Reviewerly/exHarmony huggingface dataset:
Pengaruh Self Regulated Learning dan Self Efficacy terhadap Prestasi Akademik Mahasiswa

This study aims to determine: the effect of self-regulated learning on student academic achievement, (2) the effect of Self Efficacy on academic achievement; (3) the influence of Self Regulated Learning and Self Efficacy on Academic achievement. The population in this study were 70 students in Semester II, the research sample as much as the population, namely 70 students. The method used is multiple linear regression analysis. The results showed that: (1) Self regulated learning had no effect on student academic achievement, (2). Self-efficacy has a significant effect on student academic achievement, and (3) Self regulated learning and Self-efficacy simultaneously affect student academic achievement. Keywords: Self Regulated Learning, Self Efficacy, Academic Achievement Abstrak: Penelitian ini bertujuan untuk mengetahui: pengaruh self regulated learning terhadap prestasi akademik mahasiswa, (2) pengaruh Self Efficacy terhadap prestasi Akademik; (3) pengaruh Self Regulated Learning dan Self Efficacy terhadap prestasi Akademik. Populasi dalam penelitian ini adalah Mahasiswa Semester II sebanyak 70 mahasiswa, sampel penelitian sebanyak jumlah populasi yakni 70 mahasiswa. Metode yang digunakan adalah analisis regresi linear berganda. Hasil penelitian menunjukkan bahwa: (1) Self regulated learning tidak berpengaruh terhadap prestasi akademik mahasiswa, (2). Self efficacy secara signifikan berpengaruh terhadap prestasi akademik mahasiswa, dan (3). Self regulated learning dan Self efficacy secara simultan berpengaruh terhadap prestasi akademik mahasiswa. Kata Kunci: Self Regulated Learning, Self Efficacy, Prestasi Akademik
An Empirical Study on Software Fault Prediction Using Product and Process Metrics

Product and process metrics are measured from the development and evolution of software. Metrics are indicators of software fault-proneness and advanced models using machine learning can be provided to the development team to select modules for further inspection. Most fault-proneness classifiers were built from product metrics. However, the inclusion of process metrics adds evolution as a factor to software quality. In this work, the authors propose a process metric measured from the evolution of software to predict fault-proneness in software models. The process metrics measures change-proneness of modules (classes and interfaces). Classifiers are trained and tested for five large open-source systems. Classifiers were built using product metrics alone and using a combination of product and the proposed process metric. The classifiers evaluation shows improvements whenever the process metrics were used. Evolution metrics are correlated with quality of software and helps in improving software quality prediction for future releases.
Implementation of Distance Learning in Ukraine

The pandemic has put the higher education of Ukraine to new challenges. The objectives of the article are to describe the implementation of distance learning in Ukraine, illustrate the personal learning systems and their levels, provide practical examples of the content of personal learning systems.The theoretical and methodological base of the research of distance learning consists of an activity approach that is grounded on the psychological theory of activity and the concept of personally oriented learning, directed at the formation and development of students’ subjectivity, with due regard to their individual abilities.Results are the following. Studying during the lockdown has essentially speeded the implementation of various forms and technologies of distance education, actualized the tasks to modernize its organization, search for efficient methods of managing the process of studying, has provoked the necessity for teachers to improve their level of mastering information and communication technologies.The concept of development of distance education in Ukraine has emphasized that it creates conditions for the realization of the system of continuous “life-long learning” and provides individualization of studying by mass education. Meanwhile, this organization should be built as a pedagogical system that consists of the elements (goals, principles, content, methods, and forms of the educational activity) and is realized by information and communication technologies. In S. Kuznets Kharkiv National University of Economics distance learning is supported by PNS (personal learning systems) that consist of content, interactive and autonomous levels. The content of the given levels is characterized and supported by practical examples.Conclusion: implementation of distance learning in such a form enables the renewal of content, forms and methods of studies according to current demands of the society in innovative development; individualization of the educational process with due regard to demands of those who study as well as optimization of the whole educational process.
Grounded Compositional Generalization with Environment Interactions

In this paper, we present a compositional generalization approach in grounded agent instruction learning. Compositional generalization is an important part of human intelligence, but current neural network models do not have such ability. This is more complicated in multi-modal problems with grounding. Our proposed approach has two main ideas. First, we use interactions between agent and the environment to find components in the output. Second, we apply entropy regularization to learn corresponding input components for each output component. The results show the proposed approach significantly outperforms baselines in most tasks, with more than 25% absolute average accuracy increase. We also investigate the impact of entropy regularization and other changes with ablation study. We hope this work is the first step to address grounded compositional generalization, and it will be helpful in advancing artificial intelligence research.
Pairwise Context Similarity for Image Retrieval System Using Variational Auto-Encoder

Deep-learning-to-hash models have recently achieved several breakthroughs enabling a fast and efficient image retrieval system. As supervision for deep-learning-to-hash models, pairwise label similarity which considers two images to be identical if their labels are identical plays a crucial role. However, models using only pairwise label similarity cannot incorporate rich contextual information in images because pairwise label similarity solely depends on labels. In this paper, we initially address two major limitations of using the pairwise label similarity as only supervision for the deep-learning-to-hash model. Then, we propose a novel pairwise context similarity to alleviate those limitations. The proposed pairwise context similarity is computed on the latent space of a Variational Auto-Encoder which is trained in an unsupervised fashion that does not utilize any label information. Moreover we propose the strategy of an auxiliary loss for deep-learning-to-hash models that can easily be combined with previous losses using pairwise label similarity without deteriorating the retrieval quality. In our experiments on three standard benchmark datasets, our proposed method achieved high retrieval quality for image retrieval tasks while also showing advantages with regard to the addressed limitations. Also, we empirically prove that our proposed method acts as a proper regularization term during training so that our loss term therefore helps to mitigate overfitting and stabilizes the training curves.
Analysis of human activities and identification of uncertain situations in context-aware systems

FCT – Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020
Gain Analysis of In-motion Magnetic Inductive Coupling-based Systems

Human activity recognition (HAR) using wearable sensors is becoming widely used in a large range of applications. Magnetic induction-based human activity recognition system (MI-HAR) is a new wearable-based HAR system that captures human motions by variations in the magnetic induction (MI) signals received from transmitter coils placed around the human body. In this work, we focused on the performance analysis of the MI-HAR system using experimental measurements. The main aim of this study is to show the sensitivity of MI signals to spatial translation and rotations of MI coils and verify the capability of the MI-HAR system in detecting relative motion between coils. Moreover, we compare the simulated MI-motion data generated by the MI system model with the data measured by vector network analyzer (VNA).
Gray Literature Searching Options: Million Short and Carrot2 Search Engines

Librarians searching for gray literature often turn to Google®; however, other options exist that complement Google® searches. Two search engines, Million Short and Carrot2 Search, offer alternative places to search and locate gray literature. This review examines the search engines’ features, returned results, and possibilities for the future.
Overparameterisation and worst-case generalisation: friend or foe?

Overparameterised neural networks have demonstrated the remarkable ability to perfectly fit training samples, while still generalising to unseen test samples. However, several recent works have revealed that such models' good average performance does not always translate to good worst-case performance: in particular, they may perform poorly on subgroups that are under-represented in the training set. In this paper, we show that in certain settings, overparameterised models' performance on under-represented subgroups may be improved via post-hoc processing. Specifically, such models' bias can be restricted to their classification layers, and manifest as structured prediction shifts for rare subgroups. We detail two post-hoc correction techniques to mitigate this bias, which operate purely on the outputs of standard model training. We empirically verify that with such post-hoc correction, overparameterisation can improve average and worst-case performance.
Early Stopping in Deep Networks: Double Descent and How to Eliminate it

Over-parameterized models, such as large deep networks, often exhibit a double descent phenomenon, whereas a function of model size, error first decreases, increases, and decreases at last. This intriguing double descent behavior also occurs as a function of training epochs and has been conjectured to arise because training epochs control the model complexity. In this paper, we show that such epoch-wise double descent occurs for a different reason: It is caused by a superposition of two or more bias-variance tradeoffs that arise because different parts of the network are learned at different epochs, and mitigating this by proper scaling of stepsizes can significantly improve the early stopping performance. We show this analytically for i) linear regression, where differently scaled features give rise to a superposition of bias-variance tradeoffs, and for ii) a wide two-layer neural network, where the first and second layers govern bias-variance tradeoffs. Inspired by this theory, we study two standard convolutional networks empirically and show that eliminating epoch-wise double descent through adjusting stepsizes of different layers improves the early stopping performance.
Model-Based Clustering with HDBSCAN*

We propose an efficient model-based clustering approach for creating Gaussian Mixture Models from finite datasets. Models are extracted from HDBSCAN* hierarchies using the Classification Likelihood and the Expectation Maximization algorithm. Prior knowledge of the number of components of the model, corresponding to the number of clusters, is not necessary and can be determined dynamically. Due to relatively small hierarchies created by HDBSCAN* compared to previous approaches, this can be done efficiently. The lower the number of objects in a dataset, the more difficult it is to accurately estimate the number of parameters of a fully unrestricted Gaussian Mixture Model. Therefore, more parsimonious models can be created by our algorithm, if necessary. The user has a choice of two information criteria for model selection, as well as a likelihood test using unseen data, in order to select the best-fitting model. We compare our approach to two baselines and show its superiority in two settings: recovering the original data-generating distribution and partitioning the data correctly. Furthermore, we show that our approach is robust to its hyperparameter settings. (Data and code are publicly available at: https://github.com/mjstrobl/HCEM)
Think-Aloud Exploratory Search: Understanding Search Behaviors and Knowledge Flows

This paper describes anTibau, Marcelo experiment that uses Concurrent Think-Aloud protocol (CTA) and person-to-person interviews to map searching behaviors and knowledge flows during search sessions.Siqueira, Sean W. M. The findings are: (1) the most used searching strategy during exploratory searches was the “Metacognitive Domain”; and (2) online searching experts have a fair ability to deal with ideas prompted by browsing the search results.Nunes, Bernardo Pereira The main contributions of this research lie in the understanding of the process in which people find, access, decide what content is useful and apply online data to their different information needs.
Pengembangan Teknologi ERP Modul Customer Relationship Management Studi Kasus Rumah Makan Nasi Uduk “BABE”

The implementation of Enterprise Resource Planning (ERP) as one of the supports for the operation of an integrated business sector and better data accuracy. An ERP system consists of various modules and sub-modules that can represent certain business components, one of which is CRM. Application of Customer Relationship Management (CRM) or customer relationship management which is indirectly said to be computerizing customer data. This is intended so that the company knows what the needs and desires of customers are so that good relationships can be established with customers. In East Kotawaringin, there is now a place to eat called Rumah Makan Nasi Uduk Babe. This restaurant is a small business engaged in the sale of food. This restaurant serves direct customer orders, namely coming to the restaurant and by telephone. This ordering process goes through a very long route, where the prospective customer will choose the food menu and the restaurant records the menu ordered by the customer to the notebook and calculates how much the customer has to pay, then the customer waits for confirmation of the specified price. In connection with the aforementioned problems, a web-based application design is proposed so that in the future it can provide services about detailed information on food menus, prices and orders to customers. The research method used in this report is divided into several stages, namely the data collection stage, the system design stage, the program development stage, the program trial stage and the implementation stage. The conclusion of this research is that an ERP system has been successfully developed for a web-based CRM module with a case study of the Babe Nasi Uduk Restaurant.
PySRResNet: Super Resolution for Video Satellite Imagery via Pyramid Residual Network

Video satellite is of great significance in change detection and military reconnaissance due to its high temporal resolution. However, restricted by the hardware conditions, spatial resolution must be sacrificed if temporal resolution is to be guaranteed. Because of this, how to reconstruct super resolution (SR) video satellite data is particularly important. Based on the proposed SR Residual Network (SRResNet), we proposed a Pyramid Residual Network (PySRResNet) model, using a pyramid structure to obtain features of different scales, including 1, 1/2 and 1/4, and concatenate them together to provide more detailed information for SR reconstruction. In addition, we reduced the number of blocks and removed the batch normalization layer to achieve good performance. Training with “Jilin-1” video satellite images, our PySRResNet can get superior grades than other comparing models both in PSNR and SSIM, which demonstrates the effectiveness of PySRResNet in video satellite imagery SR reconstruction.
Training on the Use of Computer Applications in Making Learning Videos (Training for MTs Miftah Assa'adah Teachers)

&lt;p&gt;This Community Service activity was carried out because they we the teacher's lack of ability in making their own learning videos. This ability is very much-needed in this pandemic, because students have to do distance learning. Universitas Pembangunan Jaya conducts training for teachers at MTs Miftah Assa'adah School located in South Tangerang area. In distance learning, the use of video media is needed by students. Teacher training is conducted using a combination of online and offline methods. The teachers were also given hand-on training to create, edit and upload the learning videos they made. Training using common applications and can be obtained easily. The applications used are also applications that do not require high computer resources, so they can be done with only standard devices. For example MS Power Point, Canva and Movie Maker, VLC and even Zoom. Of the 10 indicators used as a measure, 8 of them showed an increase of over 50%. The post-test results at the end of the training also showed an improvement in the general abilities of the training participants &lt;/p&gt;
Detection of Financial Statement Frauds Using Beneish Model: Empirical Evidence From Listed Pharmaceutical Companies in Bangladesh

Financial statement fraud had the most significant financial impact on companies when compared to the other categories of occupational fraud as per ACFE Report to the Nations 2014. This study aims to determine the value of Beneish M-Score and to identify whether selected pharmaceutical companies were manipulating their earnings or not. Financial information of 14 pharmaceutical companies was collected from their annual reports of 2014-18. This study finds that pharmaceutical companies of Bangladesh are engaged in Earnings Manipulation and they are doing it on a continuous basis. The results of our study show a deteriorating financial condition of pharmaceutical industry which should make the auditors and regulators more cautious in detecting financial statement frauds.
Online Learning Transition Based on Internet and E-Commerce Classroom Learning Management System (LMS)

In the era of digital information, especially in the field of education, online learning methods are currently developing rapidly in the learning and teaching process. The aim of this project is to provide a uniform and consistent user interface in the new learning management system (LMS) for students and faculties and to promote super users who will assist the faculty in a smooth transition from. The curriculum template is provided in the LMS for online learning processes or called e-Learning and has various training opportunities with super users that are interactive so that online learning will be effective and quality. (57%) report results from faculty feeling competent or very competent (36%) in their ability to use. Canvas ™ LMS after project implementation; There are several survey comments reflecting a positive response to the transition to Canvas ™. This project produces quality, student-centered online learning. Where is in accordance with the standardized template framework.
Professionally-oriented tasks in the formation of competencies in the field of students’ research activities

Due to the introduction and implementation of the requirements of the third-generation Federal State Educational Standards, the importance of students' involvement in research activities during their studies has increased. Currently, special importance in the higher education system is given to the issues related to the organization of such work with students. Having mastered the principal educational program, graduates should be ready to carry out professional activities in the field of applied research, as well as to solve various research tasks. To do this, the graduates must have a certain set of competencies, formed in various ways and methods in the course of professional training. One of the best ways to form competencies both in the field of future profession and research activities is professionally-oriented tasks. As a rule, such tasks are offered to students starting from the first year. Gradually, the level of complexity of the tasks increases, and to solve such tasks, senior-year students need knowledge across multiple disciplines. The article presents a list of academic disciplines, whose study using various forms can train and develop various competencies, including research ones. Universities regularly involve students in research work. However, at present, an appropriate pedagogically sound system of continuous involvement of students in this work is required.
Enhancing the performance of cancer text classification model based on cancer hallmarks

&lt;span id="docs-internal-guid-cb130a3a-7fff-3e11-ae3d-ad2310e265f8"&gt;&lt;span&gt;Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.&lt;/span&gt;&lt;/span&gt;
Multi party privacy preserving machine learning for process control

Further application of machine learning is important for the future development of semiconductor fabrication. Machine learning relies on access to large, detailed datasets. When different parts of the data are owned by different companies who do not wish to pool their data due to commercial sensitivity concerns, the benefits of machine learning can be limited resulting in reduced manufacturing performance. Imec has developed Privacy-preserving Amalgamated Machine Learning (PAML) to overcome this problem and achieve predictive performance close to models built on pooled data, without compromising sensitive raw data. In this paper we give a concrete example based on an in-house overlay metrology dataset where we apply a PAML enhanced version of a tree regression model, and quantify the performance benefit compared to separate models that don’t have access to all of the data.
Improved Evaluation Index Systems for Public Physical Education

In view of the defects of existing evaluation index systems (EIS) for the teaching quality of public PE such as it’s difficult for them to process fuzzy and uncertain information, this paper analyzed factors that can affect the teaching of public PE and extracted the key evaluation indices; then, it proposed a set of improved EISs for the teaching quality of public PE and constructed an evaluation model based on fuzzy system theory. Moreover, this paper put forward a few countermeasures for the improvement of public PE teaching quality, in the hopes of providing a good support for the said matter.