Reviewerly/exHarmony huggingface dataset:
Face Recognition based Attendance System

Human face is one of the natural traits and crucial part of human body that can uniquely identify an individual. In the current old system the roll numbers are called out by the teachers and their presence or absence is marked accordingly which is time consuming and has a lot of ambiguity that caused inaccuracy and inefficiency of attendance marking. The productive time of the class can be utilized very efficiently by implementing automated attendance system. The main purpose of this project is to build a face recognition-based attendance monitoring system for any educational institution or organization where attendance marking is the demanding task. It enhances and upgrades the current attendance system into more efficient and effective as compared to before. This attendance system which uses HaarCascade a machine learning Object Detection Algorithm used to identify faces in an image or a real time video, Local Binary Pattern Histogram (LBPH) a face recognizer algorithm used to extract features and compare by using python programming and OpenCV libraries saves time and efficiently identifies and eliminates the chances of proxy attendance. This model integrates a camera that captures an input image and training database is created by training the system with the faces of the authorized students.
Clinical Decision Support System for Knee Injuries Treatment Using Multi-Agent System

Diagnosis of sports injuries is a very critical process and its performance depends on the recognition of the relevant symptoms. In this paper, a Multi-Agent-based knee injury detection and diagnosis scheme (MFZS) is introduced that applies fuzzy rules over input symptoms and recommends the relevant treatments. Its performance is compared with traditional fuzzy system (TDFZS) under the constraints of detection accuracy and sensitivity etc. Chi-square and Fisher Exact test is also performed to verify the significance of the outcomes of both schemes.
THE IMPACT OF NTFONJENI RURAL EDUCATION CENTRE’S ELECTRICAL INSTALLATION PROGRAMME ON PROMOTION OF SELF-RELIANCE AMONG GRADUATES

This study assessed the impact of Ntfonjeni Rural Education Centre’s (REC) electrical installation programme on promotion of self-reliance among out-of-school youth and adults in Eswatini, Hhohho Region. The objectives of the study were to: establish whether the electrical installation programme objectives were met, determine the electrical installation programme activities undertaken by graduates; and determine the engagement with learning by graduates in promoting self-reliance as a result of the electrical installation programme offered at Ntfonjeni REC. This was a small-scale study that used the evaluation research design with qualitative research methods. Data were collected using interviews, observation, and documents. Participants were graduates from the electrical installation programme, electrical installation instructor and community members that were chosen using convenient, purposive, and snowballing sampling techniques, respectively. Seventeen participants were individually interviewed using structured interviews. Data were analyzed qualitatively through thematic analysis. The findings revealed that the goal of the electrical installation programme of promoting self-reliance among its graduates was partially met. The findings indicated that the electrical installation programme improved the livelihood skills of the graduates and changed their behaviour and the community members’ mind-set. The graduates showed some features of self-reliance such as becoming financially independent. The study recommended the upgrading of the electrical programme into Grade 1 Grade Testing Level in order to enable its graduates to be employable by big companies and that the Ministry of Education and Training should allocate more funding to enable graduates to start income-generation projects.
Virtual Reference Service through Mobile App Developed Using MIT App Inventor: An effective tool for Librarians in Covid-19 Pandemic Scenario

The paper reveals the responsibilities of library professionals in the contemporary world. And how to think, and how to provide information services to the user community in the critical situation resulted by COVID-19 pandemic. This paper is also give new idea about how to provide information services using “open access information sources”. Using “open access information sources” and “MIT App inventor: the mobile application developing platform” how can a library professional provide effective virtual reference service is briefly explained in this paper
Machine learning-based variable selection: An evaluation of Bagging and Boosting

Variable selection is a necessary step to build a useful regression. In this paper, an evaluation of different methods (variable selections) including Bagging and Boosting were performed. Large datasets from 1924 observations were taken and the second interaction data which contains 435 variables were employed. In big data, there is no single variable selection technique that is robust towards different families of regression algorithm. The existing variables techniques produce different results with different predictive models. Variable selections only provide the rank of important variables which means that the techniques did not have rules in selecting the suitable range of variable importance.  Each of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, and 120 highest variable important were selected. Several validations such as Sum Square of Error (SSE), R-square, and Mean Square Error (MSE) were used to compare its performances. As the result, bagging for the 90 highest variable important was better than others SSE (31077.8295), R-square (0.9210), and MSE (17.8344), respectively. Hence, the variable selection using bagging has been considered as the best model.
Attitude Towards ICT among Visually Impaired Children of Ludhiana in Relation to Gender and Locale

Present study was undertaken to investigate the level of attitude of visually impaired children towards ICT and to check the significance of difference in attitude towards ICT on the basis og gender and locale.100 children 50 boy (25 urban and 25 rural) and 50 Girl (25 urban and2 5 rural) were randomly selected from the list of visually impaired children in the schools of Ludhiana district of Punjab.Self-developed scale of attitude towards ICT for visually impaired was used by the investigator.Result of the study revealed that most of the children have moderate attitude towards ICT.Visually impaired children do not differ significantly on the basis of their gender and locale.
SIMULASI DAN PEMBELAJARAN PASAL UNDANG-UNDANG PELANGGARAN LALU LINTAS DENGAN METODE COMPUTER BASED INSTRUCTION (CBI) PADA SISWA SMK PARULIAN MEDAN

The law is a rule that must be applied in Indonesia in this case the law that is most often violated by the general public is the law on traffic violations, most traffic users often ignore this, due to a lack of understanding of the violation of the article on Constitution Computer Based Instruction is a method that makes computers as the main means or tools that communicate material to students. The computer learning model (CBI) aims to help students understand the lessons about law because this method includes Tutorial, Drill and Practice, Simulation and Games The results of the application are designed to be able to help millennials in learning and understanding about orderly and able comply with laws, especially millennials, to become role models in complying with traffic laws
Context Graphs for Ampliative Analogical Legal Reasoning and Argumentation

Legal Reasoning involves both analytic/deductive and ampliative/analogical components. The latter are tightly connected to defeasibility in legal argumentation. Yet present rule-based models of defeasible legal inference are not ampliative: what is defeasibly derivable is solely determined by the contents of their knowledge base. We argue that a proper modelling of the analogical aspects of legal reasoning allows one to capture both much of its ampliative and much of its defeasible capabilities. Context graphs enable the representation of ampliative analogies as metalogical operations. We sketch algorithms acting on Context Graphs to implement analogical reasoning.
Pengaruh Penggunaan Media Gambar Untuk Meningkatkan Hasil Belajar Bahasa Arab Siswa Kelas VII Di Madrasah Tsanawiyah Muhammadiyah 12 Palirangan

Education is a necessity for everyone. Educational activities are activities that are very important in human life and cannot be separated from their lives. With education, human needs regarding change and development can be fulfilled. The mean score of the pre-test results was 71.73, while the mean value of the post-test results was 79.61. From these data it can be seen that the pre-test result value is lower than the post-test score, so it can be interpreted that there are differences in student learning outcomes before and after using image media in the learning process. the completeness of the pre-test result value is 46.15%, while the post-test result value is 80.76%. From this data, it can be seen that using image media can affect student learning outcomes. Then from the hypothesis testing using the Product Moment Correlation test and the Paired Sample T-test with the help of SPSS 20 software in the sig (2-tailed) section, it isknown that 0.000 &lt;0.05 The condition is if r count is smaller than r table, then H0 is accepted. and Ha is rejected and vice versa if r count is greater than r table (r count&gt; r table) then H0 is rejected and Ha is accepted. In fact, r count (0.977) is greater than r table (0.404). Thus H0 is rejected and Ha is accepted. As the basis for decision making in the Product Moment Correlation test and decision guidelines based on the probability value, it can be concluded that H0 is rejected and Ha is accepted. This means that image media can affect the improvement of learning outcomes in Arabic language material for class VII at MTs. Muhammadiyah 12Palirangan
Exploring a Dynamic Change of Muscle Perception in VR, Based on Muscle Electrical Activity and/or Joint Angle

We display a virtual avatar changing its biceps appearance to fit the user’s biceps contraction. The avatar’s biceps changes its size and color based on either: (EMG) the biceps activity (recorded with a electromyography [EMG] sensor); or (ANG) the elbow angle; or (EMG + ANG) the biceps activity and elbow angle. The users also wear an ad hoc haptic device around their biceps to give the illusion of stronger biceps contractions. In the Virtual Environment, the participants (N=10) look at their right biceps and do elbow flexion. They rate EMG, ANG and EMG + ANG (with and without the haptic device) along Body Ownership and Agency. We found that ANG (without the haptic device) was rated noticeably lower on the Agency than EMG and EMG + ANG (both without the haptic device). It is likely better to change the appearance of a muscle using the user’s muscle contraction rather than the user’s movement when considering the user’s muscle ownership and agency. The addition of the ad hoc haptic device appeared to not affect the Sense of Embodiment.
Building Business Models with Blocks In Chain: Business Models in a Blockchain Environment

Disruptive technologies of the Industry 4.0 are not only disrupting the technological field, but they are also changing the competitive environment and the sources of business competitiveness. Although the business potential of blockchain seems to be promising, the existing research on blockchain technology are in most cases focused on the technical aspects. Therefore, the aim of the paper is to explore the possibilities for wider business use of blockchain technology (apart from its implementation within the financial services context) and to map its effects on the existing business models. Moreover, the paper is dealing with blockchain-inspired digital business models in an attempt to identify the main directions of new, blockchain-based business models development. As usual for the first stages in the development of a certain research area, the paper is exploratory. Moreover, it is theoretically oriented and it presents the results of relevant literature research. As a conceptual paper, it is built around the concept of the business model and the blockchain potential to affect its functioning and dynamics. The theoretical research presented by the paper reveals that blockchain technology has the potential to affect all the elements of the existing business models, but also to inspire the development of entirely new digital business models.
Application of Deep Recurrent Q Network with Dueling Architecture for Optimal Sepsis Treatment Policy

Sepsis is one of the leading causes of mortality globally, and it costs billions of dollars annually. However, treating septic patients is currently highly challenging, and more research is needed into a general treatment method for sepsis. Therefore, in this work, we propose a reinforcement learning method for learning the optimal treatment strategies for septic patients. We model the patient physiological time series data as the input for a deep recurrent Q-network that learns reliable treatment policies. We evaluate our model using an off-policy evaluation method, and the experimental results indicate that it outperforms the physicians’ policy, reducing patient mortality up to 3.04%. Thus, our model can be used as a tool to reduce patient mortality by supporting clinicians in making dynamic decisions.
Application of Machine Learning for Improving Early Cancer Diagnosis

Across the world, cancer becomes a catastrophe for a human being who is suffering from it.Cancer can be diagnosed at a premature stage to overcome the consequences at a later stage and the possibility of endurance considerably, as it can support appropriate medical action to patients.One of the frequently used innovative technologies for the diagnosis and detection of cancer is Machine learning (ML).In recent times ML has been used for the prediction and prognosis of cancer.Machine learning enables the creation of algorithms that can learn and make predictions.Various Machine Learning techniques can build a model to diagnose cancer based on finding accuracy level.It is possible for early detection of cancer through machine learning where we train the machine with previous data.This paper aims to predict cancer type based on symptoms given by the user.Here we adopted a supervised learning algorithm and then use the Logistic Regression based on accuracy and recall score i.e., the algorithm which gives high accuracy level and recall score.The proposed System executes with good performance as it generates accurate results.
PEMBELAJARAN MEMBACA PUISI PADA SISWA KELAS V SD DENGAN METODE CLASSWIDE PEER TUTORING

This research was written because of the low poetry reading skills of the fifth grade students of SDN 041 Cibuntu Warung Muncang. The purpose of this research is to improve the learning achievement of fifth grade elementary school students towards learning Indonesian, especially reading poetry through the Class Wide Peer Tutoring (CWPT) method. In addition, the study also aims to improve teacher performance in applying appropriate learning methods. After implementing the CWPT method, there were 87.6% who could reach the KKM of 30 students. Based on the data on student learning outcomes, it was found that with increased self-confidence, their learning outcomes would also increase. This self-confidence was obtained after the implementation of the CWPT development method which used peer tutors to improve their ability to read poetry. The results of this study are expected to have a positive impact on improving teacher performance and student learning outcomes.
A New Method for Image Steganography using Artificial Bee Colony algorithm (ABC)

Today computer and networking technologies provide easy mode and medium for communication. While transferring the data among distributed user, the confidentiality and privacy of data should be maintain. Ethical hackers or third party members like intruders can access the secret data, so security is also major issue. Steganography can be precisely defined as the science and art of hiding secret message into image, audio and video or text file. The most common use of steganography is to hide a file inside another file. Bio inspired computing is vast and advance concept that is also used as to provide data security. Artificial Bee Colony Algorithm (ABC Algorithm), is one of the finest algorithms used for stenography. This paper proposes an Image steganography scheme using Artificial Bee Colony algorithm. The proposed scheme is best for the searching good optimal solution very efficiently. The proposed image stenography scheme is implemented using Blowfish algorithm. Experimental results validate the theories of the proposed scheme and reported significant improvements as compared with state of the art.
Designing AI-Based Systems for Qualitative Data Collection and Analysis

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS.Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups. With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups.
A Framework for Developing Agricultural Product Forecasting System Application for Farmers

In the Philippines, farming employs one-third of the workforce. Farmers played an essential part in delivering fresh food to every family on a regular basis for our wellbeing. Assisting farmers in building a stronger network to market their products resulted in the agricultural sector's long-term viability. The development of an agricultural forecasting mobile application will benefit both farmers and traders by ensuring that produced crops are profitable. Agricultural forecasting applications will use the developed architecture design. The study looked into the aspects that went into the development of a mobile application to help farmers. The employment of various statistical methods aided in the provision of in-depth analysis, resulting in a more widely acceptable design for farmers. The findings demonstrate that identifying some common concerns in offering online services will also help to resolve some challenges and provide answers to the best practises in agricultural production forecasting.&nbsp; It stated that having a mobile agricultural forecasting application solved the problems associated with farm waste production. The application will assist farmers in determining the degree of crop demand in the market and navigating to the location where farm produce demand is high in the participating trading post. This helped farmers and the government enhance the agricultural sector, which was lagging behind other developing countries.
現代日本語における副詞の感動詞用法をめぐって －｢ちょっと｣の主観化を中心に－

This study focuses on chotto among the adverbs that are interjectionally used and describes the process of how the meaning and function of adverbs has derived from adverbial usage to interjectional usage in case of increased subjectification. (1)Chotto precedes the predication and indicates low degree or small quantity. (2)The acquisition of affective meaning by chotto is probably enabled by contexts in which speaker’s negative feelings or attitude to the addressee. In such uses, it appears to have lost its original meaning, small extent as an adverb. Chotto occurs without a predicate as a response but it is not procedural with interjectional meaning. (3)Chotto is used as an interjection to call someone casually. It occurs in clause-initial position. Moreover, it can stand alone as an utterance. It functions at a discourse expressive level. As addressed above, chotto illustrates increased subjectification; originating in a clause-internal degree modifier (i.e. propositional function), it comes to show the speaker’s emotional involvement as an interjection (i.e. expressive function).
Cyber Crime Awareness among Teacher Trainees

Cybercrime's 'definitions' are largely determined by the context in which it is used.The root of cybercrime is a small number of actions that compromise the confidentiality, credibility, or availability of computer data or systems.Beyond that, computer-related actions for personal or financial gain or harm, such as identity-related crime and computer content-related acts (all of which come under a broader definition of the word "cybercrime"), do not lend themselves easily to legal definitions of the term.The aim of this study was to look into the knowledge of cybercrime among Teacher Trainees.The study included 200 B.Ed. students from three separate education colleges in Ludhiana: B.C.M. College of Education, Malwa College of Education for Women, and Partap College of Education.There were 50 males and 150 females among the 200 B.Ed. students.The researcher developed his own scale of cybercrime knowledge.This scale has 42 sentences, 24 of which are positive statements and the other 18 are negative statements.Each claim was rated on a five-point scale.According to the findings male and urban Teacher Trainees were both more conscious about cybercrime than female and rural Teacher Trainees respectively.