An improved background segmentation method for ghost removals

With ongoing research assessment in higher education and the introduction of master’s‐level work in initial teacher education, the growing need for teacher educators to develop research identities is discussed in relation to mentoring and support in two universities. Twelve interviews—with three teacher educators and three research mentors from each university—were carried out, in order to identify effective mentoring practices and other forms of support, as well as any barriers or problems encountered in developing a research profile. An innovative aspect of the methodological approach is that beginning researchers from the teacher education faculty in both universities undertook the interviewing and co‐authored the article. The need for an entitlement to and protection of research time is stressed, as well as a range of supportive practices within an active research culture. It is argued that this aspect of teacher educators’ professional development requires as much attention as the pedagogical aspects of their rol

이상치 탐지를 위한 적대적 사전 학습 알고리즘

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 박종우.In this thesis, we propose a semi-supervised dictionary learning algorithm that learns representations of only non-outlier data. The presence of outliers in a dataset is a major drawback for dictionary learning, resulting in less than desirable performance in real-world applications. Our adversarial dictionary learning (ADL) algorithm exploits a supervision dataset composed of known outliers. The algorithm penalizes the dictionary expressing the known outliers well. Penalizing the known outliers makes dictionary learning robust to the outliers present in the dataset. The proposed method can handle highly corrupted dataset which cannot be effectively dealt with using conventional robust dictionary learning algorithms. We empirically show the usefulness of our algorithm with extensive experiments on anomaly detection, using both synthetic univariate time-series data and multivariate point data.본 논문에서는 이상치가 아닌 데이터의 희소 표현만을 학습하는 준지도 사전 학습 알고리즘을 제안한다. 데이터셋에 섞여 있는 이상치는 사전 학습의 주요한 문제로, 실제 문제에 적용 시 바람직하지 않은 성능을 초래한다. 본 연구에서 제안하는 적대적 사전 학습(ADL) 알고리즘은 이상치 데이터로 구성된 감독 데이터셋을 학습에 이용한다. 우리의 알고리즘은 주어진 이상치 데이터를 잘 표현하는 사전에 페널티를 주고, 이것은 사전이 학습 데이터셋에 섞여 있는 이상치에 강건하게 학습되도록 한다. 제안된 방법은 기존의 사전 학습 방법들과 비교해 이상치의 비중이 높은 데이터셋에서도 효과적으로 사전을 학습해 낸다. 이 연구에서는 인공적인 단변량 시계열 데이터와 다변량 점 데이터에 대한 이상치 탐지 실험을 통해 알고리즘의 유용성을 경험적으로 검증한다.1 Introduction 1 1.1 Related Works 4 1.2 Contributions of This Thesis 5 1.3 Organization 6 2 Sparse Representation and Dictionary Learning 7 2.1 Sparse Representation 7 2.1.1 Problem De nition of Sparse Representation 7 2.1.2 Sparse representation with l0-norm regularization 10 2.1.3 Sparse representation with l1-norm regularization 11 2.1.4 Sparse representation with lp-norm regularization (0 < p < 1) 12 2.2 Dictionary Learning 12 2.2.1 Problem De nition of Dictionary Learning 12 2.2.2 Dictionary Learning Methods 14 3 Adversarial Dictionary Learning 18 3.1 Problem Formulation 18 3.2 Adversarial Loss 19 3.3 Optimization Algorithm 20 4 Experiments 25 4.1 Data Description 26 4.1.1 Univariate Time-series Data 26 4.1.2 Multivariate Point Data 29 4.2 Evaluation Process 30 4.2.1 A Baseline of Anomaly Detection 30 4.2.2 ROC Curve and AUC 34 4.3 Experiment Setting 35 4.4 Results 36 5 Conclusion 43 Bibliography 45 국문초록 50Maste

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

HIV Drug Resistant Prediction and Featured Mutants Selection using Machine Learning Approaches

HIV/AIDS is widely spread and ranks as the sixth biggest killer all over the world. Moreover, due to the rapid replication rate and the lack of proofreading mechanism of HIV virus, drug resistance is commonly found and is one of the reasons causing the failure of the treatment. Even though the drug resistance tests are provided to the patients and help choose more efficient drugs, such experiments may take up to two weeks to finish and are expensive. Because of the fast development of the computer, drug resistance prediction using machine learning is feasible. In order to accurately predict the HIV drug resistance, two main tasks need to be solved: how to encode the protein structure, extracting the more useful information and feeding it into the machine learning tools; and which kinds of machine learning tools to choose. In our research, we first proposed a new protein encoding algorithm, which could convert various sizes of proteins into a fixed size vector. This algorithm enables feeding the protein structure information to most state of the art machine learning algorithms. In the next step, we also proposed a new classification algorithm based on sparse representation. Following that, mean shift and quantile regression were included to help extract the feature information from the data. Our results show that encoding protein structure using our newly proposed method is very efficient, and has consistently higher accuracy regardless of type of machine learning tools. Furthermore, our new classification algorithm based on sparse representation is the first application of sparse representation performed on biological data, and the result is comparable to other state of the art classification algorithms, for example ANN, SVM and multiple regression. Following that, the mean shift and quantile regression provided us with the potentially most important drug resistant mutants, and such results might help biologists/chemists to determine which mutants are the most representative candidates for further research