Reviewerly/exHarmony huggingface dataset:
Methodological Features of Formation of Functional Literacy at Younger Schoolchildren

The article deals with the issues of formation of functional literacy of primary school pupils (on the example of one school) as an indicator of the level of mastery of competences necessary in education. The authors research methodological peculiarities of the development of the ability of teachers and pupils to conduct the joint search for solutions of the current problems and solve modern tasks in real life situations, which determines the practical and theoretical significance of the research. The main functional qualities of the personality of pupils are initiative, ability to think creatively and find non-standard solutions in the context of solving this strategically important task for the country.
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Purpose– This study aims to analyze the spillover effects of external real and financial shocks on the Korean economy during January 2000...
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness

We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to adversarial attacks. SPLASH units have both a simple parameterization and maintain the ability to approximate a wide range of non-linear functions. SPLASH units are: 1) continuous; 2) grounded (f(0) = 0); 3) use symmetric hinges; and 4) the locations of the hinges are derived directly from the data (i.e. no learning required). Compared to nine other learned and fixed activation functions, including ReLU and its variants, SPLASH units show superior performance across three datasets (MNIST, CIFAR-10, and CIFAR-100) and four architectures (LeNet5, All-CNN, ResNet-20, and Network-in-Network). Furthermore, we show that SPLASH units significantly increase the robustness of deep neural networks to adversarial attacks. Our experiments on both black-box and open-box adversarial attacks show that commonly-used architectures, namely LeNet5, All-CNN, ResNet-20, and Network-in-Network, can be up to 31% more robust to adversarial attacks by simply using SPLASH units instead of ReLUs.
Environmental Control System for Locked-in Syndrome Patients Using Eye Tracker

Locked-in syndrome or LIS is a whole body voluntary muscles paralysis excluding muscles that control the eye movements but they are awake and conscious. The cognitive function is usually not affected and they can communicate through blinking or eye movements. LIS patient loses his productivity since their everyday living are greatly affected and needs a continuous help. Nowadays, assistive technology is widely used to compensate for the impairments they experience. The main objective of this research is to design and develop an assistive technology controlled by an eye tracker to help improve the lives of those patients suffering from LIS in controlling some electrical/electronic devices. The study utilized the experimental research design and the creation of a functional prototype for its hardware and software requisites. The researchers were able to successfully develop an environmental control system that is made through designing an appliance controller and a software where user can switch on or off four appliances through the use of eye gaze as their input. Statistical treatment of t-test was applied and the results showed that there is no significance difference between the user's actual gaze point coordinate to the center coordinate of the appliances buttons.
Solar Autopilot Drone

Advances in technology have made the drone an affordable tool for various purposes. This article focuses on gaining knowledge of drone at a working and conceptual level. Firstly, there is a detailed explanation of the construction of the drone. Some of the most essential elements of a drone include frame, propellers, engine, system of power the electronic control and communication system. Whether you fly your drone for commercial or recreational purposes, staying in the air as long as possible is the goal. But of course, the battery life of the drone can put a damper on how much you can accomplish while you’re flying.Batteries serve as a major drawback because they get exhausted after 15 minutes of flight and thereby landing the drone on ground. The batteries used for powering the drones are lithium-polymer batteries.This project aims to provide an ingenious solution to this hurdle by introducing the current popular photovoltaic system into the UAV power system design.Solar drones use solar cells powered directly from the sun and solve major issues related to conventional drones such as increasing the flight time and risk of the drone losing connectivity with its controller. The design is to be modular for easy module upgrade and replacement. Using photovoltaic system minimizes the environmental impact, an issue that can be controversial for large projects built for utilities because they tend to spread across hundreds of acres of land in remote regions.
Project 1: Launching, Analyzing, and Reporting a Survey Using R and LimeSurvey

When I was a graduate student, a former student who had gone into industry came back to visit us a year post-graduation. He was working for a company in marketing, a rare departure from the bulk of our comrades who had gone into academia. Talking with him, I asked what the biggest difference was. He smiled and told me of a meeting he’d recently been in with the sales team. They were very happy that sales were up that month compared to last month. He asked “What is the standard deviation of monthly sales?” They looked at him perplexed, until one of them asked “Is that important?”
Project 4: The R Powered Presentation

Years ago, I was asked by a colleague to give a presentation to a group of my peers on statistical reliability and validity. It was at the end of a “retreat” day that was also a Friday – at 3:00 PM. Nobody wants to give or be in a presentation at 3:00 PM on a Friday for any reason. Yet it was the situation I was in.
Query-independent dynamic similitude data models for edge-computing

Privacy-preserving data release is an increasingly important problem in today's ubiquitous computing. Performing data processing on edge devices saves CPU time, network traffic and improves latency. The savings are especially significant for edge devices with low-power and limited resources. In addition, processing and privacy-preserving release of the data on the edge devices prevents sensitive data from leaving the devices at all. Thus, solving a bunch of potential problems in data protection. We propose a usage of query-independent, similitude models for privacy-preserving data release on the edge devices. Those models build a compact representation of the data for any possible query, as opposite to a common method of query-specific data release. Compared to the query-specific models, similitude models can be combined and “subtracted” when a new edge devices appear or shutdown. In addition, a data oriented approach allows further processing in a fog, near the edge devices.
Efﬁcient GeMSS Based Ring Signature Scheme

The ring signature scheme has an important usage area of public key crypto-system. It can be used for e-voting, as well as leaking information without revealing identity within a group. However, most of these systems relies on traditional crypto-systems which are not secure against quantum computing related attacks. Multivariate cryptography is one of the most popular research areas on quantum resilient crypto-systems. In this work, we propose an efficient ring signature scheme based on GeMSS, where we achieve smaller signature size and faster verification time with respect to other alternatives.
Robots in a World of Data

Data fusion is considered an important component for many of the new technology initiatives that are being developed. We define data fusion in the next section of the chapter. Data fusion is an enabling technology in many domains, but currently it has been brought to prominence by the development of autonomous vehicles, intelligent automation, and collaborating autonomous mobile robots.
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning

Zero-shot learning (ZSL) addresses the unseen class recognition problem by leveraging semantic information to transfer knowledge from seen classes to unseen classes. Generative models synthesize the unseen visual features and convert ZSL into a classical supervised learning problem. These generative models are trained using the seen classes and are expected to implicitly transfer the knowledge from seen to unseen classes. However, their performance is stymied by overfitting, which leads to substandard performance on Generalized Zero-Shot learning (GZSL). To address this concern, we propose the novel LsrGAN, a generative model that Leverages the Semantic Relationship between seen and unseen categories and explicitly performs knowledge transfer by incorporating a novel Semantic Regularized Loss (SR-Loss). The SR-loss guides the LsrGAN to generate visual features that mirror the semantic relationships between seen and unseen classes. Experiments on seven benchmark datasets, including the challenging Wikipedia text-based CUB and NABirds splits, and Attribute-based AWA, CUB, and SUN, demonstrates the superiority of the LsrGAN compared to previous state-of-the-art approaches under both ZSL and GZSL. Code is available at https: // github. com/ Maunil/ LsrGAN
일본 개정형법의 성범죄 처벌규정에 관한 검토 - 처벌규정의 체계 및 시사점을 중심으로 -

On June 16, 2017, the “Law Amending a Part of the Penal Code”, which mainly addresses the amendment of penalties for sex crimes, was enacted in Japan, and the law went into enforcement on July 13.This amendment bill is a major amendment to the provisions related to sex crimes in Japan 110 years after the enactment of the Criminal Act. The main contents are revision of constitutional requirement for rape, change od crime’s name, increase of statutory form, creation of punishment such as impurity act due to the influence of detainee, abolishment of offense subject to complaint.This amendment is highly appreciated in that it reflected the opinions of people from various fields including practitioners, scholars, psychiatrists, and clinical psychologists as well as sex crime victims in the drafting stage and the deliberation stage at the Parliament for revising the penalties for sexual crimes.The contents of the amendment have great implications for legal theories and practices in Korea.
Л.С. ЛАРИЧЕВА: ВЕХА В РАЗВИТИИ ФИЗИКО-МАТЕМАТИЧЕСКОГО ПЕДАГОГИЧЕСКОГО ОБРАЗОВАНИЯ НА АЛТАЕ

The article highlights the contribution of the famous scientist and teacher Laricheva Lidia Sergeyevna - the head of the faculty of physics and mathematics and mathematics, the head of the departments of mathematics and mathematical analysis of the Barnaul State Pedagogical Institute in the formation and development of pedagogical physics and mathematics education in the Altai Territory, the training of teachers of mathematics and physics in 50-80s of the XX century. Dedicated to her 90th birthday.
Blind hierarchical deconvolution

Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of model-based inversion techniques require knowledge on the convolution kernel to recover an accurate reconstruction and additionally prior assumptions on the regularity of the signal are needed. To overcome these limitations, we parametrise the convolution kernel and prior length-scales, which are then jointly estimated in the inversion procedure. The proposed framework of blind hierarchical deconvolution enables accurate reconstructions of functions with varying regularity and unknown kernel size and can be solved efficiently with an empirical Bayes two-step procedure, where hyperparameters are first estimated by optimisation and other unknowns then by an analytical formula.
Combining sequential model-based algorithm configuration with default-guided probabilistic sampling

General-purpose automated algorithm configuration procedures have enabled impressive improvements in the state of the art in solving challenging problems from AI, operations research and other areas. The most successful configurators combine multiple techniques to search vast combinatorial spaces of parameter settings for a given algorithm as efficiently as possible. Specifically, two of the most prominent general-purpose algorithm configurators, SMAC and irace, can be seen as combinations of Bayesian optimisation and racing, and of racing and an estimation of distribution algorithm, respectively. Here, we investigate an approach that combines all three of these techniques into one single configurator, while exploiting prior knowledge contained in expert-chosen default parameter values. We demonstrate significant performance improvements over irace and SMAC on a broad range of running time optimisation scenarios from AClib.
Generalization Guarantees for Multi-Modal Imitation Learning.

Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies. In this paper, we present rigorous generalization guarantees for imitation learning by leveraging the Probably Approximately Correct (PAC)-Bayes framework to provide upper bounds on the expected cost of policies in novel environments. We propose a two-stage training method where a latent policy distribution is first embedded with multi-modal expert behavior using a conditional variational autoencoder, and then fine-tuned in new training environments to explicitly optimize the generalization bound. We demonstrate strong generalization bounds and their tightness relative to empirical performance in simulation for (i) grasping diverse mugs, (ii) planar pushing with visual feedback, and (iii) vision-based indoor navigation, as well as through hardware experiments for the two manipulation tasks.
An Anonymous Trust-Marking Scheme on Blockchain Systems

In this paper, we propose an anonymous trust-marking scheme on blockchain systems that is universally applicable to any cryptocurrency. In our scheme, entities called token admitters are allowed to generate tokens adding trustworthiness or untrustworthiness to addresses. Anyone can anonymously verify whether these tokens were issued by a token admitter. Simultaneously, only the designated auditor and no one else, including nondesignated auditors, can identify the token admitters. Our scheme is based on accountable ring signatures and commitment, and confirm that our scheme is applicable to Bitcoin, Ethereum, and NEM.
Многообразие и характерные особенности сценической танцевальной культуры российского казачества

In recent decades, the Russian Federation has been actively reviving and developing the Cossacks, as a special sub-ethnic group, with its own unique history, traditions and culture. Dance is an integral part of the Cossack culture, it has its own characteristics that are belong to different Cossack communities inhabiting various territories of our country. Over time, the traditional Cossack dances acquired life on the concert and theatrical stage, also the traditions of their professional performance were determined. The study of historically formed traditional dances of the Cossacks with the peculiarities of their stage forms, the allocation of their inherent choreographic vocabulary and the basic methods of performance are important for maintaining the dance culture of the Cossacks. The fertile material for the practical development of the article were dances, which are part of the modern repertoire of the largest Russian folk dance ensembles. The concert programs of these groups most fully reflect the diversity of the dance culture of the Russian Cossacks. Based on the data obtained, the characteristic features of the dances of several large Cossack communities are described.
The Semiotic Foundation of Ingarden’s Analysis of Music

The papers collected in this volume vividly reflect the strikingly wide range of interests characterizing current research in phenomenology inspired by Roman Ingarden. One of Husserl’s closest and most devoted students, and at the same time one of his earliest and sharpest critics, Ingarden himself explored numerous fields of philosophy in considerable depth. While he remains best known for his groundbreaking work in aesthetics, ontology, and metaphysics, he also dealt extensively in ethics, epistemology, philosophical anthropology, and cognitive science, and his work was characterized throughout by a deep and abiding interest in the sciences and an unwavering respect for painstakingly thorough logical investigation. Issues from all of these areas of study provide the topics dealt with by the authors contributing to this volume. Several authors focus explicitly on historical matters, often casting surprising new light on the development of early phenomenology in general and of Ingarden’s own ‘realist’ phenomenology in particular. Other authors have concentrated instead on specific areas or particular topics of long-standing interest, such as the aesthetic experience, the philosophy of music, and artistic creation, while others have explored the relevance of Ingarden’s ontological and anthropological analyses to current research into everything from the interpretation of texts to the study of technological posthumanization. With contributions from both established experts and young scholars, this collection brings together three generations of researchers who share the same basic philosophical goals and methodology, yet exhibit noticeably distinct styles, making this collection not only accessible and topical but also unusually lively.
A model for scholarly journals

Computer-Aided Civil and Infrastructure EngineeringVolume 35, Issue 10 p. 1044-1045 EDITORIAL A model for scholarly journals Bruce R. Ellingwood, Corresponding Author Bruce R. Ellingwood bruce.ellingwood@colostate.edu Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, USA Correspondence Bruce R. Ellingwood, Department of Civil and Environmental Engineering, Colorado State University, Ft. Collins, CO 80523, USA. Email: bruce.ellingwood@colostate.eduSearch for more papers by this author Bruce R. Ellingwood, Corresponding Author Bruce R. Ellingwood bruce.ellingwood@colostate.edu Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, USA Correspondence Bruce R. Ellingwood, Department of Civil and Environmental Engineering, Colorado State University, Ft. Collins, CO 80523, USA. Email: bruce.ellingwood@colostate.eduSearch for more papers by this author First published: 01 September 2020 https://doi.org/10.1111/mice.12615Read 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. Volume35, Issue10October 2020Pages 1044-1045 RelatedInformation
Product recommendation system using optimal switching hybrid algorithm

The recommendation system works as a heart in the business strategy of e-commerce. By employing various techniques and methods it recommends the desirable items to the user. Recent studies suggest by applying proper methods the accuracy of the recommendation can be improved. Traditional techniques like collaborative filtering face the cold start problem, so in this paper, we propose an optimal switching hybrid approach (OSHA) to overcome the issue. Here, K-nearest neighbour algorithm is used to predict the similar kind of users and the experimental results show that the proposed algorithm performs better than the standalone technique.
Scheele in Uppsala

ItUppsala was probably in order to get better access to a laboratory that Scheele moved to UppsalaUppsala in 1770. Others have suggested that it was in order to get into contact with Bergman and the University, but given the fact that Bergman had rejected the papers he had submitted to the Academy in StockholmStockholm, and that it would take some effort to persuade Scheele to visit Bergman, it appears less likely.
Can ATMs Get Out the Vote? Evidence from a Nationwide Field Experiment

We report on a large-scale field experiment to assess ATMs (automatic teller machines) capacity to get out the vote. This is a heretofore unexploited method. Our experimental design used the universe of functioning ATMs in Portugal. We randomly selected a set of treatment civil parishes, where a civic message took over the totality of ad time, which we compare with a set of control areas. The campaign we follow was active for three days before and during the 2017 local elections. Although we do not achieve statistical significance on a stable but small average treatment effect, when we consider the intensity of treatment, results show a statistically significant increase in the likelihood of voting. Placebo tests using turnout rates in previous elections strengthen our interpretation. We ran a post-treatment survey around ATMs located in two neighbouring civil parishes, one treated, the other not. We found a sizeable difference in recall.
Efficiently Constructing Adversarial Examples by Feature Watermarking

With the increasing attentions of deep learning models, attacks are also upcoming for such models. For example, an attacker may carefully construct images in specific ways (also referred to as adversarial examples) aiming to mislead the deep learning models to output incorrect classification results. Similarly, many efforts are proposed to detect and mitigate adversarial examples, usually for certain dedicated attacks. In this paper, we propose a novel digital watermark based method to generate adversarial examples for deep learning models. Specifically, partial main features of the watermark image are embedded into the host image invisibly, aiming to tamper and damage the recognition capabilities of the deep learning models. We devise an efficient mechanism to select host images and watermark images, and utilize the improved discrete wavelet transform (DWT) based Patchwork watermarking algorithm and the modified discrete cosine transform (DCT) based Patchwork watermarking algorithm. The experimental results showed that our scheme is able to generate a large number of adversarial examples efficiently. In addition, we find that using the extracted features of the image as the watermark images, can increase the success rate of an attack under certain conditions with minimal changes to the host image. To ensure repeatability, reproducibility, and code sharing, the source code is available on GitHub