Reviewerly/exHarmony huggingface dataset:
Pure Differentially Private Summation from Anonymous Messages

The shuffled (aka anonymous) model has recently generated significant interest as a candidate distributed privacy framework with trust assumptions better than the central model but with achievable error rates smaller than the local model. In this paper, we study pure differentially private protocols in the shuffled model for summation, a very basic and widely used primitive. Specifically: - For the binary summation problem where each of n users holds a bit as an input, we give a pure e-differentially private protocol for estimating the number of ones held by the users up to an absolute error of O_{e}(1), and where each user sends O_{e}(log n) one-bit messages. This is the first pure protocol in the shuffled model with error o(√n) for constant values of e. Using our binary summation protocol as a building block, we give a pure e-differentially private protocol that performs summation of real numbers in [0, 1] up to an absolute error of O_{e}(1), and where each user sends O_{e}(log³ n) messages each consisting of O(log log n) bits.- In contrast, we show that for any pure e-differentially private protocol for binary summation in the shuffled model having absolute error n^{0.5-Ω(1)}, the per user communication has to be at least Ω_{e}(√{log n}) bits. This implies (i) the first separation between the (bounded-communication) multi-message shuffled model and the central model, and (ii) the first separation between pure and approximate differentially private protocols in the shuffled model. Interestingly, over the course of proving our lower bound, we have to consider (a generalization of) the following question that might be of independent interest: given γ ∈ (0, 1), what is the smallest positive integer m for which there exist two random variables X⁰ and X^1 supported on {0, … , m} such that (i) the total variation distance between X⁰ and X^1 is at least 1 - γ, and (ii) the moment generating functions of X⁰ and X^1 are within a constant factor of each other everywhere? We show that the answer to this question is m = Θ(√{log(1/γ)}).
Fast Traffic Sign Detection Approach Based on Lightweight Network and Multilayer Proposal Network

Vision-based traffic sign detection plays a crucial role in intelligent transportation systems. Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches. However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited. In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow. This paper proposes a deep learning-based approach to improve the performance of small traffic sign detection in driving environments. First, a lightweight and efficient architecture is adopted as the base network to address the issue of the inference speed. To enhance the performance on small traffic sign detection, a deconvolution module is adopted to generate an enhanced feature map by aggregating a lower-level feature map with a higher-level feature map. Then, two improved region proposal networks are used to generate proposals from the highest-level feature map and the enhanced feature map. The proposed improved region proposal network is designed for fast and accuracy proposal generation. In the experiments, the German Traffic Sign Detection Benchmark dataset is used to evaluate the effectiveness of each enhanced module, and the Tsinghua-Tencent 100K dataset is used to compare the effectiveness of the proposed approach with other state-of-the-art approaches on traffic sign detection. Experimental results on Tsinghua-Tencent 100K dataset show that the proposed approach achieves competitive performance compared with current state-of-the-art approaches on traffic sign detection while being faster and simpler.
Collusion Resistant Watermarkable PRFs from Standard Assumptions

A software watermarking scheme can embed a message into a program without significantly changing its functionality. Moreover, any attempt to remove the embedded message in a marked program will substantially change the functionality of the program. Prior constructions of watermarking schemes focus on watermarking cryptographic functions, such as pseudorandom function (PRF), public key encryption, etc. A natural security requirement for watermarking schemes is collusion resistance, where the adversary’s goal is to remove the embedded messages given multiple marked versions of the same program. Currently, this strong security guarantee has been achieved by watermarking schemes for public key cryptographic primitives from standard assumptions (Goyal et al., CRYPTO 2019) and by watermarking schemes for PRFs from indistinguishability obfuscation (Yang et al., ASIACRYPT 2019). However, no collusion resistant watermarking scheme for PRF from standard assumption is known. In this work, we solve this problem by presenting a generic construction that upgrades a watermarkable PRF without collusion resistance to a collusion resistant one. One appealing feature of our construction is that it can preserve the security properties of the original scheme. For example, if the original scheme has security with extraction queries, the new scheme is also secure with extraction queries. Besides, the new scheme can achieve unforgeability even if the original scheme does not provide this security property. Instantiating our construction with existing watermarking schemes for PRF, we obtain collusion resistant watermarkable PRFs from standard assumptions, offering various security properties.
DetReco: Object-Text Detection and Recognition Based on Deep Neural Network

Deep learning-based object detection method has been applied in various fields, such as ITS (intelligent transportation systems) and ADS (autonomous driving systems). Meanwhile, text detection and recognition in different scenes have also attracted much attention and research effort. In this article, we propose a new object-text detection and recognition method termed “DetReco” to detect objects and texts and recognize the text contents. The proposed method is composed of object-text detection network and text recognition network. YOLOv3 is used as the algorithm for the object-text detection task and CRNN is employed to deal with the text recognition task. We combine the datasets of general objects and texts together to train the networks. At test time, the detection network detects various objects in an image. Then, the text images are passed to the text recognition network to derive the text contents. The experiments show that the proposed method achieves 78.3 mAP (mean Average Precision) for general objects and 72.8 AP (Average Precision) for texts in regard to detection performance. Furthermore, the proposed method is able to detect and recognize affine transformed or occluded texts with robustness. In addition, for the texts detected around general objects, the text contents can be used as the identifier to distinguish the object.
Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

Background Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. Objective We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. Materials & methods We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. Results Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85. Discussion We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.
Pain intensity estimation based on a spatial transformation and attention CNN

Models designed to detect abnormalities that reflect disease from facial structures are an emerging area of research for automated facial analysis, which has important potential value in smart healthcare applications. However, most of the proposed models directly analyze the whole face image containing the background information, and rarely consider the effects of the background and different face regions on the analysis results. Therefore, in view of these effects, we propose an end-to-end attention network with spatial transformation to estimate different pain intensities. In the proposed method, the face image is first provided as input to a spatial transformation network for solving the problem of background interference; then, the attention mechanism is used to adaptively adjust the weights of different face regions of the transformed face image; finally, a convolutional neural network (CNN) containing a Softmax function is utilized to classify the pain levels. The extensive experiments and analysis are conducted on the benchmarking and publicly available database, namely the UNBC-McMaster shoulder pain. More specifically, in order to verify the superiority of our proposed method, the comparisons with the basic CNNs and the-state-of-the-arts are performed, respectively. The experiments show that the introduced spatial transformation and attention mechanism in our method can significantly improve the estimation performances and outperform the-state-of-the-arts.
Pyjamask: Block Cipher and Authenticated Encryption with Highly Efficient Masked Implementation

This paper introduces Pyjamask, a new block cipher family and authenticated encryption proposal submitted to the NIST lightweight cryptography standardization process. Pyjamask targets side-channel resistance as one of its main goal. More precisely, it strongly minimizes the number of nonlinear gates used in its internal primitive in order to allow efficient masked implementations, especially for high-order masking in software. Compared to other block ciphers, our proposal has thus among the smallest number of binary AND computations per input bit at the time of writing. Even though Pyjamask minimizes such an important criterion, it remains rather lightweight and efficient, thanks to a general bitslice construction that enables to computation of all nonlinear gates in parallel. For authenticated encryption, we adopt the provably secure AEAD mode OCB which has been extensively studied and has the benefit to offer full parallelization. Of course, other block cipher-based modes can be considered as well if other performance profiles are to be targeted.The paper first gives the specification of the Pyjamask block cipher and the associated AEAD proposal. We also provide a detailed design rationale for the block cipher which is guided by our aim of software efficiency in the presence of high-order masking. The security of the design is analyzed against most commonly known cryptanalysis techniques. We finally describe efficient (masked) implementations in software and provide implementation results with aggressive performances for masking of very high orders (up to 128). We also provide a rough estimation of the hardware performances which remain much better than those of an AES round-based implementation.
Enseñanza virtual en educación física en primaria en México y la pandemia por COVID-19

The theoretical review addresses the virtual physical education established in Mexico based on social confine-ment by COVID-19, based on the problem, already existing in Mexico, of sedentary lifestyle and childhood obesi-ty in primary school children, in whom, derived from social confinement, an increase in body mass is estimated, which carries greater health risks. When analyzing the recent bibliography, which relates COVID-19 with physi-cal education and physical activity, the opportunity for physical education teachers to conduct virtual schemes in the contents of the educational program is identified, in this way, to contribute to the decrease in sedentary lifestyle in primary school students, prioritizing physical literacy.
Improved Hybrid Approach for Side-Channel Analysis Using Efficient Convolutional Neural Network and Dimensionality Reduction

Deep learning-based side channel attacks are burgeoning due to their better efficiency and performance, suppressing the traditional side-channel analysis. To launch the successful attack on a particular public key cryptographic (PKC) algorithm, a large number of samples per trace might need to be acquired to capture all the minor useful details from the leakage information, which increases the number of features per instance. The decreased instance-feature ratio increases the computational complexity of the deep learning-based attacks, limiting the attack efficiency. Moreover, data class imbalance can be a hindrance in accurate model training, leading to an accuracy paradox. We propose an efficient Convolutional Neural Network (CNN) based approach in which the dimensionality of the large leakage dataset is reduced, and then the data is processed using the proposed CNN based model. In the proposed model, the optimal number of convolutional blocks is used to build powerful features extractors within the cost limit. We have also analyzed and presented the impact of using the Synthetic Minority Over-sampling Technique (SMOTE) on the proposed model performance. We propose that a data-balancing step should be mandatory for analysis in the side channel attack scenario. We have also provided a performance-based comparative analysis between proposed and existing deep learning models for unprotected and protected Elliptic curve (ECC) Montgomery Power ladder implementations. The reduced network complexity, together with an improved attack efficiency, promote the proposed approach to be effectively used for side-channel attacks.
Investigating the Adoption of Hybrid Encrypted Cloud Data Deduplication With Game Theory

Encrypted data deduplication, along with different preferences in data access control, brings the birth of hybrid encrypted cloud data deduplication (H-DEDU for short). However, whether H-DEDU can be successfully deployed in practice has not been seriously investigated. Obviously, the adoption of H-DEDU depends on whether it can bring economic benefits to all stakeholders. But existing economic models of cloud storage fail to support H-DEDU due to complicated interactions among stakeholders. In this article, we establish a formal economic model of H-DEDU by formulating the utilities of all involved stakeholders, i.e., data holders, data owners, and Cloud Storage Providers (CSPs). Then, we construct a multi-stage Stackelberg game, which consists of Holder Participation Game, Owner Online Game, and CSP Pricing Game, to capture the interactions among all system stakeholders. We further analyze the conditions of the existence of a sub-game perfect Nash Equilibrium and propose a gradient-based algorithm to help the stakeholders choose near-optimal strategies. Extensive experiments show the feasibility of the proposed algorithm in achieving the Nash Equilibrium of the Stackelberg game. Additionally, we investigate the effects of parameters related to CSP, data owners and data holders on H-DEDU adoption. Our study advises all stakeholders the best strategies to adopt H-DEDU.
The Genomic Observatories Metadatabase

Molecular Ecology ResourcesVolume 20, Issue 6 p. 1453-1454 EDITORIAL The Genomic Observatories Metadatabase Benjamin Sibbett, Corresponding Author Benjamin Sibbett bsibbett@wiley.com orcid.org/0000-0001-9547-1355 John Wiley & Sons, Chichester, West Sussex, UKSearch for more papers by this authorLoren H. Rieseberg, Loren H. Rieseberg orcid.org/0000-0002-2712-2417 Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, CanadaSearch for more papers by this authorShawn Narum, Shawn Narum Hagerman Genetics Laboratory, Columbia River Inter-Tribal Fish Commission, Hagerman, ID, USASearch for more papers by this author Benjamin Sibbett, Corresponding Author Benjamin Sibbett bsibbett@wiley.com orcid.org/0000-0001-9547-1355 John Wiley & Sons, Chichester, West Sussex, UKSearch for more papers by this authorLoren H. Rieseberg, Loren H. Rieseberg orcid.org/0000-0002-2712-2417 Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, CanadaSearch for more papers by this authorShawn Narum, Shawn Narum Hagerman Genetics Laboratory, Columbia River Inter-Tribal Fish Commission, Hagerman, ID, USASearch for more papers by this author First published: 15 October 2020 https://doi.org/10.1111/1755-0998.13283Citations: 8Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume20, Issue6November 2020Pages 1453-1454 RelatedInformation
"The Personal is Political"

Initially conceived as problem-focused programming events, hackathons have expanded to encompass a range of issue areas, stakeholders and activities. There have been important critiques of hackathons in relation to their format and structure, their epistemological assumptions, and their outputs and impacts. Scholars working in Feminist HCI have proposed design considerations for more inclusive hackathons that focus on social justice outcomes for marginalized groups. Evaluative work on hackathons has assessed entrepreneurial contributions, skill development, and affective impacts, but largely absent from the analysis is a view of long-term personal impacts on participants. What kinds of lasting impacts (if any) do issue-focused hackathons have on participants themselves? In this paper, we describe a post-hoc qualitative study with participants and organizers of a postpartum health hackathon in the U.S., one year after the event took place. Our goals were to understand people's motivations for participating, what impact (if any) their participation had on their lives, and how (if at all) their participation shaped how they now understand postpartum health. Our findings indicate that the hackathon functioned as a space of "feminist consciousness raising" in that it provided space for navigating and sharing personal experiences, contextualizing and connecting those experiences to structural oppression, and developing participants' self- and collective-efficacy to create design interventions and enact social change. Feminist consciousness raising is not just "awareness-raising", but rather a specific historic and contemporary practice which we describe and situate in relation to personal experiences of oppression around stigmatized topics. With these findings, we situate feminist consciousness raising in relation to the literature on hackathons and feminist HCI, speculate which aspects of the design of the event led to it fostering feminist consciousness raising, and generate recommendations for how to intentionally bring feminist consciousness raising to the design of hackathons and innovation events.
Multimodal Semi-Supervised Learning Framework for Punctuation Prediction in Conversational Speech

In this work, we explore a multimodal semi-supervised learning approach for punctuation prediction by learning representations from large amounts of unlabelled audio and text data. Conventional approaches in speech processing typically use forced alignment to encoder per frame acoustic features to word level features and perform multimodal fusion of the resulting acoustic and lexical representations. As an alternative, we explore attention based multimodal fusion and compare its performance with forced alignment based fusion. Experiments conducted on the Fisher corpus show that our proposed approach achieves ~6-9% and ~3-4% absolute improvement (F1 score) over the baseline BLSTM model on reference transcripts and ASR outputs respectively. We further improve the model robustness to ASR errors by performing data augmentation with N-best lists which achieves up to an additional ~2-6% improvement on ASR outputs. We also demonstrate the effectiveness of semi-supervised learning approach by performing ablation study on various sizes of the corpus. When trained on 1 hour of speech and text data, the proposed model achieved ~9-18% absolute improvement over baseline model.
Vulnerabilities of Connectionist AI Applications: Evaluation and Defense

This article deals with the IT security of connectionist artificial intelligence (AI) applications, focusing on threats to integrity, one of the three IT security goals. Such threats are for instance most relevant in prominent AI computer vision applications. In order to present a holistic view on the IT security goal integrity, many additional aspects, such as interpretability, robustness and documentation are taken into account. A comprehensive list of threats and possible mitigations is presented by reviewing the state-of-the-art literature. AI-specific vulnerabilities, such as adversarial attacks and poisoning attacks are discussed in detail, together with key factors underlying them. Additionally and in contrast to former reviews, the whole AI life cycle is analyzed with respect to vulnerabilities, including the planning, data acquisition, training, evaluation and operation phases. The discussion of mitigations is likewise not restricted to the level of the AI system itself but rather advocates viewing AI systems in the context of their life cycles and their embeddings in larger IT infrastructures and hardware devices. Based on this and the observation that adaptive attackers may circumvent any single published AI-specific defense to date, the article concludes that single protective measures are not sufficient but rather multiple measures on different levels have to be combined to achieve a minimum level of IT security for AI applications.