Reviewerly/exHarmony huggingface dataset:
Status Analysis and Future Development Planning of Fitness APP Based on Intelligent Word Frequency Analysis

In order to analyze the current situation analysis and future development plan of fitness APP, this paper analyzes the current situation of fitness APP combined with the intelligent word frequency analysis algorithm. In order to deal with the problem that the existing qPMS algorithm is very time-consuming to discover motifs in the large DNA sequence dataset D, a structural motif discovery algorithm for large DNA sequence datasets, SMS, is proposed. Moreover, this paper performs motif discovery by mining substrings with high frequency in the input sequence. Through data research, it can be seen that the fitness APP based on intelligent word frequency analysis proposed in this paper has good results. On this basis, this paper analyzes the problems existing in the existing fitness APP and provides suggestions for its future development.
DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks

Deep learning is attracting interest across a variety of domains, including natural language processing, speech recognition, and computer vision. However, model training is time-consuming and requires huge computational resources. Existing works on the performance prediction of deep neural networks, which mostly focus on the training time prediction of a few models, rely on analytical models and result in high relative errors. %Optimizing task scheduling and reducing job failures in data centers are essential to improve resource utilization and reduce carbon emissions. This paper investigates the computational resource demands of 29 classical deep neural networks and builds accurate models for predicting computational costs. We first analyze the profiling results of typical networks and demonstrate that the computational resource demands of models with different inputs and hyperparameters are not obvious and intuitive. We then propose a lightweight prediction approach DNNAbacus with a novel network structural matrix for network representation. DNNAbacus can accurately predict both memory and time cost for PyTorch and TensorFlow models, which is also generalized to different hardware architectures and can have zero-shot capability for unseen networks. Our experimental results show that the mean relative error (MRE) is 0.9% with respect to time and 2.8% with respect to memory for 29 classic models, which is much lower than the state-of-the-art works.
Smart Business Characteristics as an Approach to Develop Business Performance

The current study aimed at examining the impact of smart business networks' characteristics on organizational performance from the perspective of industrial sector's decision makers in Jordan. Variables of (Adaptability, Agility, Innovation, Ability to meet requirements, and Robust Behavior) were adopted as the main and most obvious characteristics of SBN. Quantitative approach was adopted and (112) general managers, projects managers and operations managers responded to the questionnaire. Results of study indicated that respondents within industrial sector in Jordan had very positive and supportive attitudes towards the idea of SBN among organizations; they also seemed to have been a part of an SBN of an industrial nature and they have experienced its positive impacts on their and their organizational performance. Results of study also indicated that all hypotheses of study were accepted and characteristics of SBN were supportive in developing organizational performance, among the most influential variables of study appeared to be the robust behavior which seemed to have influences the long-term plans that are adopted by the organization through robustly supporting every milestone adopted. Study recommended granting extra attention to IT infrastructure within organizations in order to be able to cope with the concept of SBN and benefit from all its ramifications.
A Study on the Perceptions of EFL Students towards Using the Cambridge Unlock Classroom App

This paper deals with the importance of integrating Cambridge Classroom App in the classroom. The researchers based the idea of the study upon the newly published second edition of Unlock Series titled Reading, Writing, and Critical Thinking alongside Listening and Speaking and Critical thinking. Side by side with the online Cambridge accounts, students should download a mobile application known as “Cambridge Pocket”, which is easily accessible by students via App store and Android systems. In fact, this paper aims at analysing the attitudes of Birzeit University students towards the integrated mobile application within their English language curriculum (Cambridge Unlock). It precisely reveals important findings concerning how responsively, emotionally, motivationally and educationally this application works. All such hypotheses and questions have been clarified quantitatively and qualitatively. To achieve this purpose, 120 students were randomly selected from Unlock B1.2., Intermediate English and given a questionnaire. Concerning the qualitative data, it has been sought by analysing students’ responses to a set of open-ended questions within the questionnaire in order to reflect on their experience with the second edition of Unlock Books. Hence, this research shows the importance of integrating such apps within the English language classroom.
Why does Throwing Away Data Improve Worst-Group Error?

When facing data with imbalanced classes or groups, practitioners follow an intriguing strategy to achieve best results. They throw away examples until the classes or groups are balanced in size, and then perform empirical risk minimization on the reduced training set. This opposes common wisdom in learning theory, where the expected error is supposed to decrease as the dataset grows in size. In this work, we leverage extreme value theory to address this apparent contradiction. Our results show that the tails of the data distribution play an important role in determining the worst-group-accuracy of linear classifiers. When learning on data with heavy tails, throwing away data restores the geometric symmetry of the resulting classifier, and therefore improves its worst-group generalization.
Deep CNN with Skip Connection and Network in Network for Super-Resolving Blurry Text Images

&lt;p&gt;Deep convolutional neural networks (Deep CNN) have achieved hopeful performancefor single image super-resolution. In particular, the Deep CNN skip Connection andNetwork in Network (DCSCN) architecture has been successfully applied to naturalimages super-resolution. In this work we propose an approach called SDT-DCSCN thatjointly performs super-resolution and deblurring of low-resolution blurry text imagesbased on DCSCN. Our approach uses subsampled blurry images in the input and origi-nal sharp images as ground truth. The used architecture is consists of a higher numberof filters in the input CNN layer to a better analysis of the text details. The quantitativeand qualitative evaluation on different datasets prove the high performance of our modelto reconstruct high-resolution and sharp text images. In addition, in terms of computa-tional time, our proposed method gives competitive performance compared to state ofthe art methods.&lt;/p&gt;
Unsupervised Difference Learning for Noisy Rigid Image Alignment

Rigid image alignment is a fundamental task in computer vision, while the traditional algorithms are either too sensitive to noise or time-consuming. Recent unsupervised image alignment methods developed based on spatial transformer networks show an improved performance on clean images but will not achieve satisfactory performance on noisy images due to its heavy reliance on pixel value comparations. To handle such challenging applications, we report a new unsupervised difference learning (UDL) strategy and apply it to rigid image alignment. UDL exploits the quantitative properties of regression tasks and converts the original unsupervised problem to pseudo supervised problem. Under the new UDL-based image alignment pipeline, rotation can be accurately estimated on both clean and noisy images and translations can then be easily solved. Experimental results on both nature and cryo-EM images demonstrate the efficacy of our UDL-based unsupervised rigid image alignment method.
A Data Science Approach to Social Media Analytics for Discovering Topic-Specific Influential patterns from Tweets

Social media services like Twitter facilitate communication among people beyond geographical boundaries on the topics / events of their current interest which might differ from time to time.Topic detection from Twitter data would be helpful to find trendy topics / products and current events for target marketing in recommender systems.The information associated with each tweet should be analyzed in three different perspectives involving textual content, social context and the temporal aspects to discover high quality clusters representing the topics and accordingly generate topic-specific influential patterns relating the users.This paper proposes a new framework for discovering topic specific influential patterns and maintaining them as snapshots along the time line.It involves topic identification through tweet clustering based on the textual content, followed by their refinement based on social context that involves information inherent in the tweeting behavior of users in addition to the timing of the tweet.Finally the framework proceeds to find the topic-specific influential patterns relating the users interested in a common topic.Dynamically finding patterns of influence enables tracking of information diffusion from influential users to their potential followers.
FORMATION OF STUDENT SPIRITUALITY BY MEANS CREATIVE ATMOSPHERE OF SCHOOL AND FAMILY

Within the frames of his article the author tries to solve problem offormation of personality’s inner values with the help of fiction word. The main role is given to creative atmosphere of school and family as a condition of decision of the problem.
INTEREST IN PEDAGOGICAL THEORY IS IMPORTANT PREREQUISITE FOR PREPARING THE FUTURE TEACHER TO PROFESSIONAL ACTIVITY

The article considers the interest in the pedagogic-scientific knowledge as a main means of professional making of a person; ways of its forming in a high school's training process are shown.
Not too little, not too much: a theoretical analysis of graph (over)smoothing

We analyze graph smoothing with \emph{mean aggregation}, where each node successively receives the average of the features of its neighbors. Indeed, it has quickly been observed that Graph Neural Networks (GNNs), which generally follow some variant of Message-Passing (MP) with repeated aggregation, may be subject to the oversmoothing phenomenon: by performing too many rounds of MP, the node features tend to converge to a non-informative limit. In the case of mean aggregation, for connected graphs, the node features become constant across the whole graph. At the other end of the spectrum, it is intuitively obvious that some MP rounds are necessary, but existing analyses do not exhibit both phenomena at once: beneficial ``finite'' smoothing and oversmoothing in the limit. In this paper, we consider simplified linear GNNs, and rigorously analyze two examples for which a finite number of mean aggregation steps provably improves the learning performance, before oversmoothing kicks in. We consider a latent space random graph model, where node features are partial observations of the latent variables and the graph contains pairwise relationships between them. We show that graph smoothing restores some of the lost information, up to a certain point, by two phenomenon: graph smoothing shrinks non-principal directions in the data faster than principal ones, which is useful for regression, and shrinks nodes within communities faster than they collapse together, which improves classification.
Problem-Based Multimedia E-books: The Effect on Students Creative Thinking Skills in Physics Course

Creative thinking abilities are essential to address real-world problems and adapt flexibly to changing demands.Teaching materials are very necessary for learning but the existence of books is still dominantly verbal and textual, so it is less interesting for students to read them.The purpose of this study was to determine the influence of problem-based multimedia e-books on students' ability to think creatively.This study used a quasi-experimental design with a non-equivalent control group.The research was undertaken at a public senior high school in Central Java, Indonesia in the physics classroom on sound waves topic.The sampling technique used purposive sampling.The research sample was 68 students consisting of the experimental and control class.Data were collected using a creative thinking ability test instrument in the form of multiple-choice questions.The measured creative thinking skills consist of fluency, flexibility, originality, and elaboration.Data were analyzed quantitatively using the t-test (t-test).The results showed that the Sig.(2-tailed) < 0.005 which means that there is a significant difference between students' creative thinking skills in learning using problembased multimedia e-books.The effect size result obtained is 0.61 in the medium category.It can be inferred that problem-based multimedia e-books effectively improve students' creative thinking skills in physics courses.
Machine Learning Approach for Classification and Identification of Blood Cells

In the medical field, blood testing is considered one of the most important clinical examinations.A complete blood cell count is important for any medical diagnosis.Traditionally manual equipment is used to do this task which is timeconsuming.Therefore, there is a need to research for an automated blood cell detection system that will help physicians to solve the problem efficiently.This paper presents a machine learning approach for the automatic identification and classification of three types of blood cells using a Single-shot Multi-Box detector (SSD) network.This framework has been trained on the BCCD Dataset of blood smear images to automatically identify red blood cells, White blood cells, and platelets.
The Implementation of Health Protocols in Facing Pandemic Covid-19 at Ragunan Wildlife Park

This study aims to determine the health protocol for the New Normal preparation for the tourism sector in the Ragunan Wildlife Park. This research is a type of research that uses descriptive data through data collection techniques of observation, interviews, documentation and literature study. The data analysis technique used for this writing is qualitative with descriptive data analysis techniques based on the data analysis literature, data reduction, data processing and research conclusions. The results of this study indicate that the existence of this pandemic has had a major impact on the tourism sector of the Ragunan Wildlife Park. As a result, many activities cannot be carried out because this pandemic has resulted in the closure of tourist attractions and no income. However, this makes the welfare of the existing animals calmer, living like in real nature without many tourist visits. The existing animals do not lack food or medicine at all because this has received a subsidy from the DKI Jakarta Provincial Government of 70% of 100% of which 30% is income from ticket sales. The Ragunan Wildlife Park has established a health protocol that is enforced according to the government's decision, both for tourists and animals and hopes that this pandemic will end quickly and return to normal for the welfare of all.
Application of Chaotic Neural Network in Cryptography

Cryptography is the method of protecting information and multimedia through the use of codes.So, that only the desired person can read and process the information.In the recent years there has been quite a development in the field of artificial intelligence mainly the introduction of the artificial neural networks (ANN).In this paper, data or information are encrypted and decrypted using artificial neural networks based on chaotic algorithm.Chaotic system generates random numbers which is highly sensitive to initial conditions.So, in this cryptosystems, we have utilized the advantages of randomness of chaotic theory, rigorous structure of ANN for encryption and decryption.Chaotic Neural Network (CNN) has been implemented for encryption and decryption of on array of numbers, a word and RGB image.Results indicated that Chaotic Neural Network can be used effectively in cryptography.
PERSONAL ORIENTATION OF HIGHER EDUCATION SCHOOLS AS A MEANS OF INCREASING THE LEVEL OF CREATIVE STUDENT ACTIVITIES

The article deals with the problem of improvement of the intended teacher's level of creative activity by means of personal orientation of classes in higher school as one of the factors of his professionally creative self realization.
Digitalization as a new educational paradigm

The rapid development of digital and communication technologies in the second decade of the XXI century has had a significant impact on all spheres of human life and accelerated the transition to a post-industrial society. Information has become as accessible as never before, moreover everyone who has a gadget with Internet access becomes an active content producer. In this article the authors analyze five major trends that have emerged in education at global and individual levels in this period. These trends significantly influence traditional methods and approaches. According to the authors, they can be an indicator of the shift to a new paradigm in education.
DAMPAK ROLE PLAY METHOD TERHADAP PEMBELAJARAN KETERAMPILAN BERBICARA SISWA KELAS XI SMAN 1 MONTONG GADING

The purpose of this study was to determine the impact of the application of the Role Play Method on speaking skills in class XI students of SMA 1 Montong Gading for the 2020/2021 academic year. The type of research used is experimental. Data collection techniques are interviews and documentation. The data analysis techniques in this research are descriptive analysis techniques and independent data analysis techniques. The results of the analysis conducted in class XI of SMAN 1 Montong Gading by looking at the students' speaking ability using the Role Play Method and has been tested statistically to obtain a t-count result that is greater than t-table. the results of the t-test obtained t-count = 5.23 at a significant level = 5% with degrees of freedom = n1+ n2-2 = 34+34-2 = 66, so that the experimental class showed an increase. These results were obtained by using hypothesis testing with t-test, from the calculation it can be calculated = 5.23 while t table = 2.00 which means t count ttable which states HO is rejected and Ha is accepted. This shows that there is a significant impact between the experimental class and the control class on students' speaking ability by role play method. Keywords : Role Play, Communication Skill, Experiment
FC1: A Powerful, Non-Deterministic, Symmetric Key Cipher

&lt;p&gt; In this paper we describe a &lt;strong&gt;symmetric key algorithm&lt;/strong&gt; that offers an &lt;strong&gt;unprecedented grade of confidentiality&lt;/strong&gt;. Based on the uniqueness of the modular multiplicative inverse of a positive integer a modulo n and on its computability in a polynomial time, this &lt;strong&gt;non-deterministic cipher&lt;/strong&gt; can easily and quickly handle &lt;strong&gt;keys of millions or billions of bits&lt;/strong&gt; that an attacker does not even know the length of. &lt;strong&gt;The algorithm’s primary key is the modulo,&lt;/strong&gt; while the ciphertext is given by the concatenation of the modular inverse of blocks of plaintext whose length is randomly chosen within a predetermined range. In addition to the full specification, we present &lt;strong&gt;a&lt;/strong&gt; &lt;strong&gt;working implementation of it in Julia Programming Language&lt;/strong&gt;, accompanied by real examples of encryption and decryption. &lt;br&gt; &lt;/p&gt;
CMS Wordpress Based Academic Information System Design at SMPN 17 Marusu

School is one of the formal education facilities that must be able to provide the best services or facilities for students and also for parents. One of the right ways is to use information technology, namely accessing academic values through websites. The specific objective of this research is to produce an academic information system based on Wordpress Content Management System (CMS) that can be used by SMPN 17 Marusu in Maros Regency. The current website-based academic information system is made using a complex programming language and requires special skills to create the website. Wordpress is a platform for creating websites instantly. In this study, the information system was built using the prototype method so that it can be adapted to user needs. The resulting software is presented to the user, starting from the requirements analysis, design to implementation stage, and users are given the opportunity to provide input so that the resulting software really fits the user's needs. The prototype method consists of several stages, namely, requirements analysis, system analysis, system design, system testing, and system implementation. The design stage includes interface design, front end and back end using WordPress CMS. With this school website, it is hoped that it can help government programs in improving school performance and productivity.
Using Maple and GRTensorIII in relativistic spherical models

This article presents some aspects and experience in the use of algebraic manipulation software applied to general relativity. Some years ago certain results were reported using computer algebra platforms, but the growing popularity of graphical platforms such as Maple allows us to approach the problem of the simplifications of many expressions from another point of view. Some simple algebraic programming procedures are presented (in Maple with the GrTensorIII package) to obtain and study material distributions with spherical symmetry and to search for exact solutions of the Einstein field equations. The purpose is to show how useful a computer algebra system can be. All calculations were performed using the GRTensorIII computer algebra package, which runs on Maple 2017, along with several Maple routines that we have used specifically for the simplification of many of the algebraic expressions that are very common in this type of problem.
TASK APPROACH AND PRINCIPLE PRINCIPLE PERSPECTIVES IN PEDAGOGICAL PROCESSES - THE BASIS OF PROFESSIONAL ACTIVATION STUDENT POSITIONS

This article is devoted to forming of active professional position of future teachers by means of using role perspective principle and problem approach during pedagogical training.
Is quantum computing green? An estimate for an energy-efficiency quantum advantage

The quantum advantage threshold determines when a quantum processing unit (QPU) is more efficient with respect to classical computing hardware in terms of algorithmic complexity. The "green" quantum advantage threshold $-$ based on a comparison of energetic efficiency between the two $-$ is going to play a fundamental role in the comparison between quantum and classical hardware. Indeed, its characterization would enable better decisions on energy-saving strategies, e.g. for distributing the workload in hybrid quantum-classical algorithms. Here, we show that the green quantum advantage threshold crucially depends on (i) the quality of the experimental quantum gates and (ii) the entanglement generated in the QPU. Indeed, for NISQ hardware and algorithms requiring a moderate amount of entanglement, a classical tensor network emulation can be more energy-efficient at equal final state fidelity than quantum computation. We compute the green quantum advantage threshold for a few paradigmatic examples in terms of algorithms and hardware platforms, and identify algorithms with a power-law decay of singular values of bipartitions $-$ with power-law exponent $α\lesssim 1$ $-$ as the green quantum advantage threshold in the near future.