Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection

In this paper, we consider the problem of detecting counterfeit identity documents in images captured with smartphones. As the number of documents contain special fonts, we study the applicability of convolutional neural networks (CNNs) for detection of the conformance of the fonts used with the ones, corresponding to the government standards. Here, we use multi-task learning to differentiate samples by both fonts and characters and compare the resulting classifier with its analogue trained for binary font classification. We train neural networks for authenticity estimation of the fonts used in machine-readable zones and ID numbers of the Russian national passport and test them on samples of individual characters acquired from 3238 images of the Russian national passport. Our results show that the usage of multi-task learning increases sensitivity and specificity of the classifier. Moreover, the resulting CNNs demonstrate high generalization ability as they correctly classify fonts which were not present in the training set. We conclude that the proposed method is sufficient for authentication of the fonts and can be used as a part of the forgery detection system for images acquired with a smartphone camera

퇴행성 슬 관절염의 객관적 평가를 위한 기계학습 기반의 보행 데이터 분석 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2020. 8. 김희찬.Osteoarthritis (OA) is a disease that affects above 30% of the elderly population aged 60 years and older. Western Ontario and McMaster Osteoarthritis (WOMAC) and radiographic-based Kellgren–Lawrence (KL) grade methods are currently used to evaluate the severity of knee osteoarthritis (KOA). However, the WOMAC is a subjective method which cannot be performed to certain patients, and is not suitable for tracking changes in severity over time. KL grade requires highly trained experts and is a time consuming process. This dissertation hypothesized that objective and biomechanical gait data can supplement unmet needs of current gold standard. It was hypothesized that specific features from gait data would reflect the severity of KOA. Therefore, this study aims to identify key gait features associated with the severity of KOA and provide a new objective and explainable evaluation method for KOA based on gait analysis. Features were extracted from the gait signal and an automated severity evaluation model was designed based on machine learning technique for WOMAC severity evaluation model. To develop an automated severity evaluation algorithm for KL grade, features were extracted from the plain radiography image using deep learning network, and machine learning was applied to select features from the gait data. Both image and gait features were used to develop a machine learning algorithm for KL grade evaluation. The evaluation algorithm for WOMAC and KL grade showed a correlation of 0.741 and an accuracy of 75.2% with gold standard method, respectively. This dissertation proposed a new evaluation method for KOA and showed the clinical utility of the gait data application that was limited in clinical practice due to the complexity of the signal.퇴행성 관절염은 60세 이상의 노인 인구 약 30%에서 발병하는 질병이다. 현재 퇴행성 슬 관절염의 진단은 Western Ontario and McMaster Osteoarthritis (WOMAC) 방법과 방사선 촬영 기반의 Kellgren–Lawrence (KL) grade 방법이 사용되고 있다. 그러나 WOMAC 환자의 주관적인 판단을 토대로 중증도를 정량화하는 방법이어서 일부 환자들에게 적용이 불가능하고, 수술 후의 중증도를 반영하지 못한다는 단점이 있다. KL grade은 고도로 훈련된 전문가를 필요로 하며, 정확한 진단을 위하여서는 많은 시간을 필요로 한다. 반면 보행 신호는 환자의 보행에 따른 객관적인 생체 역학 신호를 제공하며, 보행이 가능한 모든 사람에게 적용이 가능하며, 주기적인 추적 관찰에 용의하다. 따라서 본 연구는 보행 신호를 이용하여 객관적이며, 결과에 대한 생체 역학적 이유를 알 수 있는 퇴행성 슬 관절염의 새로운 분석 방법을 제시함에 있다. 먼저 자동으로 WOMAC 방법을 진단하기 위해 보행신호에서 특징들을 추출하고 기계학습 기법을 이용하여 평가하는 모델을 개발하였다. 또한 KL grade 방법을 평가하기 위해 방사선 영상에서 딥러닝 알고리즘으로 추출한 특징들과 보행신호에서 추출한 특징들을 기계학습 기법을 이용하였다. 제안하는 퇴행성 슬 관절염의 평가 방법은 WOMAC 및 KL grade 방법과 각각 상관관계 0.741, 정확도 75.2%를 보였다. 본 연구는 퇴행성 슬 관절염의 새로운 평가 방법을 제시하였으며, 신호의 복잡성으로 인하여 임상에서 사용되지 못했던 보행 신호의 임상적 활용성을 보여주었다.1. Introduction 1 1.1. Knee Osteoarthritis 2 1.2. Severity Evaluation of Knee Osteoarthritis 4 1.2.1. Symptomatic Severity evaluation 4 1.2.2. Structural Severity evaluation 5 1.3. Unmet Clinical Needs 7 1.4. Gait analysis and KOA 8 1.5. Thesis objectives 12 2. Symptomatic Severity of Knee Osteoarthritis 14 2.1. Introduction 15 2.2. Methods 18 2.2.1. Participants 18 2.2.2. Gait Data Collection 20 2.2.3. Statistical Analysis and WOMAC Estimation Model 21 2.3. Results 25 2.4. Discussion 34 2.5. Conclusion 41 3. Structural Severity of Knee Osteoarthritis 42 3.1. Introduction 43 3.2. Methods 49 3.2.1. Participants 49 3.2.2. Gait Data Collection 52 3.2.3. Radiographic Assessment 53 3.2.4. Feature Extraction and Classification 54 3.3. Results 62 3.3.1. Feature Analysis 62 3.3.2. Deep Learning Approach Based on Radiographic Images 72 3.3.3. Proposed Model Based on Gait Data and Radiographic Images 74 3.4. Discussion 76 3.5. Conclusion 83 4. Conclusion 84 4.1. Thesis Summary and Contributions 85 4.2. Future Direction 87 Bibliography 89 Abstract in Korean 98Docto

Interpreting Labor Supply Regressions in a Model of Full and Part-Time Work

We construct a family model of labor supply that features adjustment along both the intensive and extensive margin. Intensive margin adjustment is restricted to two values: full time work and part-time work. Using simulated data from the steady state of the calibrated model, we examine whether standard labor supply regressions can uncover the true value of the intertemporal elasticity of labor supply parameter. We find positive estimated elasticities that are larger for women and that are highly significant, but they bear virtually no relationship to the underlying preference parameters.

Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning

Effectively operating an electric vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. By utilizing the flexibility of EV charging needs, the EVCS can reduce the total electricity cost for meeting the EV demand. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces unfortunately grows with the number of EVs, which becomes very large and time-varying. This dimensionality issue affects the efficiency and convergence performance of generic RL algorithms. To this end, we advocate to develop aggregation schemes for state/action according to the emergency of EV charging, or its laxity. A least-laxity first (LLF) rule is used to consider only the total charging power of the EVCS, while ensuring the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Using the proposed aggregation scheme, the policy gradient method is applied to find the best parameters of a linear Gaussian policy. Numerical tests have demonstrated the performance improvement of the proposed representation approaches in increasing the total reward and policy efficiency over existing approximation-based method

EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences.

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns

The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation

Recent work on dense optical flow has shown significant progress, primarily in a supervised learning manner requiring a large amount of labeled data. Due to the expensiveness of obtaining large scale real-world data, computer graphics are typically leveraged for constructing datasets. However, there is a common belief that synthetic-to-real domain gaps limit generalization to real scenes. In this paper, we show that the required characteristics in an optical flow dataset are rather simple and present a simpler synthetic data generation method that achieves a certain level of realism with compositions of elementary operations. With 2D motion-based datasets, we systematically analyze the simplest yet critical factors for generating synthetic datasets. Furthermore, we propose a novel method of utilizing occlusion masks in a supervised method and observe that suppressing gradients on occluded regions serves as a powerful initial state in the curriculum learning sense. The RAFT network initially trained on our dataset outperforms the original RAFT on the two most challenging online benchmarks, MPI Sintel and KITTI 2015

Redirected Walking in Infinite Virtual Indoor Environment Using Change-blindness

We present a change-blindness based redirected walking algorithm that allows a user to explore on foot a virtual indoor environment consisting of an infinite number of rooms while at the same time ensuring collision-free walking for the user in real space. This method uses change blindness to scale and translate the room without the user's awareness by moving the wall while the user is not looking. Consequently, the virtual room containing the current user always exists in the valid real space. We measured the detection threshold for whether the user recognizes the movement of the wall outside the field of view. Then, we used the measured detection threshold to determine the amount of changing the dimension of the room by moving that wall. We conducted a live-user experiment to navigate the same virtual environment using the proposed method and other existing methods. As a result, users reported higher usability, presence, and immersion when using the proposed method while showing reduced motion sickness compared to other methods. Hence, our approach can be used to implement applications to allow users to explore an infinitely large virtual indoor environment such as virtual museum and virtual model house while simultaneously walking in a small real space, giving users a more realistic experience.Comment: https://www.youtube.com/watch?v=s-ZKavhXxd