Reviewerly/exHarmony huggingface dataset:
Theoretical Evaluation of Ensemble Machine Learning Techniques

The use of ensemble techniques is widely recognized as the most advanced approach to solving a variety of problems in machine learning. These strategies train many models and combine the results from all of those models in order to enhance the predictive performance of a single model. During the period of the last several years, the disciplines of artificial intelligence, pattern recognition, machine learning, neural networks, and data mining have all given a considerable consideration to the concept of ensemble learning. Ensemble Learning has shown both effectiveness and usefulness across a broad range of problem domains and in significant real-world applications. Ensemble learning is a technique thatinvolves the construction of many classifiers or a group of base learneis and the merging of their respective outputs in order to decrease the total variance. When compared to using only one classifier or one base learner at a time, the accuracy of the results achieved by combining numerous dassifiers or the set of base learners is greatly improved. It has been shown that the use of ensemble methods may increase the predicted accuracy of machine learning models for a range of tasks, including classification, regression, and the identification of outliers. This study will discuss about ensemble machine learning techniques and its various methods such as bagging, boosting, and stacking. finally, all the factors involved in bagging, boosting, and stacking are compared.
FHIR4FAIR: Leveraging FHIR in health data FAIRfication process: In the case of VODAN-A

This article describes FHIR as a healthcare data exchange standard to attain the FAIR data principles taking the VODAN-A implementation as a showcase. FHIR relies upon the basic unit “resources” and the interaction among them. A new FAIR enabling architecture is proposed.
Two-Stage Framework for Faster Semantic Segmentation

Semantic segmentation consists of classifying each pixel according to a set of classes. Conventional models spend as much effort classifying easy-to-segment pixels as they do classifying hard-to-segment pixels. This is inefficient, especially when deploying to situations with computational constraints. In this work, we propose a framework wherein the model first produces a rough segmentation of the image, and then patches of the image estimated as hard to segment are refined. The framework is evaluated in four datasets (autonomous driving and biomedical), across four state-of-the-art architectures. Our method accelerates inference time by four, with additional gains for training time, at the cost of some output quality.
Persuading programmers to detect and mitigate bias in technology design: The role of motivational appeals and the speaker

Few studies have empirically investigated the role of individual programmers in correcting or overcoming bias, specifically regarding motivating programmers to engage in bias detection. In this study, we present and test a conceptual framework for the effectiveness of motivational appeals aimed at programmers, considering the role of framing, the speaker's race and gender, and the individual differences in recipients’ social dominance orientation-egalitarianism (SDO-E) in driving bias detection outcomes. The framework suggests that a problem framing, "You are part of the problem," will be more effective than a solution framing, "You are part of the solution," when the speaker is white and male rather than black and female, but this only applies to respondents with low levels of SDO-E and will be reversed for respondents with high levels of SDO-E, due to the pursuit of egalitarian values that automatically inhibits the activation of stereotypes. The authors recruited 575 real US programmers via Prolific. They participated in a between-subjects experiment that measured their ability to detect potential bias in a 2 (problem versus solution framing) × 2 (speaker, white male versus black female) design. Results provide important conceptual and practical implications for understanding the role of programmers in addressing bias.
Role-Playing Methods: Efforts to Stimulate the Development of Early Childhood Numeracy Literacy

The development of early childhood numeracy literacy in the form of the ability to apply the concept of numbers and counting operation skills in everyday life needs to be stimulated by various methods, one of which is role-playing. This research aims to determine efforts that can stimulate the development of early childhood numeracy literacy with role-playing methods. The data collection technique in this research uses literature studies by exploring journals relevant to the study. The results showed that role-playing efforts have a pattern in their implementation that is adapted to goals that demand a certain form of participation. Efforts to stimulate children's numeracy literacy can be by buying and selling fruits in a market, children will play based on their respective roles, namely as sellers and buyers and children demonstrate both language, numeracy and behavior like these characters in real life.
Speed Control Analysis of an Electrical Vehicle by Using Fuzzy- PID and ANFIS Controllers

Electrical vehicles (EVs) will be the transportation system <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">,</sup> s future when it overcomes some disadvantages like battery life., charging stations., and speed control limitations. In EV., the brushless DC motor is often used because of its high torque and high efficiency. however., it <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">,</sup> s difficult to control the speed of the DC motor. This paper presents the speed control of an EV by using different controlling methods such as PID (Proportional-Integral-Derivative)., Fuzzy-PID., and ANFIS (Adaptive-Neuro Fuzzy Inference System) controllers. The comparison of the results of all three techniques is discussed in this paper. From the results of the simulation., it can be noticed that the PID-Fuzzy offers better speed control than the conventional PID and augmenting ANFIS with PID gives better results than the PID-Fuzzy and PID alone.
Multi-Content Interaction Network for Few-Shot Segmentation

Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies. Most existing approaches focus on extracting high-level representations of the same layers for support-query correlations, neglecting the shift issue between different layers and scales, due to the huge difference between support and query samples. In this paper, we propose a Multi-Content Interaction Network (MCINet) to remedy this issue by fully exploiting and interacting with the multi-scale contextual information contained in the support-query pairs to supplement the same-layer correlations. Specifically, MCINet improves FSS from the perspectives of boosting the query representations by incorporating the low-level structural information from another query branch into the high-level semantic features, enhancing the support-query correlations by exploiting both the same-layer and adjacent-layer features, and refining the predicted results by a multi-scale mask prediction strategy, with which the different scale contents have bidirectionally interacted. Experiments on two benchmarks demonstrate that our approach reaches SOTA performances and outperforms the best competitors with many desirable advantages, especially on the challenging COCO dataset.
Developing STEAM-based Students’ Worksheet with the Ecoprint Technique in Biology Subject

This research aimed at developing a STEAM-based students’ worksheet with the Ecoprint technique and analysing the developed product's validity, feasibility and practicality. This research used the 4D development method (Define, Design, Develop, and Disseminate). The research subjects were 102 students of the tenth-grade science class in Senior High School. The instruments employed expert validation tests and questionnaires. The data were analysed by processing validator response data with a Likert scale converted into qualitative data. The results showed that the STEAM-based students’ worksheet with the Ecoprint technique obtained a moderate level for its validity, while the feasibility aspects were 90% based on the students’ views and 91% from the teachers. The results of the practicality test were 87% from the teachers and 92% by students, respectively. It can be concluded that the STEAM-based students’ worksheets with the Ecoprint technique are valid, feasible and practical to use as a supporting learning media of Biology. Steam-based students’ worksheet with the Ecoprint technique is very suitable to be applied as an innovative learning media which is relevant to 21 st century demands by emphasising 4C skills.
Information loss and run time from practical application of quantum data compression

Abstract We examine information loss, resource costs, and run time from practical application of quantum data compression. Compressing quantum data to fewer qubits enables efficient use of resources, as well as applications for quantum communication and denoising. In this context, we provide a description of the quantum and classical components of the hybrid quantum autoencoder algorithm, implemented using IBMs Qiskit language. Utilizing our own data sets, we encode bitmap images as quantum superposition states, which correspond to linearly independent vectors with density matrices of discrete values. We successfully compress this data with near-lossless compression using simulation, and then run our algorithm on an IBMQ quantum chip. We describe conditions and run times for training and compressing our data on quantum devices, and relate trainability to specific characteristics and performance metrics of our parametric quantum circuits.
Leveraging the State-of-the-Art Machine-Learning Techniques for Amplifying the Credit Card Fraud Detection

In this paper mainly focused on credit card fraud detection for in real world. Initially collect the credit card datasets for trained dataset. Then will provide the user credit card queries for testing data set. After classification process of random forest algorithm using to the already analyzing data set and user provide current dataset. Finally optimizing the accuracy of the result data. Then will apply the processing of some of the attributes provided can find affected fraud detection in viewing the graphical model visualization. The performance of the techniques is evaluated based on accuracy, sensitivity, and specificity, precision. The results indicate about the optimal accuracy for Decision tree are 98.6% respectively. Keywords: credit card fraud detection, random forest algorithm, fraud detection, visualization, Decision tree.
Diffusion Models for Non-autoregressive Text Generation: A Survey

Non-autoregressive (NAR) text generation has attracted much attention in the field of natural language processing, which greatly reduces the inference latency but has to sacrifice the generation accuracy. Recently, diffusion models, a class of latent variable generative models, have been introduced into NAR text generation, showing an improved text generation quality. In this survey, we review the recent progress in diffusion models for NAR text generation. As the background, we first present the general definition of diffusion models and the text diffusion models, and then discuss their merits for NAR generation. As the core content, we further introduce two mainstream diffusion models in existing work of text diffusion, and review the key designs of the diffusion process. Moreover, we discuss the utilization of pre-trained language models (PLMs) for text diffusion models and introduce optimization techniques for text data. Finally, we discuss several promising directions and conclude this paper. Our survey aims to provide researchers with a systematic reference of related research on text diffusion models for NAR generation. We present our collection of text diffusion models at https://github.com/RUCAIBox/Awesome-Text-Diffusion-Models.
AI-Based Virtual Assistant Using Python: A Systematic Review

Abstract: A software agent that will carry out tasks or provide services in response to a user's privately supported instructions or inquiries is known as an intelligent virtual assistant (IVA) or intelligent personal assistant (IPA). A virtual assistant capable of being accessed via web chat is sometimes called a "chatbot." Online chat systems can occasionally only be used for amusement. Some virtual assistants are equipped to comprehend spoken language and answer with synthetic voices. Users can use voice commands to manage other basic chores like email, to-do lists, and calendars in addition to asking their assistants questions, controlling home automation devices, and controlling media playing. One of the best applications of artificial intelligence is the virtual personal assistant (VPA), which offers a new way for people to delegate tasks to machines. To create a Virtual Personal Assistant (VPA) and use it in various software applications, certain approaches and principles are used. To enable users to communicate with virtual assistants, speech recognition systems—also known as Automatic Speech Recognition, or ASR—play a crucial role.
A new paradigm of Luca Pacioli’s bookkeeping on blockchain phenomenon

AbstractThe objective of this study is to examine the double and triple entry bookkeeping related to blockchain technology bookkeeping. The data we obtained for 30 articles from the database we searched for were provided by major publishers (Elsevier (www.sciencedirect.com), Emerald (www.emeraldinsight.com), Springer (www.springerlink.com), and Wiley (www. wiley.com)) and by service libraries (Complete Business Resources and Web of Science). We show the thoughts of philosopher Foucault regarding the power of knowledge, which will be associated with the phenomenon of the triple-entry and double-entry bookkeeping system paradigms. In addition, we document the blockchain technology has changed the paradigms of both bookkeeping systems. The novelty in this article is the implementation of the philosopher Foucault's thought in the paradigm of the triple-entry and double-entry bookkeeping systems. Our study contributes to expanding the existing literature by providing some arguments and findings regarding the blockchain bookkeeping systems and the future research agenda.AbstrakTujuan dari penelitian ini adalah untuk mengkaji pembukuan double dan triple-entry yang terkait dengan pembukuan teknologi blockchain. Data yang kami peroleh adalah 30 artikel dari database penerbit besar (Elsevier (www.sciencedirect.com), Emerald (www.emeraldinsight.com), Springer (www.springerlink.com), dan Wiley (www.wiley.com)) dan dari perpustakaan layanan (Sumber Daya Bisnis Lengkap dan Web of Science). Kami tunjukkan pemikiran filsuf Foucault mengenai kekuatan pengetahuan, yang akan dikaitkan dengan fenomena paradigma sistem pembukuan triple-entry dan double-entry. Selain itu, kami mendokumentasikan teknologi blockchain telah mengubah paradigma kedua sistem pembukuan tersebut. Kebaruan dalam artikel ini adalah implementasi pemikiran filsuf Foucault dalam paradigma sistem pembukuan triple-entry dan double-entry. Studi kami berkontribusi untuk memperluas literatur yang ada dengan memberikan beberapa argumen dan temuan mengenai sistem pembukuan blockchain dan agenda penelitian di masa depan.
Validating the Paraphrasing Task with the Framework of AUA

This paper aims to justify the Paraphrasing task in the Advanced English course’s final achievement test with the framework of Assessment Use Argument (AUA) and finds out the implications for classroom teaching and learning. By articulating the claims with warrants of collected evidence combining the syllabus, the students’ scores, and their answers in the questionnaire, the research shows that the Paraphrasing task is plausible for the Junior English Majors in their final test, paraphrasing practice could improve students’ reading comprehension and academic writing.
English listening and speaking test platform for Chinese high school students

In the era of rapid technological development, the use of artificial intelligence technology to help education can not only quickly improve the efficiency of work, but also help students improve their grades and academic performance. This study proposes to build an English listening and speaking test platform that provides automatic scoring of high school English speaking exams for Chinese high school students and gives scores and final scores for each scoring index, thus reducing the workload of examiners and allowing students to practice for their questions to improve their performance.
Impact of Foreign Direct Investments on the Hospitality Sectors in Ethiopia

Purpose: The study sought to analyze the impacts of foreign direct investments on the hospitality sectors in Ethiopia.&#x0D; Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The results show that showed that the success of the hospitality industry in developing countries depends on the levels of foreign direct investment. Although many developing countries have natural features such as beaches, rivers, and other natural resources, local capital to invest in those resources is unavailable. The tourism industry is one of the largest and fastest-growing sectors in the global economy and a key driver of socio-economic development, as it is labor-intensive and stimulates SME growth and investment. It has been used in other countries as an economic driver for growth which can widely support poverty reduction.&#x0D; Unique Contribution to Theory, Practices and Policies: The capital market theory, investment development path theory and the eclectic theory may be used to anchor future studies in the tourism sector. The study results will also benefit other stakeholders such as the policy makers as well as researchers and scholars from different parts of the world. The top management of hospitality management companies in the country will also use the study findings to improve tourism performance in all their activities and programs. The study recommends that the adoption of effective social protection development policies in the tourism sector will help to improve efficiency in their major operations and activities.
HybriD-GM: A Framework for Quantum Computing Simulation Targeted to Hybrid Parallel Architectures

This paper presents the HybriD-GM model conception, from modeling to consolidation. The D-GM environment is also extended, providing efficient parallel executions for quantum computing simulations, targeted to hybrid architectures considering the CPU and GPU integration. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables granularity control, optimizing hardware resources in distributed computations organized as tree data structures. In the HybriD-GM evaluation, simulations of Shor’s and Grover’s algorithms achieve significant performance improvements in comparison to the previous D-GM version, and also with other related works, for example, LIQUi|⟩ and ProjectQ simulators.
An Automatic System for Identifying and Categorizing Tribal Clothing Based on Convolutional Neural Networks

The quantity of internet businesses providing tribal clothes is constantly increasing, and people tend to exaggerate how often they shop at such sites. However, we are concerned about the authenticity of the outfits. The study recommends using Convolutional Neural Networks (CNN) to automatically identify and categorize authentic images of particular tribal dresses used by some Bangladeshi tribes into predetermined categories. The study's impetus comes from the expansion of commerce and the desire to spread these traditional clothes over the globe. In order to categorize the clothing, we obtained images from actual tribal residences, shops, and a few online marketplaces. To that end, we made an effort to provide a dataset we've labeled “TribalBd,” which has 680 samples, including six different classes. Then, use the YOLOv5, YOLOv6, and YOLOv7 models to put these datasets for detection and classification on our CNN. As a means of evaluating the efficacy of our model, we have experimented with a number of different CNN topologies and tweaks. We put the model through its tests with YOLOv6 and YOLOv7. YOLOv5 achieved the best results among these models. The final result shows that the YOLOv6 model gives 86.24%, the YOLOv7 model gives 71.28% accuracy whereas YOLOv5 gives 89.97% accuracy in classifying the images in the training and testing sets which are best compared to the other two models.
River Flood Early Warning System Based on Internet of Things in Binjai City

Flood is an event of inundation of land, which is usually dry, by water originating from water sources around the land. Binjai City is an area prone to flash floods. This is because in Binjai City there are 5 (five) upstream rivers namely the Bingei river, Mencirim river, Bangkatan river, Diski river and Rambai river. A flood early warning system is a series of systems that function to notify an impending flood disaster. With the existence of a flood early warning system, it can provide information to the community and can reduce victims or losses due to the community's unpreparedness in dealing with flood disasters. This study aims to create a prototype of a river flood early warning system based on the internet of things (IoT). The method used is to design and create a flood early warning system prototype, then perform system testing. The system is designed using the Arduino Mega2560 microcontroller as the system control center, the HC-SR04 ultrasonic sensors and the ESP32-Cam camera module as system input, as well as buzzer, LCD and website as system output. The transmission medium used is wireless via a 4G WiFi Modem connected to the internet. System prototype testing will be carried out in the Bangkatan river area in Binjai City. From the results of the discussion, it was found that the river flood warning system using the HC-SR04 ultrasonic sensor and the ESP32-Cam camera module based on the Internet of Things was successfully designed and implemented in prototype form and worked well. Ultrasonic sensors work well in measuring river water level with an average error percentage of 3.642%. The ESP32-Cam camera module works well in capturing images of river water conditions up to a distance of 200 cm. (9 pt).&#x0D;
Traversing the Metaverse: the new frontiers for computer-mediated communication and natural language processing

The primary objective of the following paper is to explore the concept of the Metaverse encompassing the Internet revolution, the information revolution, and the artificial intelligence technology revolution, which further incorporates virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. Due to the fact that the current, fourth wave of computing innovation can be regarded as driven by immersive, spatial technologies, the Metaverse as the so-called post-reality universe and multi-user virtual environment has a considerable potential to become the future of the digital discourse. With Natural Language Processing (NLP) conceptualized as a subfield of artificial intelligence and linguistics, the following paper argues for the inclusion of NLP-based methods in the developing discourse revolving around the transformative idea of the Metaverse. At the same time, computer-mediated communication (CMC), can potentially be extended to the new context of the extensive online world of the Metaverse.