Reviewerly/exHarmony huggingface dataset:
Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT

A surrogate model based hyperparameter tuning approach for deep learning is presented. This article demonstrates how the architecture-level parameters (hyperparameters) of deep learning models that were implemented in Keras/tensorflow can be optimized. The implementation of the tuning procedure is 100% accessible from R, the software environment for statistical computing. With a few lines of code, existing R packages (tfruns and SPOT) can be combined to perform hyperparameter tuning. An elementary hyperparameter tuning task (neural network and the MNIST data) is used to exemplify this approach
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation

Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an Abstract Syntax Tree (AST). However, such a traversal order may not be suitable for handling all multi-branch nodes. In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multi-branch nodes. Particularly, since the selection of expansion orders is a non-differentiable multi-step operation, we optimize the selector through reinforcement learning, and formulate the reward function as the difference of model losses obtained through different expansion orders. Experimental results and in-depth analysis on several commonly-used datasets demonstrate the effectiveness and generality of our approach. We have released our code at https://github.com/DeepLearnXMU/CG-RL.
Enhanced performance of Automatic Single-axis Solar Tracking system fed Induction Motor Drive

Nowadays Nonconventional Energy sources got vital Role to meet the increase in energy demand because of depletion of fossil fuels. This paper is to design and construct a solar tracker system increases the electricity generation that follows the Sun direction because the continuous movement of the sun limits maximum sun light irradiation absorption. The proposed system is simple and low-cost price solar tracker system with single axis.UAisingleiaxisitrackericaniincreaseiproductionibetween 25%ito 35%. The proposed systemidevelopsiwith PIC microcontroller, LDR’s are used to detect the Sun direction.gTheioperationiofithe experimentalzmodelgofkthekdeviceaisptoxdrivewInductionumotorxintelligentlyacontrolledpbybakdedicatedcdrivehunitkthatmovesdaoPVzpanelpaccordingstouthexsignalsmreceivedpfromztwowsimplenbutwefficientblightfsensors.nIt runs a single-phase induction motor through an Inverter circuit. The main aim of this paper is that it compares the power produced with and without solar tracker at some maximum point in different temperature conditions.
LibGuides: Digital Citizenship: Video Links

List of Tools and Tips to not only assist you in becoming the best student you can be, but also the best 21st Century Learner! With the continued use of Social Media and other digital communication networks, it is important for us to all realize we are no
The Online Use of Language for Communication in Tourist Village Community of Pujon Kidul Malang

The current language phenomenon is developing under the influence of situations and conditions that humans always execute, whether consciously or unconsciously.It is undoubtedly that the impact of human behavior on their language system and language choice leads to an inseparable bond between the former and its respective socio-cultural environment.This study aims to acknowledge the manifestation of language choice performed by the community of Tourist Village Pujon Kidul, Malang when they interact online, especially in the domain of social networking.The method utilized in this study relies on a qualitative design to portray the general depiction of a certain occurrence without any given treatment.The technique used is threefold; interviewing, note-taking, and recording to document the community interaction within the domain of online or social media, such as Facebook and Instagram administered by Komunitas Desa Wisata Pujon Kidul Malang (Tourist Village Pujon Kidul Malang Community).What motivates such an approach is the concern to prevent Covid-19 from worsening should there be field research, therefore online-based research data are deemed significant.This study targets the fundamental policy of the use of language in a specific community within the domain of online media while taking into account the values of local culture.The result shows that the manifestation of language choice portrayed in social media such as Facebook and Instagram are of four categories: Indonesian, Javanese, English, and a mix of the three languages; Indonesian-Javanese, Indonesian-English, and Javanese-Indonesian.Meanwhile, the interaction process is divided into three; the language use, the status context, and the dialogue room.
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as the lack of convergence and the potential for catastrophic forgetting across real-world heterogeneous devices. In this paper, we demonstrate that self-attention-based architectures (e.g., Transformers) are more robust to distribution shifts and hence improve federated learning over heterogeneous data. Concretely, we conduct the first rigorous empirical investigation of different neural architectures across a range of federated algorithms, real-world benchmarks, and heterogeneous data splits. Our experiments show that simply replacing convolutional networks with Transformers can greatly reduce catastrophic forgetting of previous devices, accelerate convergence, and reach a better global model, especially when dealing with heterogeneous data. We release our code and pretrained models at https://github.com/Liangqiong/ViT-FL-main to encourage future exploration in robust architectures as an alternative to current research efforts on the optimization front.
Unsupervised Person Re-identification via Multi-Label Prediction and Classification based on Graph-Structural Insight

This paper addresses unsupervised person re-identification (Re-ID) using multi-label prediction and classification based on graph-structural insight. Our method extracts features from person images and produces a graph that consists of the features and a pairwise similarity of them as nodes and edges, respectively. Based on the graph, the proposed graph structure based multi-label prediction (GSMLP) method predicts multi-labels by considering the pairwise similarity and the adjacency node distribution of each node. The multi-labels created by GSMLP are applied to the proposed selective multi-label classification (SMLC) loss. SMLC integrates a hard-sample mining scheme and a multi-label classification. The proposed GSMLP and SMLC boost the performance of unsupervised person Re-ID without any pre-labelled dataset. Experimental results justify the superiority of the proposed method in unsupervised person Re-ID by producing state-of-the-art performance. The source code for this paper is publicly available on 'https://github.com/uknownpioneer/GSMLP-SMLC.git'.
Layanan Mandiri di Perpustakaan SMA Negeri 5 Magelang: Menjawab Tantangan Generasi Milenial

School library is a library located in a school that shelters it. The library has the main objective, namely to support teaching and learning activities. In the current era, pemustaka in senior high schools (SMA) are users who are classified as the millennial generation. Millennial generation has characteristic of making technology a lifestile. Millennials want speed in their every life. The speed at which they wanted made them prefer something simple. Millennials are also more comfortable using technology so that they can do things independently and in a structured manner. Libraries need to innovate in providing services to visitors to match the characteristics of the millennial generation. SMA Negeri 5 Magelang Library implements independent services in responding to the challenges of the millennial generation. The implementation of self-service in the SMA Negeri 5 Magelang Library can be said to be easy and simple. However, there is a need for hardware upgrades so that self-service can run better. It should also be noted regarding security and librarians must be diligent in doing database backups so that at any time when a problem occurs, the data is well preserved.
Existing Software Tutors

From early childhood through to adulthood, ECAs are already being used for a range of education and training applications. In early childhood the focus so far has been on literacy, however social skills are also a promising emergent area.
Semantic sentence similarity: size does not always matter

This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge. We produce synthetic and natural spoken versions of a well known semantic textual similarity database and show that our VGS model produces embeddings that correlate well with human semantic similarity judgements. Our results show that a model trained on a small image-caption database outperforms two models trained on much larger databases, indicating that database size is not all that matters. We also investigate the importance of having multiple captions per image and find that this is indeed helpful even if the total number of images is lower, suggesting that paraphrasing is a valuable learning signal. While the general trend in the field is to create ever larger datasets to train models on, our findings indicate other characteristics of the database can just as important important.
Review on Aspect based Sentiment Analysis on Social Data

Aspect-based sentiment analysis is a computationally determining opinions, sentiments, and semantic conception of the text. Alternatively, it is a specific sentiment analysis process with an aim to extract important features that describe the entities along with its polarity of every aspect present in the textual content. This paper reviews the recent research work in aspect based sentiment analysis to extract aspects in the text and corresponding sentiment. The paper discusses various aspect methods and sentiment prediction methods. The accuracy of IMAN is observed as 83.59 on Restaurant. The CNN-GA Hybrid model is observed with increased precision, accuracy, recall, and F1 calculation. Finally, we identify research opportunities in aspect or feature-based sentiment analysis.
ALGORITHM FOR ESTIMATING PARAMETERS OF THE NONLINEAR SYSTEM

The article presents an algorithm for estimating the parameters of nonlinear systems, which is one of the main tasks of classical statistical analysis. The parametric estimation of the coefficients of models that are based on experimental data is the basis. The basis for evaluating parameters. Key words: estimation algorithm, nonlinear systems, statistical analysis, nonlinear function, experiment, models.
Thoreau’s Religion: Walden Woods, Social Justice, and the Politics of Asceticism by AdaBalthrop‐Lewis (Cambridge: Cambridge University Press2021), xxiv + 308 pp.

Modern TheologyEarly View Book Review Thoreau’s Religion: Walden Woods, Social Justice, and the Politics of Asceticism by Balthrop-Lewis, Ada ( Cambridge: Cambridge University Press 2021), xxiv + 308 pp. Brett Gray, Corresponding Author bcg24@cam.ac.uk Sidney Sussex College, Cambridge, CB2 3HU UKSearch for more papers by this author Brett Gray, Corresponding Author bcg24@cam.ac.uk Sidney Sussex College, Cambridge, CB2 3HU UKSearch for more papers by this author First published: 30 May 2021 https://doi.org/10.1111/moth.12722Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Data Quality Analysis based Machine Learning models for Credit Card Fraud Detection

Misrepresentation of money is a developing issue in monetary business with far-reaching consequences and keeping in mind that many processes have been found. Data quality management with data mining has been effectively applied to data sets to mechanize the investigation of massive amounts of complex information. Data mining has likewise played a notable role in identifying credit card fraud in online exchanges. Fraud detection in credit cards is a data quality management issue that considered under data mining, tested for two important reasons — first, the profiles of ordinary and false practices habitually change, and also because of the explanation that charge card fraud information is exceptionally slow. This research paper examines the performance of Decision Trees, Logistics Regression, and Random Forest rely strategically on profoundly skewed credit card fraud data. The dataset of credit card transactions is sourced from Kaggle (a publically accessible dataset repository) with 284,807 transactions. These methods are applied to raw data values and data preprocessing techniques. Assessment of the performance of techniques depends on accuracy, sensitivity, specificity, precision, and recall. Results indicate the optimal accuracy for the decision trees, logistics regression, and random forest classifiers with 90.8%, 98.5%, and 99.1% respectively.
Educación en línea en tiempos de COVID-19 en el área económico-administrativa en la Universidad Tecnológica de León

The coronavirus pandemic (COVID-19) broke into the lives of people around the world, (WHO, 2020) led to changes in the way of working and studying. For students it represents a challenge. The educational community has made efforts to sustain student learning; This represented the need to use information technologies and depend on their own resources to guarantee the continuity of learning, forcing them to take distance classes through digital means (UNESCO, 2020). This research will allow to know the conditions and how the students of the Administrative Economic area of the Technological University of León face the challenges. For this, a questionnaire was designed in order to collect information, it was validated to measure reliability and it was applied to a sample of 249 students. The data was analyzed and showed how many of them have a computer; if it is for exclusive use or shared; how many have internet and the quality of the service; how many have cell phones; how many hours they use their devices for classes; the platforms they use; if they have what it takes to work at home and if they prefer distance or face-to-face classes.
COVID-19 확산 속에서 비대면 수업의 적용에 따른 보건계열 학과 1, 2학년 대학생들의 전공 몰입과 전공 만족도의 변화

The purpose of this study is aimed to analyze the changes in major commitment and satisfaction of the 1st and 2nd grades university students in health-related departments through the application of un-contact lecture during COVID-19 pandemic. Study subjects were a total of 181 students in the 1st and 2nd grades in health-related departments. They participated in the un-contact lectures for 15 weeks through COVID-19 pandemic and the lectures consisted of real-time online lectures and the lectures using video contents based on the guidelines of Ministry of Education. Assessment of major commitment was performed using the major commitment scale and the major satisfaction was performed using the major satisfaction scale. As the results, the statistically significant decrease of major commitment and satisfaction showed after the application of un-contact lectures. Based on these, the application of un-contact lectures acts the students to decrease major commitment and satisfaction. Therefore, in the application of un-contact lectures, appropriate consideration should be needed for the increase of major commitment and satisfaction of 1st and 2nd grades university students in health-related departments.
Efficient Image Inpainting Using Dimensional Space Reduction via Adaptive Patch-based Concept and Rank Lowering Technique

In this paper, we present a new algorithm for image inpainting using low dimensional feature space. In our method, projecting a low dimensional space from the original space is accomplished firstly using SVD, which is named low rank component, and then the missing pixels are filled in the new space. Finally, the original image is inpainted so that adaptive patch size is considered by quad-tree based on the previous step. In our algorithm, the missing pixels in the target region are estimated twice, one in low dimension feature space and another in the original space. It is noticeable that both processes estimate the unknown pixels using patch-based idea and rank lowering concept. Experimental results of this algorithm show better consistency in comparison with state-of-the-art methods.
Mapping Psychological Spaces: Psychotherapeutic Applications of System Dynamics in Fuzzy Cognitive Maps of Personal Constructs: Mapping Psychological

espanolEste articulo presenta un procedimiento innovador para derivar tanto Mapas Cognitivos Borrosos como Graficos de Comportamiento vs. Tiempo a partir de la Tecnica de Rejilla de la Psicologia de los Constructos Personales, a traves del paso intermedio de obtener una adaptacion de Rejillas de Implicaciones Bipolares. Esto permite tener un modelo funcional a la vez que estructural del Sistema de Constructos Personales y probar y simular su dinamica anticipada en escenarios hipoteticos, asi como comprender mas plenamente sus propiedades sistemicas. El articulo se centra en el procedimiento en si, que se ilustra mediante un estudio de caso para resaltar sus importantes implicaciones para la investigacion y la practica de la psicoterapia. EnglishThis paper presents an innovative procedure for deriving both Fuzzy Cognitive Maps and Behavior Over Time Graphsfrom Personal Construct Psychology’s Repertory Grids, via the intermediate step of eliciting an adaptation of Bipolar Implications Grids. This makes it possible to have a functional as well as structural model of the Personal Construct System and to test and simulate its anticipated dynamics in hypothetical scenarios, as well as to understand more fully its systemic properties. The paper focuses on the procedure itself, and it is illustrated by means of a case study so as to highlight its significant implications for psychotherapy research and practice.
Survei Awareness Mahasiswa PGSD UNM Bone Terhadap Teknologi Virtual Reality

Virtual reality technology is experiencing rapid development in the global market, but not in the Indonesian market. When other parts of the world took advantage of this technology for online learning during the Covid-19 pandemic, something similar was almost never happened in Indonesia. This research seeks to research the awareness level of students of PGSD UNM Kampus VI Bone towards virtual reality. The survey method was carried out on 160 samples who were randomly selected from a population of 571 students, whose characteristics tended to be homogeneous. The results showed that the level of awareness of VR technology was still low (index 0.36). The run test also shows that the selected sample is indeed random, so the study assumed to be well-represent the population. It is recommended to the stakeholders, for increasing promotion and ease the access to virtual reality technology so level of awareness can be increased.
Towards Heterogeneous Clients with Elastic Federated Learning

Federated learning involves training machine learning models over devices or data silos, such as edge processors or data warehouses, while keeping the data local. Training in heterogeneous and potentially massive networks introduces bias into the system, which is originated from the non-IID data and the low participation rate in reality. In this paper, we propose Elastic Federated Learning (EFL), an unbiased algorithm to tackle the heterogeneity in the system, which makes the most informative parameters less volatile during training, and utilizes the incomplete local updates. It is an efficient and effective algorithm that compresses both upstream and downstream communications. Theoretically, the algorithm has convergence guarantee when training on the non-IID data at the low participation rate. Empirical experiments corroborate the competitive performance of EFL framework on the robustness and the efficiency.
ParticleAugment: Sampling-based data augmentation

We present an automated data augmentation approach for image classification. We formulate the problem as Monte Carlo sampling where our goal is to approximate the optimal augmentation policies. We propose a particle filtering formulation to find optimal augmentation policies and their schedules during model training. Our performance measurement procedure relies on a validation subset of our training set, while the policy transition model depends on a Gaussian prior and an optional augmentation velocity parameter. In our experiments, we show that our formulation for automated augmentation reaches promising results on CIFAR-10, CIFAR-100, and ImageNet datasets using the standard network architectures for this problem. By comparing with the related work, we also show that our method reaches a balance between the computational cost of policy search and the model performance.
LTL2Action: Generalizing LTL Instructions for Multi-Task RL

We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. Instructions are expressed in a well-known formal language -- linear temporal logic (LTL) -- and can specify a diversity of complex, temporally extended behaviours, including conditionals and alternative realizations. Our proposed learning approach exploits the compositional syntax and the semantics of LTL, enabling our RL agent to learn task-conditioned policies that generalize to new instructions, not observed during training. To reduce the overhead of learning LTL semantics, we introduce an environment-agnostic LTL pretraining scheme which improves sample-efficiency in downstream environments. Experiments on discrete and continuous domains target combinatorial task sets of up to $\sim10^{39}$ unique tasks and demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.