Reviewerly/exHarmony huggingface dataset:
Advancing the AmbientGAN for learning stochastic object models

Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks. Variation in the objects to-be-imaged is an important source of variability that can significantly limit observer performance. This object variability can be described by stochastic object models (SOMs). In order to establish SOMs that can accurately model realistic object variability, it is desirable to use experimental data. To achieve this, an augmented generative adversarial network (GAN) architecture called AmbientGAN has been developed and investigated. However, AmbientGANs cannot be immediately trained by use of advanced GAN training methods such as the progressive growing of GANs (ProGANs). Therefore, the ability of AmbientGANs to establish realistic object models is limited. To circumvent this, a progressively-growing AmbientGAN (ProAmGAN) has been proposed. However, ProAmGANs are designed for generating two-dimensional (2D) images while medical imaging modalities are commonly employed for imaging three-dimensional (3D) objects. Moreover, ProAmGANs that employ traditional generator architectures lack the ability to control specific image features such as fine-scale textures that are frequently considered when optimizing imaging systems. In this study, we address these limitations by proposing two advanced AmbientGAN architectures: 3D ProAmGANs and Style-AmbientGANs (StyAmGANs). Stylized numerical studies involving magnetic resonance (MR) imaging systems are conducted. The ability of 3D ProAmGANs to learn 3D SOMs from imaging measurements and the ability of StyAmGANs to control fine-scale textures of synthesized objects are demonstrated.
A cloud-based centerline algorithm for Studierfenster

A practical method to analyze blood vessels, like the aorta, is to calculate the vessel's centerline and evaluate its shape in a CT or CTA scan. This contribution introduces a cloud-based centerline tool for the aorta, which computes an initial centerline from a CTA scan with two user given seed points. Afterwards, this initial centerline can be smoothed in a second step. The work done for this contribution was implemented into an existing online tool for medical image analysis, called Studierfenster. In order to evaluate the outcome of this contribution, we tested the smoothed centerline computed within Studierfenster against 40 baseline centerlines from a public available CTA challenge dataset. In doing so, we computed a minimum, maximum, and mean distance between the two centerlines in mm for every data sample, resulting in the smallest distance of 0.59mm, an overall maximum distance of 14.18mm, and a mean distance for all samples of 3.86mm with a standard deviation of 0.99mm.
End-to-End Egospheric Spatial Memory

Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments. However, most existing artificial memory modules have difficulty recalling information over long time periods and are not very adept at storing spatial information. We propose a parameter-free module, Egospheric Spatial Memory (ESM), which encodes the memory in an ego-sphere around the agent, enabling expressive 3D representations. ESM can be trained end-to-end via either imitation or reinforcement learning, and improves both training efficiency and final performance against other memory baselines on both drone and manipulator visuomotor control tasks. The explicit egocentric geometry also enables us to seamlessly combine the learned controller with other non-learned modalities, such as local obstacle avoidance. We further show applications to semantic segmentation on the ScanNet dataset, where ESM naturally combines image-level and map-level inference modalities. Through our broad set of experiments, we show that ESM provides a general computation graph for embodied spatial reasoning, and the module forms a bridge between real-time mapping systems and differentiable memory architectures.
MicrothingsChain: Blockchain-based Controlled Data Sharing Platform in Multi-domain IoT

The vision of the Internet of Things (IoT) is creating an open and shared world connected to everything, making people's lives more smart, convenient, and efficient.Currently, there are various isolated IoT applications developed by different IoT manufacturers for a single specific function.The IoT nodes are classified into different administrative domains according to their affiliation, resulting in a multi-domain IoT environment.In order to realize the true IoT vision with multi-domain data sharing, this paper introduces a multi-domain IoT data sharing architecture based on blockchain to address the current phenomenon of information isolation.A control approach including capability-based cross-domain access control and risk-based access control in a domain is proposed to ensure the security of full data sharing processes.With these properties, diverse isolated applications can constitute a whole system for secure data exchange.
The Design and Assignment of Microgestures to Commands for Virtual and Augmented Reality Tasks

Researchers have designed 3D (non-contacting) gestures for human-computer interaction (HCI) for desktop computers, automobiles, and augmented and virtual reality (AR/VR) to improve functionality, and experience. 3D gestures provide a method for HCI in clean and dirty environments and settings where both hands need to be free for other tasks such as warehouse work. However, long-term use of 3D gestures may pose a risk of musculoskeletal fatigue especially if the gestures require movement at larger joints such as the shoulder or elbow. Microgestures involve motions of just the fingers and hands thereby reducing muscle fatigue and distraction. Current microgesture lexicons are not well designed for VR or AR systems because of differences in interactive requirements of the commands and context for their use. The purpose of this study was to design microgestures for AR and VR that follow user experiences with existing gestures while considering application, user preference and hand/wrist postures.
Combining Forensics and Privacy Requirements for Digital Images

This paper proposes to study the impact of image selective encryption on both forensics and privacy preserving mechanisms. The proposed selective encryption scheme works independently on each bitplane by encrypting the s most significant bits of each pixel. We show that this mechanism can be used to increase privacy by mitigating image recognition tasks. In order to guarantee a trade-off between forensics analysis and privacy, the signal of interest used for forensics purposes is extracted from the 8--s least significant bits of the protected image. We show on the CASIA2 database that good tampering detection capabilities can be achieved for s $\in$ {3,. .. , 5} with an accuracy above 80% using SRMQ1 features, while preventing class recognition tasks using CNN with an accuracy smaller than 50%.
Doubly Stochastic Generative Arrivals Modeling

We propose a new framework named DS-WGAN that integrates the doubly stochastic (DS) structure and the Wasserstein generative adversarial networks (WGAN) to model, estimate, and simulate a wide class of arrival processes with general non-stationary and random arrival rates. Regarding statistical properties, we prove consistency and convergence rate for the estimator solved by the DS-WGAN framework under a non-parametric smoothness condition. Regarding computational efficiency and tractability, we address a challenge in gradient evaluation and model estimation, arised from the discontinuity in the simulator. We then show that the DS-WGAN framework can conveniently facilitate what-if simulation and predictive simulation for future scenarios that are different from the history. Numerical experiments with synthetic and real data sets are implemented to demonstrate the performance of DS-WGAN. The performance is measured from both a statistical perspective and an operational performance evaluation perspective. Numerical experiments suggest that, in terms of performance, the successful model estimation for DS-WGAN only requires a moderate size of representative data, which can be appealing in many contexts of operational management.
Enseñanza y aprendizaje virtual en contexto de pandemia. Experiencias y vivencias de docentes y estudiantes de la Facultad de Psicología en el primer semestre del año 2020

The pandemic caused by SARS-CoV-2 impacted on the way in which all activities of our daily lives were carried out. In Uruguay, since March 13th, when the first cases were confirmed, a state of health emergency was declared, with the educational system being one of the most radically affected aspects. The Faculty of Psychology at Universidad de la Republica, as an educational community, was able to cope with adversity and achieve, in a very short period of time, a real response in order to maintain the delivery of the courses in a virtual way. Once the first semester is over, it is vital to know how the new reality was lived and experienced by students and teachers. This study analyzes the experiences of teachers and students at the Faculty of Psychology. The data was obtained through online forms designed by ProREn (Teaching Renewal Program) and applied from July 8th to 22nd, 2020. The sample of participants was of 693 students and 55 teachers. Descriptive analyses of common dimensions to both questionnaires were carried out. The general results show that the abrupt passage to virtual teaching was properly achieved with good outcomes. However, some dimensions of the experiences of students and teachers during this transition warn us about the most vulnerable aspects of each population.
A Systematic Design Methodology of Formally Proven Side-Channel-Resistant Cryptographic Hardware

This article proposes a formal design system for automatically generating provably secure register transfer level description of cryptographic hardware based on generalized masking scheme. -Rosario Cammarota, Intel Labs -Francesco Regazzoni, University of Amsterdam and Università della Svizzera Italiana
Noisy Truncated SGD: Optimization and Generalization

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Noisy Truncated SGD: Optimization and GeneralizationYingxue Zhou, Xinyan Li, and Arindam BanerjeeYingxue Zhou, Xinyan Li, and Arindam Banerjeepp.468 - 476Chapter DOI:https://doi.org/10.1137/1.9781611977172.53PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Recent empirical work on stochastic gradient descent (SGD) applied to over-parameterized deep learning has shown that most gradient components over epochs are quite small. Inspired by such observations, we rigorously study properties of Truncated SGD (T-SGD), that truncates the majority of small gradient components to zeros. Considering non-convex optimization problems, we show that the convergence rate of T-SGD matches the order of vanilla SGD. We also establish the generalization error bound for T-SGD. Further, we propose Noisy Truncated SGD (NT-SGD), which adds Gaussian noise to the truncated gradients. We prove that NT-SGD has the same convergence rate as T-SGD for non-convex optimization problems. We demonstrate that with the help of noise, NT-SGD can provably escape from saddle points and requires less noise compared to previous related work. We also prove that NT-SGD achieves better generalization error bound compared to T-SGD because of the noise. Our generalization analysis is based on uniform stability and we show that additional noise in the gradient update can boost the stability. Our experiments on a variety of benchmark datasets with various network architectures verify that NT-SGD matches the speed and accuracy of vanilla SGD while effectively working with sparse gradients, and can successfully escape poor local minima. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Hidden semi-Markov models to segment reading phases from eye movements

Our objective is to analyze scanpaths acquired through participants achieving a reading task aiming at answering a binary question: Is the text related or not to some given target topic? We propose a data-driven method based on hidden semi-Markov chains to segment scanpaths into phases deduced from the model states, which are shown to represent different cognitive strategies: normal reading, fast reading, information search, and slow confirmation. These phases were confirmed using different external covariates, among which semantic information extracted from texts. Analyses highlighted some strong preference of specific participants for specific strategies and more globally, large individual variability in eye-movement characteristics, as accounted for by random effects. As a perspective, the possibility of improving reading models by accounting for possible heterogeneity sources during reading is discussed.
The corona crisis, data protection and tracking apps in the EU

The sudden outbreak of COVID-19 put governments under pressure toswiftly introduce measures to protect citizens and react appropriately to theemerging threat. This paper focuses on geo-location tracking mobile phoneapplications developed in the Czech Republic and Austria to monitorpersonal movement of those positively tested for COVID-19 to prevent thefurther spread of the disease. The aim of the analysis is to answer thequestion of whether the appsʼ functionalities complied with the EU dataprotection standards and to what extent the citizensʼ right to control thecollection, evaluation and preservation of their personal data has beenviolated. Both countries belonged to the pioneers in COVID-19 apps. Whilethey differed in several areas such as the legal and political circumstancesunder which the apps were developed and public communication,similarities between them were identified in the area of public trust in theapps and their utilisation. In both countries, certain illiberal issues wererecognised as well.
INTERVENÇÕES DE ENFERMAGEM EM IDOSOS DEPRESSIVOS: UMA REVISÃO DA LITERATURA/NURSING INTERVENTIONS IN DEPRESSED ELDERLY: A LITERATURE REVIEW

part of public health, as this period of life brings with it several changes and challenges and nursing has an indispensable role in this phase of life.This study aims to search the literature for the main nursing interventions in elderly patients with depressive conditions.It is important to emphasize that the intervention of nurses in the depressive phase of the elderly makes a difference, it is through this interaction that the evolution happens in the lives of the elderly, depression is a disease that affects many people regardless of age, but the elderly have an index greater to develop this pathology.The present study consists of a systematic review of the literature, which is a study based on a review of other researches already carried out by other authors and which address the same line of research or subject.Articles were used for all theoretical bases and preparation of the current study.According to the literature, it can be concluded that the elderly population has grown on a large scale and the cases of depression in this age group too, however nursing has made a big difference with their interventions in a direct and indirect way, providing health and well-being for these elderly people, they act preventively and during the onset of the disease, coping with depression is not easy and a third person needs support.
Precise Multi-Neuron Abstractions for Neural Network Certification.

Formal verification of neural networks is critical for their safe adoption in real-world applications. However, designing a verifier which can handle realistic networks in a precise manner remains an open and difficult challenge. In this paper, we take a major step in addressing this challenge and present a new framework, called PRIMA, that computes precise convex approximations of arbitrary non-linear activations. PRIMA is based on novel approximation algorithms that compute the convex hull of polytopes, leveraging concepts from computational geometry. The algorithms have polynomial complexity, yield fewer constraints, and minimize precision loss. We evaluate the effectiveness of PRIMA on challenging neural networks with ReLU, Sigmoid, and Tanh activations. Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness for up to 16%, 30%, and 34% more images than prior work on ReLU-, Sigmoid-, and Tanh-based networks, respectively.
Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.
'Shiny' Crypto Assets: A Systemic Look at Gold-Backed Cryptocurrencies during the COVID-19 Pandemic

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Survey on Software Changes: Reasons and Remedies

Software systems play a key role in most businesses nowadays. Building robust, reliable and scalable software systems require going through a software production cycle (or process). However, it has been noticed that software systems are subjected to changes, whether those amendments are important or not. Those changes to software systems are viewed as a considerable issue in software engineering; they are considered as a burden and cost a lot, especially in cases such as enterprises and large-scale software systems. This study aims to identify the reasons that cause software changes and suggest remedies for those reasons. We survey the opinions of experts such as technical managers, team leaders, and senior developers. We collected 81 responses to our questionnaire, which aimed to establish common software development practices in the local industry. We also conducted 16 semi-structured interviews targeting the most senior experts, in which we directly discussed the reasons and remedies for software changes. Our results highlight the most influential reasons that cause changes to software systems, such as changes to user requirements, requests for new features, software development methodology, solving bugs, refactoring, and weak user experience design. Most importantly, the study solicited solutions that can reduce the need for software changes
Educación a distancia en tiempos de COVID-19 en la carrera de Enseñanza Básica de la Universidad Técnica de Machala

Objective: The paper aims at assessing the teaching-learning process outcomes of the Technical University of Machala Basic Education major in times of COVID-19.Methods: The descriptive study follows a quantitative-qualitative paradigm, systematized through hermeneutical methods, statistical and content analysis, and the survey technique.Results: The effectiveness of the virtual learning environment implemented is mediated by endogenous factors, such as organizing content and activities limitations, and exogenous factors such as, the limited technological readiness of tutors, as well as low use cooperative work in virtual classroom tool potentials, and the lack of availability of some students in using technological equipment and Internet connectivity.Conclusions: The researcher arrived at the conclusion that Basic Education major professors need to develop skills in managing virtual environments and active methodologies, and thus take advantage of both, the benefits offered by the platforms selected by the university for the teaching process, and the students’ digital born skills. Likewise, radio and television will be needed to meet the needs of the total number of students.
Sarkasme pada Media Sosial Twitter dan Implikasinya Terhadap Pembelajaran Bahasa Indonesia di SMA

This research is a sarcasm on Twitter social media. The purpose of this study is to describe the form of sarcasm on Twitter social media and to describe the implications of learning Indonesian in high school. The approach used in this research is to use a qualitative approach, the method used is the equivalent method. The data is obtained by reading the social media account comments column @ fadlizon then recording words and sentences containing the style of sarcasm and analyzed. The results of this study use the Elizabeth Camp theory there are 4 types of sarcasm, namely designation sarcasm, lexical sarcasm, sarcasm and illocutionary sarcasm by finding 24 data and then implicated in Indonesian language learning in high school class X semester II curriculum 2013. Keywords: Sarcasm, twitter, Indonesian language learning.
Faculty Survey on the Distance Learning of Engineering Education during the COVID-19

This work-in-progress paper describes a project of a faculty survey at our university about distance learning. The purpose is to assure the quality of distance learning. Therefore, we adopted a questionnaire survey for faculty members in charge of classes. From the survey results, the attendance rate and the degree of understanding of distance learning were equitable or more to those of face-to-face learning. Faculty members also considered the communication environment and students with handicapped. Although there are some concerns, it makes clear that distance learning is conducted smoothly.
Arbitrating Disputes Involving Blockchains, Smart Contracts, and Smart Legal Contracts

Blockchain-based distributed ledgers (“Blockchain Ledgers”) provide an immutable, secure, and tamper evident alternative to conventional transactional modalities, one which also yields enhanced accountability, traceability, and transparency. The inherent benefits, and hence growing adoption of, Blockchain Ledgers, Smart Contracts, and quite recently Smart Legal Contracts (the latter two being built on blockchains), across a wide range of the economy, has caused and is now accelerating a fundamental paradigm shift that, in certain sectors of society, is increasingly displacing traditional written and oral contracts in favor of automatically executing blockchain-implemented agreements. This paper offers a short historical background of the societal need underlying blockchains; describes, at a high level, the technological basis of blockchains, smart agreements and smart legal agreements; addresses various legal issues and disputes that are likely to arise; and discusses the use and advantages of arbitration, as the only viable approach, for resolving those disputes.