Healthcare Event and Activity Logging.

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean

커널 서포트와 평형점을 활용한 차분 프라이버시 다중 클래스 분류 기법

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022.2. 이재욱.In this paper, we propose a multi-class classification method using kernel supports and a dynamic system under differential privacy. We find support vector machine (SVM) algorithms have a fundamental weaknesses of implementing differential privacy because the decision function depends on some subset of the training data called the support vectors. Therefore, we develop a method using interior points called equilibrium points (EPs) without relying on the decision boundary. To construct EPs, we utilize a dynamic system with a new differentially private support vector data description (SVDD) by perturbing the sphere center in the kernel space. Empirical results show that the proposed method achieves better performance even on small-sized datasets where differential privacy performs poorly.본 논문에서는 커널 서포트와 평형점을 활용한 차분 프라이버시 다중 클래스 분류 기법을 제시한다. 서포트 벡터 분류 기법은 데이터 분석과 머신 러닝에 활용성이 높아 사용자의 데이터를 보호하며 학습하는 것이 필수적이다. 그 중 가장 대중적인 서포트 벡터 머신(SVM)은 서포트 벡터라고 불리는 일부 데이터에만 분류에 의존하기 때문에 프라이버시 차분 기법을 활용하기 어렵다. 데이터 하나가 변경되었을 때 결과의 변화가 적어야 하는 차분 프라이버시 상황에서 서포트 벡터 하나가 없어진다면 분류기의 결정 경계는 그 변화에 매우 취약하다는 문제가 있다. 이 문제를 해결하기 위해 본 연구에서는 평형점이라고 불리는 군집 내부에 존재하는 점을 활용하는 차분 프라이버시 다중 클래스 분류 기법을 제시한다. 이를 위해, 먼저 커널 공간에서 구의 중심에 섭동을 더해 차분 프라이버시를 만족하는 서포트 벡터 데이터 디스크립션(SVDD)을 구하고 이를 레벨집합으로 활용해 동역학계로 극소점들을 구한다. 평형점을 활용하거나 고차원 데이터의 경우 초입방체를 만들어, 학습한 모델을 추론에 활용할 수 있는 (1) 서포트 함수를 공개 하는 방법과 (2) 평형점을 공개하는 방법을 제시한다. 8개의 다양한 데이터 집합의 실험적인 결과는 제시한 방법론이 노이즈에 강건한 내부의 점을 활용해 기존의 차분 프라이버시 서포트 벡터 머신보다 성능을 높이고, 차분 프라이버시가 적용되기 어려운 작은 데이터셋에도 활용될 수 있다는 기술임을 보여준다.Chapter 1 Introduction 1 1.1 Problem Description: Data Privacy 1 1.2 The Privacy of Support Vector Methods 2 1.3 Research Motivation and Contribution 4 1.4 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Differentially private Empirical risk minimization 6 2.2 Differentially private Support vector machine 7 Chapter 3 Preliminaries 9 3.1 Differential privacy 9 Chapter 4 Differential private support vector data description 12 4.1 Support vector data description 12 4.2 Differentially private support vector data description 13 Chapter 5 Differentially private multi-class classification utilizing SVDD 19 5.1 Phase I. Constructing a private support level function 20 5.2 Phase II: Differentially private clustering on the data space via a dynamical system 21 5.3 Phase III: Classifying the decomposed regions under differential privacy 22 Chapter 6 Inference scenarios and releasing the differentially private model 25 6.1 Publishing support function 26 6.2 Releasing equilibrium points 26 6.3 Comparison to previous methods 27 Chapter 7 Experiments 28 7.1 Models and Scenario setting 28 7.2 Datasets 29 7.3 Experimental settings 29 7.4 Empirical results on various datasets under publishing support function 30 7.5 Evaluating robustness under diverse data size 33 7.6 Inference through equilibrium points 33 Chapter 8 Conclusion 34 8.1 Conclusion 34석

Experiences of aiding autobiographical memory Using the SenseCam

Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

Experiences of aiding autobiographical memory using the sensecam

Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

Multi-factor Physical Layer Security Authentication in Short Blocklength Communication

Lightweight and low latency security schemes at the physical layer that have recently attracted a lot of attention include: (i) physical unclonable functions (PUFs), (ii) localization based authentication, and, (iii) secret key generation (SKG) from wireless fading coefficients. In this paper, we focus on short blocklengths and propose a fast, privacy preserving, multi-factor authentication protocol that uniquely combines PUFs, proximity estimation and SKG. We focus on delay constrained applications and demonstrate the performance of the SKG scheme in the short blocklength by providing a numerical comparison of three families of channel codes, including half rate low density parity check codes (LDPC), Bose Chaudhuri Hocquenghem (BCH), and, Polar Slepian Wolf codes for n=512, 1024. The SKG keys are incorporated in a zero-round-trip-time resumption protocol for fast re-authentication. All schemes of the proposed mutual authentication protocol are shown to be secure through formal proofs using Burrows, Abadi and Needham (BAN) and Mao and Boyd (MB) logic as well as the Tamarin-prover

Novelty, distillation, and federation in machine learning for medical imaging

The practical application of deep learning methods in the medical domain has many challenges. Pathologies are diverse and very few examples may be available for rare cases. Where data is collected it may lie in multiple institutions and cannot be pooled for practical and ethical reasons. Deep learning is powerful for image segmentation problems but ultimately its output must be interpretable at the patient level. Although clearly not an exhaustive list, these are the three problems tackled in this thesis. To address the rarity of pathology I investigate novelty detection algorithms to find outliers from normal anatomy. The problem is structured as first finding a low-dimension embedding and then detecting outliers in that embedding space. I evaluate for speed and accuracy several unsupervised embedding and outlier detection methods. Data consist of Magnetic Resonance Imaging (MRI) for interstitial lung disease for which healthy and pathological patches are available; only the healthy patches are used in model training. I then explore the clinical interpretability of a model output. I take related work by the Canon team — a model providing voxel-level detection of acute ischemic stroke signs — and deliver the Alberta Stroke Programme Early CT Score (ASPECTS, a measure of stroke severity). The data are acute head computed tomography volumes of suspected stroke patients. I convert from the voxel level to the brain region level and then to the patient level through a series of rules. Due to the real world clinical complexity of the problem, there are at each level — voxel, region and patient — multiple sources of “truth”; I evaluate my results appropriately against these truths. Finally, federated learning is used to train a model on data that are divided between multiple institutions. I introduce a novel evolution of this algorithm — dubbed “soft federated learning” — that avoids the central coordinating authority, and takes into account domain shift (covariate shift) and dataset size. I first demonstrate the key properties of these two algorithms on a series of MNIST (handwritten digits) toy problems. Then I apply the methods to the BraTS medical dataset, which contains MRI brain glioma scans from multiple institutions, to compare these algorithms in a realistic setting