Reviewerly/exHarmony huggingface dataset:
University brand: factors, tools and main tendencies of development information about authors

Trends towards transformation of the system of higher education create the need for universities to adapt to the dynamically changing conditions of the external competitive environment. In this connection, university branding as a specific distinguishing characteristic from competitors is an integral part of the development strategy of a modern university, and the effectiveness of marketing activities to form a brand of an educational organization depends largely on the influence of internal and external factors and a set of tools applied. However, in the scientific literature, these aspects have come into common use mainly in relation to commercial organizations, giving short shrift to the institutions of higher education, which determines the goal setting of the study, which is to identify the main directions of the development of a university’s brand under present-day conditions of functioning. To achieve this goal, the authors used methods of synthesis, systematization, logical-structural, comparative and cause-and-effect analysis of the literature which highlights the theoretical and practical issues of university branding. The directions of brand formation identified as a result of the study are based on the method of an expert survey conducted in May 2021. Overall, the systematization of university branding factors has been carried out;&#x0D; a set of tools for creating a brand of a higher educational institution in the context of the transformation of the education system has been proposed; and the main directions for the development of the university brand from an expert survey have been identified.The authors have found that the most promising areas of university branding are corporate, educational and reputational aspects of activities.
High school mathematics knowledge level of technical university students

The lack of knowledge of high school mathematics is one of the key problems for students at technical universities. They face this problem not only right after entering the university, but they fight with this handicap during their whole studies. In the first part of this paper, we introduce the results of the questionnaire survey among first-year students which consists of the comparison of their subjective opinion on the level of their knowledge with the true results of the entrance test. The second part of the paper is dedicated to our new online courses of high school mathematics for our students. We believe that our effort will become an effective way how to supply the students with the knowledge, which they should already have and which is expected for further extension in regular university courses.
Pengenalan Kepribadian Seseorang Melalui Bentuk Tulisan Tangan Menggunakan Metode Radial Basis Function Neural Network (RBFNN)

Humans are created with a variety of different characters and characters, so that the personality that is formed is very diverse according to the character and character of each person. With a variety of personalities, it appears that there are differences in quality and quantity between humans from one another in the view of society. There are various ways to get to know a person's personality, one of which is through handwriting with specific letters of the alphabet. In this study, handwriting is limited to the letter 't' only and has been generated from 26 images of 't' training which were previously processed using the PCA method, there are 10 images according to the type 't' 1 and 16 images according to the type 't ' 2nd and no image matches the 3rd 't' type.
Abrupt changes detection using wavelet-based denoising analysis

Abstract Switching power supply is an essential power supply method that exists in the development of the electronic information industry. Drive power supply has various quality problems, primarily due to the loss of power inductors; for power inductor capacity testing, making a feasibility assessment is of great significance. In this study, we present a wavelet-based approach for detecting sudden changes in the power inductor current curve. To reliably identify rapid changes in the saturation of the inductor current after energization, the discrete wavelet transform is utilized to analyze the signal within a specified subset of all scaling and translation values, displaying different local signal characteristics.
Internet of Things with Smart Contract

Abstract- Smart contract is an automatic contract with the agreement between two parties, written into few lines of code. Agreements contained live across a distributed, decentralized blockchain network. Code controls the execution and transactions that are tractable and not changed [1]. This are the digital contract stored in block chain and executes automatically when condition meets [1]. IOT is network of physical devices that embedded with different sensors, software for exchanging data over internet [2].this are having advantages in all sectors including medical science, home automation, security etc. [2]. Future is IOT and smart contract technology is bright in near future as all IOT has lot of applications with use of smart contract technology to maintain integrity of data [2]. Keywords – Smart Contract, IOT, blockchain
On the Comparison and translation of Marked Passive Sentences in China and Korea

There are many differences and some similarities between Chinese and Korean. Even if it is common, it will not be expressed in exactly the same way because they are two different languages. There are many problems in translation class, including word collocation and grammar. Passive sentence is one of the most difficult grammatical problems to solve. There is no symmetry between Chinese and Korean in the translation of passive sentences. These are not only the problems encountered in translation, but also the concerns of students who study Chinese or Korean. Taking marked passive sentences as an example, this paper investigates the types and translation of marked passive sentences in Chinese and Korean. The results show that passive sentences in Korean are more complex than those in Chinese. Passive sentences of the same sentence pattern in Chinese will be translated into different passive sentence patterns in Korean. On the other hand, there is no problem with passive sentence selection in Korean. At the same time, this paper also summarizes the situation that should be paid attention to when translating passive sentences. For example, we should pay attention to some verbs that can not enter the passive sentence.
Cyber Crime Education

Cyber Crime is not new for the readers, Electronic gadgets (either wireless or wired), whenever are used to harm public and business organization are called cyber crimes. It may be done in the form of a virus attack, Phishing attack, Spoofing, Identify Theft or any of the tricks to misguide the victim. There is significant and worth investigation by criminologists because victims of Cyber-Crimes are increasing more quickly than cyber police can detect, arrest and prosecute. With the advent of the Internet, people have changed the way in which they communicate or interact with others, shop. These changes in organization's have increased the probability of being any kind of cyber threat.
Implementing secure, policy-driven data access in a cloud environment

The last several years have given rise to an increased emphasis on the protection of personal digital data. Businesses realize the value that their data hold - if they can provide the right set of data for a specified purpose to a specified user entity according to policy. For example, the European Union's General Data Protection Regulation (GDPR) went into effect in 2018, and strictly defines rules on the access, storage and transfer of personal data.
Non-linear Input Transformations and Kernels

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Chinese Culture Education in College Chinese Teaching Based on Big Data

<p style="text-align: justify;"><span style="font-family: "times new roman";">With the advancement of society and the increase of computer, network and digital media technology, big data technology has become an indispensable part of the modern service industry. Big data technology has been developed in all aspects of the education industry, and the proportion of the value of products and services created has gradually increased. Big data plays an important role in technology in language teaching and other aspects. This article mainly introduces the research of Chinese teaching reform based on the era of big data. This article uses the research of Chinese culture education in Chinese teaching in colleges and universities based on big data, starting from the two aspects of Chinese teaching and Chinese culture, and rationally analyzes the feasibility of Chinese teaching reform. From the labor market in our country, the demand for Chinese culture talents is concentrated on applied talents, but the purpose of language teaching in many schools is mostly in traditional research-based teaching, resulting in a decline in the quality of education, and it is difficult to meet the market's demand for applied talents in Chinese culture and classroom reforms. It is the main body that determines the course content and teaching mode. The experimental results of this paper show that based on the research of Chinese culture education in Chinese teaching in the era of big data, the efficiency of Chinese teaching has increased by 18%. The current situation of Chinese teaching reform in the era of big data and the limitations of research are important to the cultivation of Chinese culture in the era of big data. Methods and approaches are analyzed, discussed and summarized so as to enrich the academic research results.</span>
Blockchain Framework for Artificial Intelligence Computation

Blockchain is an essentially distributed database recording all transactions or digital events among participating parties. Each transaction in the records is approved and verified by consensus of the participants in the system that requires solving a hard mathematical puzzle, which is known as proof-of-work. To make the approved records immutable, the mathematical puzzle is not trivial to solve and therefore consumes substantial computing resources. However, it is energy-wasteful to have many computational nodes installed in the blockchain competing to approve the records by just solving a meaningless puzzle. Here, we pose proof-of-work as a reinforcementlearning problem by modeling the blockchain growing as a Markov decision process, in which a learning agent makes an optimal decision over the environment’s state, whereas a new block is added and verified. Specifically, we design the block verification and consensus mechanism as a deep reinforcement-learning iteration process. As a result, our method utilizes the determination of state transition and the randomness of action selection of a Markov decision process, as well as the computational complexity of a deep neural network, collectively to make the blocks not easy to recompute and to preserve the order of transactions, while the blockchain nodes are exploited to train the same deep neural network with different data samples (state-action pairs) in parallel, allowing the model to experience multiple episodes across computing nodes but at one time. Our method is used to design the next generation of public blockchain networks, which has the potential not only to spare computational resources for industrial applications but also to encourage data sharing and AI model design for common problems.
Dinamika pendidikan vokasi pembelajaran praktikum konservasi arsip di masa pandemi

Introduction. The COVID-19 pandemic has forced face-to-face learning activities to change into distance learning. This paper discusses the impact of the pandemic on the learning process in the hands-on archives conservation practicum at the Archives Preservation Laboratory, Vocational School, Universitas Gadjah Mada. This paper also analyzes several perspectives across memory institutions as well as the potential and challenges through the choice of methods used to deliver the practical skills in distance learning. &#x0D; Data Collection Method. Field observations and interviews’ data were analysed using descriptive analytics approach and contrasted with findings in the literature reviews to draw conclusions.&#x0D; Results and Discussion. The dimensions of experience and tactile skills from hands-on conservation practice require specific equipments and laboratory which cannot be fully replaced in online classes. However, the pandemic has urged the university to arrange new learning methods for laboratory-based practicum.&#x0D; Conclusion. Although distance learning prompts plenty opportunities, it remains a challenge to replace the face-to-face model of conservation practicum. There is a great urgency for the best model and media to ensure effective learning which addresses the limitations during a pandemic. It is recommended that universities collaborate with stakeholders and Libraries, Archives and Museums (LAM) institutions to improve the current online practicum scheme.
The Impact of the Number of Distractors in Multiple-choice Test Items on the Psychometric Characteristics of the Items and Item Information Function According to the Two-Parameter Logistic Model in the Item Response Theory

The aim of the current study was to identify the impact of the number of distractors in multiple-choice test items on the psychometric characteristics of the test items and the item information function according to the twoparameter logistic model in the Item Response Theory (IRT).To answer the study questions, an achievement test was built covering all school tests course that measures teachers' degree of knowledge to build school tests.The test consisted of (42) items and was of 3 different forms varying in the number of options.The study sample consisted of 356 male and female teachers from Ma'an governorate and Wadi al-Sair district and was selected in a simple random manner.The statistical program (SPSS) and the (Bilog-mg3) program were used to analyse the responses of the study sample.The results of the study showed that there were statistically significant differences between the means of the values of the test items information function in favor of the four-distractors test form compared to the three-and-five-distractors test form.The results, furthermore, showed that there were statistically significant differences between the means of the values of the test items information function in favor of the five-distractors test form compared to the three-distractors test form.The results, further, showed that there were no differences between the means of the estimate of the items difficulty parameter due to the number of distractors of the item.It was indicated that there were differences between the means of the estimate of the items discrimination parameter concerning the three-and-four-distractors tests in favor of the four-distractors test.It was, besides, shown that there were statistically significant differences between the means of the estimate of the items discrimination parameter concerning the three-distractors and five-distractors tests' forms in favor of the for the five-distractors test.
A Theoretical CNN Compression Framework for Resource-Restricted Environments

Convolutional Neural Network (CNN) is considered as one of the most significant algorithms of deep learning that has made impressive achievements in many areas of computer vision and natural language processing. In the current times of big data, input data dimensions keep on increasing which leads to the development of complex CNN models for processing such big data. This has made CNN computationally intensive and limits its practical application to some extent. To address the aforementioned issue, this paper presents a detailed review of various network compression methods existing in literature. Two most commonly deployed network compression methods have been discussed including pruning and quantization which can be coupled with CNN to increase its applicability. The main goal of presenting this comprehensive review of the state-of-the-art pruning and quantization-based network compression schemes is to significantly improve trade-off between CNN architectural size and computational cost versus its performance in resource restricted environments. Additionally, this paper also exploits the challenges posed by these techniques when implemented for large-scale CNNs. In this context, paper also presents a novel framework to perform network compression of CNN to meet the requirements of resource-restricted devices.
Cloud computing, emerging computing technology of new age

Cloud computing is seen as an emerging technology highly utilized by different industries and businesses to optimize their processes conservation resources. Its popularity opens various loopholes and barriers, which are useful, especially when improving its overall performance. This study is conducted cloud computing further technological improvements suggested. This technology has been attractive developers and end-users because it provides both parties with an increased revenue opportunity. Entailed for and maintenance of physical hosting has been drastically eliminated from them for the end-user. Also true for the service providers since the increasing number integrating cloud computing to their systems increased revenue. However, the technology is facing challenges with security and privacy, workflow optimization, provisioning, cloud interoperability, data management, need or architecture modifications. These barriers inhibit cloud computing from achieving their maximum potential in terms of Service (QoS) and cost-effectiveness. Nevertheless, metrics monitored and measurements be done cloud computing's overall performance for of both the operation and economic aspects future studies is good &amp; interesting.
study of school students’ perception towards value education and advertising

Today, children are becoming silent by being busy on smart-phones or TV and while waiting for their parents to return from work; they often expect an enticing product instead of a warm hug. Sexual eroticism and its effect can be seen in increasing cases of teenage pregnancy, violence derived from the desire for expensive products by children who can barely have food; child obesity encouraged by the extreme exposure to junk food ads; and depression resulting from being unheard are some of the consequences that burden our children. India today is facing a value crisis where humanity seems to have lost its voice in the face of unending desire for wealth and pleasure. The present study is a deliberate attempt in this direction where there is a severe need for some remedial actions against the ill effects of advertising on innocent school students’ minds.
IoT Challenges and Future Directions

The development potential for the enterprises in the modern era is huge as they invest in diverse technologies. Internet of Things is a leading technology that has immense growth opportunities. It is predicted that 41.6 billion IoT devices will be operational in 2025. Typically, with the Internet of Things (IoT) expanding at large, there are numerous technical challenges that are encountered. Given the ongoing advancements in the IoT sector, it is quite obvious that IoT may penetrate all the sectors like finance, healthcare, agriculture, smart wearables, to name a few. The IoT has a potential to introduce a major revolution amongst the various sectors. This paper presents an overview on the IoT advancements and discusses the Future applications and investigation Challenges. Keywords- Internet of Things, IoT Applications , future technologies, IoT Challenges, IoT Future.
Regional infrasonic and seismic event classification with machine learning

The present work details how convolutional and recurrent deep learning networks can be used to classify infrasonic and seismic events of regional-field rocket demolitions, rocket motor burns, quarry blasts, and earthquakes. To accelerate machine learning adoption within the geophysical sciences, we illustrate the full machine learning pipeline: data acquisition and cleaning, preprocessing, model construction and training, and model understanding using feature-space analysis. Multiple deep learning architectures are evaluated to provide practical lessons learned and insights. The LSTM-RNN suffers from degraded learning on long geophysical signals, while a CNN is more robust to variance in data quality and length. Geophysical time-series signals should be learned with the instrument response deconvolved to avoid gross resampling or decimation of the signal. Frequency-domain feature inputs like spectrograms exhibit improved classification performance, and mapping event-based attributes to the learned feature space of deep networks can provide explainable physical context. All network configurations are validated on geophysical data collected in the Utah region, with experimental results including ablation studies examining different input types, preprocessing strategies, and hyperparameter settings.
Emotional Intelligence Of Students Mastering The Profession Of A Defectologist

The topicality of the given research is explained by high requirements for the teachers of speech pathologists' education, whose professional activity is performed in complex conditions, associated with the support of the development of children with disabilities in the educational process, with their conflictfree integration into everyday life.In this regard, this article is devoted to the emotional intelligence of future defectologists.It characterizes an ability to accept and understand both other people's emotions and feelings and their ones and control the way communicative behaviour is realized.We consider emotional intelligence as one of the professionally crucial qualities of a person, a key element of professional suitability and readiness to implement adequate psychological and pedagogical support for children with disabilities.The leading research method is an experiment that opens up the opportunity to re-evaluate the personal preparedness of future defectologists for independent pedagogical activity and improve their professional training.In the experimental work, the original author's methodology was used.The research participants were students mastering a Bachelor's degree program in Special (defectological) education.The article presents the peculiarities of students' reactions to their feelings and others' feelings, to the need to control emotions when fulfilling professional duties.