Reviewerly/exHarmony huggingface dataset:
Automation and Digitalization of Business Processes – New Challenges Arising, Inter Alia, from the COVID-19 Pandemic

The scientific and technical progress is unstoppable. Information technologies have an impact on every spheres of social life, including accounting. The automation of business processes is a common part of business and accounting practice. Manual, regularly occurring transactions, which are made by accountants nowadays, are gradually being replaced by information technologies and automatic robotic systems. The accounting entities fully use cloud storages, artificial intelligence, blockchain technologies, etc. It is even assumed that some professions, due to robotization and automation of business processes, will gradually disappear within a few decades. The digitalization and automation of business processes have recently been accelerated in many cases by the crisis related to the COVID-19 pandemic. The paper focuses on the assessment of the level of automation in different types of accounting entities, with an accent on automation in accounting practice, as well as on new challenges arising from it, many of which are the result of measures taken in the last two years due to the COVID-19 pandemic.
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows

Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-free training of flows and proposes the energy objective, an alternative sample-based loss based on proper scoring rules. The energy objective is determinant-free and supports flexible model architectures that are not easily compatible with maximum likelihood training, including semi-autoregressive energy flows, a novel model family that interpolates between fully autoregressive and non-autoregressive models. Energy flows feature competitive sample quality, posterior inference, and generation speed relative to likelihood-based flows; this performance is decorrelated from the quality of log-likelihood estimates, which are generally very poor. Our findings question the use of maximum likelihood as an objective or a metric, and contribute to a scientific study of its role in generative modeling.
Seismic source characterization from GNSS data using deep learning

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Geophysical Research - Solid Earth. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Seismic source characterization from GNSS data using deep learningAuthorsGiuseppeCostantinoiDSophieGiffard-RoisinDavidMarsanLouMarilliDMathildeRadiguetiDMauroDalla MuraGaëlJanexAnneSocquetSee all authors Giuseppe CostantinoiDCorresponding Author• Submitting AuthorUniversité Grenoble AlpesiDhttps://orcid.org/0000-0002-8445-7117view email addressThe email was not providedcopy email addressSophie Giffard-RoisinUniversité Grenoble Alpesview email addressThe email was not providedcopy email addressDavid MarsanUniversite de Savoie Mont Blancview email addressThe email was not providedcopy email addressLou MarilliDISTerreiDhttps://orcid.org/0000-0003-3610-5341view email addressThe email was not providedcopy email addressMathilde RadiguetiDISTerreiDhttps://orcid.org/0000-0002-3877-9393view email addressThe email was not providedcopy email addressMauro Dalla MuraGIPSA-Lab; Institut Universitaire de Franceview email addressThe email was not providedcopy email addressGaël JanexUniversité Grenoble Alpesview email addressThe email was not providedcopy email addressAnne SocquetUniversité Grenoble Alpesview email addressThe email was not providedcopy email address
Key Extraction Using Ambient Sounds for Smart Devices

To secure communications, this article presents a key extraction scheme for smart devices using ambient sounds. Specifically, it is designed for the scenario when smart devices do not have any pre-loaded secrets. Moreover, it can be implemented when smart devices have no access to the online trusted third party or the Network Time Protocol server. In our scheme, smart devices achieve synchronization by making use of ambient sounds. Then, they calculate and obtain the pairing distance and secure distance by applying a band-pass filter. Completing these operations, two smart devices can directly extract a communication key. We analyze the security of our scheme and evaluate the performance of it by implementing the scheme using a few off-the-shelf devices. The experimental results indicate that compared with related schemes, the bit generation rate of our scheme has significant improvement (increases at least 80%), and it can reach 312 bit/s.
The Moderating Effect of Employee Well-Being between Employee Engagement and Firm Performance: Evidence from the Apparel Industry in Sri Lanka

Owing to globalization and dynamic business environment, organizations quest for sustaining their business performance by crafting various compelling strategies. Recently, employee engagement and well-being have gained dramatic popularity among managers and practitioners as a salient strategy to augment firm performance. Notably, the research studies on the nexus among employee engagement, employee well-being and firm performance are still nascent, and past findings are obscure. Moreover, the studies that have been conducted in developed countries cannot be generalisable to the developing countries since the variables are subject to country culture specific nature. To fill the void left by the earlier studies, the present study is designed to investigate the moderating role of employee well-being on the relationship between employee engagement and firm performance based on the sampled from Sri Lankan apparel industry. Using the convenience sampling technique, data were marshalled through a self-reported questionnaire from 177 employees working in the apparel industry in Sri Lanka. The data were analyzed using SmartPLS. The study shows a significant positive relationship between employee engagement and firm performance. The study further reveals a moderating effect of employee well-being on the relationship between employee engagement and firm performance”. The study contributed to the fronters of extant HRM literature and provided many useful practical implications that have been discussed at the end of the paper. Importantly, the present study adds to the evolving debate on the critical role of employee engagement in enhancing firm performance through employee well-being.
A framework for designing applications to support knowledge construction on learning ecosystems

We are building a society increasingly immersed in digital contexts. In this transformation process, mainly because of the daily use of technology, several contexts of our lives are changing, including education and the school itself, which have especially received IT support to be carried out in an informal and personalized way. Learning ecosystems, if properly promoted, could be a partner of this school. In this paper, we propose a Framework to support the modeling of Smart Learning Environments (SLE) capable of stimulating interactions (including location-based interactions) in the diverse ecosystems in which we participate and thus support the construction of knowledge. We also present some SLEs designed with the Framework to demonstrate the practical result of its use and others for validation purposes.
Evaluating and Inducing Personality in Pre-trained Language Models

Standardized and quantified evaluation of machine behaviors is a crux of understanding LLMs. In this study, we draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. Originating as a philosophical quest for human behaviors, the study of personality delves into how individuals differ in thinking, feeling, and behaving. Toward building and understanding human-like social machines, we are motivated to ask: Can we assess machine behaviors by leveraging human psychometric tests in a principled and quantitative manner? If so, can we induce a specific personality in LLMs? To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors; MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories. By systematically evaluating LLMs with MPI, we provide the first piece of evidence demonstrating the efficacy of MPI in studying LLMs behaviors. We further devise a Personality Prompting (P^2) method to induce LLMs with specific personalities in a controllable way, capable of producing diverse and verifiable behaviors. We hope this work sheds light on future studies by adopting personality as the essential indicator for various downstream tasks, and could further motivate research into equally intriguing human-like machine behaviors.
Introduction

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book aggregates the research of many well-regarded scholars from across the continent on social media dissidence in a variety of African countries and the legal and extra-legal efforts governments have invented to contain the vibrance of the social media scene. It traces the history and evolution of social media in Africa, explains the factors that aided the growth of social media on the continent, and argues that the progressive quotidianisation of social media in Africa signals a demotic turn in the media ecology of the continent. The book then takes a political economy perspective to show how the state can exclude marginal voices from the social media public sphere through collusion with neoliberal telecommunications companies, which are regulated by the government, to price data plans beyond the reach of economically dislocated but politically active citizens.
A Study on Big Data and It’s Security Approaches

Big data has steadily become a trendy topic in study and business, and it has been rapidly expanding. It is a collection of organized, informal, and unorganized data that is continuously rising from many origins such as web servers, cellular phones, social media, private and public clouds, and so on. Big data is used by many organizations and corporations, and its security and privacy have become progressively important. However, there is a glaring inconsistency between the security and privacy of big data and its broad use. In this paper, the difficulties of security and privacy in big data are discussed. Then some of the strategies and approaches that may be employed to ensure the security and privacy of large data were found. Big Data, Big Data Dimensions, Security, and Privacy are all terms used to describe large amounts of data.
A Simple Data Mixing Prior for Improving Self-Supervised Learning

Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component for advancing recognition models. In this paper, we focus on studying its effectiveness in the self-supervised setting. By noticing the mixed images that share the same source images are intrinsically related to each other, we hereby propose SDMP, short for $\textbf{S}$imple $\textbf{D}$ata $\textbf{M}$ixing $\textbf{P}$rior, to capture this straightforward yet essential prior, and position such mixed images as additional $\textbf{positive pairs}$ to facilitate self-supervised representation learning. Our experiments verify that the proposed SDMP enables data mixing to help a set of self-supervised learning frameworks (e.g., MoCo) achieve better accuracy and out-of-distribution robustness. More notably, our SDMP is the first method that successfully leverages data mixing to improve (rather than hurt) the performance of Vision Transformers in the self-supervised setting. Code is publicly available at https://github.com/OliverRensu/SDMP
Total Factor Productivity and Institutional Indicators: Panel Data Analysis

Total Factor Productivity (TFP) plays a very important role in achieving growth and development. The TFP level and its development process over time are an important determinant of the “diversity in the level of development” existing in today’s economies. This study examines the theorotical studies on the place and importance of TFP in economic theory and aims to discuss the relationships with respect to the institutions in the context of current discussions. In the study, the effect of institutional indicators on TFP for the period of 2005-2018 was analyzed with the panel data method in the model of 117 countries. The results of the analysis showed that the institutional indicators and the human development index have a positive and significant effect on the TFP level of countries.
An Efficient HTN to STRIPS Encoding for Concurrent Plans

The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems in terms of decompositions of tasks into subtaks. Many techniques have been proposed to solve such hierarchical planning problems. A particular technique is to encode hierarchical planning problems as classical STRIPS planning problems. One advantage of this technique is to benefit directly from the constant improvements made by STRIPS planners. However, there are still few effective and expressive encodings. In this paper, we present a new HTN to STRIPS encoding allowing to generate concurrent plans. We show experimentally that this encoding outperforms previous approaches on hierarchical IPC benchmarks.
Abstraction Based Text Summarization using NLTK

As there is huge amount of content is produced in our day-to-day life using electronic devices in various form fields. The main problem arises from here huge form information once to analyzing and understanding the meaning of text become difficult and time taking, so the Text summarization is introduced. There are two types of text summarization types one is Extraction based text summarization and Abstraction based text summarization.
TEACHING OF SPEAKING USING ASYNCHRONOUS E-LEARNING APPLICATIONS

This research aimed to find out the use of asynchronous e-learning applications in teaching speaking and describe students’ perceptions of asynchronous e-learning applications in online English-speaking courses. The data in this research was collected qualitatively by using observation and focus group discussion. The observation was conducted on the use of asynchronous e-learning applications in teaching speaking. The focus group discussions have been done with thirteen students of Tadris English Department, UIN Datokarama Palu, who were taught using asynchronous e-learning in 2020/2021. This research showed that 1) in this asynchronous teaching and learning, lecturer and students use FlipGrid, WhatsApp group, and Google classroom. 2) the students had a positive perception of the use of asynchronous e-learning in online speaking courses, such as independent learning, and becoming more active, whereas the students also have negative perceptions such as lack of interaction and technical support.
ANALYSIS OF EMOTIONALLY MEANINGFUL WORDS IN MICROCONTEXTS

The article provides an extensive analysis of emotionally meaningful words in microcontexts. In it focuses on words made using text emotionality and metaphors. The peculiarity of contextual emotionality is that it is clearly manifested as a result of an in-depth special analysis of similar or similar contexts, in the presence of author's comments in the text that reveal the essence of contextual emotionality. Text is also a communicative unit through which people communicate, express their thoughts and emotions.
Spam Detction Using Information Retrieval And Machine Learning

In the 21st century, World has become a large ball stuffed with large amounts of information. People habituated to this type of communication, they're messages, emails and call etc. Keeping of these things in mind, it's obvious to induce spam messages, most of them are commercial, but repeatedly such emails or messages may contain some phishing links that have malware. so as to beat from this problem, we might prefer to propose a mechanism to detector identity such spam emails so time and memory space of the system may be stored-up to an excellent extent. during this paper we've got come up with an answer to detect the spam, involving the concepts and algorithms of data retrieval and Machine Learning.
Sistem Informasi Akademik Berbasis Web Pada Sekolah Menengah Kejuruan Negeri 2 Kota Bengkulu

The development of technology is currently very influential in the world of education. Inthis era of ever-increasing technology, it is required to work quickly and precisely. Likewise in the world of education, especially in vocational high schools, it is very necessary to have technology that is able to work in the field of school operations and for the smooth activities of students and teachers. Academic information system is a system built to assist educational institutions in managing student academic data, such as teacher data, student data, grade data, and other academic data. This system is used to make it easier to manage and display information related to academics. State Vocational High School 2 Bengkulu City is an institution engaged in the field of education that has utilized technology such as computers. However, at SMK Negeri 2 Bengkulu City, there is still no web-based academic information system. This study aims to create an Academic Information System at SMK Negeri 2 Bengkulu City. The research method used is the method of system design and development, such as the design of Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), Flowcharts, Databases (Files), Menus, Interfaces (Input/Output). Academic information system is made using CodeIgniter Framework, Hypertext Preprocessor (PHP) and MySQL database. The result of the research is that the academic information system created can assist in simplifying the data collection process for teachers, students, classes, making lesson schedules and assessments using a website-based academic information system.
survey of designing tools for chatbot application

The invention of chatbot brought us to the new era of technology. As Artificial Intelligence continues to gain traction in modern customer service, chatbots are becoming an integrated part of user experience. A chatbot has the capability to study and influence user behavior by asking and answering user’s questions. Chatbots help with human- computer interaction and address customers’ queries instantly, without the need for a support agent. Now a days there are many tools available for designing and deploying the chatbot such as Google dialogflow, Amazon Lex, Microsoft bot, Facebook Wit.AI, RASA, IBM Watson, Botpress, Botkit, Botsify, Pandorabots, MobileMonkey, SAP Conversational AI, CoRover, Chatfuel and many more. This paper represents the different types of tools used for developing chatbot.
Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records (Preprint)

<sec> <title>BACKGROUND</title> Electronic Health Records (EHR) with large sample sizes and rich information offer great potential for dementia research but current methods of phenotyping cognitive status are not scalable. </sec> <sec> <title>OBJECTIVE</title> To evaluate whether Natural Language Processing (NLP)-powered semi-automated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status </sec> <sec> <title>METHODS</title> In this diagnostic study, we developed and evaluated a semi-automated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) Healthcare using NAT or traditional chart reviews. Patient charts contained EHR data from two datasets: (1) Records from January 1, 2017 to December 31, 2018 for 100 Medicare beneficiaries from the MGB Accountable Care Organization (ACO), and (2) Records from 2-years pre-COVID diagnosis to the date of COVID diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. </sec> <sec> <title>RESULTS</title> NAT adjudication provided higher interrater agreement (Cohen k=0.89 vs. k=0.80) and significant speed up (time difference mean [SD]: 1.4 [1.3] minutes, P &lt; 0.001; ratio median [min, max]: 2.2 [0.4, 20]) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen k=0.67). In the cases that exhibited disagreement with manual chart review, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semi-automated NLP features. </sec> <sec> <title>CONCLUSIONS</title> NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHR. </sec>
체험위생교육이 초등 저학년 아동의 개인위생 태도와 실천에 미치는 영향

Objectives This study was an interventional study verifying the effects of experiential hygiene education on lower grade of elementary school student’s personal hygiene attitudes and practices&#x0D; Methods In July 2020, data collection and intervention were conducted among 16 lower grade lower grade elementary students who visited the regional children's center and participated in experiential hygiene education. The data was analyzed using SPSS/WIN 21.0.&#x0D; Results The analysis showed that the perception of personal hygiene after experiential hygiene education (t = 4.39, p = .001), attitude toward hand hygiene (t = 6.70, p &lt; .001), and degree of practice of hand hygiene (t = 3.02, p = .009) and wearing a mask (t = 7.04, p &lt; .001) were significantly improved.&#x0D; Conclusions The experiential hygiene education program developed in this study can be used to influence the attitudes toward and practice of personal hygiene, such as hand hygiene and wearing masks, in lower grade elementary students.&#x0D;
Issues of introduction of electronic personnel document management in medical organizations

Informatization is gradually spreading to all new areas of labor relations. Therefore, it is logical to switch from the transfer to electronic form of individual documents (electronic sick leave, electronic workbooks, etc.) to the comprehensive automation of the entire personnel document flow. The proposed material presents an analysis of changes in labor legislation devoted to this area.