Reviewerly/exHarmony huggingface dataset:
COVID-19 2.0. INTERNATIONAL LAW AND THE PROTECTION OF THE RIGHT TO HEALTH IN CYBERSPACE: A PERSPECTIVE FROM ECUADOR

The purpose of this article is to explore the opportunity offered by the Covid-19 crisis in Ecuador to interpret international human rights law and international humanitarian law to protect the right to health, and also health-care systems in cyberspace Through a descriptive methodology that allows a comprehensive analysis of the issue from a legal point of view, this article: synthesizes the main international debates on the scope of the protection of the right to health under the international human rights law against cyber threats;studies the protection that international humanitarian law provides to citizens and health-care systems;and examines Ecuador's situation, as a country facing several challenges in the application of the necessary policies to ensure the protection of the right to health and health-care systems The article concludes that the legal developments in both sectors have not managed to establish means of protection which are acceptable and effective, with clear obligations for States in cyberspace to protect individuals and health-care systems Additionally, it argues that the gaps in international law regarding the protection of health in cyberspace are exacerbated in Ecuador by a discursive dissonance between foreign and domestic policy, with an impact on the protection of the right to health
Concept Driven Search and Visualization System for Exploring Scientific Repositories

In this paper we present a scientific document retrieval system targeted at answering specific information needs for a diverse set of professionals This became a crying need in the wake of the pandemic when researchers, clinicians, virologists, epidemiologists and health policy makers are regularly exploring a large collection of scientific literature related SARS-COV2 and COVID-19 The system facilitates exploration and easy comprehension of the results at multiple granularity through visual and textual summaries The system uses novel NLP techniques to extract, aggregate and analyse textual information The system also creates unique visual components to view key information of interest at individual and aggregate levels The system is implemented as a web application © 2021 Owner/Author
Application of Unsupervised and Supervised Machine Learning on Geochemical Signatures of Norwegian Continental Shelf Oils

Summary Rigorous data quality assessment has been performed on two sets of geochemical data of oils from two geographical areas (Norwegian Sea and Barents Sea). Existing geochemical understanding and geological knowledge has been exerted to group these oils with regard to geochemical provinces. The same data sets were used for two machine learning approaches: unsupervised clustering and supervised classification of oil types, the latter one uses a test set of already assigned oil family typed oils. Our results indicate that both supervised and unsupervised machine learning can differentiate between different petroleum provinces in a very consistent manner and may give additional insight into outlier samples. Unsupervised clustering of oil samples produces classes that are, overall, consistent with geological understanding of the origin of the oils. Therefore, machine learning algorithms appear to be an appropriate tool to generate an effective and coherent understanding of accumulated hydrocarbons in the subsurface.
The Influence of Human Development, Institutional Quality and ISIS Emergence on Foreign Direct Investment in Iraq

This study investigates the influence of human development and institutional quality on the foreign direct investment net inflows on the gross domestic product in Iraq for the period (2004-2016) by using OLS regression. The empirical results show that governance sub-indicators do not actually matter much in attracting Foreign Direct Investment (FDI) inflows in Iraq. This variable shows fixed results with a different sign; the Political Stability (PS), Government Effectiveness (GE) and Rule of Law (RL) are the sub-indicators of the governance with a positive sign which is insignificant, while the Voice and Accountability (VA), Regulatory Quality (RQ) and Control of Corruption (CC) are the sub-indicators of the governance index with a negative sign and an insignificant impact on FDI inflows. The value of Worldwide Governance Indicators (WGI) coefficient is negative and significant at 10% level. Hence it has been concluded that the explanatory variables are negatively associated with the dependent variable FDI. In contrast, the Human Development Index (HDI) has a positive and significant impact on FDI inflows. This study found that Islamic State of Iraq and Syria (ISIS) emergence after (2013) had a significant coefficient, meaning that ISIS has affected the level of FDI inflows.
Simple method for the estimation of solar spectral radiation

In order to know the energy production of photovoltaic modules, it is necessary to have an idea of the spectral distribution of solar radiation, because each technology is characterized by a different spectral resolution. The objective of this work is to determine the irradiance in ultraviolet, visible, and infrared from the triangular shape of the global solar spectrum for any sky state. According to statistical indicators, the coefficient of determination R² varies between 97% and 99%.
REFERENCE KNOWLEDGE IN TEACHER EVALUATION

Evaluation activity is a special form of human activity. It appeared along with human activities and is an important characteristic of it. Assessment allows you to know the surrounding reality, to establish a reliable and useful relationship with it. The teacher also, in assessing the knowledge and skills of the student, must learn about his academic achievements and shortcomings. To organize the assessment, the teacher must learn the organization mechanisms, the stages of its implementation. However, in order to successfully carry out the assessment, the teacher must have formed supporting knowledge. The article is devoted to the study of these issues.
KELAYAKAN MATERI AJAR SASTRA DARI KOMPONEN NILAI PENDIDIKAN KARAKTER BUKU BAHASA INDONESIA SMP KELAS VII

Indonesian language textbooks are the main source of teaching and learning activities used by teachers and students and are used as a reference for gathering information as well as supporting learning activities. The appropriateness of the content of the material is one part of the criteria in the assessment of textbooks. The main problem in this study is how the appropriateness of the content of literary teaching materials, especially the fairy tales of the components of character education values contained in the contents of the Indonesian language book for grade VII SMP. The purpose of this research is to describe the feasibility of the content of literary teaching materials, especially fairy tales from the components of the character education values contained in the Indonesian language book for grade VII SMP.
Monitoring Geologic Change in an Evolving World

Summary Geologic-related change evaluation can be challenging because there may be multiple transformations occurring at the same time and place, albeit at different temporal and spatial scales. But being able to identify specific changes is especially important now when climate change may be affecting landscapes and digital transformation is altering how business intelligence is conducted. This presentation provides examples of data analytical and visualization strategies used to improve the potential for actionable change detection from multisource, multiscale and multitemporal geospatial sources. Examples of results from applying these approaches to natural resource assessment, sustainable development, and strategic monitoring projects throughout the world.
Wi-Fi Devices Individual Identification based on Envelope Front Characteristic

Abstract There are some characteristics are used for traditional Wi-Fi devices individual identification methods, such as: amplitude and phase characteristics during the period that Wi-Fi devices are turned-on or turned-off, MAC address, and IP address. In fact, on one hand, MAC address and IP address are easily to be changed. Traditional methods can’t identify individual Wi-Fi devices if MAC address or IP address changed. On the other hand, the amplitude and phase characteristics of Wi-Fi devices can only be identified during the short period of turned-on or turned-off. The traditional methods have application limitations. Therefore, the Wi-Fi devices individual identification based on envelope front characteristic is proposed in this paper. Firstly, envelopes of Wi-Fi signals are extracted. Secondly, front characteristics of Wi-Fi envelope are extracted. Then envelope front characteristics are used to compare and match with models saved in the library to realize Wi-Fi devices individual identification. By using the method proposed in this paper, Wi-Fi devices individual identification can be realized whether MAC address or IP address changed. Scope of identification application can be effectively expanded into the whole time when Wi-Fi devices transmit signals by using this method. Since envelope front characteristics have the advantages of non-changing and non-copying, accuracy of Wi-Fi individual identification is also improved by using this method.
Problem Space Identification for Developing Virtual Reality Learning Environments

Our study argues that the extant literature on virtual reality-based learning environments (VRLEs) currently lacks proper definitions and context descriptions for a problem space, which is fundamental for conducting design science research (DSR).Without properly conducted problem space identification, the most pivotal problems cannot be identified resulting solutions lacking validity and unreliable evaluations.This is a major challenge for the DSR in the educational field, but also for the research on VRLEs.The purpose of this paper is to introduce a novel DSR method to support rigorous problem space identification, which would allow rigorous and profound problem space analysis.The instantiation of our method is depicted with a VRLE development project.In the problem space identification -process we adopt the concepts of selfdetermination theory and learning path to study and consider individual and a system level of the current VRLE artifact.This theoretical lens enables us to identify the problem space for VRLEs and also suggest how the to-be-developed artifact to be later evaluated.This paper contributes by introducing a general problem space identification for VRLEs and a DSR method to guide the future DSR in the educational field.
영어 듣기 문항의 발화 속도와 언어적 요소 분석―1994-2018학년도 대학수학능력시험

The purpose of this study is to examine the changing patterns of speech rate, linguistic features, and speech length in 404 English listening test items on the Korean College Scholastic Ability Test (CSAT) between 1994 and 2018. For the calculation of the speech rate and length, Praat, MS Word and MS Excel were used. The Coh-Metrix tool was used to measure the linguistic features of the listening items and to present them as numerical figures. The analysis revealed that the length of the listening texts in the CSAT has increased over 25 years while the speech rate has shown rather a static pattern. The difficulty of the texts, measured by Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL), was found to have increased compared to previous years. The findings suggest that the usage of authentic materials in English listening classes is urgent while waiting for changes to be made to the CSAT listening items.
NAHAS: Neural Architecture and Hardware Accelerator Search

Neural architectures and hardware accelerators have been two driving forces for the rapid progress in deep learning. Although previous works have optimized either neural architectures given fixed hardware, or hardware given fixed neural architectures, none has considered optimizing them jointly. In this paper, we study the importance of co-designing neural architectures and hardware accelerators. To this end, we propose NAHAS, an automated hardware design paradigm that jointly searches for the best configuration for both neural architecture and accelerator. In NAHAS, accelerator hardware design is conditioned on the dynamically explored neural networks for the targeted application, instead of fixed architectures, thus providing better performance opportunities. Our experiments with an industry-standard edge accelerator show that NAHAS consistently outperforms previous platform-aware neural architecture search and state-of-the-art EfficientNet on all latency targets by 0.5% - 1% ImageNet top-1 accuracy, while reducing latency by about 20%. Joint optimization reduces the search samples by 2x and reduces the latency constraint violations from 3 violations to 1 violation per 4 searches, compared to independently optimizing the two sub spaces.
The Extended Corporate Mind: When Corporations Use AI to Break the Law

Algorithms may soon replace employees as the leading cause of corporate harm. For centuries, the law has defined corporate misconduct — anything from civil discrimination to criminal insider trading — in terms of employee misconduct. Today, however, breakthroughs in artificial intelligence and big data allow automated systems to make many corporate decisions, e.g., who gets a loan or what stocks to buy. These technologies introduce valuable efficiencies, but they do not remove (or even always reduce) the incidence of corporate harm. Unless the law adapts, corporations will become increasingly immune to civil and criminal liability as they transfer responsibility from employees to algorithms.This Article is the first to tackle the full extent of the growing doctrinal gap left by algorithmic corporate misconduct. To hold corporations accountable, the law must sometimes treat them as if they “know” information stored on their servers and “intend” decisions reached by their automated systems. Cognitive science and the philosophy of mind offer a path forward. The “extended mind thesis” complicates traditional views about the physical boundaries of the mind. The thesis states that the mind encompasses any system that sufficiently assists thought, e.g. by facilitating recall or enhancing decision-making. For natural people, the thesis implies that minds can extend beyond the brain to include external cognitive aids, like rolodexes and calculators. This Article adapts the thesis to corporate law. It motivates and proposes a doctrinal framework for extending the corporate mind to the algorithms that are increasingly integral to corporate thought. The law needs such an innovation if it is to hold future corporations to account for their most serious harms.
Weak NAS Predictor Is All You Need

Neural Architecture Search (NAS) finds the best network architecture by exploring the architecture-to-performance manifold. It often trains and evaluates a large amount of architectures, causing tremendous computation cost. Recent predictor-based NAS approaches attempt to solve this problem with two key steps: sampling some architecture-performance pairs and fitting a proxy accuracy predictor. Existing predictors attempt to model the performance distribution over the whole architecture space, which could be too challenging given limited samples. Instead, we envision that this ambitious goal may not be necessary if the final aim is to find the best architecture. We present a novel framework to estimate weak predictors progressively. Rather than expecting a single strong predictor to model the whole space, we seek a progressive line of weak predictors that can connect a path to the best architecture, thus greatly simplifying the learning task of each predictor. It is based on the key property of the predictors that their probabilities of sampling better architectures will keep increasing. We thus only sample a few well-performed architectures guided by the predictive model, to estimate another better weak predictor. By this coarse-to-fine iteration, the ranking of sampling space is refined gradually, which helps find the optimal architectures eventually. Experiments demonstrate that our method costs fewer samples to find the top-performance architectures on NAS-Bench-101 and NAS-Bench-201, and it achieves the state-of-the-art ImageNet performance on the NASNet search space.
Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling

Inspired by the phenomenon of performance disparity between languages in machine translation, we investigate whether and to what extent languages are equally hard to conditional-language-model. Our goal is to improve our understanding and expectation of the relationship between language, data representation, size, and performance. We study one-to-one, bilingual conditional language modeling through a series of systematically controlled experiments with the Transformer and the 6 languages from the United Nations Parallel Corpus. We examine character, byte, and word models in 30 language directions and 5 data sizes, and observe indications suggesting a script bias on the character level, a length bias on the byte level, and a word bias that gives rise to a hierarchy in performance across languages. We also identify two types of sample-wise non-monotonicity --- while word-based representations are prone to exhibit Double Descent, length can induce unstable performance across the size range studied in a novel meta phenomenon which we term erraticity. By eliminating statistically significant performance disparity on the character and byte levels by normalizing length and vocabulary in the data, we show that, in the context of computing with the Transformer, there is no complexity intrinsic to languages other than that related to their statistical attributes and that performance disparity is not a necessary condition but a byproduct of word segmentation. Our application of statistical comparisons as a fairness measure also serves as a novel rigorous method for the intrinsic evaluation of languages, resolving a decades-long debate on language complexity. While these quantitative biases leading to disparity are mitigable through a shallower network, we find room for a human bias to be reflected upon. We hope our work helps open up new directions in the area of language and computing that would be fairer and more flexible and foster a new transdisciplinary perspective for DL-inspired scientific progress.
EDUCATIONAL REFORMS AND SOCIAL ADAPTATION DURING PANDEMIC

This study represents an attempt to briefly analyse the labour market situation in Bulgaria during the pandemic crisis and to find a solution to new challenges our education system shall face. Through changes in the educational environment the authors tried to formulate and define teaching methods, organization and management of the values changing process, creation of new culture of the school community, emphasis on the project-based educational process, increase in students` learning motivation through experience, development of key competencies so that learners can meet the requirements of the dynamic labour market – the ability to independently gain knowledge and planning skills.
CONCORDANCE: Stata module to harmonize classification codes

concordance provides an algorithm for concording industrial classification codes consistently over time. It is similar to Pierce and Schott (2012), but implemented as a Stata command that internally calls Java programs relying on a graph algorithm developed by Sedgewick and Wayne (2011).
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors

Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks. In this work, we focus on estimating predictive distributions for bounding box regression output with variance networks. We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean. We propose to use the energy score as a non-local proper scoring rule and find that when used for training, the energy score leads to better calibrated and lower entropy predictive distributions than NLL. We also address the widespread use of non-proper scoring metrics for evaluating predictive distributions from deep object detectors by proposing an alternate evaluation approach founded on proper scoring rules. Using the proposed evaluation tools, we show that although variance networks can be used to produce high quality predictive distributions, ad-hoc approaches used by seminal object detectors for choosing regression targets during training do not provide wide enough data support for reliable variance learning. We hope that our work helps shift evaluation in probabilistic object detection to better align with predictive uncertainty evaluation in other machine learning domains. Code for all models, evaluation, and datasets is available at: https://github.com/asharakeh/probdet.git.
Graph Deformer Network

Convolution learning on graphs draws increasing attention recently due to its potential applications to a large amount of irregular data. Most graph convolution methods leverage the plain summation/average aggregation to avoid the discrepancy of responses from isomorphic graphs. However, such an extreme collapsing way would result in a structural loss and signal entanglement of nodes, which further cause the degradation of the learning ability. In this paper, we propose a simple yet effective Graph Deformer Network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images. Local neighborhood subgraphs (acting like receptive fields) with different structures are deformed into a unified virtual space, coordinated by several anchor nodes. In the deformation process, we transfer components of nodes therein into affinitive anchors by learning their correlations, and build a multi-granularity feature space calibrated with anchors. Anisotropic convolutional kernels can be further performed over the anchor-coordinated space to well encode local variations of receptive fields. By parameterizing anchors and stacking coarsening layers, we build a graph deformer network in an end-to-end fashion. Theoretical analysis indicates its connection to previous work and shows the promising property of graph isomorphism testing. Extensive experiments on widely-used datasets validate the effectiveness of GDN in graph and node classifications.