Digital Library[ Search Result ]
Latent Dirichlet Allocation Based Crime Code Clustering and Crime Prediction
http://doi.org/10.5626/JOK.2020.47.1.45
Predicting crime using crime data has become one of the most actively researched disciplines in major cites. Based on the research, law enforcement officials are shifting their efforts from crime investigation to crime prevention through predictive policing. Predictive policing highly relies on mathematics and statistics and identifies the underlying patterns of crimes. Based on these patterns, law enforcement officials can identify potential criminal activities. For accurate prediction, crime data must be well organized and managed. We first introduce one of the popular crime data set actively used by researchers. The data set categorizes each incident through a crime code. Examining the frequency of these codes allows regional agencies to predict the type of potential crimes, leading to effective predictive policing. In this research, we introduce a machine learning-based approach that can identify the similarity between the codes. Based on these similarities, we compute the frequencies of clusters and predict the code of potential crimes. Our experimental results show how our algorithm outperforms the statistical method.
ESS Operation Scheduling Scheme Using LSTM for Peak Demand Reduction
Yeongung Seo, Seungyoung Park, Myungjin Kim, Sungbin Lim
http://doi.org/10.5626/JOK.2019.46.11.1165
In recent years, blackouts have become more likely in South Korea as the peak demand has sharply increased. In order to address this issue, an energy storage system (ESS) operation scheduling technique has been investigated for its ability to reduce the peak demand by utilizing the power stored in the ESS. If the power demand information is known in advance, an optimal ESS operation scheduling technique can be applied in consideration of both the power stored in the ESS and the power demand to be generated in the future. However, it is difficult to predict the peak demand in advance because it only occurs in a relatively short time period, and the instance of its occurrence differs substantially from day-to-day. Therefore, it is very difficult to implement an optimal ESS operation scheduling technique that requires exact information on power demands in advance. Thus, in this paper, we proposed an ESS operation scheduling method with which to reduce the peak demand by using only historical power demands. Specifically, we employed a long short-term memory (LSTM) network and trained it using the historical power demands and their corresponding optimal ESS discharge powers. Then, we applied the trained network to approximate the optimal ESS operation scheduling. We showed the validity of the proposed method through computer simulations using historical power demand data from four customers. In particular, it was shown that the proposed scheme reduced the peak demand per year by up to about 82.42% compared to the optimal scheme that is only feasible when the exact future power demands are available.
Prediction of Compound-Protein Interactions Using Deep Learning
http://doi.org/10.5626/JOK.2019.46.10.1054
Characterizing the interactions between compounds and proteins is an important process for drug development and discovery. Structural data of proteins and compounds are used to identify their interactions, but those structural data are not always available, and the speed and accuracy of the predictions made in this way ware limited due to the large number of calculations involved. In this paper, compound-protein interactions were predicted using S2SAE (Sequence-To-Sequence Auto-Encoder), which is composed of a sequence-to-sequence algorithm used in machine translation as well as an auto-encoder for effective compression of the input vector. Compared to the existing method, the method proposed in this paper uses fewer features of protein-compound complex and also show higher predictive accuracy.
An Approach to Detect Macros via Self-similarity of Mobile Input
http://doi.org/10.5626/JOK.2019.46.9.951
Macros that repeats specified in-game actions without the need for human interaction are a major cause of unfairness in computer gaming. For the success of a game service, the organizational use of macros which destroys the game’s economy and can deteriorate a user’s game motivation should be prohibited. It is particularly easy for macros to be generated and used in mobile games, because a mobile game’s design and playing sequence are likely to be relatively simple compared to those of PC games because of the limited hardware resources and, inefficient input methods of mobile devices compared to PCs. At the same time, the current macro detection methods used in mobile games can consume substantial amounts of resources. Thus, macro detection is still a challenge in mobile game services. In this paper, we propose a method to detect macros via self-similarity based on the mobile input. Our proposed method sets the unit for effectively obtaining self-similarity with fewer resources. We applied the proposed method to two mobile games and showed that macro and human activities can be distinguished with high accuracy.
Design of Video Advertisement Analysis via Analysis of Internet Term Sensitivity
Sejin Kim, Jieun Kim, Wonyoung Seong, Yoonhee Kim
http://doi.org/10.5626/JOK.2019.46.9.919
Analysis of the increasing influence of video advertisements via Social Networking Service (SNS) is important in identifying their effects. However, the traditional methods of survey-based analysis are not suitable for measurement of the effectiveness of SNS advertisements that are distributed rapidly via smartphone use and the current system does not consider the sensitivity of users expressed in various forms, such as slang, and emoticons. This study proposes an automated system for the analysis of the effects of video ads via video comments, reflecting the characteristics of short Korean sentences.
This system uses machine learning for the interpretation of Internet terms and compilation of a sentiment dictionary specializing in SNS short sentences. Emoticon, which is used to emphasize the sensitivity of users in comments, is used for sentiment analysis when applied to Korean syntax rules, and the system is designed and implemented for more sophisticated emotional analysis by calculating the emotional values of nouns that are subject to sentiment.
The analysis of Loan status and Comparison of Default Prediction Performances based on Personal Credit Information Sample Database
http://doi.org/10.5626/JOK.2019.46.7.627
In this paper, we analyze the status of loans and defaults and present statistical data according to the borrower"s gender, age, month, etc. by using the personal credit information sample database offered as a trial service from Korea Credit Information Services. In addition, since domestic and foreign banks are paying attention to minimize the loss caused by default of the borrower, we used the personal credit information sample database to create a predicting model of borrower default and evaluated the model performance. To predict the default for a certain month, the borrower"s demographic information and loan information for the previous six months were processed to generate characteristic data, and a default prediction model was created using Recurrent Neural Network and machine learning algorithm. Based on the performance of each model, Recurrent Neural Network was showed as the model to demonstrate the best performance with Recall of 0.96 and AUC of 0.85 for the default borrower.
English-to-Korean Machine Translation using Image Information
Jangseong Bae, Hyunsun Hwang, Changki Lee
http://doi.org/10.5626/JOK.2019.46.7.690
Machine translation automatically converts a text in one language into another language. Conventional machine translations use only texts for translation which is a disadvantage in that various information related to input text cannot be utilized. In recent years, multimodal machine translation models have emerged that use images related to input text as additional inputs, unlike conventional machine translations which use only textual data. In this paper, image information was added at decoding time of machine translation according to recent research trends and used for English-to-Korean automated translation. In addition, we propose a model with a decoding gate to adjust the textual and image information at the decoding time. Our experimental results show that the proposed method resulted in better performance than the non-gated model.
Image Caption Generation using Object Attention Mechanism
http://doi.org/10.5626/JOK.2019.46.4.369
Explosive increases in image data have led studies investigating the role of image caption generation in image expression of natural language. The current technologies for generating Korean image captions contain errors associated with object concurrence attributed to dataset translation from English datasets. In this paper, we propose a model of image caption generation employing attention as a new loss function using the extracted nouns of image references. The proposed method displayed BLEU1 0.686, BLEU2 0.557, BLEU3 0.456, BLEU4 0.372, which proves that the proposed model facilitates the resolution of high-frequency word-pair errors. We also showed that it enhances the performance compared with previous studies and reduces redundancies in the sentences. As a result, the proposed method can be used to generate a caption corpus effectively.
Data-driven Path Selection for Improving Industrial-Strength Static Analyzers
http://doi.org/10.5626/JOK.2019.46.4.363
We propose a data-driven method to improve path-sensitive industrial-strength static analyzers. Most industrial static analyzers adopt path-sensitive techniques and path selection holds the key to their performance. We propose a method to automatically learn new cost-effective path-selection heuristics from an existing analyzer with a manually tuned path-selection heuristic. We evaluated our method on an industrial static C code bug-finder from Sparrow as a baseline analyzer with 17 C open-source benchmark programs. The experimental results showed that with the newly-learned path-selection heuristic, the analyzer reported 90.8% of the defects in only 38% of the analysis time, compared to the baseline analysis. This method reported more defects in less time than the baseline path-selection heuristic under similar path search space constraints.
Korean Machine Reading Comprehension using S³-Net based on Position Encoding
Choeneum Park, Changki Lee, Hyunki Kim
http://doi.org/10.5626/JOK.2019.46.3.234
S³-Net is a deep learning model that is used in machine reading comprehension question answering (MRQA) based on Simple Recurrent Unit and Self-Matching Networks that calculates attention weight for own RNN sequence. The answers to the questions in the MRQA occur within the passage, because any passage is made up of several sentences, so the length of the input sequence becomes longer and the performance deteriorates. In this paper, a hierarchical model that adds sentence-level encoding and S³-Net that applies position encoding to check word order information to solve the problem of long-term context degradation are proposed. The experimental results show that the S³-Net model proposed in this paper has a performance of 69.43% in EM and 81.53% in F1 for single test, and 71.28% in EM and 82.67 in F1 for ensemble test.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr