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Lightweight Temporal Segment Network for Video Scene Understanding: Validation in Driver Assault Detection
Juneyong Lee, Joon Kim, Junhui Park, Jongho Jo, Ikbeom Jang
http://doi.org/10.5626/JOK.2024.51.11.987
"The number of driver assaults in transportation such as taxis and buses has been increasing over the past few years. It can be especially difficult to respond quickly to assaults on drivers by drunks late at night. To address this issue, our research team proposed a lightweight CNN-based Temporal Segment Network (TSN) model that could detect driver assaults by passengers in real time. The TSN model efficiently processes videos by sampling a small number of image frames and divides videos into two streams for learning: one for spatial information processing and the other for temporal information processing. Convolutional neural networks are employed in each stream. In this research, we applied a lightweight CNN architecture, MobileOne, significantly reducing the model size while demonstrating improved accuracy even with limited computing resources. The model is expected to contribute to rapid response and prevention of hazardous situations for drivers when it is integrated into vehicular driver monitoring systems."
A Feature Selection Technique in the Neural Network for Demand Forecasting of Mobile Payment System
Ho-Joon Kim, Yun-Seok Cho, Kyungmi Kim
http://doi.org/10.5626/JOK.2018.45.4.370
In this paper, we present a time series prediction technique based on neural network as a methodology for forecasting service demand of mobile payment system. We propose a two-stage neural network model for the feature selection process and the prediction process. Three types of fuzzy membership functions were adopted for the representation of feature data, and a hyperbox-based neural network model is used for the evaluation of feature relevance factor. The proposed feature selection technique reduces the amount of computation and eliminates erroneous feature data in the learning data set. We evaluated the usefulness of the proposed method through experiments using two years of data obtained form actual smart campus systems.
Automatic Construction of Reduced Dimensional Cluster-based Keyword Association Networks using LSI
Han-mook Yoo, Han-joon Kim, Jae-young Chang
http://doi.org/10.5626/JOK.2017.44.11.1236
In this paper, we propose a novel way of producing keyword networks, named LSI-based ClusterTextRank, which extracts significant key words from a set of clusters with a mutual information metric, and constructs an association network using latent semantic indexing (LSI). The proposed method reduces the dimension of documents through LSI, decomposes documents into multiple clusters through k-means clustering, and expresses the words within each cluster as a maximal spanning tree graph. The significant key words are identified by evaluating their mutual information within clusters. Then, the method calculates the similarities between the extracted key words using the term-concept matrix, and the results are represented as a keyword association network. To evaluate the performance of the proposed method, we used travel-related blog data and showed that the proposed method outperforms the existing TextRank algorithm by about 14% in terms of accuracy.
Policy Based DDoS Attack Mitigation Methodology
Hyuk Joon Kim, Dong Hwan Lee, Dong Hwa Kim, Myung Kil Ahn, Yong Hyun Kim
Since the Denial of Service Attack against multiple targets in the Korean network in private and public sectors in 2009, Korea has spent a great amount of its budget to build strong Internet infrastructure against DDoS attacks. As a result of the investments, many major governments and corporations installed dedicated DDoS defense systems. However, even organizations equipped with the product based defense system often showed incompetency in dealing with DDoS attacks with little variations from known attack types. In contrast, by following a capacity centric DDoS detection method, defense personnel can identify various types of DDoS attacks and abnormality of the system through checking availability of service resources, regardless of the types of specific attack techniques. Thus, the defense personnel can easily derive proper response methods according to the attacks. Deviating from the existing DDoS defense framework, this research study introduces a capacity centric DDoS detection methodology and provides methods to mitigate DDoS attacks by applying the methodology.
Dynamic Parameter Visualization and Noise Suppression Techniques for Contrast-Enhanced Ultrasonography
This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.
A Malicious Traffic Detection Method Using X-means Clustering
Myoungji Han, Jihyuk Lim, Junyong Choi, Hyunjoon Kim, Jungjoo Seo, Cheol Yu, Sung-Ryul Kim, Kunsoo Park
Malicious traffic, such as DDoS attack and botnet communications, refers to traffic that is generated for the purpose of disturbing internet networks or harming certain networks, servers, or hosts. As malicious traffic has been constantly evolving in terms of both quality and quantity, there have been many researches fighting against it. In this paper, we propose an effective malicious traffic detection method that exploits the X-means clustering algorithm. We also suggest how to analyze statistical characteristics of malicious traffic and to define metrics that are used when clustering. Finally, we verify effectiveness of our method by experiments with two released traffic data.
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