Reviewerly/exHarmony huggingface dataset:
Reducing Costs by the Use of Cloud Accounting

Cloud technologies have developed intensively during the last years. Cloud accounting is one of the most important factors that assist economic units in keep up of scientific and technological development and its exploitation in the organization of accounting work based on the Internet-based on systems designed to carry out all the accounting procedures of the economic unit, Dematerialization of accounting documents and migration of certain accounting operations to cloud-based electronic platforms have overcome the research phase, as the Kurdistan region already includes numerous companies that develop and provide cloud accounting based accounting applications. The research aims to review the concept of cloud accounting and its advantages. It found that reliance on cloud accounting contributes to cost reduction as well as its contribution to the organization of accounting work. The present research seeks to verify the hypothesis claiming that the cost-saving generated by the utilization of a cloud accounting based application is significant enough to represent a relevant criterion when selecting the internet based accounting solution.
Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System

Attendance is the fact that someone is present at an event or goes regularly to an institution, or attendance at an event is the number of people present at that time. The Saifiatul Amaliyah school itself is one of the many schools in Indonesia where the attendance of students or attendance is still done manually. This can cause problems, namely allowing fraud when filling in attendance and errors in data recapitulation. Therefore, in this study a computerized face attendance was created, which was formed using the K-Nearest Neighbor (K-NN) method and combined with the extraction of the Principal Component Analysis (PCA) feature where the attendance process can be done with a person's face. The face attendance system using the K-NN and PCA methods has an accuracy of 82%.
Feature Selection Strategy for Intrahospital Mortality Prediction after Coronary Artery Bypass Graft Surgery on an Unbalanced Sample

The aim of the study is to develop models of intrahospital mortality (IHM) prediction on an unbalanced sample of patients with coronary artery disease (CAD) post coronary artery bypass graft (CABG) surgery. Methods. Models for IHM prediction were built following the analysis of 866 electronic case histories based on the analysis of CAD patients, revascularized with the CABG operation. The patient cohort consisted of two groups. The first included 35 (4%) patients who died within the first 30 days after CABG, the second - 831 (96%) patients with a favorable operation outcome. We analyzed 99 factors, including the results of clinical, laboratory and instrumental studies obtained before CABG. For feature compilation, classical filtering and model selection methods were used (wrapper method). The primary drawback to applying a classical approach was the unbalanced sample as one cohort only consisted of 4% of subjects. In that case, it was not possible to apply the cross-validation procedure with three types of samples, standard quality metrics and multi-category factors. Results. Features searching approach using the multi-stage selection procedure, which combined the validation of predefined predictors, filtering methods and multifactor model development based on logistic regression, random forest (RF) and artificial neural networks (ANNs) was proposed. The models' accuracy was evaluated by a combined quality metric. RF and ANNs based models allowed not only to build more accurate forecasting tools, but also assisted in verifying five additional IHM predictors.
Google Translate Accuracy Evaluation

Google Translate (GT), as the most used language translation application, has been significantly improved when artificial neural network (ANN) based Neural Machine Translate System was introduced.The purpose of this paper is to evaluate the translation accuracy of the GT system when used for education purposes.The text in the English language is translated into two languages, by professional human translators and by GT.It is IT related text and refers to computer networks.Students' comprehension of the given text is evaluated regarding language fluency, adequacy, meaning and severity.
Political Instability and Firm Performance: A Microeconomic Evidence from Ivory Coast

In this paper, we investigate how political instability affects firms' performance in Ivory Coast.Using data from the World Bank's 2016 survey of 361 firms in Ivory Coast, we perform a robust endogenous treatment model to assess the causal impact of political instability on firm productivity.The results indicate that political instability is statistically significant and negatively associated with firm performance.More specifically, firms that perceive political instability as an obstacle have XOF 511 million less annual sales than those which do not perceive political instability as an obstacle.Therefore, the government of Ivory Coast should develop strategies to reduce political instability in order to ensure efficient and performing firms.
Word Autobots: Using Transformers for Word Association in the Game Codenames

Winning the social game Codenames involves combining cooperative and language understanding capabilities. We developed six cooperative bots designed to play the Codemaster and Guesser roles in the Codenames AI Competition and tested them using the provided framework and a round-robin tournament set. The bots are based on term frequency - inverse document frequency (TF-IDF), Naive-Bayes and GPT-2 Transformer word embedding. Additionally, Transformer-based bots were assessed and compared with the concatenation of word2vec and GloVe baseline bot developed by Codenames AI Competition creators. Results from this Transformer implementation rivals the concatenated bot in terms of win rates and guess precision and outperforms it in terms of minimum and average turns taken to win the game and training data load time. Additionally, in an initial evaluation performed with 10 human players, the Transformer agent performed slightly better than the baseline as Codemaster, but worse as a Guesser.
Squirrel – Crawling RDF Knowledge Graphs on the Web

The increasing number of applications relying on knowledge graphs from the Web leads to a heightened need for crawlers to gather such data. Only a limited number of these frameworks are available, and they often come with severe limitations on the type of data they are able to crawl. Hence, they are not suited to certain scenarios of practical relevance. We address this drawback by presenting Squirrel, an open-source distributed crawler for the RDF knowledge graphs on the Web, which supports a wide range of RDF serializations and additional structured and semi-structured data formats. Squirrel is being used in the extension of national data portals in Germany and is available at https://github.com/dice-group/squirrel under a permissive open license.
Identifying Fine-Grained Opinion and Classifying Polarity on Coronavirus Pandemic

In this paper, we explore the fine-grained opinion identification and polarity classification tasks using twitter data on the COVID-19 pandemic in Brazilian Portuguese. We trained machine learning-based classifiers using a few different methods and tested how well they performed different tasks. For polarity classification, we tested a cross-domain strategy in order to measure the performance of the classifiers among different domains. For fine-grained opinion identification, we provide a taxonomy of opinion aspects and employed them in conjunction with machine learning methods. Based on the obtained results, we found that the cross-domain data improved the results of the polarity classification. For fine-grained opinion identification, the use of a domain taxonomy presented competitive results for the Portuguese language.
Design of Intelligent Sorting Trash Dustbin Based on STM32

—Garbage sorting is related to many issues such as living environment, resource conservation and social civilization. Aiming at the problem of garbage sorting, an intelligent sorting trash dustbin was designed on the mechanical structure designed by ourselves, using the STM32F103ZET6 chip, the LD3320 speech recognition module, and the ultrasonic module. The garbage dustbin realizes the identification of garbage types, the compressed storage of recyclable garbage, and automatic bag sealing. This design not only protects the environment, reduces the risk of disease, but also makes resources recyclable, which indirectly brings unexpected benefits to humans.
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability

In networks of independent entities that face similar predictive tasks, transfer machine learning enables to re-use and improve neural nets using distributed data sets without the exposure of raw data.As the number of data sets in business networks grows and not every neural net transfer is successful, indicators are needed for its impact on the target performance-its transferability.We perform an empirical study on a unique real-world use case comprised of sales data from six different restaurants.We train and transfer neural nets across these restaurant sales data and measure their transferability.Moreover, we calculate potential indicators for transferability based on divergences of data, data projections and a novel metric for neural net similarity.We obtain significant negative correlations between the transferability and the tested indicators.Our findings allow to choose the transfer path based on these indicators, which improves model performance whilst simultaneously requiring fewer model transfers.
Training Of Organizers Of Educational Process In Using Automated Control Systems

The article provides an analysis of the training of managerial personnel of a higher educational institution in the field of using automated university management systems using various information and communication and pedagogical technologies, considering the specific activities of each specialist and their qualification requirements.The relevance of this study is due to the contradiction between the need and need for high-quality use of modern information technologies and the low level of ICT competence of the organizers of the educational process at the university.The procedure for the introduction of ACS in the university and the appropriate training of employees for professional activities in this system should be integral, systemic and then the methodology of the systematic approach is applicable to this procedure.The training of the organizers of the educational process in the field of using automated control systems should be carried out purposefully for a specific selected automated control system (ACS) implemented in a university, for which it is necessary to conduct an analysis of existing automated control systems of a university, to determine what types of activities and processes are being automated.The process of organizing the educational process in a university, as well as issues of university management, require comprehensive automation of its work.One of the main conditions for increasing the effectiveness of university management is the most effective informational relationship.Thus, we can say that ACS is a complex complex, the creation and implementation of which should be implemented by high-level specialists.
The Design of Earthquake Detector Using Pendulum Swing Based on ATMega328

Earthquake is a vibration that occur on the surface of the earth, earthquakes are usually caused by the movement of the earth's crust (earth's plates). Earthquakes are also used to indicate the area from which the earthquake occurred. Even though our earth is solid, it always moves and earthquakes occur when the pressure caused by that movement is too large to be able to withstand. One of the effects of the earthquake vibration itself that reaches the earth's surface and if the vibration is large enough can damage buildings and other infrastructure such as roads and bridges, railroad tracks, dams and others, causing casualties and property losses. So that we can avoid the danger caused by an earthquake, it is necessary to design an earthquake detection device with a pendulum swing method based on the ATMega328 Microcontroller. The ATMega328 microcontroller is the core of all the systems that exist in this design. In the design of earthquake measuring device using infrared sensors and photodiodes. Where the infrared beam construction is determined by the pendulum which detects the swing.
Comprehensive Program Of Professionally Oriented Training Of Specialists In Communication

The paper discusses a problem, which deals with modern higher education, specifically, professionally oriented training of highly qualified specialists in the field of communication.The problem is that a university graduate, having received knowledge and skills is not always able, according to the research, to apply them in a professional life.The purpose of the study is to analyze the current state of training of specialist communicators at a university, to propose, based on analysis and experience, a comprehensive program of professionally oriented training of specialist communicators at a university and to verify its efficiency.To solve the problem, the authors developed a comprehensive program of professionally oriented training of specialists communicators, built on integrative-competency, integrated and professional approaches, principles, implemented using the organizational and pedagogical conditions, innovative educational technologies and methods, information and technological resources.The goal of the comprehensive program is to develop a high level of professional competencies among future communicators.An integrated approach made it possible to include into the program a set of methods of theoretical and practical training, creative development, and motivation for independence and activity in both classroom and extracurricular activities.The main feature of this program regarding the development of the professional competencies of future specialist occurs not with the application of a traditional, knowledge-based approach, but with the creation and saturation of a valuable educational environment that develops motivation for independent work and student initiative.
Properties of Switch-List Representations of Boolean Functions

In this paper, we focus on a less usual way to represent Boolean functions, namely on representations by switch-lists, which are closely related to interval representations. Given a truth table representation of a Boolean function f the switch-list representation of f is a list of Boolean vectors from the truth table which have a different function value than the preceding Boolean vector in the truth table. The main aim of this paper is to include this type of representation in the Knowledge Compilation Map by Darwiche and Marquis and to argue that switch-lists may in certain situations constitute a reasonable choice for a target language in knowledge compilation. First, we compare switch-list representations with a number of standard representations (such as CNF, DNF, and OBDD) with respect to their relative succinctness. As a by-product of this analysis, we also give a short proof of a longstanding open question proposed by Darwiche and Marquis, namely the incomparability of MODS (models) and PI (prime implicates) representations. Next, using the succinctness result between switch-lists and OBDDs, we develop a polynomial time compilation algorithm from switch-lists to OBDDs. Finally, we analyze which standard transformations and queries (those considered by Darwiche and Marquis) can be performed in polynomial time with respect to the size of the input if the input knowledge is represented by a switch-list. We show that this collection is very broad and the combination of polynomial time transformations and queries is quite unique. Some of the queries can be answered directly using the switch-list input, others require a compilation of the input to OBDD representations which are then used to answer the queries.
Recreational potential of metaphorization in mental self-regulation of the students

Introduction: In the era of dynamic modern transformations in the education system, there is a need for effective psychological tools for updating individual psychological and life resources, self-regulation of students' mental states, contributing to the preservation and support of their mental health.&#x0D; Purpose: to substantiate the recreational potential of the metaphorization of mental states as a recreational method of self-regulation in the system of psychological well-being, to identify its individual psychological characteristics in students.&#x0D; Methodology: Experimental work was carried out on the basis of Kyiv National University of Culture and Arts, Tavriya National University named after V. Vernadsky. Five hundred and ten respondents were involved in the research process (18 - 45 years, 65% female; study specializations: “Arts”, “Design”, “Management”, “Psychology”). The research was based on the analysis of free associative experiment, method of cognitive interpretation, praximetric and executive methods for distributing certain semantic units&#x0D; Results: the most popular among students causes of dissatisfaction in the situation with stress and lack of satisfaction in basic psychological needs, negative and positive conditions at different levels, life furnishings, those situations when there are stinks; on the basis of the experience of the SUM inquirer, there are positive changes in the most of the majority of students. On the basis of the developed model of self-regulation of mental states by means of metaphorization (5 stages of the act of metaphorical creativity of resource states), when its recreational functions are realized, transformations of strategies of thinking and life take place, internal resources are activated&#x0D; Conclusions: The developed model of metaphorization of search of resource states is an effective means of stabilization and self-regulation of a mental condition of students that allows to be guided quickly in changing living and educational conditions, to choose optimum for each separate case methods and means of mental self-regulation
The impact of user involvement in software development process

&lt;p&gt;In software development process, user can take part in any phase of the process, depending on what model is being applied. Lack of user involvement can result in a poorly designed solution, or even a solution that conflicts with user’s needs. This review paper presents the impact of user involvement in software development process. In this study, different software development processes will be reviewed, show where the user usually gets involved in different models such as: Structural (Waterfall, V-model) and incremental (Scrum-extreme programming XP). As each model differs from the other, each of them has a different perspective of where user should take part and where they should not. This can be an asset that helps project managers, and leaders to develop suitable strategies to follow in their projects.&lt;/p&gt;
Neural Speed Reading Audited

Several approaches to neural speed reading have been presented at major NLP and machine learning conferences in 2017-20; i.e., "human-inspired" recurrent network architectures that learn to "read" text faster by skipping irrelevant words, typically optimizing the joint objective of minimizing classification error rate and FLOPs used at inference time.This paper reflects on the meaningfulness of the speed reading task, showing that (a) better and faster approaches to, say, document classification, already exist, which also learn to ignore part of the input (I give an example with 7% error reduction and a 136x speed-up over the state of the art in neural speed reading); and that (b) any claims that neural speed reading is "human-inspired", are ill-founded.
ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection

Accurate detection of emotions in usergenerated text was shown to have several applications for e-commerce, public well-being, and disaster management.Currently, the stateof-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data.In this paper, we propose Emotion-Sensitive TextRank (ESTeR ), an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs.Our model combines large-scale word co-occurrence information with wordassociations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings.Our similarity function can also be computed efficiently.We study a diverse range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.
Overview of the SustaiNLP 2020 Shared Task

We describe the SustaiNLP 2020 shared task: efficient inference on the SuperGLUE benchmark (Wang et al., 2019).Participants are evaluated based on performance on the benchmark as well as energy consumed in making predictions on the test sets.We describe the task, its organization, and the submitted systems.Across the six submissions to the shared task, participants achieved efficiency gains of 20× over a standard BERT (Devlin et al., 2019) baseline, while losing less than an absolute point in performance.
Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation

Recent work on the lottery ticket hypothesis has produced highly sparse Transformers for NMT while maintaining BLEU.However, it is unclear how such pruning techniques affect a model's learned representations.By probing Transformers with more and more lowmagnitude weights pruned away, we find that complex semantic information is first to be degraded.Analysis of internal activations reveals that higher layers diverge most over the course of pruning, gradually becoming less complex than their dense counterparts.Meanwhile, early layers of sparse models begin to perform more encoding.Attention mechanisms remain remarkably consistent as sparsity increases.
A Cryptographic Escrow for Treaty Declarations and Step-by-Step Verification

The verification of arms-control and disarmament agreements requires states to provide declarations, including information on sensitive military sites and assets. There are important cases, however, in which negotiations of these agreements are impeded because states are reluctant to provide any such data, because of concerns about prematurely handing over militarily significant information. To address this challenge, we present a cryptographic escrow that allows a state to make a complete declaration of sites and assets at the outset and commit to its content, but only reveal the sensitive information therein sequentially. Combined with an inspection regime, our escrow allows for step-by-step verification of the correctness and completeness of the initial declaration so that the information release and inspections keep pace with parallel diplomatic and political processes. We apply this approach to the possible denuclearization of North Korea. Such approach can be applied, however, to any agreement requiring the sharing of sensitive information.