Reviewerly/exHarmony huggingface dataset:
Impact of Internal Auditing Practices on the Internal Control System: Pre- &amp; Post-Pandemic Comparative Study

Internal auditing is an impartial practice, which involves providing assurance and consulting services that are aimed at improving a company’s business operations. The recent business malfunctions and increased fraudulent and corruption cases in both developed and developing countries have raised questions over the effectiveness of internal auditing practices. The key objective of this study was to assess the effect of internal auditing practices on the internal control system quality pre- and post-COVID-19 pandemic. This study proposes organizational independence, objectivity, audit work performance, scope of work, and professional proficiency as the solution for business operations. The study also advocates that risk assessment and control activities are essential elements of the internal control system to evaluate and enhance the quality of audit work. The data for this study were collected from 89 respondents from 31 Iraqi banks using a survey questionnaire technique. The data were analyzed using SPSS and Smart-PLS. The findings present that the organizational independence impacted the control activities and the scope of work impacted the risk assessment before the pandemic. Objectivity impacted control activities and objectivity, organizational and scope of work impacted risk assessment post COVID pandemic. Moreover, the multigroup technique was used for comparative analysis and the results also showed that only organizational independence showed significant impact on control activities. This study is unique in a way that this is the only study that compares the pre- and post-COVID pandemic impact of internal auditing practices on the internal control system.
Internet of Things Based Blockchain Technology for Gas Station

The protection of information sent over the Internet has become very important because of the possibility of being hacked and leaking important or confidential information. Therefore, many studies have been conducted in the field of information security and many ways have emerged to protect the information, but blockchain technology is the most prominent technology currently due to its high potential in maintaining the study was conducted on the most important blockchain algorithms, namely the Proof of Authority (POA) algorithm and the Proof of Work (POW) algorithm. The study aimed now at the best and most reliable algorithm to protect the parameters sent via the Internet of Things technology. The POA algorithm is the first candidate to win the advantage in theory, but After applying the study and verifying the actual results, it was confirmed that the POA algorithm is superior and highly capable of protecting information, in addition to speed in executing calculations, less memory consumption, less execution time, fewer Nonce to obtain the correct hash, and many other advantages, and the obtained result was the PoA algorithm is significantly faster with a difference of 46/s to create the blockchain, and it also requires less memory than the PoW algorithm, with a difference of 1024KB. Blockchain (B.C) algorithms will be applied to the Internet of Things (IoT) technology to obtain IoT technology that is completely encrypted from hacking and information cannot be tampered with. The transmission within the network, whether it is a local or global network. The new technology will be applied to a gas station information transmission system to generate electric power. The aim of this paper are to make the smart grid of the gas station more secure and private.
gTheoryShiny: An online application for interactive G-theory inference

gTheoryShiny is an R/Shiny application for interactive construction, and inference of generalizability theory (also named G-theory) for performance assessment, which provide a friendly GUI for users lacking of programming skills. It's heavely based on lme4 for multilevel modeling. This manual illustrates the functionality of the app.
Legal Service Post Application at the Soppeng District Attorney Office

With the development of technology, it makes work faster and more efficient, such as in legal services or the provision of legal services by the State Attorney (JPN) in writing or orally to the public, which includes individuals and legal entities as well as legal service posts. However, with the habit of people coming to the office to carry out consultations which take time for services, this prompted us to create a Web-Based Legal Service Post Application at the Soppeng District Attorney's Office. With the waterfall research method in application design there is analysis, design, implementation, testing and maintenance so as to produce a Web-Based Legal Service Post Application that can facilitate the community and prosecutor's staff in the legal service process so that this activity can be carried out without having to go directly to the office.
The Use of the Token Economy in ESL Classrooms During the COVID-19 Pandemic

The token economy is a pivotal notion in educational settings for maintaining good classroom behaviours among learners. Despite the burgeoning number of studies conducted to determine the efficiency of the token economy, most have been primarily interested in the learners’ perspective. With a gap in the literature to investigate the phenomenon from the educators’ perspectives, the present study surveyed 154 English teachers in Malaysia during the COVID-19 pandemic. The respondents who taught at government schools were involved in examining the token economy as an approach to behavioural management and learning engagement. The data collection was performed through a self-completed questionnaire survey of Google Form via various online platforms. The study showed that Malaysian English teachers from government schools were in complete agreement that the implementation of the token economy in ESL classrooms is effective in eliciting desired behaviours and reducing disruptive behaviours among learners. Social and physical reinforcement were the most effective forms of the token economy in the classroom as opposed to other types of reinforcement. The implications of the study include the possibility of the token economy becoming a catalyst in the educational landscape for changing behaviour and boosting learning motivation, particularly among young learners who are usually emotionally sensitive.
Integrating Blockchain and Machine Learning for Healthcare Systems

Health care systems are that systems which help the patients reach directly the concerned specialist. These are the systems which are having lots of advantages during the pandemic situation and in high emergency situations.In the proposed work user will search for the disease summary (disease and treatment related information) by giving symptoms as a query in the search engine. Initially when a pdf is downloaded and saved in the system it first performs per processing on the data in the document and the extracted relevant data is stored in the database. The symptoms entered by the user are further classified using SVM classifier to make the further process easier to find the semantic keyword which helps to identify the disease easily and quickly. Then the semantic keyword found is matched with the stored medical input database to identify the exact disease related to that keyword present. Once the disease related to the symptom is identified, it is sent to medical database to extract the articles pertaining to that disease. The preprocessing process involves tokenization, removal of stop words and stemming. Followed by that, relevant information is extracted using the keyword searching algorithm. In our implementation of our proposed system, we have used SVM classifier which gives us an improved result. The decentralized database where transactions get recorded in append only shared ledger has many advantages in healthcare industry. In medical treatment, the complete history of patient is very important and value is added when same information is accessed by different parties. The convergence of these two technologies can give highly accurate results in terms of machine learning with the security and reliability of Blockchain Technology. Keywords: Machine Learning, Tokenization, Stemming, SVM classifier, Naïve Bayes, Decision Trees, Blockchain.
A Unified Dialogue Framework Based on Joint Generation of Dialogue Acts and Responses

Abstract Currently, building an end-to-end dialogue system for multi-domain task-oriented dialogue has some enormous challenges. Dialogue systems must obtain entire dialogue states from all relevant domains in order to respond correctly. However, multiple domains are involved in the actual dialogue process, which increases the difficulty of obtaining the dialogue state. In addition, dialogue systems process diverse information such as dialogue history, dialogue state, dialogue act, and database across domains, resulting in natural responses. These complex dialogue information brings greater difficulties to response generation. In this paper, we propose a novel unified dialogue framework for multi-domain dialogue in task-oriented dialog, including three modules: Encoder, Dialogue State Tracker and Multiple Decoders. First, encoder module encode all text input into continuous representations. Secondly, we train the dialogue state tracker module with a stacked-attention architecture.It learns information from slot-atten structure and domain-atten structure to track dialogue state. Then, multiple decoders module consists of act decoder structure and response decoder structure. It combines information from different textual inputs while modeling dialogue act. Finally, we jointly train all the above modules to generate system responses. We conducted extensive experiments on the dataset MultiWOZ. The experimental results show that our model achieves state-of-the-art results on evaluation metrics compared to models from previous work.
Men dominate conference Q&amp;A sessions — including online ones

‘Question and manswer’ sessions are the norm at both in-person and virtual events, even when there’s a good gender balance. ‘Question and manswer’ sessions are the norm at both in-person and virtual events, even when there’s a good gender balance.
Cognitive Development in the Golden Ages

When children develop their cognitive abilities, two processes occur within them, namely the first is the process of organizing information, namely the process of linking information received with existing knowledge structures, and the second is the process of adaptation, which contains two activities; (1) combining received knowledge (assimilation) and (2) changing the existing knowledge structure with a new knowledge structure (accommodation). This change results in a multi-faceted interaction network. Developmental factors are also influenced by the phase before birth (prenatal), at birth (perinatal) and after birth (postnatal). At least it can be concluded that every child is unique and no two children have exactly the same cognitive abilities, even though they were born in identical twins from one egg cell. Early childhood is in the most rapid phase of cognitive development. Cognitive development of children begins in the womb. It is influenced by many factors, including physical maturity, experiences and interactions between children and the people around them. Cognitive skills in children occur through an elaboration process in the brain (mind) not outside the brain a brief statement of the results and implications, if any.
Time-series association based dynamic graph evolution for recommendation

Abstract Modeling the interactions between users and items to accurately predict ratings is very crucial for improving the performance of recommendation. Although existing graph-based methods have achieved great progress in predicting ratings for recommendation, they usually need additional side information which is difficultly obtained, and ignore the temporal associations between items (users) when constructing graphs. In this paper, we propose a time series association based dynamic graph evolution model for recommendation, which can capture not only the information propagation on multiple graphs but also the temporal association between items (users) by constructing a time series item association graph and a user similarity graph. Specifically, the proposed model consists of two main components: recurrent graph construction component and message propagation component. The former recurrently constructs the time series item association graph and the user similarity graph only from the interactions between items (users) to capture the temporal associations between items (users), which further helps the process of constructing two auxiliary graphs. The latter refines user and item representations by aggregating the influence information propagated from multiple high-order neighbors. Finally, the refined representations of users and items are used to predict ratings on all items a user has not interacted with. The experimental results illustrate that our method outperforms the state-of-the-art methods on five real-world datasets.
Termination of Recursive Functions by Lexicographic Orders of Linear Combinations

This paper presents an improvement to Isabelle/HOL’s lex- icographic termination algorithm. This paper also shows how to encode positive vector-component maximisation as a linear program.
ARENA: Enhancing Abstract Refinement for Neural Network Verification

Formal verification on neural networks is earnestly needed to guarantee its robustness property. In this paper, we propose an abstract refinement process that leverages the double description method and the new activation relaxation technique to improve the verification precision and efficiency. We implement our proposal into a verification framework named ARENA. And the experimental results show that ARENA yields significantly better verification precision compared to the existing abstract-refinement-based tool DeepSRGR. It also identifies adversarial examples, with reasonable execution efficiency. Lastly, it verifies more images than a state-of-the-art verifier PRIMA.
Reconstruction and Development of Indonesian Language PAT Question Items Aspects of Class VII Literary Expression MTs N 1 Banjarnegara Academic Year 2021/2022

Assessment in the 2013 curriculum is seen as having more complexity than the assessment system in the previous curriculum. So that in this study will discuss "Reconstruction and Development of Instruments for Assessment of Indonesian Language Learning Outcomes Aspects of Literary Expression for Class VII Students of MTs 1 Banjarnegara for the 2021/2022 Academic Year" as an effort to reduce errors in making assessment questions about aspects of literary expression and present a form of improvement for errors that have been conducted. The approach in this research is a qualitative approach. The type of research used is content analysis research. Data analysis refers to the rules for writing questions contained in the 2013 Curriculum Assessment Guide issued by the Ministry of Education and Culture with due regard to material, construction, and language/cultural aspects. The results showed that there were still 2 Indonesian Year End Assessment (PAT) questions for class VII MTs 1 Banjarnegara for the 2021/2022 academic year in terms of literary expression which did not meet good quality standards.
Case Study of Narrative Text Transitivity System in Junior English Teaching Materials

This research was conducted due to the need of close analysis towards reading text to make conclusions about the appropriate reading passages in textbooks in the school instruction. Transitivity system analysis based on Halliday's theory is the important elements in order to pursue the students' successful learning of reading comprehension by analyzing the process types that characterizes the reading text. This study aims at investigating how the transitivity system in Narrative text of Think Globally Act Locally grade 9th textbook are constructed. This research was conducted using a descriptive method. Data source of the research was text document of narrative text of Junior High School English Textbook for grade 9 th entitled Think Globally Act Locally. The data were analyzed based on theory proposed by Gay et.al [1] ;Data Managing, Reading, Description, Classifying, Interpreting and Writing the Report.This study found that material process was the most dominant process types in narrative text.Most of the process valences found in the reading passages were monovalent and bivalent.The most dominant circumstances elements are adverb of place.
THE ROLE OF COMMUNITIES LIVING IN WATERSHEDS TOWARDS FLOOD DISASTER MANAGEMENT : SYSTEMATIC REVIEW

Introduction. Disaster is a series of events that can threaten and disrupt human life, caused by natural and non-natural factors. A flood disaster is a natural disaster that can occur due to extreme storms in the upper watersheds, climate change, and geomorphological conditions of soil slopes, erosion, and sediment material. Flood disasters will have an impact on the social situation, people's economy, health status and death. Method. A systematic review was conducted through several stages, namely making questions, identifying, eligibility, selecting article inclusions, screening, and appraisal. The selection process was listed in the Systematic review framework and 10 articles are obtained. Later, articles were summarized and rated according to The Joanna Briggs Institute (JBI) in each article. Result&amp;Analysis. The data shows that the results vary according to the system, and the conditions in which the study was conducted. Increasing the role of the community in flood disaster management efforts is done by increasing community knowledge, skills and experience, related to an efficient disaster management system, with several methods, namely by giving questionnaires and conducting interviews with the community or family, special training and simulations. Discussion. To increase the awareness and role of the community in managing flood disasters, several methods are applied. The simulation method is an educational effort that can be used to improve people's knowledge, skills, and experience in conducting flood management activities.
Remote Health Monitoring System for the Elderly based on mobile computing and IoT

Due to the increasing technological innovation over the last decades, the average life expectancy of a human being has been increasing exponentially. Although this is an excellent step forward for humanity, it has led older population to being more prone to illness, making them more vulnerable to accidents such as falls. In this article a study is made on the existing literature in non-intrusive remote health monitoring systems, towards the design and implementation of an IoT system capable of identifying fall situations and monitor cardiac data. A Systematic Literature Review (SLR) method was considered in this work, focused on reviewing the existing literature on remote health monitoring systems, having fall detection algorithms, based in IoT. The Design Science Research (DSR) methodology was used to seek to enhance technology and science knowledge about this paper’s topic, through the creation of an innovative artifact.The system includes a smart watch (Lily-Go T-Watch-2020 V2), programmable in C under Arduino IDE to detect falls and a photoplethysmography monitoring unit (PPG) based on a Onyx 9560 Bluetooth oximeter, capable of measuring the user’s blood oxygen percentage (SpO2) and heart rate, in real time. It also provides remote monitoring through a user-friendly website to visualize live data about the status of the user. The system was tested in volunteers to show the effectiveness of remote health monitoring systems for the elderly population.
APPLICATION OF FRACTIONAL DOMINO MEDIA TO IMPROVE STUDENT ACTIVITIES AND LEARNING OUTCOMES IN ELEMENTARY SCHOOLS

This study aims to determine the implementation and improvement of 5th grade mathematics learning outcomes through the use of fractional domino media. The research method uses Action Research Classroom in two cycles with the research procedure consisting of four stages, namely: 1) planning stage, 2) implementation stage, 3) observation stage, and 4) reflection stage. The subjects in this study were 28 fifth grade students at SDN Sentul 3 Blitar City. Research data in the form of observations of student learning activities and test data on learning outcomes. From the data collected, it was obtained that the learning activities of students increased from 70.36% (good) in the first cycle to 87.50% (very good) in the second cycle. Student learning outcomes have also increased from 54% with an average 73,21 in the first cycle to 93% with an average 83,56 in the second cycle.
Elements of effective machine learning datasets in astronomy

In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increasingly important tool for analyzing and understanding the large-scale flood of data in astronomy. To take advantage of these tools, datasets are required for training and testing. However, building machine learning datasets for astronomy can be challenging. Astronomical data is collected from instruments built to explore science questions in a traditional fashion rather than to conduct machine learning. Thus, it is often the case that raw data, or even downstream processed data is not in a form amenable to machine learning. We explore the construction of machine learning datasets and we ask: what elements define effective machine learning datasets? We define effective machine learning datasets in astronomy to be formed with well-defined data points, structure, and metadata. We discuss why these elements are important for astronomical applications and ways to put them in practice. We posit that these qualities not only make the data suitable for machine learning, they also help to foster usable, reusable, and replicable science practices.
Modelling a Blockchain for Smart Contract Verification using DeepSEA

To create trustworthy programs, the 'gold standard' is specifications at a high-enough level to clearly correspond to the informal specifications, and also a refinement proof linking these high-level specifications down to, in our case, executable bytecode. The DeepSEA system demonstrates how this can be done, in the context of smart contracts on the Ethereum blockchain. A key component of this is the model of the blockchain on which the smart contracts reside. When doing proofs in DeepSEA, it is critical to have such a model, which allows for the writing of specifications at a high-level clearly corresponding to informal specifications. A candidate model for doing so and its usefulness for carrying out proofs is discussed in this paper.