Reviewerly/exHarmony huggingface dataset:
PENINGKATAN HASIL BELAJAR SISWA KELAS XI MIPA 2 SMAN 1 TIDORE KEPULAUAN MELALUI PENERAPAN MODEL PEMBELAJARAN COOPERATIVE LEARNING TIPE JIGSAW

The application of a varied learning model needs to be done by the teacher to improve student learning outcomes. Jigsaw Cooperative Learning allows students to be actively involved in the learning process so that it has a positive impact on the quality of interaction and communication among students. The method used in this research is a quasi experiment. Data obtained through actions to fix problems in the learning process with a cycle consisting of 4 stages. The results showed that student learning outcomes increased, marked by increased student attention in listening to teacher directions, a responsible attitude emerged and worked together in working on group assignments. Communication between groups is well established, students work on group assignments creatively, students help each other, and give opinions to other groups. Students also learn to respect and appreciate the opinions of others and the emergence of a spirit of leadership in students. In addition, student learning outcomes also increase with the use of multi-methods and learning media, as well as different study room designs for each action, so as to create a conducive and enjoyable learning atmosphere for students.
Discussion on Measures to Improve the Practical Skills of Students Majoring in Construction Engineering Technology in Higher Vocational Colleges

According to the author's years of teaching experience in higher vocational construction engineering specialty and engineering site management experience, combined with the current demand of construction engineering talents, this paper put forward some measures from many aspects to improve the practical ability and skills of students majoring in construction engineering technology in higher vocational colleges, including the construction of teaching staff, the development of practical courses, the improvement of practical training conditions, the in-depth of school-enterprise cooperation, the promotion of teaching and learning by competition.Through the above measures, it aims to improve students' professional quality, cooperation quality, innovation and moral quality.In addition, it targeted at training students' learning ability, communication and cooperation ability, sense of responsibility, problem-solving ability, innovative thinking ability, argumentation ability and self-discipline ability, so as to obtain the practical skills better and faster to adapt to the needs of enterprise and the society.
What to Prune and What Not to Prune at Initialization

Post-training dropout based approaches achieve high sparsity and are well established means of deciphering problems relating to computational cost and overfitting in Neural Network architectures. Contrastingly, pruning at initialization is still far behind. Initialization pruning is more efficacious when it comes to scaling computation cost of the network. Furthermore, it handles overfitting just as well as post training dropout. It is also averse to retraining losses. In approbation of the above reasons, the paper presents two approaches to prune at initialization. The goal is to achieve higher sparsity while preserving performance. 1) K-starts, begins with k random p-sparse matrices at initialization. In the first couple of epochs the network then determines the fittest of these p-sparse matrices in an attempt to find the lottery ticket p-sparse network. The approach is adopted from how evolutionary algorithms find the best individual. Depending on the Neural Network architecture, fitness criteria can be based on magnitude of network weights, magnitude of gradient accumulation over an epoch or a combination of both. 2) Dissipating gradients approach, aims at eliminating weights that remain within a fraction of their initial value during the first couple of epochs. Removing weights in this manner despite their magnitude best preserves performance of the network. Contrarily, the approach also takes the most epochs to achieve higher sparsity. 3) Combination of dissipating gradients and kstarts outperforms either methods and random dropout consistently. The benefits of using the provided pertaining approaches are: 1) They do not require specific knowledge of the classification task, fixing of dropout threshold or regularization parameters 2) Retraining of the model is neither necessary nor affects the performance of the p-sparse network. We evaluate the efficacy of the said methods on Autoencoders and Fully Connected Multilayered Perceptrons. The datasets used are MNIST and Fashion MNIST.
Strategi Transformatif Perpustakaan Tingkat Desa dalam Gerakan Literasi Nasional

The village library is one type of public library that is a place to attract users who are in the village environment and have a very important role. People who have a desire to read, while their reading material is incomplete, this library will be the support for that desire. As for the government’s efforts to foster literacy throughout the archipelago, one of which is the national literacy movement. There are six dimensions that have become the benchmarks for this national literacy movement, including: (1) reading and writing literacy (2) numeracy literacy (3) scientific literacy (4) digital literacy (5) financial literacy (6) cultural and civic literacy. These six dimensions are tools used by the government to develop and deepen knowledge for society. The transformative strategy of village libraries in realizing this national literacy movement includes, (1) virtual or digital library collections, (2) providing services in the form of mobile libraries, (3) promoting to users
Optimization of Computer Programming System Based on Deep Learning Mathematical Algorithm

With the rapid development of information technology, a series of information technologies, such as big data, artificial intelligence and cloud computing, have brought profound changes to our lives.And these, in fact, are inseparable from computer programming.In programming, data structure and algorithm constitute the program itself, and the optimization of algorithm directly determines the efficiency of program execution.Mathematical algorithm can play a good role in program optimization, which is particularly important for computer programming.Computer programming and mathematical algorithm have an inseparable close relationship, because mathematics and computer science have a very close relationship, the organic integration of the two is an effective way to carry out efficient programming.With the advent of the computer age, the influence industry of computer in people's daily life is becoming more and more important, and the connection with other disciplines is also closer, especially the proportion of cross development with the integration of mathematics discipline has become more and more, and the mathematical algorithm is indispensable for the computer.The efficiency and quality of computer programming can be effectively improved by optimizing mathematical algorithm in computer programming.Scientific data structure is closely related to the performance of the algorithm.In order to get close to the variables, it is more convenient to use the algorithm reasonably, reduce the memory space required by variables, and effectively improve the efficiency and level of mathematical algorithm.In this paper, a series of researches on the optimization and development of computer programming system are carried out by discussing mathematical algorithms.
LibGuides: Golf Course Management: Multimedia

Golf Course Management is the prestigious credential that today’s golf course industry professionals are looking for.
Response Modeling of Hyper-Parameters for Deep Convolutional Neural Networks

Hyper-parameter optimization (HPO) is critical in training high performing Deep Neural Networks (DNN). Current methodologies fail to define an analytical response surface and remain a training bottleneck due to their use of additional internal hyper-parameters and lengthy evaluation cycles. We demonstrate that the low-rank factorization of the convolution weights of intermediate layers of a CNN can define an analytical response surface. We quantify how this surface acts as an auxiliary to optimizing training metrics. We introduce a dynamic tracking algorithm -- autoHyper -- that performs HPO on the order of hours for various datasets including ImageNet and requires no manual tuning. Our method -- using a single RTX2080Ti -- is able to select a learning rate within 59 hours for AdaM on ResNet34 applied to ImageNet and improves in testing accuracy by 4.93% over the default learning rate. In contrast to previous methods, we empirically prove that our algorithm and response surface generalize well across model, optimizer, and dataset selection removing the need for extensive domain knowledge to achieve high levels of performance.
Predicting software vulnerabilities with unsupervised learning techniques

As software is produced more and more every year, software also gets exploited more. This exploitation can lead to huge monetary losses and other damages to companies and users. The exploitation can be reduced by automatically detecting the software vulnerabilities that leads to exploitation. Unfortunately, the state-of-the-art methods for this automated process are not perfect and thus more research is needed to address this issue. This research was partly done at ING, one of the banks of The Netherlands, in order to find a software vulnerabilities prediction method that is more efficient than their already deployed static code analysis tool Fortify Static Code Analyzer. This report proposes a method to predict software vulnerabilities in code using unsupervised learning methods. The data set is comprised of software metrics of code written by developers of ING, in conjunction with its corresponding label whether the code was vulnerable or non-vulnerable, confirmed by a security expert. Principal component analysis reduced the dimensions of the data set. From here on, the unsupervised learning technique k-means was used to build our prediction model and a distance-based anomaly detection technique was applied to find the software vulnerabilities. This produced poor results. In a final attempt to find better results, k-nearest neighbor was used to build a new prediction model and another distance-based anomaly detection technique was applied. The outcome of this latter method was surprisingly good.
LibGuides: PSYC B1A: Psychological Disorders: Finding Books

This LibGuiide was created for Professor Young-Gomez's Psychological Disorders Writing Assignment. How to find books and e-books in our library.
Junior Middle School English Education and Teaching Practice Under the Micro-class Mode

In the traditional English classroom teaching process, we are used to treating teachers as the center of classroom activities, so that teachers can play a leading role in the teaching process.Its disadvantage is that it is easy to ignore the initiative of students, limit the creativity and imagination of students, and cannot teach students in accordance with their aptitude.In order to study the improvement of students' learning enthusiasm and teachers' teaching efficiency to the greatest extent under junior high school English education and teaching, based on the micro-class model, this paper uses example analysis and questionnaire survey methods to collect data from five middle schools in the city.The English education and teaching practice system.The experimental results prove that in the micro-class mode, the students' understanding of the knowledge learned is about 50% higher than that in the traditional mode.In the after-class test, the scores far exceed those of the students in the traditional curriculum.Teachers have an effect on this teaching mode.Satisfaction is also above 80%, which is much higher than the traditional teaching model.This shows that the junior high school English teaching model based on micro-classes is more conducive to students' integration of knowledge, and also conducive to improving students' learning experience and promoting their generative cognition.
Lessons Learned: Deep Learning for Mineral Exploration

Summary The application of commercial-scale deep learning to mineral exploration is in its infancy, with the intersection of affordable computation, large data volumes, novel applications of 3D deep learning and commercial buy-in all contributing to recent developments. The DeepMine team at IBM have spent the last three years developing and applying 3D Convolutional Neural Networks (CNNs) to the prediction of economic-grade resource in a hard-rock mining context. The iterative process has resulted in successes and shortcomings, yielding valuable insights for the resource exploration community. The approach involves representing subsurface data as point cloud information, which is then input as voxelated training examples into the model. 3D CNN models were constructed on top of PyTorch’s Deep Learning framework, developed by Facebook’s AI research group. The success of the project has been judged primarily by the ability of the model to reliably predict economic mineralization at greater distances from existing drillhole data upon each enhancement. Most recent applications have begun to incorporate non-drillhole data sources, such as Vertical Time-Domain Electro-Magnetic (VTEM) and Airborne Gravity and Magnetic data. It was found that key challenges and opportunities existed in the areas of efficient and accurate data representation, bespoke data augmentation techniques and the 3D CNN formulation itself. New improvements continue to be made to the IBMs body of work on the subject, DeepMine.
Approximate Birkhoff-von-Neumann decomposition: a differentiable approach

The Birkhoff-von-Neumann (BvN) decomposition is a standard tool used to draw permutation matrices from a doubly stochastic (DS) matrix. The BvN decomposition represents such a DS matrix as a convex combination of several permutation matrices. Currently, most algorithms to compute the BvN decomposition employ either greedy strategies or custom-made heuristics. In this paper, we present a novel differentiable approach to approximate the BvN decomposition. Our algorithm builds upon recent advances in Riemannian optimization on Birkhoff polytopes. We offer an empirical evaluation of this approach in the fairness of exposure in rankings, where we show that the outcome of our method behaves similarly to greedy algorithms. Our approach is an excellent addition to existing methods for sampling from DS matrices, such as sampling from a Gumbel-Sinkhorn distribution. However, our approach is better suited for applications where the latency in prediction time is a constraint. Indeed, we can generally precompute an approximated BvN decomposition offline. Then, we select a permutation matrix at random with probability proportional to its coefficient. Finally, we provide an implementation of our method.
RETRACTED: The current situation and the countermeasures of football teaching in university campus under the new environment – Taking Jiangxi science and technology normal university and YuZhang normal college as examples

Football course teaching is the key project of college physical education course, which has the nature of unity and entertainment and attracts the interest of college students. However, there are still many problems in football education in our country, so in fact, the football teaching system in university campus is not perfect, which makes students’ interest in sports reduced. Based on this, this paper first analyzes the current situation of football teaching on campus in the world, and then discusses the effective measures to do well in the new environment. So as to provide a reference for people from all walks of life.
DESAFIOS DA CONFORMIDADE DE REGISTRO DE GESTÃO: o caso do Instituto Federal de Sergipe

As an instrument of internal control and efficiency of public institutions, management record compliance acts as a preventive control related to budgetary, financial and patrimonial execution, which aims to reduce the risks inherent in the use of public resources and, if necessary, guide through corrective measures in a timely manner. Thus, this study aims to analyze the management record compliance process at the Federal Institute of Sergipe. The study is qualitative, characterized as a case study. Bibliographic and documentary researches were carried out, in addition to interviews with the official servers responsible for the management record compliance. As a result, it is noteworthy that there are elements that weaken the procedure, such as the lack of more practical training, staff turnover, budget limitations and the absence of an information channel between conformists. However, it was seen that, despite obstacles, the institution attributes greater relevance and endeavors to improve management record compliance, since it established actions so that the deficiencies initially identified by the Internal Audit were minimized.
INVESTIGATION OF THE DIGITAL PODCAST TO DEVELOP STORYTELLING METHOD IN ISLAMIC SCHOOL DURING PANDEMIC DISRUPTION

This paper suggested integrating podcast application to improve Islamic education storytelling sessions during pandemic COVID-19 disruption in Indonesia. The findings of a study about learning in schools and madrasahs believe that learning during a pandemic disturbance can be overcome with a remote learning system with the help of learning media with various digital applications. For this reason, we have reviewed various literature and online-based learning applications and scientific publications of the latest findings on how to deal with learning disabilities during COVID-19 to deepen new understanding to prove the assumption that many learning applications can help students learn during temporary shelters but open classrooms at a long distance. With the evidence of existing studies from various literature and scientific publications on online learning and pandemic disruption, we can say that the effectiveness of integrating the Podcast application has become an alternative superiority of distance learning media among students in Islamic boarding schools in Indonesia. Therefore, we highly recommend pesantren's academics, especially students in the Islamic education environment, and teachers outside the Islamic boarding school to integrate digital media podcasts as an alternative distance learning solution during disruption and the new normal learning period because of the advantages and effectiveness of podcasts application for learning in Indonesia.
Integrasi SMOTE pada Naive Bayes dan Logistic Regression Berbasis Particle Swarm Optimization untuk Prediksi Cacat Perangkat Lunak

Software defects are one of the main contributors to information technology waste and lead to rework, thus consuming a lot of time and money. Software defect prediction has the objective of defect prevention by classifying certain modules as defective or not defective. Many researchers have conducted research in the field of software defect prediction using NASA MDP public datasets, but these datasets still have shortcomings such as class imbalance and noise attribute. The class imbalance problem can be overcome by utilizing SMOTE (Synthetic Minority Over-sampling Technique) and the noise attribute problem can be solved by selecting features using Particle Swarm Optimization (PSO), So in this research, the integration between SMOTE and PSO is applied to the classification technique machine learning naïve Bayes and logistic regression. From the results of experiments that have been carried out on 8 NASA MDP datasets by dividing the dataset into training and testing data, it is found that the SMOTE + PSO integration in each classification technique can improve classification performance with the highest AUC (Area Under Curve) value on average 0,89 on logistic regression and 0,86 in naïve Bayes in the training and at the same time better than without combining the two.
Las Webquest como recurso tecnológico para el fortalecimiento del trabajo colaborativo

This degree work is an investigation that was carried out with the purpose of presenting the digital tool ¨Webquest¨ as a facilitator of the search for information on the web. The Webquest is an activity based on constructivist assumptions of the teaching-learning process that is based on group work techniques and Internet search.Developing this tool is simple, each member of the group is assigned a different role and they carry out the final task together, which will culminate with a multimedia product.In addition, the tutor / teacher will propose various resources that the network has so that the search complies with the established contents which are easily accessible on the Internet.
Analysing the Update step in Graph Neural Networks via Sparsification

In recent years, Message-Passing Neural Networks (MPNNs), the most prominent Graph Neural Network (GNN) framework, have celebrated much success in the analysis of graph-structured data. In MPNNs the computations are split into three steps, Aggregation, Update and Readout. In this paper a series of models to successively sparsify the linear transform in the Update step is proposed. Specifically, the ExpanderGNN model with a tuneable sparsification rate and the Activation-Only GNN, which has no linear transform in the Update step, are proposed. In agreement with a growing trend in the relevant literature the sparsification paradigm is changed by initialising sparse neural network architectures rather than expensively sparsifying already trained architectures. These novel benchmark models enable a better understanding of the influence of the Update step on model performance and outperform existing simplified benchmark models such as the Simple Graph Convolution (SGC). The ExpanderGNNs, and in some cases the Activation-Only models, achieve performance on par with their vanilla counterparts on several down-stream graph prediction tasks, while containing exponentially fewer trainable parameters. In experiments with matching parameter numbers our benchmark models outperform the state-of-the-art GNNs models. These observations enable us to conclude that in practice the Update step often makes no positive contribution to the model performance.
The English speaking anxiety of public health students of STIK Bina Husada Palembang

The learning process is an adaptation method dole out by people to grasp and master the science. During this learning period that the individual adjusts himself to the changes that occur quickly and surely. Speaking publically will cause anxiety as a result of each activity dole out by humans contains a tendency to develop anxiety. Horwitz (2001) expresses that one-third of the scholars who study foreign languages expertise a minimum of at a median level. The subjects were 13 students of the sixth semester who took English for Public Health in the academic year of 2020/2021. The frequency and the English speaking anxiety level became the purpose of this study to be found out. To collect the data English speaking anxiety questionnaire was used. The first questionnaire results showed that 2 students (15%) answered very often, 4 students (30%) often, 6 students (46%) rarely, and 1 student (7%) never felt anxious when speaking English. The second questionnaire showed that 2 students (15%) in high category, 10 students (76%) in medium, and 1 student (7%) in low anxiety. In accordance to the students’ answer, it is hoped that scholars understand that it is natural to form mistake in form