Hyuk-Yoon Kwon

Taehee Kim, Hyuk-Yoon Kwon

Advanced metering infrastructure (AMI) has been widely used as an intelligent energy consumption measurement system. Electric power was the representative energy source that can be collected by AMI; most existing studies to detect abnormal energy consumption have focused on a single energy source, i.e., power. Recently, other energy sources such as water, gas, and heating have also been actively collected. As a result, it is necessary to develop a unified methodology for anomaly detection across multiple energy sources; however, research efforts have rarely been made to tackle this issue. The inherent difficulty with this issue stems from the fact that anomalies are not usually annotated. Moreover, existing works of anomaly definition depend on only individual energy sources. In this paper, we first propose a method for defining anomalies considering not only individual energy sources but also correlations between them. Then, we propose a new Correlation-driven Multi-Level Multimodal Learning model for anomaly detection on multiple energy sources. The distinguishing property of the model incorporates multiple energy sources in multi-levels based on the strengths of the correlations between them. Furthermore, we generalize the proposed model in order to integrate arbitrary new energy sources with further performance improvement, considering not only correlated but also non-correlated sources. Through extensive experiments on real-world datasets consisting of three to five energy sources, we demonstrate that the proposed model clearly outperforms the existing multimodal learning and recent time-series anomaly detection models, and we observe that our model makes further the performance improvement as more correlated or non-correlated energy sources are integrated.

Min-Seon Kim, Bo-Young Lim, Han-Sub Shin et al.

In this study, we deal with the problem of judging the credibility of movie reviews. The problem is challenging because even experts cannot clearly and efficiently judge the credibility of a movie review and the number of movie reviews is very large. To tackle this problem, we propose historical credibility that judges the credibility of reviews based on the historical ratings and textual reviews written by each reviewer. For this, we present three kinds of criteria that can clearly classify the reviews into trusted or distrusted ones. We validate the effectiveness of the proposed historical credibility through extensive analysis. Specifically, we show that characteristics between the trusted or distrusted reviews are quite distinguishable in terms of three viewpoints: 1) distribution, 2) statistics, and 3) correlation. Then, we apply historical credibility to a weakly supervised model to classify a given review as a trusted or distrusted one. First, we show that it is significantly efficient because the entire data set is annotated according to the predefined criteria. Indeed, it can annotate 6,400 movie reviews only in 0.093 seconds, which occupy only 0.55%~1.88% of the total learning time when we use LSTM and SVM as the learning model. Second, we show that the historical credibility-based classification model clearly outperforms the textual review-based classification model. Specifically, the classification accuracy of the former outperforms that of the latter by up to 11.7%~13.4%. In addition, we clearly confirm that our classification model shows higher accuracy as the data size increases.