Vol. 45, No. 2,
Feb. 2018
Digital Library
Distributed Processing Method of Hotspot Spatial Analysis Based on Hadoop and Spark
Changsoo Kim, Joosub Lee, KyuMoon Hwang, Hyojin Sung
http://doi.org/10.5626/JOK.2018.45.2.99
One of the spatial statistical analysis, hotspot analysis is one of easy method of see spatial patterns. It is based on the concept that "Adjacent ones are more relevant than those that are far away". However, in hotspot analysis is spatial adjacency must be considered, Therefore, distributed processing is not easy. In this paper, we proposed a distributed algorithm design for hotspot spatial analysis. Its performance was compared to standalone system and Hadoop, Spark based processing. As a result, it is compare to standalone system, Performance improvement rate of Hadoop at 625.89% and Spark at 870.14%. Furthermore, performance improvement rate is high at Spark processing than Hadoop at as more large data set.
Identification of Attack Group using Malware and Packer Detection
Heaeun Moon, Joonyoung Sung, Hyunsik Lee, Gyeongik Jang, Kiyong Kwak, Sangtae Woo
http://doi.org/10.5626/JOK.2018.45.2.106
Recently, the number of cyber attacks using malicious code has increased. Various types of malicious code detection techniques have been researched for several years as the damage has increased. In recent years, profiling techniques have been used to identify attack groups. This paper focuses on the identification of attack groups using a detection technique that does not involve malicious code detection. The attacker is identified by using a string or a code signature of the malicious code. In addition, the detection rate is increased by adding a technique to confirm the packing file. We use Yara as a detection technique. We have research about RAT (remote access tool) that is mainly used in attack groups. Further, this paper develops a ruleset using malicious code and packer main feature signatures for RAT which is mainly used by the attack groups. It is possible to detect the attacker by detecting RAT based on the newly created ruleset.
SWAT: A Study on the Efficient Integration of SWRL and ATMS based on a Distributed In-Memory System
Myung-Joong Jeon, Wan-Gon Lee, Batselem Jagvaral, Hyun-Kyu Park, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.2.113
Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.
Enhancing the Performance of Recommender Systems Using Online Review Clusters
Giseop Noh, Hayoung Oh, Jaehoon Lee
http://doi.org/10.5626/JOK.2018.45.2.126
The recommender system (RS) has emerged as a solution to overcome the constraints of excessive information provision and to maximize profit and reputation for information providers. Although the RS can be implemented with various approaches, there is no study on how to appropriately utilize the information generated from the review of the recommended object. We propose a method to improve the performance of RS by using cluster information generated from online review. We implemented the proposed method and experimented with real data, and confirmed that the performance is significantly improved compared to the existing approaches.
Knowledge Embedding Method for Implementing a Generative Question-Answering Chat System
Sihyung Kim, Hyeon-gu Lee, Harksoo Kim
http://doi.org/10.5626/JOK.2018.45.2.134
A chat system is a computer program that understands user"s miscellaneous utterances and generates appropriate responses. Sometimes a chat system needs to answer users’ simple information-seeking questions. However, previous generative chat systems do not consider how to embed knowledge entities (i.e., subjects and objects in triple knowledge), essential elements for question-answering. The previous chat models have a disadvantage that they generate same responses although knowledge entities in users’ utterances are changed. To alleviate this problem, we propose a knowledge entity embedding method for improving question-answering accuracies of a generative chat system. The proposed method uses a Siamese recurrent neural network for embedding knowledge entities and their synonyms. For experiments, we implemented a sequence-to-sequence model in which subjects and predicates are encoded and objects are decoded. The proposed embedding method showed 12.48% higher accuracies than the conventional embedding method based on a convolutional neural network.
A Prediction-based Dynamic Component Offloading Framework for Mobile Cloud Computing
http://doi.org/10.5626/JOK.2018.45.2.141
Nowadays, mobile computing has become a common computing paradigm that provides convenience to people’s daily life. More and more useful mobile applications’ appearance makes it possible for a user to manage personal schedule, enjoy entertainment, and do many useful activities. However, there are some inherent defects in a mobile device that battery constraints and bandwidth limitations. These drawbacks get a user into troubles when to run computationally intensive applications. As a remedy scheme, component offloading makes room for handling mentioned issues via migrating computationally intensive component to the cloud server. In this paper, we will present the predictive offloading method for efficient mobile cloud computing. At last, we will present experiment result for validating applicability and practicability of our proposal.
A Class Diagramming Tool for Visualizing the Latest Revision of Software Change History
Jaekyeong Sim, HeeTae Cho, Jongyeol Park, Seonah Lee
http://doi.org/10.5626/JOK.2018.45.2.150
Software visualization can assist developers to understand a software system and change its code. The recent development of bottom-up visualization tools demonstrates the advantages by revealing the code that is directly related to a software evolution task. However, the information provided by these tools is limited to the code already investigated by the developers in that task session. To broaden the scope and provide the code information that developers should explore, we propose to present the latest revision of a software system via a class diagram. When a developer clicks on a button, the proposed tool reveals the code changes committed to a configuration management system, and facilitates the understanding of code changes. We also conduct case studies illustrating the advantages of the proposed tool.
Application of Improved Variational Recurrent Auto-Encoder for Korean Sentence Generation
Sangchul Hahn, Seokjin Hong, Heeyoul Choi
http://doi.org/10.5626/JOK.2018.45.2.157
Due to the revolutionary advances in deep learning, performance of pattern recognition has increased significantly in many applications like speech recognition and image recognition, and some systems outperform human-level intelligence in specific domains. Unlike pattern recognition, in this paper, we focus on generating Korean sentences based on a few Korean sentences. We apply variational recurrent auto-encoder (VRAE) and modify the model considering some characteristics of Korean sentences. To reduce the number of words in the model, we apply a word spacing model. Also, there are many Korean sentences which have the same meaning but different word order, even without subjects or objects; therefore we change the unidirectional encoder of VRAE into a bidirectional encoder. In addition, we apply an interpolation method on the encoded vectors from the given sentences, so that we can generate new sentences which are similar to the given sentences. In experiments, we confirm that our proposed method generates better sentences which are semantically more similar to the given sentences.
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.
An HTTP Adaptive Streaming Scheme to Improve the QoE in a High Latency Network
http://doi.org/10.5626/JOK.2018.45.2.175
Recently, HAS (HTTP Adaptive Streaming) has been the subject of much attention to improve the QoE (Quality of Experience). In a high latency network, HAS degrades the QoE due to the lost RTT cycle since it replies with a response of one segment to the request of one segment. The server-push based HAS schemes of downloading multiple segments in one request cause QoE degradation due to the buffer underflow. In this paper, we propose a VSSDS (Video Streaming Scheme based on Dynamic Server-push) scheme to improve the QoE in a high latency network. The proposed scheme adjust video quality by estimating available bandwidth and determine the number of segments to be downloaded for each segment request cycle. Through the simulation, the proposed scheme not only improves the average video bitrate but also alleviates the buffer underflow.
Video Quality Maintenance Scheme for Improve QoE of HTTP Adaptive Streaming Service
Yunho Kim, Heekwang Kim, Kwangsue Chung
http://doi.org/10.5626/JOK.2018.45.2.187
Recently, Hypertext Transfer Protocol (HTTP) adaptive streaming service is attracting attention. The existing quality adaptive scheme of HTTP adaptive streaming service adjusts the video quality according to the network bandwidth or the client buffer size. However, the problem with the existing quality adaptive scheme is the QoE (Quality of Experience) degradation caused by the unnecessary quality change that occurs due to frequent bandwidth change or fixed buffer threshold. We propose a video quality maintenance scheme that improves average video quality and minimizes unnecessary quality change in order to improve the QoE of HTTP adaptive streaming service in the changing network environment. The proposed scheme maintains high quality for a long time by setting the quality maintenance duration to be long when buffer occupancy and video quality are high. The experimental results show that the proposed scheme improves QoE by improving the average video quality and minimizing the quality change.
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