• Korean Journal of Forensic Psychology
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• v.11 no.1
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• pp.1-20
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• 2020

Witnesses will be exposed to a variety of misinformation after the witnessing of the event and state at the scene of the investigation after the delay period. This study was conducted to promote correct recall reporting without being affected by factors that against correct recall. Self-Administered Interview(SAI) is known to obtain eyewitness accounts quickly and accurately. Therefore, we performed a SAI to see if it reported more information than the control group that did not perform the SAI. Also, it also performed that correct information was maintained without being affected by misinformation and delay. Eighty-eight participants were asked to perform SAI or game after showing a video of mock crime. Misinformation was presented in the first or second session to see if it affected recall. An analysis of responses from the final test conducted in the second session by participants showed that groups that conducted SAI after a four-week delay reported more correct information than control groups, while there was no difference between incorrect- and confabulation information. In particular, the timing of presenting misinformation did not affect the amount of recall. This suggests that conducting the SAI immediately after witnessing the event protects correct information even after four weeks. Finally, the significance and limitations of this study, and subsequent studies were discussed.

• 한국노년학
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• v.29 no.3
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• pp.789-803
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• 2009

We investigated the influences of positive thought about social capital on social participation of the elderly Koreans. The study design was cross-sectional analysis of the National Statistic Office Study of Korean Society Statistics Survey 2003. Participants were total of 8,586 representative samples. Two types of social participation were investigated: meeting attendance and volunteer obligations. The base model included five thoughts about social capital that must be settled as a priority in Korea, i.e., reducing the differences between the rich and the poor, reducing the crime rate, reducing regulations and corruption, improving the moral level, eliminating environmental pollution. Social participation was influenced by positive thoughts about social capital, and different kinds of thoughts had different influences on different types of social participation. The elderly who responded positively to 'reducing the differences between the rich and the poor' attended all two types of social participation more. Along with education and health status, positive thought about social capital is another important factor that influences increased social participation. It may also compensate for deteriorating health with increasing age by promoting social participation. Encouraging social participation is a good way to improve the health of the elderly, as are efforts to change thought about social capital positively.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• Information Systems Review
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• v.19 no.2
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• pp.185-209
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• 2017

In modern society, crime records have been digitized. Digital information is difficult to distinguish from original information, but the former is easy to modulate. This situation explains the increasing importance of digital forensics. However, digital forensic has several inefficiencies because of the rapid development of technology, unclear jurisdiction, and tool errors. This study surveyed digital forensic specialists and derived the priority of domestic digital forensic issues by redefining 17 issues in digital forensics from Brungs-Jamieson study in Australia. The present study was divided into four groups, namely, police, government and public corporations, private companies, and legal groups. The study could compare and analyze comparative analysis of existing studies in Australia and the US. This study can also examine differences in the results of each group in Korea. Thus, the key issues in Korea were derived as "Requirements to 'Fire Up' Original." The differences of the three groups in terms of legal issues were then identified. This finding enables us to understand differences in priorities and importance between groups and countries.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.6
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• pp.137-148
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• 2017

This study analyzed the opinions of residents for desirable urban regeneration and suggested an improvement plan for alley environments. This study conducted a questionnaire survey of the residents living in Alley Garden of Bisan 2,3-dong, Daegu Metropolitan City. In the analysis of the importance and satisfaction of Alley Garden components, items related to a safe, cleanly environment such as 'Lighting facility installation', 'Sewage and waste disposal' and 'CCTV installation' had a high level of importance. It was also found that items improved by the Residential Environment Improvement Project and Alley Garden such as 'Lighting facility installation', 'Quantity of herbaceous flowers' and 'Kinds of herbaceous flowers' had a high level of satisfaction. The IPA results showed that items such as 'Empty house maintenance', 'Rest facilities such as benches and pergolas', 'Space for resident interaction' and 'Public parking lot' had a high level of importance, but had a low level of satisfaction, which suggests that they should be improved by priority. As a result of factor analysis, Alley Garden components were classified into four factors: 'Safety and cleanliness', 'Greenness', 'Exchange and convenience facility' and 'Aesthetics renewal'. Based on this classification, a regression analysis was conducted regarding the effects of the four factors on overall satisfaction. Results showed that all four factors had a significant influence on the overall satisfaction and that 'Aesthetics renewal' and 'Safety and cleanliness', respectively showing levels of significance at 0.274 and 0.235, were highly influential to overall satisfaction. Therefore, it was concluded that spaces for resident interaction and rest facilities should be preferentially installed to improve the environment of alleys. For the improvement of the overall satisfaction of alley environments, it was also concluded that residents should be encouraged to engage in activities such as sculpture installation and mural drawing, along with the introduction of safety bells and crime prevention environment design and the implementation of alley beautification projects.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.6
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• pp.41-48
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• 2018

The aim of this study is to provide foundational knowledge of how to enhance the user satisfaction of urban parks. The study seeks to identify essential factors that influence user satisfaction and to provide better design strategies for future park design as well as the reorganization of existing ones. To measure user satisfaction, this study used factor analysis to extract essential factors - facility conditions, landscape and scenery, safety, and kindness - by using data from a survey conducted by the city of Seoul in 2017. We then used a regression analysis to infer causal relationships between the independent variables and the dependent variables (user satisfaction). The results revealed that the most significantly and positively related variable to user satisfaction in urban parks was safety (${\beta}=0.276$, p<.000), followed by landscape and scenery (${\beta}=0.230$, p<.000), facility conditions (${\beta}=0.215$, p<.000), and kindness (${\beta}=0.208$, p<.000). The results indicate that, for future urban park designs, planners and designers should prioritize the issues of safety by adopting crime prevention through environmental design (CPTED). In addition, planners and designers of future park designs should heavily weigh the selection and provision of relevant facilities for the intended use as well as well-arranged and well-managed plants and trees. Based on the results of IPA analysis, the most urgent improvement elements appeared to be the factor of kindness; however, the impact of kindness influencing user satisfaction was less important than that of safety and landscape and scenery in the urban park design processes. This study demonstrates that to maximize the user satisfaction of the urban park design processes and to provide more valuable spaces for users, it is necessary to secure park safety and to create well-composed landscape and scenery. Future research should provide more detailed and specified urban park design strategies corresponding with the importance of the factors identified in this study.

• Korean Journal of Forensic Psychology
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• v.12 no.1
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• pp.15-33
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• 2021

In this study, we analyzed the difference in survival rates of those subject to electronic supervision of sex crimes based on the tracking of the period of recidivism and whether they were recidivism, and wanted to confirm the ability of the criminal record to predict recidivism. The criteria for recidivism were defined as cases where a conviction was confirmed due to a criminal case that occurred during the execution of electronic monitoring, and the date of recidivism was the date of occurrence of a case that was confirmed guilty. A total of 122 re-offenders were used in the analysis, and all of them were charged with electronic supervision for committing sex crimes. Studies have confirmed that the subjects commit the most recidivism within three years. In addition, in this study, the difference in survival rate between groups was analyzed after classifying mixed and sex recidivism cases. The number of members was 88 for the mixed recidivism group and 34 for the sex recidivism group. The analysis confirmed that both groups had the most recidivism within three years. There was a slight difference between the survival rate of the mixed recidivism group and the survival rate of the sex recidivism group. So the Log Rank Test and the Generalized Wilcoxon Test were conducted, but no statistically significant differences were identified(Wilcoxon statistic = 2.326, df = 1, p = .13, Log Rank = 1.345, df = 1, p = .25). Next, a Cox Regression analysis was performed to confirm the ability of the criminal record to predict recidivism. As a result, the number of criminal records(sex offense, violent crime) have been confirmed to be a good predictor of recidivism(X²=27.33, df=1, p< .001). As a result, the recidivism rate is gradually decreasing due to the implementation of the electronic monitoring. However, the duration of recidivism required by sex offenders in high-risk groups was found to be rather short. Currently, security measures against felons are being strengthened, so it is necessary to select high-risk groups. Therefore, based on the related studies, the characteristics of high-risk groups and the results of recidivism studies will be used as a basis for disposal within the criminal justice system, which will play a major role in granting objectivity.

• 한국사회복지학회:학술대회논문집
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• 2003.05a
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• pp.282-309
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• 2003

This exploratory qualitative study investigates the effects of experiencing domestic violence on male adolescent offenders' masculinities. Empirical and theoretical literature suggests that negative male role models in violent families result in male adolescents' experiencing conflict in constructing gender identities, especially masculinities. Moreover. criminologists argue that masculinities are often connected with crimes as a way to prove masculine competence. This study compares male adolescent offenders who have experienced domestic violence with those who have not experienced domestic violence and explores how domestic violence experiences influence the construction of gender identities among male adolescent offenders. The study used a secondary qualitative data analysis method. The data consisted of ethnographic in-depth interview transcripts, observational field notes, and formal facility records collected at a juvenile correctional facility in Minnesota. The process of data analysis was a "constant comparative method" that sought to understand differences and similarities in the expressed gender narratives and identity patterns between the two groups of offenders. This process also examined differences within each group. The qualitative data analysis revealed that domestic violence experiences in childhood may be related to the construction of gender identities during adolescence. The findings of this study showed that male adolescent offenders who had experienced domestic violence tended to attach themselves to oppressed mothers more readily than those who had not experienced domestic violence. Next, their attachment to mothers related to the construction of more relational gender identities although most participants, regardless of domestic violence experiences, had much in common regarding gender expression. Finally, despite these relational gender identities, male adolescent offenders who had experienced domestic violence tended to depend upon violence and crimes to show masculine competence, as did male adolescent offenders who had not experienced domestic violence. The study findings suggest a need for research to understand the construction of gender identities in the context of particular experiences and the importance of building theories that advance a comprehensive understanding of the construction of masculinities and youth crime. This study also discusses the development of social work programs that protect young men from adherence to exaggerated masculinity, which is often associated with crimes.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.