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Semi-Supervised Learning for Detecting of Abusive Sentence on Twitter using Deep Neural Network with Fuzzy Category Representation
http://doi.org/10.5626/JOK.2018.45.11.1185
The number of people embracing damage caused by hate speech on the SNS(Social Network Service) is increasing rapidly. In this paper, we propose a detection method using Semi-supervised learning and Deep Neural Network from a large file to determine whether implied meaning of sentence beyond hate speech detection through comparison with a simple dictionary in twitter sentence is abusive or not. Most of the methods judge the hate speech sentence by comparing with a blacklist comprising of hate speech words. However, the reported methods have a disadvantage that skillful and subtle expression of hate speech cannot be identified. So, we created a corpus with a label on whether or not to hate speech on Korean twitter sentence. The training corpus in twitter comprised of 44,000 sentences and the test corpus comprised of 13,082 sentences. The system performance about the explicit abusive sentences of the F1 score was 86.13% on the model using 1-layer syllable CNN and sequence vector. And the system performance about the implicit abusive sentences of the F1 score 25.53% on the model using 1-layer syllable CNN and 2-layer syllable CNN and sequence vector. The proposed method can be used as a method for detecting cyber-bullying.
A Twitter News-Classification Scheme Using Semantic Enrichment of Word Features
Seonmi Ji, Jihoon Moon, Hyeonwoo Kim, Eenjun Hwang
http://doi.org/10.5626/JOK.2018.45.10.1045
Recently, with the popularity of Twitter as a news platform, many news articles are generated, and various kinds of information and opinions about them spread out very fast. But since an enormous amount of Twitter news is posted simultaneously, users have difficulty in selectively browsing for news related to their interests. So far, many works have been conducted on how to classify Twitter news using machine learning and deep learning. In general, conventional machine learning schemes show data sparsity and semantic gap problems, and deep learning schemes require a large amount of data. To solve these problems, in this paper, we propose a Twitter news-classification scheme using semantic enrichment of word features. Specifically, we first extract the features of Twitter news data using the Vector Space Model. Second, we enhance those features using DBpedia Spotlight. Finally, we construct a topic-classification model based on various machine learning techniques and demonstrate by experiments that our proposed model is more effective than other traditional methods.
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.
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%.
Inverse Document Frequency-Based Word Embedding of Unseen Words for Question Answering Systems
Wooin Lee, Gwangho Song, Kyuseok Shim
Question answering system (QA system) is a system that finds an actual answer to the question posed by a user, whereas a typical search engine would only find the links to the relevant documents. Recent works related to the open domain QA systems are receiving much attention in the fields of natural language processing, artificial intelligence, and data mining. However, the prior works on QA systems simply replace all words that are not in the training data with a single token, even though such unseen words are likely to play crucial roles in differentiating the candidate answers from the actual answers. In this paper, we propose a method to compute vectors of such unseen words by taking into account the context in which the words have occurred. Next, we also propose a model which utilizes inverse document frequencies (IDF) to efficiently process unseen words by expanding the system’s vocabulary. Finally, we validate that the proposed method and model improve the performance of a QA system through experiments.
Implementation of Adaptive Navigation for NPCs in Computer Games
Eunsol Kim, Hyeyeon Kim, Kyeonah Yu
Uniform navigation of NPCs in computer games is an important factor that can decrease the interest of game players. This problem is particularly noticeable in pathfinding when using a waypoint graph because the NPCs navigate using only predefined locations. In this paper we propose a method that enables adaptive navigations of NPCs by observing player movements. The proposed method involves modification of waypoints dynamically by observing the player"s point designation and use of the modified waypoints for NPC"s pathfinding. Also, we propose an algorithm to find the NPC-specific path by learning the landform preferences of players. We simulate the implemented algorithm in an RPG game made with Unity 4.0 and confirm that NPC navigations had more variety and improved according to player navigations.
Malware Classification System to Support Decision Making of App Installation on Android OS
Hong Ryeol Ryu, Yun Jang, Taekyoung Kwon
Although Android systems provide a permission-based access control mechanism and demand a user to decide whether to install an app based on its permission list, many users tend to ignore this phase. Thus, an improved method is necessary for users to intuitively make informed decisions when installing a new app. In this paper, with regard to the permission-based access control system, we present a novel approach based on a machine-learning technique in order to support a user decision-making on the fly. We apply the K-NN (K-Nearest Neighbors) classification algorithm with necessary weighted modifications for malicious app classification, and use 152 Android permissions as features. Our experiment shows a superior classification result (93.5% accuracy) compared to other previous work. We expect that our method can help users make informed decisions at the installation step.
MOnCa2: High-Level Context Reasoning Framework based on User Travel Behavior Recognition and Route Prediction for Intelligent Smartphone Applications
MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user’s physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user’s physical context, infer basic context regarding the user’s travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user’s travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.
Syllable-based Probabilistic Models for Korean Morphological Analysis
This paper proposes three probabilistic models for syllable-based Korean morphological analysis, and presents the performance of proposed probabilistic models. Probabilities for the models are acquired from POS-tagged corpus. The result of 10-fold cross-validation experiments shows that 98.3% answer inclusion rate is achieved when trained with Sejong POS-tagged corpus of 10 million eojeols. In our models, POS tags are assigned to each syllable before spelling recovery and morpheme generation, which enables more efficient morphological analysis than the previous probabilistic models where spelling recovery is performed at the first stage. This efficiency gains the speed-up of morphological analysis. Experiments show that morphological analysis is performed at the rate of 147K eojeols per second, which is almost 174 times faster than the previous probabilistic models for Korean morphology.
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