Reviewerly/exHarmony huggingface dataset:
Knowledge, Attitude, and Practice toward COVID-19 Questionnaire-Indonesian Version

Coronavirus disease was detected for the first time since the extraordinary incident in Wuhan, China, in December 2019 and has now spread to 204 countries. COVID-19 is a highly contagious disease. This study aimed to translate the Knowledge, Attitude, and Practice toward COVID-19 Questionnaire and evaluate its validity and reliability Indonesia version. The original was Knowledge, Attitude, and Practice toward COVID-19 Questionnaire translated from English into Indonesian. The reliability scale analysis technique does validity and reliability testing. The question item validity is sawed from the magnitude of r-table value with r-count and reliability seen through Cronbach's alpha coefficiency. This study shows the Knowledge, Attitude, and Practice toward COVID-19 Questionnaire in the context of Indonesian all items of valid and reliable.
Shadow Detection and Removal Technique using CNN

Abstract: The Shadow detection and removal Technique is used in many real-world applications, such as surveillance systems, computer vision applications and indoor outdoor system. The shape and orientation of an object, as well as the light source, can be revealed by shadows in an image. In a traffic surveillance system, the shadow can misclassify the actual target, lowering the system’s accuracy. Numerous algorithms and techniques have been developed by researchers to aid in the detection and removal of shadows in images. This paper aims to provide an overview of different shadow detection and removal techniques, their advantages and drawbacks. Also implementation of Convolutional Neural Network for shadow detection and OpenCV features to remove shadows by re-designing the output and analysing different loss functions to train the network. Keywords: Shadow Detection and Removal Techniques, Shadow Image Processing.
Efektifitas Penggunaan Metode Talking Stick dalam Meningkatkan Hasil Belajar Tamrin Kelas I Marhalah Tsanawiyah TMI Putri Al-Amien Prenduan

The purpose of this research is to see how effective the talking stick method is for learning Tamrin class I Marhalah Tsanawiyah TMI Putri Al-Amien Prenduan. This research uses quantitative and experimental research methodologies in a single class. The researcher employed a pretest in this procedure. This research's population consisted of regular first-class female students, totaling 221 female students, with a sample of 10% of the population, namely regular class I students "B," totaling 22 female students. The Tamrin material test, observation, and documentation were employed as research instruments in this study. The T-test formula is a statistical data analysis methodology that was utilized. The calculated T-test value was 2.275, based on the results of the tests performed. dk = n-1 = 22-1 = 21 in this case. T-test table = 1.721 is calculated with dk = 21 and a margin of error of 5%. T-table (2.275 &gt; 1.721) has a higher value than T-count (2.275 &gt; 1.721). As a result, Ha was accepted whereas Ho was refused, indicating that adopting the talking stick method in Tamrin learning improved learning outcomes.
B Maude: A formal executable environment for Abstract Machine Notation Descriptions

We propose B Maude, a prototype executable environment for the Abstract Machine Notation implemented in the Maude language. B Maude is formally defined and results from the implementation of the semantics of AMN as denotations in the $π$ Framework, a realization of Mosses' Component-based Semantics and Plotkin's Interpreting Automata. B Maude endows the B method with execution by rewriting, symbolic search with narrowing and Linear Temporal Logic model checking of AMN descriptions.
Relational Subgraph for Graph-based Path Prediction

Path prediction methods using graph convolutional networks (GCNs) that represent pedestrians' relationships by graphs have been proposed. These GCN-based methods consider only the distance information for the relationship between pedestrians, and the visibility state and other relationships are not taken into account. In this paper, we propose a path prediction method that represents the detailed relationship between pedestrians by introducing relational subgraphs. Each subgraph is constructed on different relationships. The proposed method inputs these relational subgraphs and the distance graph into GCNs and we extract features. Then, the features are input to a temporal convolutional network, which outputs multivariate Gaussian parameters to predict the future path. The experimental results with ETH and UCY datasets show that the proposed method outperforms the conventional method using only the distance information.
High-dimensional Assisted Generative Model for Color Image Restoration

This work presents an unsupervised deep learning scheme that exploiting high-dimensional assisted score-based generative model for color image restoration tasks. Considering that the sample number and internal dimension in score-based generative model have key influence on estimating the gradients of data distribution, two different high-dimensional ways are proposed: The channel-copy transformation increases the sample number and the pixel-scale transformation decreases feasible space dimension. Subsequently, a set of high-dimensional tensors represented by these transformations are used to train the network through denoising score matching. Then, sampling is performed by annealing Langevin dynamics and alternative data-consistency update. Furthermore, to alleviate the difficulty of learning high-dimensional representation, a progressive strategy is proposed to leverage the performance. The proposed unsupervised learning and iterative restoration algo-rithm, which involves a pre-trained generative network to obtain prior, has transparent and clear interpretation compared to other data-driven approaches. Experimental results on demosaicking and inpainting conveyed the remarkable performance and diversity of our proposed method.
Amazon extends return window during coronavirus crisis

The move came as part of Amazon's daily updates that detail the ways in which it is combatting COVID-19 in and outside its own facilities.
Cybercrime in Local Authorities: A Case Study of the Bulawayo City Council

The study sought to evaluate the impact of cybercrime practice in local authorities with special reference to Bulawayo City Council. Zimbabwean organizations both private and public, including local authorities have not been spared by the effects of cybercrime. Given this susceptibility to risk there was a deficit of cyber security best practices at the Bulawayo City Council. Chapter two of the study reviewed past works in terms of which the conceptual framework, theoretical framework and empirical framework were highlighted. The study was based on the routine activities theory that expressed that cybercrime even in local authorities was ordinary and it just required opportunity. Chapter three highlighted the research methodology. The research was largely based on a case of the Bulawayo City Council; hence the design used was a case study. The defense for using the case study was that the research evaluated the impact of cybercrime practice in local authorities with special reference to Bulawayo City Council. An aggregate of 93 respondents were sampled using judgmental sampling. The results showed that illegal access to computer systems, illegal interference and identity theft were amongst the types of cybercrime faced whilst worker training was the major strategy to fight cybercrime. Basing on the results, it was concluded that cybercrime was predominant in local authorities, with hacking, identity theft, phishing and scamming inter alia being the greatest risks faced. It was recommended that in reply to governmental and business requirements for cyber-protection specialists and personnel growth requirements, academic bodies could create specific learning programmes and courses to unite information and research, and intensify synergies in understanding cyber protection.
Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark

In recent years, deep learning-based methods have shown promising results in computer vision area. However, a common deep learning model requires a large amount of labeled data, which is labor-intensive to collect and label. What's more, the model can be ruined due to the domain shift between training data and testing data. Text recognition is a broadly studied field in computer vision and suffers from the same problems noted above due to the diversity of fonts and complicated backgrounds. In this paper, we focus on the text recognition problem and mainly make three contributions toward these problems. First, we collect a multi-source domain adaptation dataset for text recognition, including five different domains with over five million images, which is the first multi-domain text recognition dataset to our best knowledge. Secondly, we propose a new method called Meta Self-Learning, which combines the self-learning method with the meta-learning paradigm and achieves a better recognition result under the scene of multi-domain adaptation. Thirdly, extensive experiments are conducted on the dataset to provide a benchmark and also show the effectiveness of our method. The code of our work and dataset are available soon at https://bupt-ai-cz.github.io/Meta-SelfLearning/.
A Deep Neural Network Near-Universal Dielectric Meta-Atom Generator

Dielectric meta-atoms enable ultra-thin optical devices but have complex electromagnetic responses. Neural networks mitigate the complexity but have had limited predictive scope. We present a network for near-arbitrary materials and spectral regimes.
Overview of Design and Implementation of Library Circulation Model : A Case of Lagos State University Library

The beauty of Library operations is not only in the ability to provide needed and useful materials containing information for the community, but also to be able to make them accessible to the clientele as and when needed, which is what circulation is all about. The objectives of the study were, to analyse the existing information system in LASU Library circulation section, design an information model for circulation section of LASU Library, and implementation of the designed library circulation model. The implementation tools used were, HTML (the Hypertext Mark-up Language), CSS (Cascading Style Sheets), Java Script, Jquery, and Mongodb. . The architectural design used was the unified modelling language (UML) which is a general-purpose modelling language in the field of software engineering and provide a standard way to visualize the design of a system. The model will increase the effectiveness and efficiency of the activities carried out at the circulation unit of LASU Library. At the end of the study, recommendations are provided for the smooth run of the designed circulation model.
A Study on the Relationship between English Language Learning Anxiety and English Language Achievement

The main objective of this study was to examine the relationship between students’ English language learning anxiety and their English language achievement. For this purpose, 197 grade ten students were involved. The students were selected using proportional sampling technique. The instruments used to collect data were questionnaire and achievement. Then, the collected data were analyzed using quantitative methods of data analysis. The study revealed that there was statistically significant negative relationship between students’ English foreign language learning anxiety and their English achievement test result with a correlation coefficient of (r=-.194, p=>0.05). In addition, the participants of the study were found to anxious because of fear of negative evaluation, communication apprehension and test related anxieties. .The study also indicated that female students mean score was less than the mean score of male students when their anxiety levels were measured. But the independent sample t-test indicated that there is no statistically significant difference between male students and female students in their learning anxiety in EFL classrooms. However, a significant mean difference between male and female students was observed on test anxiety with (P<0.05). Therefore, this result is consistent with the findings of Tang (2005). Finally, the researchers recommended that EFL teachers should provide advice for the learners to avoid anxiety in their English language class.
PENINGKATAN KETERAMPILAN BERBICARA MENGGUNAKAN MODEL SHOW AND TELL MELALUI PEMBELAJARAN DARING DENGAN PLATFORM WHATSAPP VIDEO CALL

This study aims to improve the speaking skills of fifth-grade students of SD Negeri 1 Kalijoyo for the academic year 2020/2021 by using the Show and Tell model with the whatsapp video call platform. This research is a Classroom Action Research (CAR) which refers to Kurt Lewin (Arikunto, 2015: 42) which consists of four stages of planning, action, observation, and reflection. The research was carried out in two cycles where each cycle was carried out in four meetings. The research subjects were 16 fifth-grade students of SD Negeri 1 Kalijoyo. The techniques accumulation of data is observation, interview, document analyzing, and test. The analysis of data use triangulation technique, triangulation sources, and content validation. This TECHNIQUE research is the interactive analysis model of Miles and Huberman (Sugiyono, 2015: 337) The results of the research data in the first cycle showed that the classical completeness in the pronunciation aspect was 56.20%, the vocabulary aspect was 62.50%, the fluency aspect was 50%, and the accuracy aspect of the story content was 56.20%. Pronunciation is 93.75%, vocabulary aspect is 81.25%, fluency aspect is 87.50%, and story content accuracy is 93.75%. These data indicate that the application of the model Show and Tell with the whatsapp video call platform can improve students' speaking skills.
Implementation of Algorithms for Right-Sizing Data Centers

The energy consumption of data centers assumes a significant fraction of the world's overall energy consumption. Most data centers are statically provisioned, leading to a very low average utilization of servers. In this work, we survey uni-dimensional and high-dimensional approaches for dynamically powering up and powering down servers to reduce the energy footprint of data centers while ensuring that incoming jobs are processed in time. We implement algorithms for smoothed online convex optimization and variations thereof where, in each round, the agent receives a convex cost function. The agent seeks to balance minimizing this cost and a movement cost associated with changing decisions in-between rounds. We implement the algorithms in their most general form, inviting future research on their performance in other application areas. We evaluate the algorithms for the application of right-sizing data centers using traces from Facebook, Microsoft, Alibaba, and Los Alamos National Lab. Our experiments show that the online algorithms perform close to the dynamic offline optimum in practice and promise a significant cost reduction compared to a static provisioning of servers. We discuss how features of the data center model and trace impact the performance. Finally, we investigate the practical use of predictions to achieve further cost reductions.
Exploring Transferable and Robust Adversarial Perturbation Generation from the Perspective of Network Hierarchy

The transferability and robustness of adversarial examples are two practical yet important properties for black-box adversarial attacks. In this paper, we explore effective mechanisms to boost both of them from the perspective of network hierarchy, where a typical network can be hierarchically divided into output stage, intermediate stage and input stage. Since over-specialization of source model, we can hardly improve the transferability and robustness of the adversarial perturbations in the output stage. Therefore, we focus on the intermediate and input stages in this paper and propose a transferable and robust adversarial perturbation generation (TRAP) method. Specifically, we propose the dynamically guided mechanism to continuously calculate accurate directional guidances for perturbation generation in the intermediate stage. In the input stage, instead of the single-form transformation augmentations adopted in the existing methods, we leverage multiform affine transformation augmentations to further enrich the input diversity and boost the robustness and transferability of the adversarial perturbations. Extensive experiments demonstrate that our TRAP achieves impressive transferability and high robustness against certain interferences.
Methods of Using Programmed Means to Organize Practical Training for Improving the Professional Skills of Future Teachers of Fine Arts

The combination of theoretical and practical knowledge in the professional training of future teachers of fine arts, the importance of theoretical and practical exercises in the subject of Composition. The theoretical basis for improving the professional training of future teachers of fine arts is systematically analyzed in the article.&#x0D; The didactic possibilities of further increasing the scientific motivation of students through the use of software means in the educational process were studied.&#x0D; To form the virtual practical training in the teaching of the subject "Composition" in the direction of education 5110800-Fine Arts and Engineering Graphics in higher education, to develop the methodological recommendations for its use, are pedagogically based.&#x0D; Methodology for developing a programmed, electronic educational-methodical complex on the subject of "Composition" which covers (introduction, normative documents, lectures, practical classes, self-study topics, presentations, animations, programmed control test, keywords and terms, used literature, information about the authors) is recommended.&#x0D; Thoughts about the actual tasks of the development of the educational system in increasing the efficiency of the acquisition of the subject “Composition” are stated.
User action representation and automated reasoning for the forensic analysis of mobile devices

We propose a framework for structuring the description and results of the forensic analysis of actions of investigative interest in digital applications, and for automated reasoning on such actions. A high level of abstraction is suitable for forensic stakeholders that are not ICT experts; other levels are suitable for automating experiments on the devices to establish traces left by actions, and for associating the results of the experiments. Such results are used in a computational logic framework to conclude evidence on the occurrence of actions. The evidence can be presented to stakeholders or used in further automated reasoning, and traced back to data on the device.
Spatio-Temporal Split Learning

This paper proposes a novel split learning framework with multiple end-systems in order to realize privacypreserving deep neural network computation. In conventional split learning frameworks, deep neural network computation is separated into multiple computing systems for hiding entire network architectures. In our proposed framework, multiple computing end-systems are sharing one centralized server in split learning computation, where the multiple end-systems are with input and first hidden layers and the centralized server is with the other hidden layers and output layer. This framework, which is called as spatio-temporal split learning, is spatially separated for gathering data from multiple end-systems and also temporally separated due to the nature of split learning. Our performance evaluation verifies that our proposed framework shows nearoptimal accuracy while preserving data privacy.
Scientific and Educational Potential of The Educational Base "Gornoye" of The State University of Land Use Planning as a System of Monitoring Indicators for The Environmentally Sustainable Development of The Region

The use of green campus practices by the State University of land use planning promotes a culture of sustainable development for all stakeholders. One of the forms of interaction was the scientific and educational base (SEB) Gornoye. The territory of the SEB "Gornoye" combines unique physical, geographical and agricultural features necessary for performing the educational, scientific, applied, research and production functions of the Department and is designed to ensure the sustainable development of natural territories.
Construction of Practical Teaching System of Database Principles based on Core Literacy

The promulgation of the overall framework of Chinese students' development of core literacy has made the implementation of core literacy into specific teaching activities an urgent problem to be solved in the field of education. Based on the analysis of the problems existing in the undergraduate education under the mode of emphasizing theory and neglecting practice, this paper constructs the practical teaching system of Database Principle Course Based on the core quality, expounds the implementation strategy of practical teaching, and probes into how to train the students in the practical teaching of database principle Cultivate scientific spirit, learning ability, practical innovation, responsibility and other core literacy. After 5 rounds of practice, students have achieved better development in independent development and collaborative work, and their awareness of practical innovation has been significantly improved.
CONSTRUCTION TASKS AS A MEANS OF FORMING METHODS OF THINKING ACTIVITY

The article considers the essence of the concept of mental activity, analyzes the problem of human thinking, shows the role of building tasks in the formation of mental activity of secondary school students. The main feature of the construction tasks is that they develop search skills for solving problems, introduce them to feasible independent research, contribute to the development of geometric representations and the processing of knowledge, skills, thereby strengthening not only the applied orientation of geometry teaching, but also the polytechnic one. Construction tasks do not allow a formal approach to them, they create a problematic situation that needs to be solved, as a result of which the level of mental activity increases. Pedagogical experience shows that due to lack of time, teachers do not see the need to return to the construction tasks, and school textbooks pay little attention to the construction tasks, so elective classes and elective seminars on this topic are necessary. The optimal number of classes should be 10 hours, especially for grades 7-9. The authors tested their own methodology (ascertaining, forming and control stages) among students of the 7th grade of one of the schools in Uralsk.
LibGuides: North Country Library System Virtual Branch: Book Club in a Bag

A guide to free activities and learning resources offered by the North Country Library System.