Reviewerly/exHarmony huggingface dataset:
Influence of Guidance and Counseling as Teachers’ Discipline Management Strategies on Kenya Certificate of Secondary Examination in Public Secondary Schools in Tharaka Nithi County, Kenya

Discipline in schools is about positive behavior change in order to create conducive environment for learning. When discipline is achieved in secondary schools, it becomes instrumental in students academic performance. This study focused on assessment of the influence of teachers’ discipline management strategies on KCSE performance in public secondary schools in Tharaka Nithi County, Kenya. The objective of this study was to establish the influence of school rules on KCSE performance. The county has had a trend of grade wastage from KCPE to KCSE. The study adopted descriptive survey research design. The study targeted 104 public secondary schools in Tharaka Nithi County, with 10 boys schools, 17 girls schools and 77 mixed schools. Stratified random, purposive sampling method and Krejcie&amp; Morgan population sample table was used to determine the sample size. Principals and teachers were the respondents in this study. Interview schedule for 21 principals and 346 questionnaires for teachers were used as tools for data collection. The study sampled 42 schools, which constituted 40% of the total population size. The researcher prepared the instruments with expert judgment by supervisors. Cronbach’s Alpha Coefficient (α) was employed in determining the reliability of the instruments. The study was based on Systems Theory of Peter M. A pilot study of 10% of the sample schools and respondents was carried out from each of school category. Alpha index of 0.869 was obtained for the teachers’ questionnaires. The reliability of the interview schedule was ensured by the consistency of the questions and the order in which they were administered. The study used descriptive statistics which included frequencies, measures of central tendencies (mean) and measures of dispersion (standard deviation). Inferential statistics Pearson r was used to test the relationships of the hypotheses with the aid of Statistical Package for Social Sciences (SPSS) in analyzing the data. The data from the interview guide was arranged thematically, transcribed and then presented verbatim in order to triangulate the results. The results revealed that school rules did not influence KCSE performance positively. The findings of this study are expected to be of benefit to different stake holders like teachers, parents and policy developers,who are sensitized with new information on alternative disciplinary strategies to be used on students’ discipline in schools, the policy makers are assisted to come up with a new policy on use of alternative disciplinary methods in schools.
Modification of IDTCS Method for Touching Leukemia Cell Grouping

Morphological analysis and calculation of the number of white blood cells on microscopic images are stages in diagnosing leukemia. Constraints in developing a system for diagnosing leukemia are white blood cell segmentation and counting of the number single cells in touching cell. We propose to modify the Iterative Distance Transform For Convex Sets (IDTCS) method to separate the touching leukemia cells. The IDTCS method is used to determine markers for each cell in touching cells. The marker results from the IDTCS method are used as cell centroids and the next process is pixels clustering based on the nearest cell centroid using the euclidean distance function. The data used are microscopic images of Acute Lymphoblastic Leukemia (ALL). The experimental results show that using modified IDTCS method for clustering produces better accuracy compared to the K-Means clustering and Watershed methods.
Variazione socioprosodica nell’italiano regionale di Spilamberto

This paper explores the socioprosodic variability in the regional Italian of Spilamberto (Modena).It is based on an experimental study that involved 8 speakers of different age and gender who were asked to perform a reading task.The aim is to investigate how the variation of fundamental frequency, i.e. intonation, relates to sociolinguistic variation in the realisation of three statements, polar questions and exclamatives.Praat and Intsint plugin were used to transcribe and annotate the data.The results, obtained through both quantitative and qualitative analysis, show that 1) exclamatives are realised at higher frequency levels than other sentence types; 2) gender and age play a role in differentiating the intonational patterns of read statements.
Shock Innovation: Conceptualisation of Digital Transformation in Education during the Covid-19 Pandemic

The article considers the transition to distance learning in the context of COVID-19 pandemic as innovation. In particular, it shows that the spread of innovation in an extremely fast and compressed way does not fit the classical model of innovation diffusion by Rogers. Based on the results of the analysis, the authors supplement the innovation theory with a model of shock innovation which aims to describe the phenomenon of momentary transformations. For that reason, a comprehensive and extensive description of innovation diffusion was narrowed down to three key characteristics and linked to three levels (micro-, meso- and macro-). The narratives of school principals which have been extracted from the interviews (N=10) were compared with the characteristics of this three-levels model. The analysis revealed that a shock innovation is characterized by the fact that (1) the initial impulse has a source, external to the system, (2) requiring an obligatory response (forced change); (3) manifested by an innovative “breakthrough” due to the extreme mobilization of the resources; and (4) “densification” of traditional innovative processes for the diffusion of innovation at three levels - individual (micro), group (meso-) and systemic (macro-). The discussion highlights the aspects of the identified characteristics that should be taken into account in designing the strategies of schools’ development, as well as bridging the gaps in the educational system caused by the pandemic.
Analysis of the meaning of Transformative Competencies and Agency in 'Education 2030' in the Era of Transformation: Focused on Karen Barad’s Agential Realism

In 2018, the OECD created the ‘Education2030’ project, which emphasized developing Transformative Competencies and Agency to prepare students for challenges in the era of transformation. The purpose of this paper analyzed the contents and meaning from agential realism perspective, as suggested by Karen Barad, feminist new materialism, arguing that ‘Education 2030’ need to be studied from various perspectives. Therefore, the meaning of the core goals, competencies, and achievements of ‘Education 2030’ was analyzed by applying the Hermeneutic Phenomenological research method. As the results of this study, Transformational Competencies required the ability to create new values, control conflicts, and take the responsibility to respond to global crises. In addition, student agency and co-agency referred to growing and developing jointly by forming entangled relationships. Forward, we must make efforts to establish a educational ecosystem conducive to building Transformative Competencies and Agency. Agential realism is expected to be a useful perspective for breaking away from the dichotomous conceptual frame that distinguishes knowledge and competence in the curricula.
ANALISIS FAKTOR-FAKTOR PENYEBAB KESULITAN SISWA DALAM MENULIS KARANGAN NARASI PADA PEMBELAJARAN BAHASA INDONESIA KELAS III DI SDN 21 SEMBAWA

ABSTRACT&#x0D; The purpose of this research is to find out the factors that cause students' difficulties in writing narrative essays in class III Indonesian language learning at SDN 21 Sembawa. The method used in this research is descriptive qualitative. The informants who were the subject of this study were teachers and third grade students at SDN 21 Sembawa. Data collection techniques used in this study were observation, interviews, questionnaires and documentation. The data obtained from the research were then analyzed qualitatively and described in a qualitative descriptive form. The results of this study indicate that students' difficulties in writing narrative essays are based on indicators of writing essays, students still have difficulties in indicators of idea content, namely adjusting the title to the content. In terms of content organization indicators, some of the students' essays did not really match the content with the essay titles, and it was seen that the students' essays were still not optimal, the ideas made by students had not been developed properly. In the grammar indicators, there were many student errors in composing words in essays, mistakes in writing word and sentence structures and there were some words that were difficult to understand. On style indicators: choice of structure and diction, namely the vocabulary used by students is still limited, students still repeat the same words a lot. Spelling and punctuation indicators, errors in capitalization, and use of periods and commas. Factors that cause student difficulties are internal factors which include: student attitudes in learning activities and student motivation in learning, study habits carried out by students in class and at home. External factors consisting of the teacher's method of teaching in the classroom as well as the family and community environment.
Application of cell imaging analyser Vision Pro® system on white blood cells differentials in acute leukemia patients

Objective: This study aimed to investigate the ability of the Vision Pro® system on white blood cell differentials in acute leukemia patients at the Hematology and Blood Transfusion Center - Bach Mai Hospital from January 2023 to March 2023. Subjects and methods: Cross - sectional and retrospective research on 43 blood samples with blasts in acute leukemia patients at the Hematology and Blood Transfusion Center - Bach Mai Hospital. Results and conclusion: The blast detection ability of the Vision Pro® system was 81.4%. The percentage of similarity in the blasts ratio between the reader and the Vision Pro® system was 69.8%. Meanwhile, the similarity in segmented neutrophils and lymphocyte ratios were 76.7 and 58.1%, respectively. In addition, no statistically significant differences were observed in the similarity of the blasts ratio, the segmented neutrophil and lymphocyte ratios among patient subgroups according to types of disease, treatment history and white blood cell count.
A Deep Reinforcement Learning Method for Accurate and Efficient Anomaly Detection

Anomaly detection is a crucial service in contemporary data networks, and the development of new fast and robust algorithms capable of detecting and classifying dangerous traffic is essential to cope with evolving threats and increasing detection difficulty. In this paper, we present a novel anomaly detection algorithm that uses deep reinforcement learning (DRL) to efficiently learn and adapt to anomalies in time series data. Compared to alternative DRL methods using in anomaly detection, the approach posited in this paper presents various advantages. Primarily, it outperforms other techniques in scenarios with continuous action spaces. Additionally, it contributes to the stabilization of the learning procedure and enhances the convergence of the algorithm. Finally, it facilitates the acquisition of superior policies and circumvents the risk of being trapped in local optima.
Research on fault diagnosis algorithm of power supply based on case-based reasoning

In order to improve the accuracy and real-time of station power supply system fault repair, the case-based reasoning algorithm is applied. First, the fault case set is organized to build a fault case library and extract feature attributes and fault types; then, the KNN-based algorithm is used to calculate the similarity between the source and target cases for the unprocessed fault data set and the processed fault data set. After the contrast and the analysis, the accuracy of case retrieval after the data processing is higher.
PENERAPAN METODE CONTEXTUAL TEACHING AND LEARNING PADA MENULIS PUISI DI SMA PASUNDAN 7 BANDUNG BERBANTUAN MEDIA CANVA

This research was conducted due to the low ability of students when writing poetry texts, which was caused by a lack of self-confidence, difficulties when choosing words, and the use of less creative learning methods by educators. One of the learning methods that can increase students' creativity is the contextual teaching and learning (CTL) method. The purpose of this study was carried out to evaluate the ability of researchers when conducting research and applying the CTL method when learning to write poetry texts, students' abilities when writing poetry, students' abilities when writing poetry using the CTL method, as well as comparisons of students' abilities before and after being given contextual method treatment teaching and learning. The research method used was an experiment with a true experimental pretest-posttest control group design. The results of this study indicate that; 1) the researcher succeeded in carrying out learning to write poetry texts using the CTL method with a value of 37.2. 2) the condition of the students before being given the CTL method treatment. 3) the condition of the students after being given the CTL method. 4) differences in students' abilities before and after being given the CTL method treatment.
ENHANCING STUDENTS’ PRONUNCIATION THROUGH YOUTUBE CHANNEL: PRONUNCIATION WITH EMMA

The utilize of YouTube for teaching and learning English in this era is needed toimplement. This research aims to investigated YouTube Channel: Pronunciation withEmma able to enhance students’ pronunciation at SMK Bina Mandiri Bekasi. The designof this research is Classroom Action Research (CAR) and uses a quantitative descriptivemethod. Participants in this research were 36 students from XI TKJ F at SMK Bina MandiriBekasi. The result showed that teaching and learning English using YouTube Channel:Pronunciation with Emma as media able to enhance students’ pronunciation.There were significant enhancements both researcher and the student overall performancewithin the classroom. Researcher can make the class more active, and students have gottop notch motivation to learn good pronunciation. Based on data, there is a significantenhance in students’ mean score. In the pre-test, students’ mean score is 71 there were10 students who reached the Minimum Completeness Criteria (KKM) with the percentage27% and 26 students with the percentage 73% failed. Then, in post-test 1 score is 82 therewere 24 students who reached the KKM with the percentage 66% and 12 students with thepercentage 34% failed. In post-test 2 score is 86 there were 34 students who reached theKKM with the percentage 94% and 2 students with the percentage 6% failed. Based onthe result of the mean score cycle 1 and cycle 2, there is enhancement of student’spronunciation that are 82 to 86. It can be concluded that the use of YouTube Channel:Pronunciation with Emma is believed to be successful and able to enhance students’pronunciation.
Semi-Dense U-Net: A Novel U-Net Architecture for Face Detection

Face detection and localization has been a major field of study in facial analysis and computer vision.Several convolutional neural network-based architectures have been proposed in the literature such as cascaded approach, singlestage and two-stage architectures.Using image segmentation based technique for object/face detection and recognition have been an alternative approach recently being employed.In this paper, we propose detection of faces by using U-net segmentation architectures.Motivated from DenseNet, a variant of U-net, called Semi-Dense U-Net, is designed in order to improve the binary masks generated by the segmentation model and further post-processed to detect faces.The proposed U-Net model have been trained and tested on FDDB, Wider face and Open Image dataset and compared with state-of-the-art algorithms.We could successfully achieve dice coefficient of 95.68% and average precision of 91.60% on a set of test data from OpenImage dataset.
Combination of Adaptive Neuro Fuzzy Inference System and Machine Learning Algorithm for Recognition of Human Facial Expressions

A face recognition system's initial three processes are face detection, feature extraction, and facial expression recognition.The initial step of face detection involves colour model skin colour detection, lighting adjustment to achieve uniformity on the face, and morphological techniques to maintain the necessary face region.To extract facial characteristics such the eyes, nose, and mouth, the output of the first step is employed.Third-step methodology using automated face emotion recognition.This study's goal is to apply the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm to increase the precision of the current face recognition systems.For the purpose of removing noise and unwanted information from the data sets, independent data sets and a pre-processing technique are built in this study based on color, texture, and shape, to determine the features of the face.The output of the three-feature extraction process is given to the ANFIS model as input.By using our training picture data sets, it has already been trained.This model receives a test image as input, then evaluates the three aspects of the input image, and then recognizes the test image based on correlation.The determination of whether input has been authenticated or not is made using fuzzy logic.The proposed ANFIS method is compared to the existing methods such as Minimum Distance Classifier (MDC), Support Vector Machine (SVM), Case Based Reasoning (CBR) with the following quality measure like error rate, accuracy, precision, recall.Finally, the performance is analyzed by combining all feature extractions with existing classification methods such as MDC, KNN (K-Nearest Neighbour), SVM, ANFIS and CBR.Based on the performance of classification techniques, it is observed that the face detection failures are reduced, such that overall accuracy for CBR is 92% and it is 97% in ANFIS.
DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Fraud is the unlawful acquisition of valuable assets gained via intended misrepresentation.It is a crime committed by either an internal/external user, and associated with acts of theft, embezzlement, and larceny.The proliferation of credit cards to aid financial inclusiveness has its usefulness alongside it attracting malicious attacks for gains.Attempts to classify fraudulent credit card transactions have yielded formal taxonomies as these attacks seek to evade detection.We propose a deep learning ensemble via a profile hidden Markov model with a deep neural network, which is poised to effectively classify credit-card fraud with a high degree of accuracy, reduce errors, and timely fashion.The result shows the ensemble effectively classified benign transactions with a precision of 97 percent.Thus, we posit a new scheme that is more logical, intuitive, reusable, exhaustive, and robust in classifying such fraudulent transactions based on the attack source, cause(s), and attack time gap.
Detection of Breast Cancer using Convolutional Neural Networks with Learning Transfer Mechanisms

Breast cancer is the leading cause of mortality in women worldwide.One of the biggest challenges for physicians and technological support systems is early detection, because it is easier to treat and establish curative treatments.Currently, assistive technology systems use images to detect patterns of behavior with respect to patients who have been found to have some type of cancer.This work aims to identify and classify breast cancer using deep learning models and convolutional neural networks (CNN) with transfer learning.For the breast cancer detection process, 7803 real images with benign and malignant labels were used, which were provided by BreaKHis on the Kaggle platform.The convolutional basis (parameters) of pre-trained models VGG16, VGG19, Resnet-50 and Inception-V3 were used.The TensorFlow framework, keras and Python libraries were also used to retrain the parameters of the models proposed for this study.Metrics such as accuracy, error ratio, precision, recall and f1-score were used to evaluate the models.The results show that the models based on VGG16, VGG19 ResNet-50 and Inception-V3 obtain an accuracy of 88%, 86%, 97% and 96% respectively, recall of 84%, 82%, 96% and 96% respectively, in addition to f1-score of 86%, 83%, 96% and 95% respectively.It is concluded that the model that shows the best results is Resnet-50, obtaining high results in all the metrics considered, although it should be noted that the Inception-V3 model achieves very similar results in relation to Resnet-50, in all the metrics.In addition, these two models exceed the 95% threshold of correct results.
Refugee Shelter on Selayar Island, South Sulawesi

Refugee shelters are a crucial asylum in restoring the physical and psychological situation. Although Indonesia is a strategic stopover country for refugees, refugee shelter facilities in the country are still far from international eligibility standards. South Sulawesi, the province with the second highest number of refugees in Indonesia, currently does not have a shelter to accommodate the diverse needs of refugees. Therefore, the purpose of this design is to design a refugee shelter in South Sulawesi that complies with international eligibility standards and can meet the needs of refugees and make it easier for the government to control. To meet this goal, data collection was conducted through a literature review of existing international refugee shelter standards and precedent building. After that, a site analysis is carried out to produce a building shape that is in accordance with the condition of the site. The result of the design is a refugee shelter that can complete architectural and non-architectural aspects including characteristics, basic needs, and evacuation activities.
Market Spy: A Smart Mobile Application Featuring Market Price, Seller Reporting, and Product Monitoring System for the Bangladeshi Citizen

The market monitoring system is regarded as an essential technique to check the high pricing, bad quality, and measurement issues of any selling products or items. Currently, available research works/systems are mainly focused on offline-based market monitoring solutions. The offline-based solution is suffered from manpower and irregularity issues, thus experiencing a very low success rate in terms of market monitoring problem-solving. The available literary works did not provide any online-based market monitoring solution with the best shop selection, high pricing issues and product information checking, police reporting, and adding reviews/rating features. To encounter the existing challenges, this paper brings a smart mobile application featuring market product information and seller profit checking by investigating the QR codes and retailers’ asking prices. This paper contributes to the best shop selection for purchasing products by evaluating product names, costs, locations, and ratings. The proposed market spy mobile application provides features like online marketing, reporting against the seller, adding reviews, and rating for the seller shop. The evaluation/review results with up to 70% positive acceptance remarks declare the superiority of the proposed application over the existing schemes.