Reviewerly/exHarmony huggingface dataset:
MusiKeys: Investigating Auditory-Physical Feedback Replacement Technique for Mid-air Typing

Augmented reality headsets have great potential to transform the modern workplace as the technology improves. However, a major obstacle in bringing AR headsets into workplaces is the need for a precise, virtual, mid-air typing solution. Transitioning from physical to virtual keyboards is difficult due to loss of many physical affordances, such as the ability to tell between touching and pressing a key. We present our system, MusiKeys, as an investigation into the effects of presenting a user with auditory tones and effects as replacements for every kind of feedback that could ordinarily be perceived through touching a keyboard.
Toward Improving Student Model Estimates through Assistance Scores in Principle and in Practice

Student modeling is especially useful in educational research and instructional development, as well as intelligent tutoring systems due to a capability to estimate latent student attributes. While the widely used approaches, such as AFM or BKT, have shown satisfactory results, they can only handle binary outcomes, which makes the models inflexible and results in potential information loss. In this work, we proposed a new modeling approach, PC-AFM, to support polytomous outcomes, namely the amount of assistance a student needs. Because student errors and hint requests may not only derive from their ability, but also from non-cognitive factors (e.g., they may game the system), we wanted to first test PC-AFM on synthetic data where this source of noise is not present. We confirm that PC-AFM is indeed better than AFM in recovering the true student and knowledge component (KC) parameters and even predicts student error rates better even though estimates are based on assistance score. Applying the approach to six real-world datasets, we found that PC-AFM consistently outperforms AFM in reliable estimation of KC parameters and produces better generalization to new students (which requires better KC estimates). However, consistent with the hypothesis that student assistance behavior is driven by motivational or meta-cognitive factors beyond their ability, we found that PC-AFM was not better in reliable estimation of student parameters nor in generalization across items (which requires accurate student estimates). This paper also illustrates a set of strategies for addressing the general problem of how to compare alternative measurement models for the same desired latent outcome.
Business model canvas : ST Clothing / Nur Izzati Ismail

ST Clothing is one of the biggest clothing brands in Malaysia which offers a line of clothing that is fashionable and trendy. The product that our company wish to renovate is raincoat. The problem that arises about this product is the raincoats are not able to provide warm to the people who wears it even though it kept them dry. So, problem arises when majority of the customer that they cannot used the raincoat to keep them warm which make them protect from rain but still exposed to cold. Other than that, the original product used the material that was not eco- friendly like plastic and PVC that may bring danger to the ecosystem as the product is the one time used. The only way to make it happen in manageable manner is by doing BMC. By conducting business model canvas before anything, my company get to see the clear picture of how the new renovation will take place in customer’s eye, how its potential to be in the market, and what is so powerful about this product compared to the competitors. The objectives of this renovation are that I want to make it as the flexible clothes that is suitable to wear either it is raining, snowing or in cold weather. . Basically, I want to add water resistance fur that able to keep the people who wears it feel warm. The fur that keep the wearer warm must be skin- friendly and eco- friendly. The raincoats that was sold in the markets have the functions of durable and waterproof, light-weight and easy to pack, and breathable but it did not provide warm. Last but not least, I wish to make advancement on the raincoat by creating it using only eco- friendly materials, even though the product still contained the material of plastic but it durable enough to be used in a very long time before dispose. That way, we can avoid environment pollution.
Exact marginal prior distributions of finite Bayesian neural networks

Bayesian neural networks are theoretically well-understood only in the infinite-width limit, where Gaussian priors over network weights yield Gaussian priors over network outputs. Recent work has suggested that finite Bayesian networks may outperform their infinite counterparts, but their non-Gaussian function space priors have been characterized only though perturbative approaches. Here, we derive exact solutions for the function space priors for individual input examples of a class of finite fully-connected feedforward Bayesian neural networks. For deep linear networks, the prior has a simple expression in terms of the Meijer $G$-function. The prior of a finite ReLU network is a mixture of the priors of linear networks of smaller widths, corresponding to different numbers of active units in each layer. Our results unify previous descriptions of finite network priors in terms of their tail decay and large-width behavior.
Science in the Context of Society through QR Code in Problem Based Learning

Science education in the context of societal applications through QR code in problem-based learning (PBL) is addressed in this paper. An example from an elementary classroom where the students received mentoring by their high school peers to develop QR codes involving the Florida Everglades is presented. Through meaningful guidance it is possible to enhance elementary students knowledge of science in society and awaken their curiosity of science using QR code embedded problem-based learning.
Meta Learning Backpropagation And Improving It

Many concepts have been proposed for meta learning with neural networks (NNs), e.g., NNs that learn to control fast weights, hyper networks, learned learning rules, and meta recurrent NNs. Our Variable Shared Meta Learning (VS-ML) unifies the above and demonstrates that simple weight-sharing and sparsity in an NN is sufficient to express powerful learning algorithms (LAs) in a reusable fashion. A simple implementation of VS-ML called VS-ML RNN allows for implementing the backpropagation LA solely by running an RNN in forward-mode. It can even meta-learn new LAs that improve upon backpropagation and generalize to datasets outside of the meta training distribution without explicit gradient calculation. Introspection reveals that our meta-learned LAs learn qualitatively different from gradient descent through fast association.
Document Graph for Neural Machine Translation.

Previous works have shown that contextual information can improve the performance of neural machine translation (NMT). However, most existing document-level NMT methods failed to leverage contexts beyond a few set of previous sentences. How to make use of the whole document as global contexts is still a challenge. To address this issue, we hypothesize that a document can be represented as a graph that connects relevant contexts regardless of their distances. We employ several types of relations, including adjacency, syntactic dependency, lexical consistency, and coreference, to construct the document graph. Then, we incorporate both source and target graphs into the conventional Transformer architecture with graph convolutional networks. Experiments on various NMT benchmarks, including IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian, demonstrate that using document graphs can significantly improve the translation quality. Extensive analysis verifies that the document graph is beneficial for capturing discourse phenomena.
Design of a Distributed Ledger-Based Reward Architecture for Collaborative Tasks

With the development and progress of blockchain, its application scenarios are becoming more and more extensive. And in the development of crowd-sensing, how to get high quality sensing data and how to motivate enough users to participate is the urgent problem at present. This article is based on the existing technology, with the help of distributed ledger, crowd-sensing, incentive mechanism and other concepts, combined with collaborative tasks to achieve incentive mechanism, which can effectively solve the problem of platform centrality and realize decentralized operation. At the same time, smart contracts on the blockchain can be executed to realize complex logic, thus solving the shortcomings of the current incentive mechanism. This paper can be applied to smart city noise sensing scenario, and combined with blockchain technology, it can realize decentralized storage of data security and distributed implementation of incentive mechanism.
Optimal Sizing of Stand-alone PV System using Artificial Bee Colony Algorithm

The alarming rate of depletion of fossil fuels due to the increasing energy demands has increased the reliance on renewable energy. Renewable resources, such as solar and wind, are excellent alternatives to fossil fuels due to their sustainability and they do not produce harmful gases to the environment. The most promising renewable energy resource is the solar energy since it is available almost everywhere. Renewable energy systems have considerable high initial cost. In order to reduce the cost, the size of the system should be optimized. Optimization means increasing the output energy while reducing the cost. Recently intelligent optimization techniques have been used to solve complex engineering problems. Artificial Bee Colony (ABC) is one of these techniques. In this paper we present a sizing and optimization technique for renewable energy systems using ABC algorithm. The model for the PV panel, the battery, and the loads are used to optimize the system. The method is applied to optimize a typical system and the results of the design are optimum as proved by simulation and comparison with the results of the published methods.
UPAYA MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA MATA PELAJARAN EKONOMI DENGAN MENERAPKAN MODEL PEMBELAJARAN SEARCH, SOLVE, CREATE, AND SHARE (SSCS) DI KELAS X IIS 1 MAN 1 PEKANBARU TAHUN PELAJARAN 2019/2020

The purpose of this study was to describe the effect of applying the SSCS model (Search, Solve, Create, and Share) on student learning outcomes in economic subjects in class X IIS 1 in Madrasah Aliyah Negeri 1 Pekanbaru in the 2019/2020 academic year. Research conducted at MAN 1 Pekanbaru precisely in class X iis 1, the method used is the observation of the activities of teachers and students in 2 cycles. Data collection technique used is the percentage technique that compares the appearance of the whole multiplied by 100%. The instrument was in the form of an observation sheet of student activities, while the test of learning outcomes to determine individual completeness was determined KKM 80 and completeness classically 75% had reached the minimum completeness standard. by research instruments in the form of written tests, and observation sheets. This instrument used is an objective test in the form of multiple choice. The data collected is quantitative data. The results showed that: the application of the learning model SSCS learning model (Search, Solve, Create, and Share) can improve the economic learning outcomes of students of class X IIS 1 MAN 1 Pekanbaru on the subject matter of Business Entity Concepts in the Indonesian Economy even semester 2019 / 2020 The results of this study are expected to be useful for the improvement and improvement of learning outcomes of students, especially in the field of economic studies.
On the Frequency Bias of Generative Models

The key objective of Generative Adversarial Networks (GANs) is to generate new data with the same statistics as the provided training data. However, multiple recent works show that state-of-the-art architectures yet struggle to achieve this goal. In particular, they report an elevated amount of high frequencies in the spectral statistics which makes it straightforward to distinguish real and generated images. Explanations for this phenomenon are controversial: While most works attribute the artifacts to the generator, other works point to the discriminator. We take a sober look at those explanations and provide insights on what makes proposed measures against high-frequency artifacts effective. To achieve this, we first independently assess the architectures of both the generator and discriminator and investigate if they exhibit a frequency bias that makes learning the distribution of high-frequency content particularly problematic. Based on these experiments, we make the following four observations: 1) Different upsampling operations bias the generator towards different spectral properties. 2) Checkerboard artifacts introduced by upsampling cannot explain the spectral discrepancies alone as the generator is able to compensate for these artifacts. 3) The discriminator does not struggle with detecting high frequencies per se but rather struggles with frequencies of low magnitude. 4) The downsampling operations in the discriminator can impair the quality of the training signal it provides. In light of these findings, we analyze proposed measures against high-frequency artifacts in state-of-the-art GAN training but find that none of the existing approaches can fully resolve spectral artifacts yet. Our results suggest that there is great potential in improving the discriminator and that this could be key to match the distribution of the training data more closely.
Dynamic Structured Pruning With Novel Filter Importance and Leaky Masking Based on Convolution and Batch Normalization Parameters

Various pruning methods have been proposed to solve the overparameterized problem in deep neural networks.Most of the structured pruning methods have used magnitude-based filter importance to remove unnecessary filters.Usually, Convolutional Neural Networks (CNN) consist of blocks that proceed with batch normalization (BN) operations after convolutional (Conv) operations.Each element is calculated through cooperation between Conv and BN, so both Conv and BN parameters must be considered together in pruning.However, previous pruning methods independently used the norm of parameters, either Conv weights or BN scales, as filter importance, ignoring these CNN structures.With this intuition, we propose a new magnitude-based filter importance method that considers both Conv weights and BN scales, and provide evidence of importance through experimental analysis on the rank of the feature map as well as mathematical analysis on the feature distortion.Furthermore, in recent works, dynamically applying a mask to recover weights has enabled more accurate pruning.However, when the weights are multiplied by zero mask values, the gradients become zero, which prevents updating the weights.We name this problem the zero gradient transferring.To solve this problem, we propose a leaky masking method that replaces the zero value of the mask with a positive constant.As a result, we solve the zero gradient transferring through the leaky masking method, enabling more accurate dynamic structured pruning.We denote our proposed method as CoBaL that incorporates Conv and BN based filter importance and Leaky mask into dynamic structured pruning.Experimental results show that our CoBaL compresses 50.09% parameters and 32.51% FLOPs on ResNet 56 with the CIFAR-10 with slightly improved accuracy.Also, we possess comparable results on ImageNet with 50.74% and 29.38% reduction in the number of parameters and FLOPs at ResNet18, respectively.
Research on the Construction of Network Course Platform Based on Big Data

As an important part of education, curriculum construction is always the focus of teaching quality in colleges and universities. This study mainly discusses the construction of cloud platform for online courses of college pop singing majors in the era of big data. The user center module mainly involves user WeChat login, user view personal information, user view their study record and consumption record. Data mining model management provides users with simple and interactive operation interface to create mining model, which enables users to create mining simply and clearly and manage it conveniently. Users can use offline caching to download videos of the courses they need to study, instead of using up network or mobile data multiple times. In the final results, 26 students in the experimental group scored in the excellent range, compared with 22 students in the control group. This study is helpful to promote the development of popular singing major in colleges and universities.
Pemanfaatan Model Pembelajaran Kooperatif Tipe Make A Match Terhadap Hasil Belajar Matematika Melalui Materi Bangun Datar

In this article, the researcher takes one of the focus parts of the second problem formulation from the research thesis work which is about student learning outcomes. Mathematics education in education is given to all levels, starting from early childhood education, elementary school education, junior high school level, high school level, even to the state tertiary level. Because mathematics education is one of the foundations of science technology capabilities, while teachers do not care about students about understanding students thinking skills in learning mathematics. 3rd grade students at SD Negeri Cipocok Jaya 4 who were the subjects in this study. Class action research is the method used in this study and the approach used is a qualitative approach. Researchers conduct interviews on student learning outcomes. Transcript results from homeroom teacher and third grade students after being given action during mathematics learning using the make a match learning model on flat waking material that the learning model with given media cards in the form of questions and answers can facilitate students in learning mathematics, so that by matching cards can delight students in learning and students are not bored in learning, unsaturated, not sleepy, and many do not complain about the material given and students are very enthusiastic in learning. The seventy-five percent indicator is successful, and a researcher is deeply moved by the action through that model. With that, it can be concluded that the use of learning models make a match in mathematics learning can improve student learning outcomes in flat-up material.
Advantages of line and non-line interactive method in online teaching

One of the most important development trends in the worlds higher education is the globalization of education. The education systems of many countries are working establish of single education milestone and labor standards. In the first decade on the third millennium, there were no major changes and innovations in Azerbaijani education, politically, economy, etc. Certain achievements have been made in the fields. This article provides detailed information about these innovators.
Using Metrics for Risk Prediction in Object-Oriented Software: A Cross-Version Validation

This work aims to investigate the potential, from different perspectives, of a risk model to support Cross-Version Fault and Severity Prediction (CVFSP) in object-oriented software. The risk of a class is addressed from the perspective of two particular factors: the number of faults it can contain and their severity. We used various object-oriented metrics to capture the two risk factors. The risk of a class is modeled using the concept of Euclidean distance. We used a dataset collected from five successive versions of an open-source Java software system (ANT). We investigated different variants of the considered risk model, based on various combinations of object-oriented metrics pairs. We used different machine learning algorithms for building the prediction models: Naive Bayes (NB), J48, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Perceptron (ANN). We investigated the effectiveness of the prediction models for Cross-Version Fault and Severity Prediction (CVFSP), using data of prior versions of the considered system. We also investigated if the considered risk model can give as output the Empirical Risk (ER) of a class, a continuous value considering both the number of faults and their different levels of severity. We used different techniques for building the prediction models: Linear Regression (LR), Gaussian Process (GP), Random forest (RF) and M5P (two decision trees algorithms), SmoReg and Artificial Neural Network (ANN). The considered risk model achieves acceptable results for both cross-version binary fault prediction (a g-mean of 0.714, an AUC of 0.725) and cross-version multi-classification of levels of severity (a g-mean of 0.758, an AUC of 0.771). The model also achieves good results in the estimation of the empirical risk of a class by considering both the number of faults and their levels of severity (intra-version analysis with a correlation coefficient of 0.659, cross-version analysis with a correlation coefficient of 0.486).
Declinazioni dell’attesa nell’Ossian  in alcune spigolature montiane

For his translation of Ossian Cesarotti works a lot on the Italian language and the transformations he takes inside the language contribute to let it know the changes that animate the new Nordic aesthetics. The theme of the wait, that is present in the Poems of Ossian in several aspects, can help to analyse how much Cesarotti’s influence has been embraced by next poetry. The essay aims to examine this phenomenon along the artistic way of Vincenzo Monti, from his early young verses, like the Pensieri d’amore, to the Napoleonic works, like the Bardo della selva nera and the Spada di Federigo.
Spam Email Detection

As we probably are aware E-mail spam is an ongoing issue for each individual.it is perhaps the greatest issue in the realm of the web. The email spam is nothing it's a notice of any organization/item or any sort of infection which is getting by the email customer post box with no notice. To take care of this issue the diverse spam filtering procedure is utilized. The spam filtering strategies are used to ensure our mailbox for spam sends. In this venture, we are using the Naive Bayesian Classifier for spam grouping. The Naive Bayesian Classifier is a straightforward and productive strategy for spam grouping. Here we are utilizing the Kaggle dataset for the arrangement of spam and non-spam messages. the outcome is to build the precision of the framework. The general precision of 97.63% accomplished by my model.
INDEPENDENT WORK AS THE MAIN DIRECTION OF DEVELOPMENT OF THE MODERN PARADIGM OF EDUCATION

The paper considers the importance of independent work in the development of the modern paradigm in education. The main opportunities for the development of independent work of students in an effective way are noted. The main difficulties of performing independent work among students and teachers are highlighted. The results of research on the level of independent work performance are presented.