), Varied Tit (Parus varius) using 7 boxes (12.28%), and unidentified birds using 38 boxes (66.67%). This study is the first to consider citizen participation in the monitoring of artificial nest boxes, a survey method for the reproductive ecology of the Paridae family, including Great tits, and it can be utilized as basic data for the design of ecological monitoring combined with citizen science in the future.