Reviewerly/exHarmony huggingface dataset:
Analysis and Design of User Interface and User Experience of Regional Tax Enterprise Resources Planning System with Design Thinking Method

Regional Tax is one of the main sectors of independent income owned by each City or Regency to fund infrastructure development, education development, health services, and much more. The government agency that manages this regional tax is Badan Pendapatan Daerah, abbreviated as Bapenda. In particular, the West Bandung Bapenda has not implemented an online-based Regional Tax service system. This management system cannot be developed, and taxpayers have no independent account management. The appearance and user experience of the existing system has not matched the current design trends and the latest organizational structure. This greatly obstructs the services of Bapenda to taxpayers due to the lack of digital transparency. The system's flow is quite convoluted, thus affecting the quality and slow access to information. These limitations will be resolved through structured research using the Design Thinking and Usability Testing method for testing so that later it can be translated into an Enterprise Resources Planning system design with a good User Interface / User Experience. This method is structured following five phases: empathy, define, ideate, prototype, and test. The empathy phase aims to understand user problems, the define phase identifies and maps problems, the ideate phase aims to generate ideas, the prototype phase aims to implement the design based on the results of the previous steps, and the test phase aims to test the user. The final phase is the test phase and aims to test the user. The results obtained after applying this method follow user expectations.
Uzun Kısa Vadeli Bellek Tekrarlayan Sinir Ağı Kullanarak Bitcoin Kripto Para Birimi Fiyat Tahmini

Due to its growing popularity and commercial acceptance, cryptocurrency is playing an increasingly essential role in altering the financial system. While many people are investing in cryptocurrency, the dynamic characteristics and predictability of cryptocurrency are still largely unknown, putting investments at risk. In this paper, we attempt to anticipate the Bitcoin price by taking into account a variety of factors that influence its value with the highest possible accuracy using (LSTM) Recurrent Neural Network. The data we use in this work includes updated daily records of many aspects of Bitcoin pricing over a five-year period. Since the cryptocurrency (Bitcoin) data is so volatile, we implement an effective pre-processing of the data in order to have a better prediction result. With this solution, we gain accuracy of 95.7% and RMSE of 0.05. Furthermore, we compare this work with other existing methods based on performance and accuracy. This comparison demonstrates that utilizing LSTM with adequate hyperparameter tweaking is one of the most efficient ways for cryptocurrency price prediction.
Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia

The need for eggs that continues to increase will not increase with large egg production so that there is a shortage of egg supplies which results in high egg prices. It is necessary to group egg production in Indonesia to find out which areas fall into the high cluster and which areas fall into the low cluster. This study aims to classify the egg production of laying hens in Indonesia. The method used is the K-Means Clustering method which is a popular clustering method. To find out how optimal the number of clusters in the K-Means method is for grouping egg production in Indonesia, the researcher evaluates the DBI value of each number of existing clusters. In this study, 8 clusters were used, namely 2 clusters, 3 clusters, 4 clusters, 5 clusters, 6 clusters, 7 clusters, 8 clusters, and 9 clusters. The results of measuring the DBI value are the number of clusters 2 = 0.215, the number of clusters 3 = 0.149, the number of clusters 4 = 0.146, the number of clusters 5 = 0.157, the number of clusters 6 = 0.180, the number of clusters 7 = 0.205, the number of clusters 8 = 0.192 and the number of clusters 9 = 0.154. This study shows that the best number of clusters is the number of clusters 4 with the smallest DBI value of 0.146.
On the Use of a Convolutional Neural Network to Predict Perceptual Quality of Images without Reference for Different Viewing Distances

A plethora of image quality metrics have been proposed in the literature. These metrics aims to estimate the perceptual image quality automatically. One important key aspect that the perceived quality is dependent on is the viewing distance from the observer to the image. In this study, we propose to consider this information by estimating the quality of a given image without a reference image for different viewing distances. For that, a Convolutional Neural Network (CNN) model was used in this study. Relevant patches are first selected from the image and they are then used as inputs to the CNN. The selection is here based on saliency information. The used CNN is composed of two outputs that correspond to the predicted subjective scores for two viewing distances (50 cm and 100 cm). Our method was evaluated using the Colourlab Image Database: Image Quality (CID:IQ) that provides subjective scores at two different viewing distances. The obtained results show the efficiency of our method.
REMOTE ENGLISH TEACHING IN UKRAINE: CHALLENGES AND ACHIEVEMENTS

The goal of any modern teacher is to facilitate students’ journey to becoming life-long learners and global citizens of the 21st century. As digital natives, generations Z and Alfa feel like fish in the water with online learning as most of their life they spend online now. But for most teachers, online education has become a severe trial during the coronavirus pandemic. They felt unprepared for online teaching, especially those who have never had the opportunity to teach online beforehand. Working remotely was a new experience for them without theoretical preparation and technical provision and support. They were not competent and confident in the online educational platforms, features, and benefits. As A. Collins and R. Halverson confirm, “there are deep incompatibilities between the demands of the new technologies and the traditional school. Technology makes life more difficult for teachers. It requires new skills teachers have often not learned in their professional development.” [1]. And the quality of teaching depends on how confident you feel using technologies and what you want your students to achieve because “well-planned online learning experiences are meaningfully different from courses offered online in response to a crisis or disaster.” [2]. For longer-term learning goals like language learning, which involve the development of knowledge and skills over time, online learning requires structure, support, interaction, and teacher’s experience and competence.
Dynamic Space-Time Scheduling for GPU Inference

Serving deep neural networks in latency critical interactive settings often requires GPU acceleration. However, the small batch sizes typical in online inference results in poor GPU utilization, a potential performance gap which GPU resource sharing can address. In this paper, we explore several techniques to leverage both temporal and spatial multiplexing to improve GPU utilization for deep learning inference workloads. We evaluate the performance trade-offs of each approach with respect to resource-efficiency, latency predictability, and isolation when compared with conventional batched inference. Our experimental analysis suggests up to a 5x potential for improved utilization through the exploration of more advanced spatial and temporal multiplexing strategies. Our preliminary prototype of a dynamic space-time scheduler demonstrates a 3.23x floating-point throughput increase over space-only multiplexing and a 7.73x increase over time-only multiplexing for convolutions, while also providing better isolation and latency predictability.
Pengaruh Penerapan Model Pembelajaran Problem Based Learning Berbasis Media Komik terhadap Hasil Belajar Siswa dalam Mata Pelajaran PPKn Kelas VIII di SMP Negeri 2 Lingsar

This study aims to determine the influence of the application of the comic-based Problem Based Learning learning model on learning outcomes. The approach used is quantitative with a type of Nonequivalent Control. The research sample consisted of two classes, namely class VIII (6) and VIII (8) with Purposive Sampling techniques. The instrument used is a multiple choice test. Test The analysis requirements used for the normality test are the Liliefors Test and the homogeneity test using the variance test. For hypothesis testing using the sign test test. The results of the study obtained that P = 0.05 &gt; the price of P(2) = 0.0002, then Ho was rejected, meaning that there was a significant influence in the influence of the application of the comic-based Problem Based Learning learning model on student learning outcomes in the subject of PPKn class VIII SMPN 2 Lingsar. The application of the comic-based Problem Based Learning learning model proves that there is an increase in student learning outcomes in ppkn subjects and in accordance with their learning objectives.
Reconceptualizing breaks in translation: Breaking down or breaking through?

Various observable on-screen translator behaviors, such as extended pauses in activity, mouse hovering, cycling through tabs/windows, and different kinds of scrolling, all common occurrences during task completion, have been regarded as potential problem indicators (cf. Angelone, 2018). Their presence is often attributed to a breakdown in declarative and/or procedural knowledge at a concrete problem nexus (Angelone and Shreve, 2011). Inspired by recent translation process research on aspects of cognitive ergonomics, pause-related cognitive rhythms (Muñoz and Cardona, 2018), and Kussmaul’s notion of parallel activity in the translation process (1995), we re-examine such phenomena through a different lens. We propose these phenomena may represent the loci of volitional, potentially strategic breaks rather than problem indicators per se. That is, the breaks observed are not necessarily linked to specific problems, but rather to subjects’ cognitive resource management. Our findings suggest that apparently random behaviors, seemingly unrelated to the task, generally have a positive impact on performance from both process and product perspectives. We refer to these breaks as instances of cognitive suspension, and, based on our findings, propose that translators engage in them as a refresh mechanism when performance has either waned or runs the risk of doing so. We start by examining cognitive suspension in terms of types and scope. This is followed by an empirical analysis of its direct impact on translation performance, as established by number of errors, number of generated characters, and number of typos within established windows (areas of interest) that precede and follow its occurrence.
The Use of Rosetta Stone in Improving Speaking Skill

&#x0D; &#x0D; &#x0D; &#x0D; The purpose of this study is to determine the efficacy of using technology-based teaching media to improve the speaking skills of STP Mataram Hospitality Study Program students. Several media that use computers and the internet will be used in this study to teach speaking. Rosetta Stone is used as learning media for students' speaking learning. In this study, 30 students served as the experimental group, while 30 other students served as the control group. According to the findings of this study, the value of the t-test &gt; t table (3.462&gt; 2.676) indicates a significant difference in English speaking skills when using technology-based learning media. Finally, the use of technology-based learning media is effective in improving students' speaking abilities.&#x0D; &#x0D; &#x0D; &#x0D;
Connection-Minimal Abduction in $$\mathcal {EL}$$ via Translation to FOL

Abstract Abduction in description logics finds extensions of a knowledge base to make it entail an observation. As such, it can be used to explain why the observation does not follow, to repair incomplete knowledge bases, and to provide possible explanations for unexpected observations. We consider TBox abduction in the lightweight description logic $$\mathcal {EL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>EL</mml:mi> </mml:math> , where the observation is a concept inclusion and the background knowledge is a TBox, i.e., a set of concept inclusions. To avoid useless answers, such problems usually come with further restrictions on the solution space and/or minimality criteria that help sort the chaff from the grain. We argue that existing minimality notions are insufficient, and introduce connection minimality. This criterion follows Occam’s razor by rejecting hypotheses that use concept inclusions unrelated to the problem at hand. We show how to compute a special class of connection-minimal hypotheses in a sound and complete way. Our technique is based on a translation to first-order logic, and constructs hypotheses based on prime implicates. We evaluate a prototype implementation of our approach on ontologies from the medical domain.
Goéland: A Concurrent Tableau-Based Theorem Prover (System Description)

Abstract We describe , an automated theorem prover for first-order logic that relies on a concurrent search procedure to find tableau proofs, with concurrent processes corresponding to individual branches of the tableau. Since branch closure may require instantiating free variables shared across branches, processes communicate via channels to exchange information about substitutions used for closure. We present the proof search procedure and its implementation, as well as experimental results obtained on problems from the TPTP library.
The Technological Gap Between Virtual Assistants and Recommendation Systems

Virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks. Meanwhile, existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions. The repertoire of actions is no longer limited to the one-shot presentation of recommendation lists, which can be insufficient when the goal is to offer decision support for the user, by quickly adapting to his/her preferences through conversations. Such an interactive mechanism is currently missing from recommendation systems. This article sheds light on the gap between virtual assistants and recommendation systems in terms of different technological aspects. In particular, we try to answer the most fundamental research question, which are the missing technological factors to implement a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions. The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in daily life.
REX: General-Purpose CNL with Code Generation Support

Controlled natural languages (CNLs) have been proposed to address some of the issues of natural language when it is used to express requirements. CNLs, however, are based on formal grammar, which can easily become complex, hard to read, and especially hard to write, and the implementation of support tools can also demand a significant effort. Moreover, unanticipated constructions cannot be handled or have to be handled in unexpected and cumbersome ways. In this article, we present REX, a CNL with a simple grammar that is, thus, easy to understand and easy to support, but still general purpose. To accomplish this, instead of trying to support every conceivable construction and imposing a language on the users, through a small but comprehensive set of rules and through patterns, users specify their own language and how natural it is. Another of the benefits of CNLs is the possibility to automate the transformation of a text or specification into something useful, thereby reducing manual labor and transformation errors. In this article, we also present the support tools that were used to transform a REX text into code and a complete application. It is also shown that this CNL and its support tools can be easily adapted to suit different needs.
Evaluation of University Students’ Cyber Security Awareness and Cyberbullying and Cyber Victimization Levels in the COVID-19 Pandemic

Amaç:
Coordinated Development of Smart City and Regional Industrial Economy under the Background of Internet of Things

The development of wireless communication and the Internet of Things has promoted the construction of smart cities and is the technical basis for the construction of smart cities. The influence of regional economic development level on smart city construction cannot be ignored, and it constitutes the external environment of smart city construction. The combination of the two will play a driving role in the construction of smart cities. Therefore, the planning and construction of smart cities must be coordinated with the realization of the two. This paper adopts a combination of literature analysis and field investigation to analyze the decoupling between smart city construction and regional economic development, conduct research on relevant cities, and combine the current situation of smart city construction to collect the problems encountered in smart city construction and regional economic development. Through analysis, it provides suggestions for the development of smart cities and regional economic development. The survey results show that in the process of smart city construction and regional industrial economic development in the context of the Internet of Things, information security and lack of talent are the most prominent problems. Among the relevant suggestions on these issues, the proportion of talent introduction is about 45%, and the proportion of rational planning of industrial structure is about 28%. Based on the experience of existing research results, this study uses optimization algorithms to make the impact of various factors more specific, which is helpful for each region to make scientific planning based on its own actual situation.
Design of Mobile English Teaching Platform Based on Collaborative Filtering Algorithm

With the rapid development of information technology, today's talent training mode is no longer limited to traditional school education. At the same time, with the maturity of portable mobile devices, a new learning method-mobile learning has been born. In this paper, the narrative of data is taken into account in the way of user collaborative filtering recommendation. For prefilling the matrix, the project confidence level also needs to be considered during the whole process. The project confidence level is measured by information entropy model. In the process of correction, it combines with traditional cosine similarity, calculates the user similarity matrix, can budget equalization, and expands the original matrix. After filling the matrix, the user uses the method of similarity calculation, using Pearson similarity and combining with the Euclidean distance correction method. When comparing the particular result prediction with the actual prediction following the completion of the similarity matrix data, all results point to a considerable reduction in MAE and RMSE. The user does not assess the item score to forecast. This demonstrates how this technique may enhance the reliability and consistency of the mobile English system platform.
ANDROID-BASED MOBILE LEARNING MEDIA WITH CHARACTER

This study aims to produce a android-based mobile learning media with character. The research method is a research and development method with a 4D design which is limited to Define, Design, and Develop. The subjects in this study were students of class VIII of Public Junior High School 10 Sungai Kakap as many as 14 students. The instruments were validation sheets, questionnaires of teacher and student and posttest questions. Learning media is said to be valid, practical and effective if the percentage is above 60%. Based on the results of the research, it is found that the android-based mobile learning media is valid, practical and effective. The validation of it has a validity level of 90.53% with very valid criteria, while the practicality level of learning media is 90.16%. with very practical criteria, and the effectiveness of learning media is 85.71% with very effective criteria.
PENGEMBANGAN PERANGKAT PEMBELAJARAN MODEL SSCS DENGAN PENDEKATAN RME DAN PENGARUHNYA TERHADAP KEMAMPUAN BERPIKIR KOMPUTASIONAL

This study aims to develop a mathematics learning tool consisting of a Learning Implementation Plan (RPP), Student Worksheet (LKS), and a Computational Thinking Test (CT Test) using the Search, Solve, Create, and Share (SSCS) learning model with a Realistic approach. Mathematics Education (RME) which is valid, practical and effective and examines its effect on the computational thinking ability of junior high school students. This research combines development research (R&amp;D) and experimental research. Data collection techniques in this study used interviews, observation of student activities, observation of the implementation of learning devices, student response questionnaires, and learning outcomes tests in the form of CT tests. The results showed that the learning tools met the criteria of being valid, practical, and effective. The validity coefficients for lesson plans, worksheets, and CT tests were 3.80, respectively; 3.80; and 3.81. The value of practicality seen from the results of observations of the implementation of learning devices is in the very practical category with a percentage of 98%. The value of the effectiveness of learning tools shows that 92% of students are active, 98% of students respond positively to learning, and 77% of students complete learning. Based on the t-test on the results of experimental research, it was found that the SSCS model learning device with the RME approach had a significant effect on students' computational thinking abilities.
Factors affecting reading Comprehension in Cebuano and English Language Texts

The main objective of the study was to determine the factors affecting reading comprehension in Cebuano and English language texts of Grade Six pupils in School X, School Year 2019 – 2020 with the end view of proposing an action plan to improve students’ reading comprehension in English language. The researcher utilized descriptive – survey research design. There were two sets of instruments, one was a reading comprehension test and the other was a survey questionnaire. As used in the study, intrinsic factors include motivation, reading style, attitude, and physical condition. In addition, extrinsic factors include teachers, family members, peers/classmates, and environmental condition. Based on the result, the respondents fall in the inferential level of reading comprehension in both languages. Among the intrinsic and extrinsic factors, motivation has greatly influenced respondents’ reading comprehension in both languages. Moreover, there was no significant difference between respondents’ reading comprehension in both languages. In Cebuano and English texts, there was no significant difference between factors affecting their reading comprehension. Moreover, results showed that respondents’ level of reading comprehension in Cebuano was higher than their reading comprehension in English texts. Hence, an action program was proposed to improve respondents’ reading comprehension in English language.