Reviewerly/exHarmony huggingface dataset:
Sub-logarithmic Distributed Oblivious RAM with Small Block Size

Oblivious RAM (ORAM) is a cryptographic primitive that allows a client to securely execute RAM programs over data that is stored in an untrusted server. Distributed Oblivious RAM is a variant of ORAM, where the data is stored in $$m>1$$ servers. Extensive research over the last few decades have succeeded to reduce the bandwidth overhead of ORAM schemes, both in the single-server and the multi-server setting, from $$O(\sqrt{N})$$ to O(1). However, all known protocols that achieve a sub-logarithmic overhead either require heavy server-side computation (e.g. homomorphic encryption), or a large block size of at least $$\varOmega (\log ^3 N)$$ . In this paper, we present a family of distributed ORAM constructions that follow the hierarchical approach of Goldreich and Ostrovsky [17]. We enhance known techniques, and develop new ones, to take better advantage of the existence of multiple servers. By plugging efficient known hashing schemes in our constructions, we get the following results: We also investigate a model where the servers are allowed to perform a linear amount of light local computations, and show that constant overhead is achievable in this model, through a simple four-server ORAM protocol. From theoretical viewpoint, this is the first ORAM scheme with asymptotic constant overhead, and polylogarithmic block size, that does not use homomorphic encryption. Practically speaking, although we do not provide an implementation of the suggested construction, evidence from related work (e.g. [12]) confirms that despite the linear computational overhead, our construction is practical, in particular when applied to secure computation.
The Optimal Approximation Factor in Density Estimation.

Consider the following problem: given two arbitrary densities $q_1,q_2$ and a sample-access to an unknown target density $p$, find which of the $q_i$'s is closer to $p$ in total variation. A remarkable result due to Yatracos shows that this problem is tractable in the following sense: there exists an algorithm that uses $O(\epsilon^{-2})$ samples from $p$ and outputs~$q_i$ such that with high probability, $TV(q_i,p) \leq 3\cdot\mathsf{opt} + \epsilon$, where $\mathsf{opt}= \min\{TV(q_1,p),TV(q_2,p)\}$. Moreover, this result extends to any finite class of densities $\mathcal{Q}$: there exists an algorithm that outputs the best density in $\mathcal{Q}$ up to a multiplicative approximation factor of 3. We complement and extend this result by showing that: (i) the factor 3 can not be improved if one restricts the algorithm to output a density from $\mathcal{Q}$, and (ii) if one allows the algorithm to output arbitrary densities (e.g. a mixture of densities from $\mathcal{Q}$), then the approximation factor can be reduced to 2, which is optimal. In particular this demonstrates an advantage of improper learning over proper in this setup. We develop two approaches to achieve the optimal approximation factor of 2: an adaptive one and a static one. Both approaches are based on a geometric point of view of the problem and rely on estimating surrogate metrics to the total variation. Our sample complexity bounds exploit techniques from {\it Adaptive Data Analysis}.
MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks

Neural networks are computational models that were originally inspired by biological neural networks like animal brains.These networks are composed of many small computational units called neurons that perform elementary calculations.Instead of explicitly programming the behavior of neural networks, these models can be trained to perform tasks, like classifying images, by presenting them examples.Sufficiently complex neural networks can automatically extract task-relevant characteristics from the presented examples without having prior knowledge about the task domain, which makes them attractive for many complicated real-world applications.Reversible operations have recently been successfully applied to classification problems to reduce memory requirements during neural network training.This feature is accomplished by removing the need to store the input activation for computing the gradients at the backward pass and instead reconstruct them on demand.However, current approaches rely on custom implementations of backpropagation, which limits applicability and extendibility.We present MemCNN, a novel PyTorch framework that simplifies the application of reversible functions by removing the need for a customized backpropagation.The framework contains a set of practical generalized tools, which can wrap common operations like convolutions and batch normalization and which take care of memory management.We validate the presented framework by reproducing state-of-the-art experiments using MemCNN and by comparing classification accuracy and training time on Cifar-10 and Cifar-100.Our MemCNN implementations achieved similar classification accuracy and faster training times while retaining compatibility with the default backpropagation facilities of PyTorch.
Evaluation of nurse’s performance in telemedicine

Describe the nurse's role in the Telemedicine Program in Cardiology implanted in Pernambuco, Brazil.Qualitative study, with a target audience of nurses, performed between July and December 2016 at the Emergency Care Units. Data were collected through an online instrument, consisting of open and closed questions, performed with 19 professionals. The data were analyzed through the discourse of the collective subject by QuantiQualisoftware.The sample consisted of 19 nurses, mostly female (80%), with a mean age of 30 years old. Two central ideas were constructed: nurses' knowledge about the Telemedicine Program in Cardiology; and actions developed by nurses. Also, a flow of the program's assistance was built.The nurse in Telecardiology performs functions of assistance and continuing education of monitoring and training for patients.
VersaTL: Versatile Transfer Learning for IMU-based Activity Recognition using Convolutional Neural Networks

The advent of Deep Learning has, together with massive gains in predictive accuracy, made it possible to reuse knowledge learnt from solving one problem in solving related problems. This is described as Transfer Learning, and has seen wide adoption especially in computer vision problems, where Convolutional Neural Networks have shown great flexibility and performance. On the other hand, transfer learning for sequences or timeseries data is typically made possible through the use of recurrent neural networks, which are difficult to train and prone to overfitting. In this work we present VersaTL, a novel approach to transfer learning for fixed and variable-length activity recognition timeseries data. We train a Convolutional Neural Network and use its convolutional filters as a feature extractor, then subsequently train a feedforward neural network as a classifier over the extracted features for other datasets. Our experiments on five different activity recognition datasets show the promise of this method, yielding results typically within 5% of trained-from-scratch networks while obtaining between a 24-52x reduction in the training time.
A Comparative Research on Human Activity Recognition Using Deep Learning

In recent years, action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction due to the widespread usage of various sensors. In this study, we aimed to develop an action recognition system that is intended to recognize human actions by using only accelerometer and gyroscope data. Various deep learning approaches like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and evaluated. A data augmentation method were applied while accuracy rates were increased noticeably.%98 accuracy rate obtained by using 3 layer LSTM network which means a solid contribution. Additionally, a realtime application was developed by using LSTM network.
A Literature Review of the Practice of Educating Children About Technology Making

Inspired by the Maker Movement and attempts of integrating Making into formal education of children, we have examined how practice-oriented the research on Making and education is in Child-Computer Interaction field. Our results show that despite the growing interest practice-orientation is still weak. Making efforts rarely aim for longitudinal durable patterns and practices even though practice-driven research has started to gain prominence in the whole Human-Computer Interaction field. General ideas of what Making integrated with education can be and should be seem to be well shared among practitioners, however. We demonstrate what aspects should be considered when doing practice-oriented Making research and point out gaps in our current understanding of the practice. We also provide guidelines for how to study Making in order to develop sustainable practices.
A Primal Dual Formulation For Deep Learning With Constraints

For several problems of interest, there are natural constraints which exist over the output label space. For example, for the joint task of NER and POS labeling, these constraints might specify that the NER label ‘organization’ is consistent only with the POS labels ‘noun’ and ‘preposition’. These constraints can be a great way of injecting prior knowledge into a deep learning model, thereby improving overall performance. In this paper, we present a constrained optimization formulation for training a deep network with a given set of hard constraints on output labels. Our novel approach first converts the label constraints into soft logic constraints over probability distributions outputted by the network. It then converts the constrained optimization problem into an alternating min-max optimization with Lagrangian variables defined for each constraint. Since the constraints are independent of the target labels, our framework easily generalizes to semi-supervised setting. We experiment on the tasks of Semantic Role Labeling (SRL), Named Entity Recognition (NER) tagging, and fine-grained entity typing and show that our constraints not only significantly reduce the number of constraint violations, but can also result in state-of-the-art performance
Feature Engineering for ICU Mortality Prediction Based on Hourly to Bi-Hourly Measurements

Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56 % , sensitivity of 90.59 % , precision of 86.52 % and F 1 -score of 88.50 % . The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs.
Undergraduate students use of mobile apps to search library catalogs

Purpose The purpose of this paper is to explore undergraduate students’ use of mobile apps to search library catalogs and analyze the problems students encountered. Student opinions and suggestions regarding mobile library apps were also examined. Design/methodology/approach A total of 16 undergraduates were observed and subjected to semi-structured interviews. Four tasks were assigned, and the participants used mobile apps and laptops to search library catalogs. Follow-up in-depth interviews were conducted to gather participants’ opinions regarding the mobile library app. Findings Among the 64 total searches conducted by the 16 participants, 45 (70.31 percent) were completed when using the mobile app, and 51 (79.69 percent) were completed using the laptop. Participants spent less time completing the tasks when they used the mobile app. Mobile apps are thus an effective tool for searching library catalogs. However, the interviews revealed that none of the participants had used mobile library apps prior to the interviews conducted in this study, and half of the participants expressed that they would not install library apps on their smartphones in the future. Originality/value The study findings may help university libraries improve their understanding about undergraduates’ search behavior and opinions about mobile library apps.
Constraints and potentials of remote sensing data/techniques applied to lithium (Li)-pegmatites

Research Article| September 30, 2019 Constraints and potentials of remote sensing data/techniques applied to lithium (Li)-pegmatites Joana Cardoso-Fernandes; Joana Cardoso-Fernandes § Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre, Porto, PortugalICT (Institute of Earth Sciences) – Porto pole (Portugal) § Corresponding author e-mail address: joana.fernandes@fc.up.pt Search for other works by this author on: GSW Google Scholar Alexandre Lima; Alexandre Lima Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre, Porto, PortugalICT (Institute of Earth Sciences) – Porto pole (Portugal) Search for other works by this author on: GSW Google Scholar Encarnación Roda-Robles; Encarnación Roda-Robles Departamento de Mineralogía y Petrología, Univ. País Vasco (UPV/EHU), Apdo. 644, 48080-Bilbao, Spain Search for other works by this author on: GSW Google Scholar Ana Cláudia Teodoro Ana Cláudia Teodoro Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre, Porto, PortugalICT (Institute of Earth Sciences) – Porto pole (Portugal) Search for other works by this author on: GSW Google Scholar The Canadian Mineralogist (2019) 57 (5): 723–725. https://doi.org/10.3749/canmin.AB00004 Article history first online: 07 Oct 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Joana Cardoso-Fernandes, Alexandre Lima, Encarnación Roda-Robles, Ana Cláudia Teodoro; Constraints and potentials of remote sensing data/techniques applied to lithium (Li)-pegmatites. The Canadian Mineralogist 2019;; 57 (5): 723–725. doi: https://doi.org/10.3749/canmin.AB00004 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyThe Canadian Mineralogist Search Advanced Search Lithium (Li) is a mineral commodity increasingly sought after mainly because it is essential to the production of rechargeable Li-ion batteries mainly used in high-tech consumer electronics (like smartphones) and in the green-power industry. This increasing demand of Li is mainly driven by the electric car industry, in the pursuit for more environment-friendly means of transportation, and it will result in an intensive Li exploitation in the near future. Consequently, there is great pressure for mining and exploration companies to achieve fast but accurate results in targeting potentially economic areas. New and untraditional Li exploration methods are needed in order... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
On the Expressive Power of Query Languages for Matrices

We investigate the expressive power of MATLANG, a formal language for matrix manipulation based on common matrix operations and linear algebra. The language can be extended with the operation inv for inverting a matrix. In MATLANG + inv, we can compute the transitive closure of directed graphs, whereas we show that this is not possible without inversion. Indeed, we show that the basic language can be simulated in the relational algebra with arithmetic operations, grouping, and summation. We also consider an operation eigen for diagonalizing a matrix. It is defined such that for each eigenvalue a set of mutually orthogonal eigenvectors is returned that span the eigenspace of that eigenvalue. We show that inv can be expressed in MATLANG + eigen. We put forward the open question whether there are Boolean queries about matrices, or generic queries about graphs, expressible in MATLANG + eigen but not in MATLANG + inv. Finally, the evaluation problem for MATLANG + eigen is shown to be complete for the complexity class ∃ R.
Contour-Aware Residual W-Net for Nuclei Segmentation

Nuclei segmentation is an important pre-processing step for any vision based cytopathological diagnostic system which extracts information from nuclei to perform tasks such as cancer detection. A cell nuclei segmentation pipeline should be robust, accurate and fast. We propose a deep learning based model, Contour-Aware Residual W-Net (WRC-Net), which consists of double U-Net, [5] or W-Net. The first U-Net learns to predict nuclei boundaries and the second generates the segmentation map. Our model can accurately segment a 128x128 dimensional image in less than 0.05s. Our model can learn from a very limited training data with as low as a single training image. We tested our model on real HE (Hematoxylin and Eosin) stained cell images and it showed better overall performance against previous state-of-the-art nuclei segmentation methods.
Convex Formulation of Overparameterized Deep Neural Networks

The analysis of over-parameterized neural networks 1 has drawn significant attention in recent years.It was shown 2 that such systems behave like convex systems under various 3 restricted settings, such as for two-layer neural networks, and 4 when learning is only restricted locally in the so-called neural 5 tangent kernel space around specialized initializations.However, 6 there is a lack of powerful theoretical techniques that can analyze 7 fully trained deep neural networks under general conditions.8 This paper considers this fundamental problem by investigat-9 ing such overparameterized deep neural networks when fully 10 trained.Specifically, we characterize a deep neural network by 11 its features' distributions and propose a metric to intuitively 12 measure the usefulness of feature representations.Under certain 13 regularizers that bounds the metric, we show deep neural 14 networks can be reformulated as a convex optimization and the 15 system can guarantee effective feature representations in terms 16 of the metric.Our new analysis is more consistent with empirical 17 observations that deep neural networks are capable of learning 18 efficient feature representations.Empirical studies confirm that 19 predictions of our theory are consistent with results observed in 20 practice.
Adversarial target-invariant representation learning for domain generalization

Supervised learning results typically rely on assumptions of i.i.d. data. Unfortunately, those assumptions are commonly violated in practice. In this work, we tackle this problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions). Our work relies on a simple lemma: by minimizing a notion of discrepancy between all pairs from a set of given domains, we also minimize the discrepancy between any pairs of mixtures of domains. Using this result, we derive a generalization bound for our setting. We then show that low risk over unseen domains can be achieved by representing the data in a space where (i) the training distributions are indistinguishable, and (ii) relevant information for the task at hand is preserved. Minimizing the terms in our bound yields an adversarial formulation which estimates and minimizes pairwise discrepancies. We validate our proposed strategy on standard domain generalization benchmarks, outperforming a number of recently introduced methods. Notably, we tackle a real-world application where the underlying data corresponds to multi-channel electroencephalography time series from different subjects, each considered as a distinct domain.
How to Accurately and Privately Identify Anomalies

Identifying anomalies in data is central to the advancement of science, national security, and finance. However, privacy concerns restrict our ability to analyze data. Can we lift these restrictions and accurately identify anomalies without hurting the privacy of those who contribute their data? We address this question for the most practically relevant case, where a record is considered anomalous relative to other records. We make four contributions. First, we introduce the notion of sensitive privacy, which conceptualizes what it means to privately identify anomalies. Sensitive privacy generalizes the important concept of differential privacy and is amenable to analysis. Importantly, sensitive privacy admits algorithmic constructions that provide strong and practically meaningful privacy and utility guarantees. Second, we show that differential privacy is inherently incapable of accurately and privately identifying anomalies; in this sense, our generalization is necessary. Third, we provide a general compiler that takes as input a differentially private mechanism (which has bad utility for anomaly identification) and transforms it into a sensitively private one. This compiler, which is mostly of theoretical importance, is shown to output a mechanism whose utility greatly improves over the utility of the input mechanism. As our fourth contribution we propose mechanisms for a popular definition of anomaly ((β,r)-anomaly) that (i) are guaranteed to be sensitively private, (ii) come with provable utility guarantees, and (iii) are empirically shown to have an overwhelmingly accurate performance over a range of datasets and evaluation criteria.