Reviewerly/exHarmony huggingface dataset:
Development of Hand Gesture Command Using Leap Motion for Floorplanner Application

Architecture is part of the art of life and works best when it seems to give expression to the life that inhabits it. As technology has developed a lot, the field of architecture has also experienced many developments supported by technology such as making architectural designs. The development experienced in making architectural designs is that it is no longer required to draw design choices with side views on a lot of paper but instead use a simulation application for making architectural designs that are displayed through a monitor layer in 3D so that they can see more realistically. After many innovations emerged regarding simulation applications which generally still use hardware such as a mouse, then these innovations shifted to connecting the simulation application with the virtual world where hardware such as a mouse has been replaced by using gestures or limb movements such as parts of the hand or other parts of the body. While other fields like medic already use virtual tool interaction, architecture fields still just use mouse and keyboard. One of the virtual interaction devices used to be able to detect fingers and palms to interact with 3D simulation applications is Leap Motion. Leap Motion is starting to be widely used to carry out various ways of interacting with hands because it is more interactive and interesting to use even many people think that it can be used for hand rehabilitation or therapy too. However, even though many innovations have developed, there is still no innovation in the use of a hand gesture command system using Leap Motion for simulation applications to design architectural designs in 3D views. Based on these things, the authors designed and developed hand gesture commands using Leap Motion for Architectural Design App. The hand gesture command system used for the Floorplanner simulation application will be tested using a system usability scale by users with architectural, informatics, and unfamiliar backgrounds. Based on the tests carried out, it can be seen that the system is running properly even though it requires getting used to the device first and getting a score from the System Usability Scale of 59,167 out of scale from 0 to 100 which means it is still accepted by users or declared as "Marginally Acceptable" category.
Artificial Intelligence in Chatbot Website Platform

In computing discourse, a Chatbot is a computer programme that is specifically designed to simulate an interactive communication or conversation. The interactive communication is from the machine to the user (human) either through text media, sound media or visual media. Chatbots have been widely used for practical purposes such as online assistance, personalised services, or information acquisition, including in the world of global marketing. The purpose of this research is to describe how a chatbot platform provides its best function in supporting a marketing task of a corporation at a global level. The methods used in this research are: 1) System Analysis, which is the collection of information needed in building the system must be done in detail. Where this information will support all the components needed to obtain results that are in accordance with all the needs related to the design of the system to be input, 2) Preparation of flowcharts, namely the design by entering data on the status of conversations that are commonly carried out by the Help-Desk with customers. Where when the user enters a word or sentence in the column that is already available in the system, a word or sentence search process will be carried out based on the noun, this process is useful for matching whether the input given by the user is in the set of nouns that have been trained in dialogue flow. The result obtained is that the website becomes one of the company's main media in marketing its products because the website already includes all information related to the product and also related to the company. However, when using the website alone, there is no direct communication with potential buyers. Therefore, this research will develop a chatbot that can improve the performance of the Spicering ltd website.
Web-based learning management system as media in teaching ESP for mathematics students in higher education

This study examines the techniques used by ESP Math lecturers, the challenges they have in providing ESP learning materials, how they overcome these challenges, and the implications for ESP design. This study employed a qualitative research design by interviewing five ESP Mathematics lecturers teaching at STKIP PGRI Sidoarjo in the 2022-2023 academic year. 15 items of interview guidelines were prepared to gain a deeper understanding of what respondents have done in implementing the techniques of teaching ESP for Mathematics. The data were then examined using an interactive analysis process. The survey found that several of the lecturers had issues with vocabulary, pronunciation, grammar, and speaking fluency. The instructors overcame the obstacles by working together with other instructors and studying from printed and digital resources to better their command of the English language. Additionally, they utilized a web-based learning management system to aid in their education and facilitate the creation of instructional materials. It meant that the development of ESP for math educators had to concentrate on four areas: vocabulary, pronunciation, grammar, and fluency. The ESP curriculum might be developed using any number of platforms, including ones that cater to linguistics and mathematics.
EFEKTIVITAS METODE PERMAINAN BAHASA BISIK BERANTAI TERHADAP PENINGKATAN KETERAMPILAN MENYIMAK SISWA KELAS IV SD NEGERI 0501 HUTANOPAN

Abstract&#x0D; Students' skills must be sharpened to see the rapid developments in today's era. Children's various skills must be developed, one of which is listening skills. If students' listening skills are not good then students' understanding of the material will also be poor. The aim of this research is to help improve students' listening skills through the whispering game method. The type of research used is Classroom Action Research (PTK) which consists of 2 cycles, each cycle consisting of planning, implementation, observation and reflection. This research data collection technique was obtained through observation, written tests and documentation. The data analysis technique in this research is a qualitative data analysis technique. Based on the research results, it can be concluded that using the chain whisper game method can improve students' listening skills, students become more active. In cycle I, 24 or 63.16% of students had the "Good" listening ability category. And 14 or 36.84% of students' listening ability was "Not Good". And in cycle II 36 or 94.74% of students had the "Good" listening ability category. And 2 or 5.26% of students' listening ability was "Not Good".&#x0D; Keywords: Method, Chain Whisper Language Game, Students' Listening Skills
Panel Vector Autoregressive Modeling of Macroeconomic Interaction in Nigeria, Ghana and Cameroon

Aims: The aim of this study is to apply Panel VAR (Vector Autoregressive) modeling and estimation to analyze the macroeconomic interaction and interdependence within the context of Nigeria, Ghana, and Cameroon. The study aims to understand the trends of key macroeconomic variables, namely gross domestic product (GDP), exchange rate, and foreign reserves.&#x0D; Methodology: The study adopted three macroeconomic variables—GDP, exchange rate, and foreign reserve—and utilized annual secondary data from the World Bank spanning from 1960 to 2022. Pretests, including unit root and cointegration tests, were conducted on the variables. The panel unit root tests (Levin, Lin, and Chu t, Augmented Dickey-Fuller Fisher Chi-Square, and Phillips-Perron Fisher Chi-Square) indicated that the series had unit roots at levels but were stationary at first difference, implying integration of order one. The absence of co-integration in the panel co-integration test established the necessary conditions for estimating a panel vector autoregressive model.&#x0D; Results: The trend analysis revealed that the variables were relatively low in the 1960s and 1970s but exhibited an increasing and fluctuating pattern afterward. Descriptive statistics showed variations among the countries, with Cameroon having higher GDP per capita and greater standard deviation, indicating more significant fluctuations. Ghana, in contrast, displayed lower per capita income with a lower standard deviation. The foreign exchange rate varied, with Cameroon having the highest and Ghana the lowest mean rates.Conclusion: The fixed effect model was estimated after the Hausman Test rejected the random effect model. The results indicated that foreign exchange rates had joint significance on GDP per capita, while foreign reserves did not. The study concludes that the economies of Nigeria, Ghana, and Cameroon are responsive to GDP per capita, foreign exchange rates, and foreign reserves. The policy implication is that economic practitioners in these countries should closely monitor these variables to anticipate changes in economic indicators. Therefore, the study recommends active monitoring of the economic variables used in this research to facilitate informed decision-making.
Mining Several Kinds of Multi-Dimensional Temporal Association Rule

To improve the effectiveness and efficiency of mining tasks, constraint-based mining enables users to concentrate on mining their interested association rules instead of the complete set of association rules. Previously proposed methods are mainly contributed to handling a single constraint and only consider the constraints which are characterized by a single attribute value. In this paper, we propose an approach to mine association rules with multiple constraints constructed by multi-dimensional attribute values. Our proposed approach basically consists of three phases. First, we collect the frequent items and prune infrequent items according to the Apriority property. Second, we exploit the properties of the given constraints to prune search space or save constraint checking in the conditional databases. Third, for each item set possible to satisfy the constraint, we generate its conditional database and perform the three phases in the conditional database recursively. Our proposed algorithms can exploit the properties of constraints to prune search space or save constraint checking. Therefore, our proposed algorithm is more efficient than the revised FP (Frequent Pattern)-growth and FIC (Fractal Image Compression) algorithms.
House Price Prediction Using Data Mining with Linear Regression and Neural Network Algorithms

The need for housing in big cities is very high because most offices and economic centers are in big cities. Limited land and high demand cause house prices to rise. Many developers build housing on the outskirts of big cities with access to trains and toll roads to make transportation easier. Property developers compete by providing the best prices, various choices of house specifications, ease of the mortgage process, and attractive promotions such as no down payment. A house is a long-term investment whose price increases yearly, so proper analysis is needed to buy a place to live in. Several factors influence the price of a house, including location, land area, building area, building type, and so on. This research aims to create a house price prediction model using the Linear Regression Algorithm and Neural Network so that the results can be useful for property agents in predicting house sales or from the buyer's side in predicting house prices. The results of this research use the Linear Regression Algorithm RMSE 0.775, while the Neural Network Algorithm uses RMSE 0.645. From this research, modeling using the Linear Regression Algorithm has better results. Still, the Linear Regression Algorithm and Neural Network Algorithm have RMSE results that are close to accurate and have small errors.
Cryptocurrency-Based Financial Science Strategy In World Influence Using Causal Diagram And Machine Learning

Cryptocurrency is a digital currency that can be used for transaction on an international scale and as an investment. The potential provided by cryptocurrency in the development of the digital economy in the world has become a special attraction for individuals, organizations, and government. Blockchain system that underlies cryptocurrencies has worked flawlessly in both the financial and non-financial worlds. This study uses Basic Risk Management Ishikawa Diagram and evaluated by ARIMA predictive algorithm in determining cause and effect of the development of cryptocurrencies. It was found that the development of cryptocurrency is very influential by the state of the world, especially countries that have great influence such as USA. USA inflation have a big influence, and the model can be used as a basis for a country's government in observing.
Digital Environment Learning (DEL): Creativity in Framework of Digital Literacy

The surge in digital literacy emerged as a prominent trend during the era of online learning in elementary schools. However, the success of connecting the digital environment to the learning process is contingent upon the teacher's proficiency. Consequently, we introduced DEL (Digital Enhanced Learning), an instructional approach that fully integrates digital technology into all learning devices. This study seeks to assess students' digital literacy and creativity during DEL activities in elementary schools conducted over a period of 10 weeks. A total of 290 students participated in the DEL activities. Students' digital literacy and creativity were evaluated using two instruments: DELQ (Digital Enhanced Learning Questionnaire) and CET (Creativity Evaluation Tool). DELQ gauged students' digital literacy, while CET assessed creativity. ANOVA was employed for data analysis to evaluate the consistency and achievement of students. The findings of this study indicate that DEL activities contribute to enhanced creativity, specifically in the aspects of fluency and elaboration. Students' digital literacy plays a pivotal role in supporting the fluency and elaboration processes during learning activities. Moreover, the effectiveness of DEL activities, as perceived through students' digital literacy, indicates an improvement in knowledge related to digital literacy. In summary, this study underscores the positive impact of digital-based activities in fostering both digital literacy and creativity among elementary school students
Reconsidering Models Of Organizational Transformation Maturity

Organizational culture encompasses a set of characteristics, including norms, routines, and unspoken rules that dictate how activities are conducted within an organization. The maturity of an organization's culture can be assessed using various maturity models (MMs), and aligning the organizational culture with an appropriate MM has the potential to accelerate project and business performance. A MM is a descriptive model outlining the stages through which organizations progress as they define, implement, evolve, and enhance their processes. Typically, these stages include the initial level, repeatable level, defined level, managed level, and optimizing level (Becker et al., 2009), illustrating the organization's capacity for continuous improvement. Maturity models play a crucial role in helping companies understand their current maturity level and guide them in improving specific disciplines. By posing questions and developing action plans, MMs serve as a tool for organizations to assess and enhance their capabilities.
The Semantic Field of Sport Collocations in English

A group of words that are semantically related and they refer to a specific subject is called semantic field. In this research, the field of sport collocation is studied. Additionally, the researcher chooses sport games and distinguishes it’s two general types namely individual and team games. Moreover, three common verbs are selected which are (play, go, and do). The study attempts to find out each sport game from the two types (individual, and team games) and their collocated verbs (the selected verbs). After, the data were analyzed, it has been declared that most of the sport games collocated with the verb (play). Then, the second verb which most of the sport games collocated with is the verb (go). Lastly, the least sport games collocated with verb (do). Finally, the nature of the sport games will determine their verb collocations.
A statistical stylistic study of collected poems of (Lawandnaway kholamesh) by Tayeeb Jabbar

This study is an attempt to focus on the style and stylistics. It tries to explore what are they and how they are made and what is their importance in analyzing literary texts especially poetic texts. The collected poems of (Lawandnaway kholamesh) by Tayeeb Jabbar is analyzed using these stylistic devices. Also, the statistical stylistics is followed in this paper. The aim of the poet is to show the statistics, the range of types of verbs and styles of the collected poems. Studying any literary work of any poet through using statistical styles, demonstrates the types of poems in terms of quantity. Thus, this has a great significance in any literary work.
A Systematic Review: Detecting Phishing Websites Using Data Mining Models

As internet technology use is on the rise globally, phishing constitutes a considerable share of the threats that may attack individuals and organizations, leading to significant losses from personal and confidential information to substantial financial losses. Thus, much research has been dedicated in recent years to developing effective and robust mechanisms to enhance the ability to trace illegitimate web pages and to distinguish them from non-phishing sites as accurately as possible. Aiming to conclude whether a universally accepted model can detect phishing attempts with 100% accuracy, we conduct a systematic review of research carried out in 2018–2021 published in well-known journals published by Elsevier, IEEE, Springer, and Emerald. Those researchers studied different Data Mining (DM) algorithms, some of which created a whole new model, while others compared the performance of several algorithms. Some studies combined two or more algorithms to enhance the detection performance. Results reveal that while most algorithms achieve accuracies higher than 90%, only some specific models can achieve 100% accurate results.
Design and Analysis of a Recommendation System Based on Collaborative Filtering Techniques for Big Data

Online search has become very popular, and users can easily search for any movie title; however, to easily search for moving titles, users have to select a title that suits their taste. Otherwise, people will have difficulty choosing the film they want to watch. The process of choosing or searching for a film in a large film database is currently time-consuming and tedious. Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste. This happens especially because humans are confused about choosing things and quickly change their minds. Hence, the recommendation system becomes critical. This study aims to reduce user effort and facilitate the movie research task. Further, we used the root mean square error scale to evaluate and compare different models adopted in this paper. These models were employed with the aim of developing a classification model for predicting movies. Thus, we tested and evaluated several cooperative filtering techniques. We used four approaches to implement sparse matrix completion algorithms: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -nearest neighbors, matrix factorization, co-clustering, and slope-one.
Editorial Advancements in Learning-Based Quality Prediction for Advanced Visual Media

In this special issue of the IEEE <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Journal of Selected Topics in Signal Processing</small> , we delve into the burgeoning domain of Learning-Based Quality Prediction for Advanced Visual Media. The rapid proliferation of advanced visual media modalities, such as high dynamic range and mixed reality, has not only enhanced interactive and immersive user experiences but has also posed significant challenges in the realm of quality assessment and optimization. This issue collates pioneering research that addresses these challenges, underscoring the critical role of human perception in the acceptance and success of such applications.
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior

Reconstructing transparent objects using affordable RGB-D cameras is a persistent challenge in robotic perception due to inconsistent appearances across views in the RGB domain and inaccurate depth readings in each single-view. We introduce a two-stage pipeline for reconstructing transparent objects tailored for mobile platforms. In the first stage, off-the-shelf monocular object segmentation and depth completion networks are leveraged to predict the depth of transparent objects, furnishing single-view shape prior. Subsequently, we propose Epipolar-guided Optical Flow (EOF) to fuse several single-view shape priors from the first stage to a cross-view consistent 3D reconstruction given camera poses estimated from opaque part of the scene. Our key innovation lies in EOF which employs boundary-sensitive sampling and epipolar-line constraints into optical flow to accurately establish 2D correspondences across multiple views on transparent objects. Quantitative evaluations demonstrate that our pipeline significantly outperforms baseline methods in 3D reconstruction quality, paving the way for more adept robotic perception and interaction with transparent objects.
Recognition of Bimanual Manipulation Categories in RGB-D Human Demonstration

Humans exhibit outstanding capabilities in using both hands to perform daily tasks. Understanding bimanuality in human demonstrations is key for humanoid robots, which should learn from human observation. In this paper, we address the problem of the recognition and segmentation of bimanual action categories defined by our Bimanual Manipulation Taxonomy, based on RGB-D data. To this end, we combine object detection and human motion tracking methods to derive graph-based representations of bimanual manipulation tasks that describe spatial relations between objects and hands as well as the temporal change of these relations during the execution of the task. We train a Graph Neural Network (GNN) for simultaneous recognition and segmentation of the demonstrations and compare the results with a rule-based classification approach that only takes contact relations between objects and hands into account. For training, five kitchen tasks of the KIT Bimanual Actions Dataset are selected and complemented with two new tasks accounting for symmetrical bimanual manipulations. The evaluations show the best results for a GNN considering spatial relations and object knowledge compared to a GNN considering only contact relations between objects and hands and compared to the rule-based approach.