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A Recognition of Violence Using Mobile Sensor Fusion in Intelligent Video Surveillance Systems
HyunIn Cha, KwangHo Song, Yoo-Sung Kim
http://doi.org/10.5626/JOK.2018.45.6.533
In this paper, we propose a violence recognition model by reflecting features extracted by concurrent and continuous action in intelligent CCTV through detecting group ROI(Region of Interest) from image. And then, proposed model uses extracted motion information obtained by using Dense Optical Flow algorithm in ROI and fusing of the acceleration and angular velocity information obtained from the inertial measurement unit of the mobile device possessed by actor. Experiments were performed to evaluate the reduction of the computation time of the proposed model and improvement of the performance degradation due to the occlusion. Result of experiment, the execution time was about 51 times faster and the accuracy of recognition of violence was improved by 11% compared to previous research methods. Therefore, the proposed model can overcome the problem of real-time failure due to excessive computation and can solve the problem of invisibility due to occlusion by actor in the image in recognition of violence.
A Method for Identifying Nicknames of a User based on User Behavior Patterns in an Online Community
http://doi.org/10.5626/JOK.2018.45.2.165
An online community is a virtual group whose members share their interests and hobbies anonymously with nicknames unlike Social Network Services. However, there are malicious user problems such as users who write offensive contents and there may exist data fragmentation problems in which the data of the same user exists in different nicknames. In addition, nicknames are frequently changed in the online community, so it is difficult to identify them. Therefore, in this paper, to remedy these problems we propose a behavior pattern feature vectors for users considering online community characteristics, propose a new implicit behavior pattern called relationship pattern, and identify the nickname of the same user based on Random Forest classifier. Also, Experimental results with the collected real world online community data demonstrate that the proposed behavior pattern and classifier can identify the same users at a meaningful level.
Study on Automatic Bug Triage using Deep Learning
Sun-Ro Lee, Hye-Min Kim, Chan-Gun Lee, Ki-Seong Lee
http://doi.org/10.5626/JOK.2017.44.11.1156
Existing studies on automatic bug triage were mostly used the method of designing the prediction system based on the machine learning algorithm. Therefore, it can be said that applying a high-performance machine learning model is the core of the performance of the automatic bug triage system. In the related research, machine learning models that have high performance are mainly used, such as SVM and Naïve Bayes. In this paper, we apply Deep Learning, which has recently shown good performance in the field of machine learning, to automatic bug triage and evaluate its performance. Experimental results show that the Deep Learning based Bug Triage system achieves 48% accuracy in active developer experiments, un improvement of up to 69% over than conventional machine learning techniques.
News Topic Extraction based on Word Similarity
http://doi.org/10.5626/JOK.2017.44.11.1138
Topic extraction is a technology that automatically extracts a set of topics from a set of documents, and this has been a major research topic in the area of natural language processing. Representative topic extraction methods include Latent Dirichlet Allocation (LDA) and word clustering-based methods. However, there are problems with these methods, such as repeated topics and mixed topics. The problem of repeated topics is one in which a specific topic is extracted as several topics, while the problem of mixed topic is one in which several topics are mixed in a single extracted topic. To solve these problems, this study proposes a method to extract topics using an LDA that is robust against the problem of repeated topic, going through the steps of separating and merging the topics using the similarity between words to correct the extracted topics. As a result of the experiment, the proposed method showed better performance than the conventional LDA method.
LTRE: Lightweight Traffic Redundancy Elimination in Software-Defined Wireless Mesh Networks
Gwangwoo Park, Wontae Kim, Joonwoo Kim, Sangheon Pack
http://doi.org/10.5626/JOK.2017.44.9.976
Wireless mesh network (WMN) is a promising technology for building a cost-effective and easily-deployed wireless networking infrastructure. To efficiently utilize limited radio resources in WMNs, packet transmissions (particularly, redundant packet transmissions) should be carefully managed. We therefore propose a lightweight traffic redundancy elimination (LTRE) scheme to reduce redundant packet transmissions in software-defined wireless mesh networks (SD-WMNs). In LTRE, the controller determines the optimal path of each packet to maximize the amount of traffic reduction. In addition, LTRE employs three novel techniques: 1) machine learning (ML)-based information request, 2) ID-based source routing, and 3) popularity-aware cache update. Simulation results show that LTRE can significantly reduce the traffic overhead by 18.34% to 48.89%.
‘Hot Search Keyword’ Rank-Change Prediction
Dohyeong Kim, Byeong Ho Kang, Sungyoung Lee
http://doi.org/10.5626/JOK.2017.44.8.782
The service, "Hot Search Keywords", provides a list of the most hot search terms of different web services such as Naver or Daum. The service, bases the changes in rank of a specific search keyword on changes in its users’ interest. This paper introduces a temporal modelling framework for predicting the rank change of hot search keywords using past rank data and machine learning. Past rank data shows that more than 70% of hot search keywords tend to disappear and reappear later. The authors processed missing rank value, using deletion, dummy variables, mean substitution, and expectation maximization. It is however crucial to calculate the optimal window size of the past rank data. We proposed an optimal window size selection approach based on the minimum amount of time a topic within the same or a differing context disappeared. The experiments were conducted with four different machine-learning techniques using the Naver, Daum, and Nate "Hot Search Keywords" datasets, which were collected for 2 years.
Bug Report Quality Prediction for Enhancing Performance of Information Retrieval-based Bug Localization
Misoo Kim, June Ahn, Eunseok Lee
http://doi.org/10.5626/JOK.2017.44.8.832
Bug reports are essential documents for developers to localize and fix bugs. These reports contain information regarding software bugs or failures that occur during software operation and maintenance phase. Information Retrieval-based Bug Localization (IR-BL) techniques have been proposed to reduce the time and cost it takes for developers to resolve bug reports. However, if a low-quality bug report is submitted, the performance of such techniques can be significantly degraded. To address this problem, we propose a quality prediction method that selects low-quality bug reports. This process; defines a Quality property of a Bug report as a Query (Q4BaQ) and predicts the quality of the bug reports using machine learning. We evaluated the proposed method with 3 open source projects. The results of the experiment show that the proposed method achieved an average F-measure of 87.31% and outperformed previous prediction techniques by up to 6.62% in the F-measure. Finally, a combination of the proposed method and traditional automatic query reformulation method improved the MRR and MAP by 0.9% and 1.3%, respectively.
Addressing Low-Resource Problems in Statistical Machine Translation of Manual Signals in Sign Language
Hancheol Park, Jung-Ho Kim, Jong C. Park
Despite the rise of studies in spoken to sign language translation, low-resource problems of sign language corpus have been rarely addressed. As a first step towards translating from spoken to sign language, we addressed the problems arising from resource scarcity when translating spoken language to manual signals translation using statistical machine translation techniques. More specifically, we proposed three preprocessing methods: 1) paraphrase generation, which increases the size of the corpora, 2) lemmatization, which increases the frequency of each word in the corpora and the translatability of new input words in spoken language, and 3) elimination of function words that are not glossed into manual signals, which match the corresponding constituents of the bilingual sentence pairs. In our experiments, we used different types of English-American sign language parallel corpora. The experimental results showed that the system with each method and the combination of the methods improved the quality of manual signals translation, regardless of the type of the corpora.
Question Answering Optimization via Temporal Representation and Data Augmentation of Dynamic Memory Networks
Dong-Sig Han, Chung-Yeon Lee, Byoung-Tak Zhang
The research area for solving question answering (QA) problems using artificial intelligence models is in a methodological transition period, and one such architecture, the dynamic memory network (DMN), is drawing attention for two key attributes: its attention mechanism defined by neural network operations and its modular architecture imitating cognition processes during QA of human. In this paper, we increased accuracy of the inferred answers, by adapting an automatic data augmentation method for lacking amount of training data, and by improving the ability of time perception. The experimental results showed that in the 1K-bAbI tasks, the modified DMN achieves 89.21% accuracy and passes twelve tasks which is 13.58% higher with passing four more tasks, as compared with one implementation of DMN. Additionally, DMN’s word embedding vectors form strong clusters after training. Moreover, the number of episodic passes and that of supporting facts shows direct correlation, which affects the performance significantly.
Re-anonymization Technique for Dynamic Data Using Decision Tree Based Machine Learning
Young Ki Kim, Choong Seon Hong
In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.
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