• Journal of Platform Technology
• /
• v.11 no.3
• /
• pp.32-44
• /
• 2023

A blockchain bridge (hereinafter referred to as "bridge") is a service that enables the transfer of assets between blockchains. A bridge accepts virtual assets from users and delivers the same virtual assets to users on other blockchains. Users use bridges because they cannot transfer assets to other blockchains in the usual way because each blockchain environment is independent. Therefore, the movement of assets through bridges is not traceable in the usual way. If a malicious actor moves funds through a bridge, existing asset tracking tools are limited in their ability to trace it. Therefore, this paper proposes a method to obtain information on bridge usage by identifying the structure of the bridge and analyzing the event logs of bridge requests. First, to understand the structure of bridges, we analyzed bridges operating on Ethereum Virtual Machine(EVM) based blockchains. Based on the analysis, we applied the method to arbitrary bridge events. Furthermore, we created an automated tool that continuously collects and stores bridge usage information so that it can be used for actual tracking. We also validated the automated tool and tracking method based on an asset transfer scenario. By extracting the usage information through the tool after using the bridge, we were able to check important information for tracking, such as the sending blockchain, the receiving blockchain, the receiving wallet address, and the type and quantity of tokens transferred. This showed that it is possible to overcome the limitations of tracking asset movements using blockchain bridges.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.8
• /
• pp.371-380
• /
• 2023

Relation extraction is to extract relationships between named entities from text. Traditionally, relation extraction methods only extract relations between predetermined subject and object entities. However, in end-to-end relation extraction, all possible relations must be extracted by considering the positions of the subject and object for each pair of entities, and so this method uses time and resources inefficiently. To alleviate this problem, this paper proposes a method that sets directions based on the positions of the subject and object, and extracts relations according to the directions. The proposed method utilizes existing relation extraction data to generate direction labels indicating the direction in which the subject points to the object in the sentence, adds entity position tokens and entity type to sentences to predict the directions using a pre-trained language model (KLUE-RoBERTa-base, RoBERTa-base), and generates representations of subject and object entities through probabilistic crossover operation. Then, we make use of these representations to extract relations. Experimental results show that the proposed model performs about 3 ~ 4%p better than a method for predicting integrated labels. In addition, when learning Korean and English data using the proposed model, the performance was 1.7%p higher in English than in Korean due to the number of data and language disorder and the values of the parameters that produce the best performance were different. By excluding the number of directional cases, the proposed model can reduce the waste of resources in end-to-end relation extraction.

• Journal of the Korean Regional Science Association
• /
• v.39 no.2
• /
• pp.47-61
• /
• 2023

Satisfaction on the residential environment is a major factor influencing the choice of residence and migration, and is directly related to the quality of life in the city. As online services of real estate increases, people's evaluation on the residential environment can be easily checked and it is possible to analyze their satisfaction and its determining factors based on their evaluation. This means that a larger amount of evaluation can be used more efficiently than previously used methods such as surveys. This study analyzed the residential environment reviews of about 30,000 apartment residents collected from 'Zigbang', an online real estate service in Seoul. The apartment review of Zigbang consists of an evaluation grade on a 5-point scale and the evaluation content directly described by the dweller. At first, this study labeled apartment reviews as positive and negative based on the scores of recommended reviews that include comprehensive evaluation about apartment. Next, to classify them automatically, developed a model by using Bidirectional Encoder Representations from Transformers(BERT), a deep learning-based natural language processing model. After that, by using SHapley Additive exPlanation(SHAP), extract word tokens that play an important role in the classification of reviews, to derive determining factors of the evaluation of the residential environment. Furthermore, by analyzing related keywords using Word2Vec, priority considerations for improving satisfaction on the residential environment were suggested. This study is meaningful that suggested a model that automatically classifies satisfaction on the residential environment into positive and negative by using apartment review big data and deep learning, which are qualitative evaluation data of residents, so that it's determining factors were derived. The result of analysis can be used as elementary data for improving the satisfaction on the residential environment, and can be used in the future evaluation of the residential environment near the apartment complex, and the design and evaluation of new complexes and infrastructure.

• Journal of Hospice and Palliative Care
• /
• v.9 no.1
• /
• pp.17-29
• /
• 2006

Purpose: This research aimed to investigate the actual condition and satisfaction of volunteer activity in Australian hospital. Methods: Data was collected by self reported questionnaire from 101 volunteers and analyzed by frequency and percentage, t-test, ANOVA and Sheffe and Pearson's correlation coefficients using SPSS 12.0. Results: 1. Years involved in volunteer work were $5{\sim}10$ years (32.7%), above 10 years (30.7%), $2{\sim}3$ years (11.9%) and $3{\sim}5$ years (10.9%). Types of volunteer work were physical care (32.7%), physical and emotional care (14.9%), and others (18.8%). Types of allocation of tasks were by volunteer coordination (55.7%), and by volunteer preference and consent between volunteer and coordinator (both respectively, 20.5%). Main reasons for volunteer work were to help sick people (61.4%) and to make good use of leisure time (22.8%). Routes to start volunteer work were from his (her) own inquiries (43.4%), from hearing from other volunteers (30.7%) and from mass media (13.1%). 80.2% of volunteers had received some kinds of training or preparation for volunteer work. Suitability of volunteer's skill and ability to voluntary work were 'very well' (74.0%) and 'mostly well' (18.0%). Reimbursements or benefits received for volunteer work were token or lunch or group outing (31.7%), and token and lunch or group outing (19.8%). Evaluation frequency for volunteer work was occasionally (372%), frequently (30.9%), always (17.0%) and never (14.9%). Relationship with volunteer work coordinator was very good (85.0%). The relationship with other volunteers was very good (81.2%). The relationship with hospital staffs was very good (69.7%) and mostly good (21.2%). Family and friend's support for volunteer work was very good (83.2%). 2 The mean score of satisfaction for the hospital volunteer activity was $3.09{\pm}0.49\;(range:\;1{\sim}4)$. The highest score domain was 'social contact', $3.48{\pm}0.61$, and the lowest was 'social exchange', $1.65{\pm}0.63$. An item of the highest score was 'I have an opportunity to help other people' ($3.83{\pm}0.40$), and the lowest score item was 'I will receive compensation for volunteer work I have done ($1.10{\pm}0.78$).' 3. The satisfaction from hospital volunteer activity was shown by significant difference according to sex (t=2.038, P=0.044), marital status (F=3.806, P=0.013), years involved in volunteer work (F=3.326), nam reason to do volunteer work (F=2.707, P=0.035), receive any training or preparation for volunteer work (t=-1.982, 0=0.050), frequency of evaluation for volunteer work (F=7.877, P=0.000), suitability of volunteer's skill and ability to voluntary work (t=2.712, P=0.049), relationship with volunteer work coordinators (F=-2.517, P=0.013), relation with hospital staffs (F=5.202, P=0.007), and support of their volunteer work by their family and friends (t=-3.394, P=0.001). Conclusion: The satisfaction of hospice volunteer activity was moderate. The satisfaction for hospice volunteer activity was shown by significant difference according to sex (t=2.038, P=0.044), marital status (F=3.806, P=0.013), years involved in volunteer work (F=3.326), main reason to do volunteer work (F=2.707, P=0.035), receive any training or preparation for volunteer work (t=-1.982, 0=0.050), frequency of evaluation for volunteer work (F=7.877, P=0.000), suitability of volunteer's skill and ability to voluntary work (t=2.712, P=0.049), relationship with volunteer work coordinator (F=-2.517, P=0.013), relation with hospital staffs (F=5.202, P=0.007), and family and friend's support for volunteer work (t=-3.394, P=0.001). Therefore, it is necessary to consider various factors to improve the satisfaction of voluntary work.