Reviewerly/exHarmony huggingface dataset:
Oxide: The Essence of Rust

Rust claims to advance industrial programming by bridging the gap between low-level systems programming and high-level application programming. At the heart of the argument that this enables programmers to build more reliable and efficient software is the borrow checker - a novel approach to ownership that aims to balance type system expressivity with usability. And yet, to date there is no core type system that captures Rust's notion of ownership and borrowing, and hence no foundation for research on Rust to build upon. In this work, we set out to capture the essence of this model of ownership by developing a type systems account of Rust's borrow checker. We present Oxide, a formalized programming language close to source-level Rust (but with fully-annotated types). This presentation takes a new view of lifetimes as an approximation of the provenances of references, and our type system is able to automatically compute this information through a substructural typing judgment. We provide the first syntactic proof of type safety for borrow checking using progress and preservation. Oxide is a simpler formulation of borrow checking - including recent features such as non-lexical lifetimes - that we hope researchers will be able to use as the basis for work on Rust.
Contact Tracing: An Overview of Technologies and Cyber Risks

The 2020 COVID-19 pandemic has led to a global lockdown with severe health and economical consequences. As a result, authorities around the globe have expressed their needs for better tools to monitor the spread of the virus and to support human labor. Researchers and technology companies such as Google and Apple have offered to develop such tools in the form of contact tracing applications. The goal of these applications is to continuously track people's proximity and to make the smartphone users aware if they have ever been in contact with positively diagnosed people, so that they could self-quarantine and possibly have an infection test. A fundamental challenge with these smartphone-based contact tracing technologies is to ensure the security and privacy of their users. Moving from manual to smartphone-based contact tracing creates new cyber risks that could suddenly affect the entire population. Major risks include for example the abuse of the people's private data by companies and/or authorities, or the spreading of wrong alerts by malicious users in order to force individuals to go into quarantine. In April 2020, the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) was announced with the goal to develop and evaluate secure solutions for European countries. However, after a while, several team members left this consortium and created DP-3T which has led to an international debate among the experts. At this time, it is confusing for the non-expert to follow this debate; this report aims to shed light on the various proposed technologies by providing an objective assessment of the cybersecurity and privacy risks. We first review the state-of-the-art in digital contact tracing technologies and then explore the risk-utility trade-offs of the techniques proposed for COVID-19. We focus specifically on the technologies that are already adopted by certain countries.
La nueva educación o la educación apoyada en la tecnología por todas partes

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Semi-supervised Transfer Learning for Evaluation of Model Classification Performance

In modern machine learning applications, frequent encounters of covariate shift and label scarcity have posed challenges to robust model training and evaluation. Numerous transfer learning methods have been developed to robustly adapt the model itself to some unlabeled target populations using existing labeled data in a source population. However, there is a paucity of literature on transferring performance metrics of a trained model. In this paper, we aim to evaluate the performance of a trained binary classifier on unlabeled target population based on receiver operating characteristic (ROC) analysis. We proposed $\bf S$emi-supervised $\bf T$ransfer l$\bf E$arning of $\bf A$ccuracy $\bf M$easures (STEAM), an efficient three-step estimation procedure that employs 1) double-index modeling to construct calibrated density ratio weights and 2) robust imputation to leverage the large amount of unlabeled data to improve estimation efficiency. We establish the consistency and asymptotic normality of the proposed estimators under correct specification of either the density ratio model or the outcome model. We also correct for potential overfitting bias in the estimators in finite samples with cross-validation. We compare our proposed estimators to existing methods and show reductions in bias and gains in efficiency through simulations. We illustrate the practical utility of the proposed method on evaluating prediction performance of a phenotyping model for Rheumatoid Arthritis (RA) on a temporally evolving EHR cohort.
HALLENGES IN THE CURRENT EDUCATION SYSTEM THE UNSEEN PROBLEMS

This article showcases the current unseen challenges in the current education system. The unseen problems that can only be felt and not seen. The article also focuses a light on the education levels and shortcomings of the literacy rate for major growing economies. Researches of various authors in the past has been taken into consideration in the literature. The data has been studied accordingly to analyze the results based on previous researches and according to the same a short suggestion is prepared. The articles aim is to bring to light the unseen problems in the current education system and the challenges parents, Teachers, students and the educational institutions face accordingly.
Single UHD Image Dehazing via Interpretable Pyramid Network

Currently, most single image dehazing models cannot run an ultra-high-resolution (UHD) image with a single GPU shader in real-time. To address the problem, we introduce the principle of infinite approximation of Taylor's theorem with the Laplace pyramid pattern to build a model which is capable of handling 4K hazy images in real-time. The N branch networks of the pyramid network correspond to the N constraint terms in Taylor's theorem. Low-order polynomials reconstruct the low-frequency information of the image (e.g. color, illumination). High-order polynomials regress the high-frequency information of the image (e.g. texture). In addition, we propose a Tucker reconstruction-based regularization term that acts on each branch network of the pyramid model. It further constrains the generation of anomalous signals in the feature space. Extensive experimental results demonstrate that our approach can not only run 4K images with haze in real-time on a single GPU (80FPS) but also has unparalleled interpretability. The developed method achieves state-of-the-art (SOTA) performance on two benchmarks (O/I-HAZE) and our updated 4KID dataset while providing the reliable groundwork for subsequent optimization schemes.
Measurement-Device-Independent Quantum Key Distribution of Frequency-Nondegenerate Photons

Measurement-device-independent quantum key distribution (MDI-QKD) of frequency-nondegenerate photons was proposed and demonstrated based on a frequency-domain beam splitter, which simplified the implementation of MDI-QKD and provided network functions on the relay node.
THE ROLE OF INTUITION IN THE STRUCTURE OF SELF-REGULATION IN CONDITIONS OF UNCERTAINTY

The article updates the issues of research on the topic of intuition, emotional intelligence and self-regulation in the context of decision-making under conditions of uncertainty, focusing on the limitations of modern scientific assets on this topic, which to a greater extent reflect the nature of the confrontation between rational and intuitive approaches. Instead, this article presents a view of the modern world оf BANI, which requires a modern man to be flexible and have a developed ability to adapt. The article reflects the empirical results that demonstrate the ambivalent nature of the manifestation of indicators of the intuitive-rational approach, emphasizing the urgent need for further development of modern research with the involvement of a wider sample, and changing the conditions of the experiment. Moreover, this paper describes theoretical components of the decision-making process, which demonstrate the presence of stress and uncertainty in the process of making even everyday decisions. The concept of subjective and objective uncertainty is revealed and their possible influence on our life and the decision-making process itself is highlighted. The characteristic influence of uncertainty on the self-regulation of the individual from the point of view of a modern war is also described. The absence of a modern conceptual apparatus that could clearly outline the concept of intuition and processes that can be called intuitive is emphasized. The results of the research of modern scientific schools demonstrate the tendency of individuals to an intuitive approach when making decisions in natural conditions. Theoretical assets demonstrating the connection between intuition, affective states and emotions, which reflect the main principles of information distortion in the decision-making process, are also highlighted.
Recurrent networks, hidden states and beliefs in partially observable environments

Reinforcement learning aims to learn optimal policies from interaction with environments whose dynamics are unknown. Many methods rely on the approximation of a value function to derive near-optimal policies. In partially observable environments, these functions depend on the complete sequence of observations and past actions, called the history. In this work, we show empirically that recurrent neural networks trained to approximate such value functions internally filter the posterior probability distribution of the current state given the history, called the belief. More precisely, we show that, as a recurrent neural network learns the Q-function, its hidden states become more and more correlated with the beliefs of state variables that are relevant to optimal control. This correlation is measured through their mutual information. In addition, we show that the expected return of an agent increases with the ability of its recurrent architecture to reach a high mutual information between its hidden states and the beliefs. Finally, we show that the mutual information between the hidden states and the beliefs of variables that are irrelevant for optimal control decreases through the learning process. In summary, this work shows that in its hidden states, a recurrent neural network approximating the Q-function of a partially observable environment reproduces a sufficient statistic from the history that is correlated to the relevant part of the belief for taking optimal actions.
Random Search Hyper-Parameter Tuning: Expected Improvement Estimation and the Corresponding Lower Bound

Hyperparameter tuning is a common technique for improving the performance of neural networks. Most techniques for hyperparameter search involve an iterated process where the model is retrained at every iteration. However, the expected accuracy improvement from every additional search iteration, is still unknown. Calculating the expected improvement can help create stopping rules for hyperparameter tuning and allow for a wiser allocation of a project's computational budget. In this paper, we establish an empirical estimate for the expected accuracy improvement from an additional iteration of hyperparameter search. Our results hold for any hyperparameter tuning method which is based on random search \cite{bergstra2012random} and samples hyperparameters from a fixed distribution. We bound our estimate with an error of $O\left(\sqrt{\frac{\log k}{k}}\right)$ w.h.p. where $k$ is the current number of iterations. To the best of our knowledge this is the first bound on the expected gain from an additional iteration of hyperparameter search. Finally, we demonstrate that the optimal estimate for the expected accuracy will still have an error of $\frac{1}{k}$.
Technology-mediated Secondary Education in Paraguay: An Exploratory Study

Researchers around the world have amassed vast amounts of information about ICT use in education. This study focuses on ICT use in high school in Paraguay and the changes that took place with the advent of the COVID-19 pandemic. The pandemic has forced governments to take quarantine decisions, confine people to their homes and delay daily activities in all social and productive spheres. In Paraguay, in terms of education, classes were suspended at all educational levels, forcing the authorities to establish contingency plans. The National Government, through the Ministry of Education and Science (MEC) established an Education Plan in times of Pandemic with the Project "Your school at home" which established training mechanisms for teachers in the country for the development of classes in the virtual modality of Distance Education, in addition to creating written didactic materials, audios and videos, plans and evaluation guides for the different subjects of the Secondary Education courses, available in an online portal as digital resources and freely accessible to the actors of the educational community. Through focus group interviews with secondary teachers from several regions in the country, issues related to the effects of the pandemic and government goals were discussed. Results evidence an urgent need for greater coverage and access to the internet, technological tools, and teacher training in such tools and in the didactics of Distance Education.
GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training

Most of the currently existing vision and language pre-training (VLP) methods have mainly focused on how to extract and align vision and text features. In contrast to the mainstream VLP methods, we highlight that two routinely applied steps during pre-training have crucial impact on the performance of the pre-trained model: in-batch hard negative sampling for image-text matching (ITM) and assigning the large masking probability for the masked language modeling (MLM). After empirically showing the unexpected effectiveness of above two steps, we systematically devise our GRIT-VLP, which adaptively samples mini-batches for more effective mining of hard negative samples for ITM while maintaining the computational cost for pre-training. Our method consists of three components: 1) GRouped mIni-baTch sampling (GRIT) strategy that collects similar examples in a mini-batch, 2) ITC consistency loss for improving the mining ability, and 3) enlarged masking probability for MLM. Consequently, we show our GRIT-VLP achieves a new state-of-the-art performance on various downstream tasks with much less computational cost. Furthermore, we demonstrate that our model is essentially in par with ALBEF, the previous state-of-the-art, only with one-third of training epochs on the same training data. Code is available at https://github.com/jaeseokbyun/GRIT-VLP.
Beyond Reproduction, Experiments want to be Understood

The content of experiments must be semantically described. This topic has already been largely covered. However, some neglected benefits of such an approach provide more arguments in favour of scientific knowledge graphs. Beyond being searchable through flat metadata, a knowledge graph of experiment descriptions may be able to provide answers to scientific and methodological questions. This includes identifying non experimented conditions or retrieving specific techniques used in experiments. In turn, this is useful for researchers as this information can be used for repurposing experiments, checking claimed results or performing meta-analyses.
Learning NAT-Modeled Bayesian Network Structures with Bayesian Approach

We study Bayesian approach for learning structures of Bayesian networks (BNs) with local models.The local structures we focus on are Non-impeding noisy-AND Tree (NAT) models due to their multiple merits.We extend meta-nets to allow encoding of prior knowledge on NAT local structures and parameters.From the extended meta-nets, we develop a Bayesian Dirichlet (BD) scoring function for evaluating alternative NATmodeled BN structures.A heuristic algorithm is presented for searching through the structure space that is signicantly more complex than that of BN structures without local models.We experimentally demonstrate learning of NAT-modeled BNs, whose inference produces suciently accurate posterior marginals and is signicantly more ecient.
Calibrating the Adaptive Learning Rate to Improve Convergence of ADAM

Adaptive gradient methods (AGMs) have become popular in optimizing the nonconvex problems in deep learning area. We revisit AGMs and identify that the adaptive learning rate (A-LR) used by AGMs varies significantly across the dimensions of the problem over epochs (i.e., anisotropic scale), which may lead to issues in convergence and generalization. All existing modified AGMs actually represent efforts in revising the A-LR. Theoretically, we provide a new way to analyze the convergence of AGMs and prove that the convergence rate of \textsc{Adam} also depends on its hyper-parameter $\epsilon$, which has been overlooked previously. Based on these two facts, we propose a new AGM by calibrating the A-LR with an activation ({\em softplus}) function, resulting in the \textsc{Sadam} and \textsc{SAMSGrad} methods \footnote{Code is available at https://github.com/neilliang90/Sadam.git.}. We further prove that these algorithms enjoy better convergence speed under nonconvex, non-strongly convex, and Polyak-{\L}ojasiewicz conditions compared with \textsc{Adam}. Empirical studies support our observation of the anisotropic A-LR and show that the proposed methods outperform existing AGMs and generalize even better than S-Momentum in multiple deep learning tasks.
THE USE OF FLASHCARDS TEACHING STRATEGY IN RECALLING ENGLISH VOCABULARY

&#x0D; &#x0D; &#x0D; This research focuses on using the strategy of learning flashcards to recall English vocabulary, and students in the game group learn to recall English vocabulary types through learning flashcards. Find out what kind of vocabulary students in the English language recall playgroup through the flashcard learning strategy, analyze the process of recalling English vocabulary using the flashcard learning strategy. This study uses a qualitative method. The data source for this study will be conducted in Hooray Denpasar. Express oral data as written data by taking notes. One month of direct observation can be used to learn more about the process of using flashcards to recall English. The research methods used in this study are divided into three points. The first point is how data is collected, the second point is how data is analyzed, and the third is how data analysis is presented. This research led to the use of flashcards teaching strategy in English vocabulary recalling school. This strategy is very effective and very helpful for students to accept classroom learning.&#x0D; &#x0D; &#x0D;
Author Identification using Traditional Machine Learning Models

The Internet has many useful resources with bountiful information at our fingertips. However, there are nefarious uses to this resource, and can be misused in cybercrime, fake emails, stealing content, plagiarism etc. In many cases, the text is anonymously written, and it is important to accurately find the author to bring the criminal to justice. The topic of author identification helps with this task, where from a set of suspect authors, the writer of a given text will be determined. We aim to create a computationally non-complex model that works to find the author of a given text. The model will not require as much data as deep learning methods. This paper focuses on the use of various stylometric and word-based features as well as different machine learning models to create a classifier that gives the best accuracy. We find that the XGBoosting algorithm performs this task with a good accuracy.
Tiny Autoscalers for Tiny Workloads: Dynamic CPU Allocation for Serverless Functions

In serverless computing, applications are executed under lightweight virtualization and isolation environments, such as containers or micro virtual machines. Typically, their memory allocation is set by the user before deployment. All other resources, such as CPU, are allocated by the provider statically and proportionally to memory allocations. This contributes to either under-utilization or throttling. The former significantly impacts the provider, while the latter impacts the client. To solve this problem and accommodate both clients and providers, a solution is dynamic CPU allocation achieved through autoscaling. Autoscaling has been investigated for long-running applications using history-based techniques and prediction. However, serverless applications are short-running workloads, where such techniques are not well suited. In this paper, we investigate tiny autoscalers and how dynamic CPU allocation techniques perform for short-running serverless workloads. We experiment with Kubernetes as the underlying platform and implement using its vertical pod autoscaler several dynamic CPU rightsizing techniques. We compare these techniques using state-of-the-art serverless workloads. Our experiments show that dynamic CPU allocation for short-running serverless functions is feasible and can be achieved with lightweight algorithms that offer good performance.
Sprachsynthese -- State-of-the-Art in englischer und deutscher Sprache

Reading text aloud is an important feature for modern computer applications. It not only facilitates access to information for visually impaired people, but is also a pleasant convenience for non-impaired users. In this article, the state of the art of speech synthesis is presented separately for mel-spectrogram generation and vocoders. It concludes with an overview of available data sets for English and German with a discussion of the transferability of the good speech synthesis results from English to German language.