Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/ licenses/by/4.0

Gaussian process classification for prediction of in-hospital mortality among preterm infants

We present a method for predicting preterm infant in-hospital mortality using Bayesian Gaussian process classification. We combined features extracted from sensor measurements, made during the first 72 h of care for 598 Very Low Birth Weight infants of birth weight <1500 g, with standard clinical features calculated on arrival at the Neonatal Intensive Care Unit. Time periods of 12, 18, 24, 36, 48, and 72 h were evaluated. We achieved a classification result with area under the receiver operating characteristic curve of 0.948, which is in excess of the results achieved by using the clinical standard SNAP-II and SNAPPE-II scores. (C) 2018 Elsevier B.V. All rights reserved.Peer reviewe

Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning

Deep learning Convolutional Neural Network (CNN) models are powerful classification models but require a large amount of training data. In niche domains such as bird acoustics, it is expensive and difficult to obtain a large number of training samples. One method of classifying data with a limited number of training samples is to employ transfer learning. In this research, we evaluated the effectiveness of birdcall classification using transfer learning from a larger base dataset (2814 samples in 46 classes) to a smaller target dataset (351 samples in 10 classes) using the ResNet-50 CNN. We obtained 79% average validation accuracy on the target dataset in 5-fold cross-validation. The methodology of transfer learning from an ImageNet-trained CNN to a project-specific and a much smaller set of classes and images was extended to the domain of spectrogram images, where the base dataset effectively played the role of the ImageNet.Comment: Accepted for IEEE Digital Image Computing: Techniques and Applications, 2019 (DICTA 2019), 2-4 December 2019 in Perth, Australia, http://dicta2019.dictaconference.org/index.htm

Audio-only bird classification using unsupervised feature learning

We describe our method for automatic bird species classification, which uses raw audio without segmentation and without using any auxiliary metadata. It successfully classifies among 501 bird categories, and was by far the highest scoring audio-only bird recognition algorithm submitted to BirdCLEF 2014. Our method uses unsupervised feature learning, a technique which learns regularities in spectro-temporal content without reference to the training labels, which helps a classifier to generalise to further content of the same type. Our strongest submission uses two layers of feature learning to capture regularities at two different time scales

Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.This research was partly funded by Spanish Government grant TEC2017-84395-P

민감한 정보를 보호할 수 있는 프라이버시 보존 기계학습 기술 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 이재욱.최근 인공지능의 성공에는 여러 가지 요인이 있으나, 새로운 알고리즘의 개발과 정제된 데이터 양의 기하급수적인 증가로 인한 영향이 크다. 따라서 기계학습 모델과 데이터는 실재적 가치를 가지게 되며, 현실 세계에서 개인 또는 기업은 학습된 모델 또는 학습에 사용할 데이터를 제공함으로써 이익을 얻을 수 있다. 그러나, 데이터 또는 모델의 공유는 개인의 민감 정보를 유출함으로써 프라이버시의 침해로 이어질 수 있다는 사실이 밝혀지고 있다. 본 논문의 목표는 민감 정보를 보호할 수 있는 프라이버시 보존 기계학습 방법론을 개발하는 것이다. 이를 위해 최근 활발히 연구되고 있는 두 가지 프라이버시 보존 기술, 즉 동형 암호와 차분 프라이버시를 사용한다. 먼저, 동형 암호는 암호화된 데이터에 대해 기계학습 알고리즘을 적용 가능하게 함으로써 데이터의 프라이버시를 보호할 수 있다. 그러나 동형 암호를 활용한 연산은 기존의 연산에 비해 매우 큰 연산 시간을 요구하므로 효율적인 알고리즘을 구성하는 것이 중요하다. 효율적인 연산을 위해 우리는 두 가지 접근법을 사용한다. 첫 번째는 학습 단계에서의 연산량을 줄이는 것이다. 학습 단계에서부터 동형 암호를 적용하면 학습 데이터의 프라이버시를 함께 보호할 수 있으므로 추론 단계에서만 동형 암호를 적용하는 것에 비해 프라이버시의 범위가 넓어지지만, 그만큼 연산량이 늘어난다. 본 논문에서는 일부 가장 중요한 정보만을 암호화함으로써 학습 단계를 효율적으로 하는 방법론을 제안한다. 구체적으로, 일부 민감 변수가 암호화되어 있을 때 연산량을 매우 줄일 수 있는 릿지 회귀 알고리즘을 개발한다. 또한 개발된 알고리즘을 확장시켜 동형 암호 친화적이지 않은 파라미터 탐색 과정을 최대한 제거할 수 있는 새로운 로지스틱 회귀 알고리즘을 함께 제안한다. 효율적인 연산을 위한 두 번째 접근법은 동형 암호를 기계학습의 추론 단계에서만 사용하는 것이다. 이를 통해 시험 데이터의 직접적인 노출을 막을 수 있다. 본 논문에서는 서포트 벡터 군집화 모델에 대한 동형 암호 친화적 추론 방법을 제안한다. 동형 암호는 여러 가지 위협에 대해서 데이터와 모델 정보를 보호할 수 있으나, 학습된 모델을 통해 새로운 데이터에 대한 추론 서비스를 제공할 때 추론 결과로부터 모델과 학습 데이터를 보호하지 못한다. 연구를 통해 공격자가 자신이 가진 데이터와 그 데이터에 대한 추론 결과만을 이용하여 이용하여 모델과 학습 데이터에 대한 정보를 추출할 수 있음이 밝혀지고 있다. 예를 들어, 공격자는 특정 데이터가 학습 데이터에 포함되어 있는지 아닌지를 추론할 수 있다. 차분 프라이버시는 학습된 모델에 대한 특정 데이터 샘플의 영향을 줄임으로써 이러한 공격에 대한 방어를 보장하는 프라이버시 기술이다. 차분 프라이버시는 프라이버시의 수준을 정량적으로 표현함으로써 원하는 만큼의 프라이버시를 충족시킬 수 있지만, 프라이버시를 충족시키기 위해서는 알고리즘에 그만큼의 무작위성을 더해야 하므로 모델의 성능을 떨어뜨린다. 따라서, 본문에서는 모스 이론을 이용하여 차분 프라이버시 군집화 방법론의 프라이버시를 유지하면서도 그 성능을 끌어올리는 새로운 방법론을 제안한다. 본 논문에서 개발하는 프라이버시 보존 기계학습 방법론은 각기 다른 수준에서 프라이버시를 보호하며, 따라서 상호 보완적이다. 제안된 방법론들은 하나의 통합 시스템을 구축하여 기계학습이 개인의 민감 정보롤 보호해야 하는 여러 분야에서 더욱 널리 사용될 수 있도록 하는 기대 효과를 가진다.Recent development of artificial intelligence systems has been driven by various factors such as the development of new algorithms and the the explosive increase in the amount of available data. In the real-world scenarios, individuals or corporations benefit by providing data for training a machine learning model or the trained model. However, it has been revealed that sharing of data or the model can lead to invasion of personal privacy by leaking personal sensitive information. In this dissertation, we focus on developing privacy-preserving machine learning methods which can protect sensitive information. Homomorphic encryption can protect the privacy of data and the models because machine learning algorithms can be applied to encrypted data, but requires much larger computation time than conventional operations. For efficient computation, we take two approaches. The first is to reduce the amount of computation in the training phase. We present an efficient training algorithm by encrypting only few important information. In specific, we develop a ridge regression algorithm that greatly reduces the amount of computation when one or two sensitive variables are encrypted. Furthermore, we extend the method to apply it to classification problems by developing a new logistic regression algorithm that can maximally exclude searching of hyper-parameters that are not suitable for machine learning with homomorphic encryption. Another approach is to apply homomorphic encryption only when the trained model is used for inference, which prevents direct exposure of the test data and the model information. We propose a homomorphic-encryption-friendly algorithm for inference of support based clustering. Though homomorphic encryption can prevent various threats to data and the model information, it cannot defend against secondary attacks through inference APIs. It has been reported that an adversary can extract information about the training data only with his or her input and the corresponding output of the model. For instance, the adversary can determine whether specific data is included in the training data or not. Differential privacy is a mathematical concept which guarantees defense against those attacks by reducing the impact of specific data samples on the trained model. Differential privacy has the advantage of being able to quantitatively express the degree of privacy, but it reduces the utility of the model by adding randomness to the algorithm. Therefore, we propose a novel method which can improve the utility while maintaining the privacy of differentially private clustering algorithms by utilizing Morse theory. The privacy-preserving machine learning methods proposed in this paper can complement each other to prevent different levels of attacks. We expect that our methods can construct an integrated system and be applied to various domains where machine learning involves sensitive personal information.Chapter 1 Introduction 1 1.1 Motivation of the Dissertation 1 1.2 Aims of the Dissertation 7 1.3 Organization of the Dissertation 10 Chapter 2 Preliminaries 11 2.1 Homomorphic Encryption 11 2.2 Differential Privacy 14 Chapter 3 Efficient Homomorphic Encryption Framework for Ridge Regression 18 3.1 Problem Statement 18 3.2 Framework 22 3.3 Proposed Method 25 3.3.1 Regression with one Encrypted Sensitive Variable 25 3.3.2 Regression with two Encrypted Sensitive Variables 30 3.3.3 Adversarial Perturbation Against Attribute Inference Attack 35 3.3.4 Algorithm for Ridge Regression 36 3.3.5 Algorithm for Adversarial Perturbation 37 3.4 Experiments 40 3.4.1 Experimental Setting 40 3.4.2 Experimental Results 42 3.5 Chapter Summary 47 Chapter 4 Parameter-free Homomorphic-encryption-friendly Logistic Regression 53 4.1 Problem Statement 53 4.2 Proposed Method 56 4.2.1 Motivation 56 4.2.2 Framework 58 4.3 Theoretical Results 63 4.4 Experiments 68 4.4.1 Experimental Setting 68 4.4.2 Experimental Results 70 4.5 Chapter Summary 75 Chapter 5 Homomorphic-encryption-friendly Evaluation for Support Vector Clustering 76 5.1 Problem Statement 76 5.2 Background 78 5.2.1 CKKS scheme 78 5.2.2 SVC 80 5.3 Proposed Method 82 5.4 Experiments 86 5.4.1 Experimental Setting 86 5.4.2 Experimental Results 87 5.5 Chapter Summary 89 Chapter 6 Differentially Private Mixture of Gaussians Clustering with Morse Theory 95 6.1 Problem Statement 95 6.2 Background 98 6.2.1 Mixture of Gaussians 98 6.2.2 Morse Theory 99 6.2.3 Dynamical System Perspective 101 6.3 Proposed Method 104 6.3.1 Differentially private clustering 105 6.3.2 Transition equilibrium vectors and the weighted graph 108 6.3.3 Hierarchical merging of sub-clusters 111 6.4 Theoretical Results 112 6.5 Experiments 117 6.5.1 Experimental Setting 117 6.5.2 Experimental Results 119 6.6 Chapter Summary 122 Chapter 7 Conclusion 124 7.1 Conclusion 124 7.2 Future Direction 126 Bibliography 128 국문초록 154박

Strategic ambivalence above, selective implementation below: an institutional analysis of Kazakhstan`s policy toward skilled labor

Aiming at entering the top thirty most competitive economies in the world by 2050 Kazakhstan faces a problem of inadequate human capital. However, an objective demand for foreign skilled workers notwithstanding, Kazakhstan fails to attract as many of them as its labor market needs. Driven by this puzzle, the given study analyzes labor migration policy of Kazakhstan regarding skilled workers. It attempts to explain what factors make Kazakhstani labor migration policy ineffective under the condition when skilled foreign workers are needed. Two main factors influence the outcomes of labor migration policy implementation: decentralized decision-making and strategic ambiguity. Transferring the function of policy implementation to local-level bureaucratic institutions the state not only shifts its responsibilities to bureaucrats but also provides them with a certain degree of autonomy and discretion. However, the state and its institutions have no a coherent vision of the national interest in labor migration. Bureaucrats concerned with their professional duties have a more protectionist stance on foreign specialists’ inflows. Meanwhile, aimed at increasing these inflows the state ensures its interest through strategic ambiguity in its discourse and practices. It allows the state to reconcile an economic need in more foreign skilled workers with a political demand for a more restrictive labor migration policy. Thus, starting from above ambiguity is manipulated by local bureaucrats to meet their professional and, occasionally, personal interests when implementing the policy. As a result, the state fails to attract the needed numbers of foreign specialists. In other words, the policy through which the state aims to achieve its goals turns to be ineffective. This thesis demonstrates that an institutional approach with an emphasis on the bureaucratic model of decision-making is a better way to understand the reasons of labor ix migration policy ineffectiveness in Kazakhstan. However, it also shows that when bureaucrats are involved in the policy-making process the findings from this case can be applied to the countries other than Kazakhstan and public policies other than migratio