Reviewerly/exHarmony huggingface dataset:
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring

Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies. We focus on a specific type of time series classification which we refer to as aggregated time series classification. We consider an aggregated sequence of a multi-variate time series, and propose a methodology to make predictions based solely on the aggregated information. As a case study, we apply our methodology to the challenging problem of household water end-use dissagregation when using non-intrusive water monitoring. Our methodology does not require a-priori identification of events, and to our knowledge, it is considered for the first time. We conduct an extensive experimental study using a residential water-use simulator, involving different machine learning classifiers, multi-label classification methods, and successfully demonstrate the effectiveness of our methodology.
Review of Image Recognition from Image Repository

Abstract: The work mainly focuses on the need of building a hybrid model by combining the different available method and techniques used for features extraction, detection, and matching. The enhanced model developed is used to identify the objects based on shape. The features are extracted from universally accepted images considered as candidate object. Number of works on image extraction has pointed out the difficulties in extracting the needed image. The paper highlights the shortfalls and the need to address the shortfalls. The review conducted provides the research gap in the industry and various directions the work is heading
DATA MINING USING RANDOM FOREST, NAÏVE BAYES, AND ADABOOST MODELS FOR PREDICTION AND CLASSIFICATION OF BENIGN AND MALIGNANT BREAST CANCER

This study predicts and classifies benign and malignant breast cancer using 3 classification models. The method used in this research is Random Forest, Naïve Bayes and AdaBoost. The prediction results get Random Forest = 100%, Naïve Bayes = 80% and AdaBoost = 80%. Results using Test and Score with Number of Folds 2, 5 and 10. Number of Folds 2 Random Forest model Accuracy = 95%, Precision = 95% and Recall = 95%, Naïve Bayes Accuracy = 93%, Precision = 93% and Recall 93%, AdaBoost Accuracy = 90%, Precision = 90% and Recall = 90%. With Number of Folds 5 with Random Forest = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 93%, Precision = 93% and Recall = 93%. With Number of Folds 10 Random Forest model = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 92%, Precision = 92% and Recall = 92%. Of the 3 models used, Random Forest got the best classification results compared to the others.
Examining university students’ satisfaction regarding remote learning during the COVID-19 pandemic

The purpose of this article is to examine students’ opinions regarding English courses taken via remote learning at a private university in the city of Pasto, Colombia during the COVID-19 pandemic. To this end, the authors mentioned some important studies related to students’ satisfaction after taking remote learning lessons because these studies allow researchers, institutions, and decision-makers to propose alternative strategies to refine the teaching-learning process. Thus, an online questionnaire was applied to 127 undergraduates who studied in the different academic programs offered by the university. The method applied was mixed since the questionnaire used both closed and open-ended questions in order to collect more relevant and detailed information coming from students. After analyzing the results, the students expressed that remote learning fulfilled their expectations because it helped them to be more autonomous and develop self-efficacy.
The Interactive Musical Score: An Electronic Score To Hear The Notes From A Paper Score

The Interactive Score is a novel instrumental device for children's solfege learningPaper scores are overlaid onto a staff drawn with conductive ink and connected to an Adafruit musical box.Pressing a note in the score triggers its sound, and running fingers over the notes plays a melody.Learning to read music from the score is an essential part of Western classical music training.Traditionally, children learn the different music notes by singing or playing notes on an instrument, guided by a teacher.We envision a way for children to learn the correspondence between notation and sound by directly touching the score.The Interactive Score is effortless to use and allows children to make discoveries on their own.The correspondence between the visual, the tactile , and the sound can aid in learning .
Power distribution abnormal data identification and processing system based on correlation classification

Abstract when the conventional system classifies the power distribution data, the data category is not fine enough, resulting in low accuracy of abnormal data identification, high false detection rate and large correction error. A power distribution abnormal data identification and processing system based on correlation classification is proposed. In terms of hardware, multi-layer framework is adopted, and the overall hardware structure of the system is composed of communication modules, so as to optimize the power distribution data and signal acquisition circuit and suppress signal noise; In terms of software, set the data classification convergence conditions, iteratively increase the number of distribution data clustering centers until the convergence conditions are met, realize the fine correlation classification of data, draw the classified data curve, obtain the normal data feasible region, judge that there are abnormalities in the data beyond the feasible region, and use the vertical comparison method for correction. The results show that the designed system improves the accuracy of abnormal data identification, reduces the false detection rate and correction error, and the accuracy of abnormal data identification and processing is better than that of the conventional system.
An Enhanced Approach for Detecting Human Age and Gender Using CNN (Convolutional Neural Network) Classifier

CONCLUSIONIn this Python Mini project, we learned how to determine a person's gender and age via a convulsive neural network (CNN), which receives 99.98% (women) and 99.84% (men) according to our facial databases.CNN's algorithm affects weight based on efficiency, classification speed, and processing speed.To construct a full gender and age recognition system, the study may be extended to address recognition and task alignment utilising CNN-like structures.This approach is appropriate for real-time processing in contexts with restricted resources that require sophisticated hardware processing.With few resources, we were able to acquire the findings of our technique utilising data sets that were extremely straightforward to prepare and had a 98 percent accuracy.
Modeling and Research on Route Planning

Abstract In view of the problem of fixed and mobile threat areas in the navigation of reconnaissance aircraft, this paper proposes to connect the position information with the time information, and build a distance matrix based on the ant colony algorithm to model and solve the optimal navigation route.This article is based on the problem of the threat area in the area where the reconnaissance aircraft is sailing, since the distance between two target points is no longer a straight line distance but a curve distance, so consider building a distance matrix.The simulation experiment results show that the algorithm proposed in this paper can select the optimal path faster and has good accuracy when traversing each target point.
Pemantauan Tumbuh Kembang Balita di Posyandu Berbasis Web dengan WhatsApp Api

The implementation of the Integrated Service Post or better known as Posyandu uses a manual method by collecting data using books. This manual data collection can result in invalid data, delays in making reports on the results of Posyandu activities both by Posyandu itself and related agencies such as Puskesmas and Kelurahan. Due to the lack of information provided, there is also a lack of community participation. To overcome the problems that arise, the Posyandu Information System Web is made to connect with the WhatsApp API. The WhatsApp API is expected to help convey information to the public through broadcast messages sent by the local Posyandu. System development follows the waterfall method and system design uses UML (Unified Modeling Language). The results of this study resulted in the design and creation of a website that can manage Posyandu data, convey Posyandu information messages and to make it easier for parents to monitor their baby's growth and development
A comparative study to predict breast cancer using machine learning techniques

Detection of disease at the starting stage is a very crucial problem. As the population growth increases, the risk of death incurred by breast cancer rises exponentially. Breast cancer is the most common cancer in women, and it is also the most dangerous of all cancers. Deaths because of breast cancer have b een increasing in recent times. Earlier detection of the disease followed by treatment can reduce the risk and increase survival chances. There will be cases where even medical professionals can make mistakes in identifying the disease. This project deals with the detection of Breast cancer using the cell data of the tumor present in the breast. So, with the help of technologies in machine learning and artificial intelligence can substantially improve the diagnosis accuracy. The development of this project is beneficial in medical decision support systems. Several machine learning techniques, namely Adaboost, multi-layer perceptron (MLP) and stacking classifier; were used, and among all the algorithms, the stacking classifier results in the best accuracy. The accuracies 95.6%, 97.1%, and 99.2% respectively.
A Learner-Centered Syllabus-Based Approach to engaging master students into research activity

Abstract This paper presents a vision of a team of researchers on solving a problem of engaging future Mathematics teachers into a research activity through implementing person-centred approach to learning and teaching. The authors of the given article present a designed Learner-Centered Syllabus of a training research workshop in Mathematical Analysis for Master students, majoring in Mathematics at teacher training universities. This study presents the structure, the components and the content of a Learner-Centered Syllabus. When giving content to the sections of the syllabus, the developers take into consideration the conditions for person-centredness; creating the atmosphere of acceptance and congruence; shaping in students the personal qualities, which contribute to their research activity; arranging collaboration; engaging students into the assessment process. The researchers in the present study identify orientation at self-development as a key concept of the course policy and make each section of the syllabus personal. The following sets of attributes were used to evaluate the designed Learner-Centered Syllabus: Community, Power and Control, Assessment and Evaluation. The experimental study was done over four years, two groups of Master students majoring in Mathematics became its participants. One group was given a conventional Content-Focused Syllabus of a training research workshop in Mathematical Analysis, while the second group received a Learner-Centered Syllabus, designed in accordance with the Person-Centered Approach to Teaching and Learning. The analysis of the findings of this experiment justified the effciency of creating a learning environment, which is determined by the emotional components, such as Congruence, Acceptance and Empathic understanding. The present paper also shows a positive impact of the syllabus on the degree of students’ motivation to choose the training research workshop in Mathematical Analysis.
Fashion Recommendation System

Abstract: Artificial intelligence is becoming increasingly useful in creating personalized shopping experiences on e-commerce websites, user-specific advertisements, classifying objects, and detecting colors from images. Fashion is one of the most critical industries in our world today. Style has become one of the primary ways in which people express their personality and in which they distinguish themselves from those around them. In this project, we are creating a Fashion Recommendation System using Artificial Intelligence inorder to classify the user’s clothes and recommend the most suitable outfit for a given occasion using a recommendation algorithm. The proposed system shows that it can process the user’s clothes from the images, identify the type and color of the outfit and finally recommend the most suitable outfit for the given occasion based on the user’s existing clothes. The system provides a wardrobe, whereusers can store images of clothes that they have. Each user is associated with a wardrobe. We explore machine and deep learning techniques to classify the type of clothes from images and to identify the color of the clothes. Finally, we propose an algorithm that is used to recommend matching clothes.
Digital Business Card With Augmented Reality

The main goal of this project is to introduce Augmented Reality (AR) technology to mobile phones and turn it into a business card app.In comparison to other devices, the mobile phone makes it considerably easier to introduce AR technology to the general audience.As a result, on a portable business card, this project provides a basic explanation of Mobile Phone AR technology and its functionality.The visual 3D map will be displayed on mobile phones upon request, using a standard marked business card that aids track marking and location identification.Low-level graphics are also a barrier for this project, which aims to boost the power of mobile phones before implementing an AR-based real-time provisioning procedure.
Analysis of Cryptocurrency, People and Future

Abstract: Cryptocurrency is a technology developed 14 years ago. It represents valuable and intangible objects which are used electronically in different applications and networks such as online social games, online social networks, virtual worlds, and peer-to-peer networks. While it may not dethrone traditional fiat currency, it could change how systems around the world interact with each other. A SWOT analysis of cryptocurrency is presented, which elucidates some of the recent events and movements that could influence whether cryptocurrency contributes to a shift in economic standards. Keywords: Cryptocurrency, Blockchain, Decentralization, Bitcoin, Cryptocurrency Security, People’s Trust in Cryptocurrency, Uses of Cryptocurrency. Investment in Cryptocurrency.
MORPHOLOGICAL AWARENESS AND ITS CORRELATION WITH EFL STUDENTS’ READING COMPREHENSION IN SECONDARY SCHOOL

Morphological awareness, as one of the mediators which gives impact, plays an important role in achieving successful reading comprehension. It is believed that morphological awareness contributes to enhance students' performances by analyzing the word structures. Students are required to improve their skills in manipulating and interpreting complex words presented in a text. This study then, investigates the students’ level of morphological awareness and its relationship with their reading comprehension. It was carried out with a group of 55 second-grade EFL students from one of Gresik's senior high schools in East Java. The data were collected through reading comprehension test and morphological awareness test. The results indicates that students’ level of morphological awareness are varied (5 students = Excellent, 21 students = Good, 18 students = Mediocre, 10 students = Poor, and 1 student = Very Poor). In addition, the results show students’ morphological awareness has no link with their reading comprehension (p-value= .357, &gt; .05).
Investigating the Impact of Employees’ Internal Factors on their Job Performance in Government Organizations

Considering the current COVID-19 crisis, organizations need to hire the right person for the right job, as they can perform effectively and contribute to achieve business goals.The current study aimed to investigate the impact of employees' internal factors, for example, (overqualification, life satisfaction, person-job fit, gender discrimination), and organizational commitment, on employees' task and contextual performance in government organizations of Kuwait.This study developed a conceptual framework based on the person-job fit theory to investigate the hypothetical relationship between constructs.A cross-sectional research approach was applied to collect data from 275 respondents working in different government organizations in Kuwait.Thus, we developed a questionnaire based on a seven-point Likert scale (1=strongly disagree to 7=strongly agree) to collect data.Data were analyzed through the "structural equation modeling" technique via Smart PLS software.The findings indicated a strong positive relationship between gender discriminations, life satisfaction, person-job fit, organizational commitment, task performance, and contextual performance.Thereby, we found overqualification negatively impacts organizational commitment.Therefore, organizational commitment mediates the relationship between person-job fit, life satisfaction, gender discrimination, task performance, and contextual performance.On the other hand, organizational commitment did not mediate the relationship between overqualification, task performance, and contextual performance.Hence, the findings of this study confirmed that the employees' job performance significantly based on life satisfaction,
THE FORMATION OF THE SOCIO-PEDAGOGICAL COMPETENCEOF THE CAMP-COUNSELORS IN THE CAMP-COUNSELOR TRAINING SCHOOL “LEAD!”

The paper deals with the problem of the formation of the social and pedagogical competence of camp-counselors in the camp-counselor training school. In the composition of the socio-pedagogical competence of camp-counselors cognitive, personal and technological components are determined. The levels of formation of camp-counselors’ socio-pedagogical competence are presented as basic, imitative and creative. The content of the camp-counselor training program is analyzed from the standpoint of socio-pedagogical competence with the presentation of forms, methods and means of training. The author presents and analyzes the data of an empirical study of the levels of camp-counselors’ socio-pedagogical competence formation.
Analysis of General Groundwater Flow Equation with Fractal Derivative

A concept of differential operators with power law setting have been recognized to capture some self-similar features found in nature and called a fractal derivative. The fractal order or dimension represents in our context the degree of self-similarities of the confined aquifer. The differential operator was applied to groundwater flowing models within fractured confined aquifers. Numerical analyses are presented in details and numerical simulations for different values of fractal dimension.
Gesture Controlled Virtual Mouse with the Support of Voice Assistant

Abstract: This work offers a cursor control system that utilises a web cam to capture human movements and a voice assistant to quickly traverse system controls. Using MediaPipe, the system will let the user to navigate the computer cursor with their hand motions. It will use various hand motions to conduct activities such as left click and dragging. It also allows you to choose numerous items, adjust the volume, and adjust the brightness. MediaPipe, OpenCV etc advanced libraries in python are used to build the system. A hand gesture and a voice assistant used to physically control all i/o operations. To recognise hand gestures and vocal instructions, the project employs cutting-edge machine learning and computer vision techniques, which operate effectively without the need for extra computer hardware. Hand gestures are a simple and natural way to communicate. Keywords: MediaPipe, Gesture Recognition, Voice Assistant, Machine Learning, Virtual Mouse
Introduction to robot path planning and an assumption Path planning

Artificial intelligence such as robots has played an increasingly significant role in numerous fields nowadays, and motion planning is definitely one of the core technologies of robots, which deserves our deeper investigation.This paper introduced some main and widely used algorithms used in motion planning, including methods based on search such as Depth First and Width First algorithms and A* algorithm, methods based on sampling such as RRT and a series of its optimizations. Every method has its searching process and results. There is another searching algorithm called Markov decision process model, we tried to combine it with RRT but failed due to the different domains they apply.
Hand Gesture Recognition using Machine Learning with Convolutional Neural Network (CNN)

Many deaf and hard-of-hearing people rely heavily on sign languages as a means of communication.Sign languages are the native languages of the Deaf community and provide full access to communication.Despite the fact that it is an effective mode of communication, communicating with speech impaired people remains a barrier for those who do not understand sign language.The purpose of this paper is to create a Web application that will convert hand gesture to English in the form of text.hence facilitating sign language communication.The web application uses the computer's webcam to collect visual data, which is then pre-processed using a combinational method before being recognized via template matching.This project tried different machine learning algorithms including SVM, RNN and CNN (Convolutional Neural Network).With the accuracy of 91% CNN algorithm has proven to be the most accurate classification tool.
Instagram-Assisted Language Learning in Islamic Higher Education: Toward Online Collaboration

The purpose of this study is to investigate instagram use among English pre-service teachers (EPST) to support their language learning in a particular course. This study sought to explore the following research questions: (1) How do EPST explore instagram to accomplish Online Task-Based Language Learning (OTBLL)? (2) What contents are created and shared by EPST to accomplish OTBLL? To collect the data, this study applied observation and content analysis. In addition several tasks were designed to be accomplished by EPST. The findings shows that EPST were able to integrate both the ability to use instagram and the ability to integrate it for OTBLL. The contents that EPST created and shared met the requirement of OTBLL. The contents included several types: creating video to explain topic assigned, writing phonetic transcription, and other tasks. The results of this study are expected to contribute in improving EPST language skills and their ability to explore social media use for language learning. Being able to seize social media to support language learning is a necessary skill to be nurtured for future language teacher in the 21st century.