Reviewerly/exHarmony huggingface dataset:
FEATURES OF THE ORGANIZATION OF BLENDED LEARNING AT TERNOPIL V. HNATIUK NATIONAL PEDAGOGICAL UNIVERSITY UNDER THE CONDITIONS OF MARTIAL LAW

The full-scale invasion of Russian troops into Ukraine prompted the Ministry of Education and Science to organize the educational process under martial law and introduce restrictions that would guarantee the safety of students and teaching staff. Universities have started the educational process in 2022–2023 in different formats, taking into account the security situation in the region, guided by the Recommendations of the Ministry of Education and Culture of Ukraine and within the limits of their autonomy. Ternopil Volodymyr Hnatyuk National University began to work in a mode that is as close as possible to the traditional format, with a flexible application of elements of blended learning.&#x0D; The purpose of the article is to characterize the peculiarities of the organization of blended learning in the conditions of martial law. The views of scientists on the definition of the term «blended learning» were analyzed and the ways of implementing the distance learning format at the university were outlined. The main focus is on the use of the educational content management system MOODLE and the overall organization of the TNPU distance learning center. A number of measures have been outlined to implement the full functioning of the educational process during the COVID-19 pandemic: development of a regulatory framework, technological support, establishing of communication with teachers and students, and improving staff qualifications.&#x0D; Based on the data received after the student questionnaire, it was established that the advantages of the distance format are: learning in a comfortable and familiar environment, the flexibility of the educational process, and the disadvantages are insufficient self-organization of students, the risk of biased assessment, the lack of necessary skills and problems with the Internet. It was determined that, in general, the effectiveness of distance learning was rated by the respondents as average. The survey results showed that the university is not only an academic environment but also performs an important social function. This justifies the importance of direct interaction between students and teachers in the classroom. Taking into account the above, we conclude that in the conditions of martial law in our region, the mixed format of learning is the most optimal. It guarantees students a safe environment, accessibility, inclusiveness, and individuality of learning.
So, How <i>Can</i> We Measure UX?

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Fused-Layer-based DNN Model Parallelism and Partial Computation Offloading

With the development of Internet of Things (IoT) and the advance of deep learning, there is an urgent need to enable deep learning inference on IoT devices. To address the computation limitation of IoT devices in processing complex Deep Neural Networks (DNNs), partial computation offloading is developed to dynamically adjust computation offloading assignment strategy in different channel conditions for better performance. In this paper, we take advantage of intrinsic DNN computation characteristics, and propose a novel Fused-Layer-based (FL-based) DNN model parallelism method to accelerate inference. The key idea is that a DNN layer can be converted to several smaller layers to increase partial computation offloading flexibility, and thus further create better computation offloading solution. However, there is a trade-off between parallelism computation offloading flexibility and model parallelism overhead. Then, we discuss the optimal DNN model parallelism and the corresponding scheduling and offloading strategies in partial computation offloading. In particular, we present a Minimizing Waiting (MW) method, which explores both the FL strategy, the path scheduling strategy, and the path offloading strategy to reduce time complexity. Finally, we validate the effectiveness of the proposed method in commonly used DNNs. The results show that the proposed method can reduce the DNN inference time by an average of 18.39 times compared with No FL (NFL) algorithm, and is very close to the optimal solution Brute Force (BF) with greatly reduced time complexity.
Strong Language

Political speech, now one of the most highly-protected forms of speech in the United States, wasn't always protected. In times of stress, particularly times of war, free speech has been curtailed: Congress passed the Sedition Act of 1798 to silence critics of the Adams administration, and it passed the Espionage and Sedition Acts of 1917/18 to silence critics of US participation in WW I. Both laws openly flouted constitutional free-speech guarantees. In a series of high-profiile cases over the course of the twentieth century, the US Supreme Court developed a First Amendment doctrine that gave more and more types of speech First Amendment protection. Starting with the Charlie Hebdo massacre in Paris in 2015, this chapter looks at increasing protections for political speech from 1791 to the present, including free speech for students. We assess the continuing impact of the doctrine that might be called "free speech, but." Yes, speech is protected, but there is still some speech considered outside the framework of legal protection.
Efficiency-Improved Privacy-Preserving Weighted Similarity Query over Outsourced eHealthcare Data

Weighted similarity query has been an essential primitive to enable personalized disease diagnosis in eHealthcare. With the prevalence of cloud computing, a new paradigm is to outsource weighted similarity range query services to the cloud. Meanwhile, the query services are usually processed over encrypted data considering data privacy. Although many existing schemes are available to achieve privacy-preserving weighted similarity query over encrypted data, they have some security and query efficiency drawbacks. This paper addresses this problem by proposing an efficient and privacy-preserving weighted similarity range query scheme. First, we employ a k-d tree to index the outsourced dataset and present a k-d tree based weighted similarity range query algorithm. Then, we propose our scheme by applying the MASPE scheme to protect the privacy of the k-d tree based weighted similarity queries. Privacy preservation of our scheme is proved through security analysis. Efficiency improvement is confirmed by the extensive experiments that indicate that our scheme improves 7 x query efficiency than the state-of-the-art scheme.
Towards the engineering of a quantum key distribution receiver robust to ambient light

Quantum Key Distribution (QKD) has the potential to secure indoor optical wireless links. In a typical room scenario, an indoor optical wireless link will have a transmitter on the ceiling and a receiver on a desk. Ambient light from room (typically LED) lighting on the ceiling and sunlight coming through the windows present a challenging environment for free-space QKD links to operate, and a key challenge is to mitigate the noise induced by ambient light, particularly sunlight. A combination of spectral and spatial filtering can be used to reduce the effect of ambient light, with a narrowband optical filter typically used. Moreover, the wavelength of operation is key to further reduce the impact of ambient light. Wavelengths in ‘quiet’ regions of the solar spectrum, such as the atmospheric absorption bands, are promising candidates. We are currently working on a system that operates at 1370 nm, where water and carbon dioxide absorption band in the atmosphere attenuate the solar spectrum substantially. This paper reports the design and modelling of the system, with a series of validation measurements to characterise the effect of solar radiation on a typical photon-counting detector as would be used in a QKD system. The aim of this work is to show the feasibility of the wavelength region around 1370 nm as a necessary step towards a low noise QKD receiver for indoor optical wireless links in a practical environment.
LANGUAGE DETECTION USING MACHINE LEARNING

In today's globalized world, where people from all over the world are able to communicate and share information with each other.Language detection is important as it helps to bridge the gap between different cultures and languages.Language detection is a useful tool in many applications, such as machine translation, text summarization, and sentiment analysis.In these applications, it is important to first determine the language of the text before processing it further.Overall, language detection using NLP is a powerful tool for analyzing and processing natural language data.It can be used in a variety of applications to improve the accuracy and efficiency of natural language processing systems.
COMPARATIVE STUDY OF FAKE NEWS DETECTION BETWEEN MACHINE LEARNING AND DEEP LEARNING APPROACHES

Most of the people now-a-days prefer to read the news via social media over internet.Many websites are publishing the news and provide the source of authentication.The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter, and other micro blogs & social networking sites.It is harmful for the society to believe on the rumors and pretend to be a news.The need of an hour is to stop the rumors and focus on the correct, authenticated news articles.Aim of this project is to develop two models for detecting fake news by using Machine Learning algorithms (usingSVM) and Deep Learning algorithms (using LST) respectively.With the help of Machine learning and Deep Learning, it is tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine and Long Short-Term model.For the model, the dataset is cleaned and the data is pre-processed.Then on the pre-processed data, feature extraction techniques are used and the model is trained using both the algorithms separately to get two different models using SVM algorithm and LSTM algorithm respectively.Confusion Matrix, Classification reports and accuracies of both the models are calculated and compared to find the best model for Fake News Detection.SVM Classifier gave 99.29% accuracy and LSTM classifier gave 99.54% accuracy.Both the algorithms gave good accuracies but LSTM gave better results compared to SVM in classifying the news articles.The major contribution of this project is to find the better fit algorithm and techniques for Fake News Detection between SVM and LSTM algorithms by comparing their accuracies.
Research on Strategies of Improving Mathematics Reading Ability in Secondary Vocational School

Reading is the premise of learning.In the information age, the cultivation of secondary vocational school students' mathematics reading ability is an important way to cultivate students' comprehensive learning ability and improve the core quality of mathematics.Therefore, more and more teachers attach importance to the cultivation of students' mathematics reading habits and the improvement of reading ability, so as to promote students' mathematical language expression ability, arouse students' thinking about mathematical problems, and promote students' mathematical language transformation ability.This paper discusses the relationship between middle school students' reading habits and mathematics achievement, and gives some suggestions on how to cultivate secondary vocational school students' good reading habits and improve their mathematics reading level The current situation of mathematics reading ability of secondary vocational school studentsThe nature of mathematics curriculum mentioned in the Mathematics Curriculum Standards for Secondary Vocational Schools is that mathematics is the science of studying quantitative relations and spatial forms, the basis of other professional and technical learning, the necessary tool for solving practical problems in production practice, and one of the necessary qualities for the future career development of secondary vocational students.In today's era of information and big data, mathematics plays an increasingly important role in the follow-up education and work services.Because mathematics requires students to have good mathematical abstraction and logical reasoning ability, secondary vocational school students generally think that mathematics is a very difficult subject to learn, many students mention that the understanding of concepts is always vague or unclear, information extraction is difficult or biased, and they are afraid of common mathematical terms [2] .Therefore, many secondary vocational school students no longer like to read mathematics books and learn mathematics knowledge on their own initiative, and they are almost not interested in extracurricular mathematics reading.Secondary vocational school students in junior high school are weak in learning, and they often rely on intuition to learn and perceive mathematics, only pay attention to the conclusion of the problem, and are unwilling to spend time to understand the logical relationship, so the development of students'mathematical literacy quality is limited.In secondary vocational schools, with the
Research on College Chinese Online Learning System Based on Content Recommendation Algorithm

With the development of Internet scale, recommendation algorithm is widely used in e-commerce, advertising, community and so on, and collaborative filtering algorithm is also widely used because of its simple implementation and clear process. However, the traditional recommendation algorithm has the problems of long cold start time, insufficient suppression of Matthew effect and insufficient utilization of item information. In the existing methods for cold start problems, the recommended contents are not fully utilized. If we want to use the recommended content as the information source of the recommendation system, we need an appropriate modeling method to analyze the content. Therefore, the focus of this paper is how to analyze and model the College Chinese online learning system, and finally use this information for recommendation.
An Industrial Software Model Checking Method Based on Machine Learning and Its Application in Education

In the era of big data, the traditional industrial software model detection methods can not meet the actual needs in the massive data. This paper studies an industrial software system, which is widely used in many industrial fields. A comprehensive analysis of its behavior and correctness has practical needs and significance. At present, there are symbolic, combinatorial and statistical model detection methods to verify the correctness of the system interaction and random behavior. Because there are many variables in the detection system, it is easy to cause the state space explosion of verification; When using abstract technology, verification is incomplete. To solve the above problems, we focus on the processing of variables in the detection system.
Design and Implementation of Dynamic Grouping Algorithm Based on Sports Track

Track and field is the “mother of sports” and the basis of all kinds of sports. In the commonly used games management software, the grouping and lane division of sports track events mostly adopt fixed eight lanes, and the AR arrangement algorithm is fixed, which has some defects. The traditional manual arrangement has low operation efficiency, low accuracy, heavy copying task and heavy workload, which requires a lot of human and material resources. The arrangement of order book by computer makes the tedious organization and arrangement work completed manually realized through simple operation, and makes the organization and management of sports meeting simple, efficient and flexible. In this paper, a dynamic grouping algorithm is proposed, the design idea of the algorithm is discussed in detail, and the order arrangement is realized by computer, so as to realize the cumbersome organization and arrangement manually through simple operation, simplify the organization and management of the game, and be efficient and flexible. Using the dynamic grouping algorithm proposed in this paper, the sports track events can be grouped flexibly and quickly, which makes up for the deficiency of the existing sports meeting management software to a certain extent.
Research on the Construction of Business English Education Translation Platform Based on “Internet+”

With the implementation of China’s reform and opening up and China’s accession to the World Trade Organization (WTO), China’s business English translation plays a more and more important role in international trade activities. Business English translation is not something that can be done only by understanding English. Many scholars at home and abroad have devoted themselves to the study of business English translation theory. In this context, it has brought unprecedented opportunities to the construction of business English translation platform. Based on the analysis of the problems existing in the process of business English translation, the research on the construction of business English translation platform based on “Internet” provides reference for translation or related learners, and also wants to attract more scholars to pay attention to business English and its translation.
The Design of the English Teaching Resource Management System Based on Collaborative Recommendation

The design of English teaching resource management system based on collaborative recommendation is a book published by Dr. Li in January 2012. The book was first published as a master’s thesis at Seoul National University in South Korea in 2006. This book describes the design and implementation of an e-learning system for English teaching based on collaborative recommendation. This article will teach you how to design an effective resource management system so that it can be used as a model for other schools. This book will help you improve your teaching skills and ensure that you can get the best results from your students. The author uses this book to show all the steps involved in designing such a system and explain why it should be done in this way rather than in any other way.
A Siamese Inverted Residuals Network Image Steganalysis Scheme based on Deep Learning

With the rapid proliferation of urbanization, massive data in social networks are collected and aggregated in real time, making it possible for criminals to use images as a cover to spread secret information on the Internet. How to determine whether these images contain secret information is a huge challenge for multimedia computing security. The steganalysis method based on deep learning can effectively judge whether the pictures transmitted on the Internet in urban scenes contain secret information, which is of great significance to safeguarding national and social security. Image steganalysis based on deep learning has powerful learning ability and classification ability, and its detection accuracy of steganography images has surpassed that of traditional steganalysis based on manual feature extraction. In recent years, it has become a hot topic of the information hiding technology. However, the detection accuracy of existing deep learning based steganalysis methods still needs to be improved, especially when detecting arbitrary-size and multi-source images, their detection efficientness is easily affected by cover mismatch. In this manuscript, we propose a steganalysis method based on Inverse Residuals structured Siamese network (abbreviated as SiaIRNet method, Sia mese- I nverted- R esiduals- Net work Based method). The SiaIRNet method uses a siamese convolutional neural network (CNN) to obtain the residual features of subgraphs, including three stages of preprocessing, feature extraction, and classification. Firstly, a preprocessing layer with high-pass filters combined with depth-wise separable convolution is designed to more accurately capture the correlation of residuals between feature channels, which can help capture rich and effective residual features. Then, a feature extraction layer based on the Inverse Residuals structure is proposed, which improves the ability of the model to obtain residual features by expanding channels and reusing features. Finally, a fully connected layer is used to classify the cover image and the stego image features. Utilizing three general datasets, BossBase-1.01, BOWS2, and ALASKA#2, as cover images, a large number of experiments are conducted comparing with the state-of-the-art steganalysis methods. The experimental results show that compared with the classical SID method and the latest SiaStegNet method, the detection accuracy of the proposed method for 15 arbitrary-size images is improved by 15.96% and 5.86% on average, respectively, which verifies the higher detection accuracy and better adaptability of the proposed method to multi-source and arbitrary-size images in urban scenes.
A New Method for Evaluating the Accuracy of the Orientation Measured by Different Kikuchi Pole Combinations in Kikuchi Patterns

Abstract In this study, high-precision coordinates of Kikuchi poles were obtained for orientation calculation by locating the lattice plane traces of Kikuchi patterns of stainless steel. Orientation can be measured by selecting three noncoincident Kikuchi poles in a Kikuchi pattern, and different combinations of Kikuchi poles provide different orientation solutions. The “deviation” defined in this study and the orientation calculation error of each combination are generally positively correlated. Therefore, the deviation can be used to evaluate the accuracy of the orientation calculation for each combination without the theoretical orientation, and can also be used as a criterion for selecting Kikuchi pole combinations for orientation calculations. The results show that the average misorientation angles for all combinations are 1.983° (orientation a), 2.768° (orientation b), and 2.175° (orientation c) before the screening of the Kikuchi pole combinations by deviation. For each pattern, the first five combinations with the smallest deviation are selected, and the average misorientation angles of these combinations are 0.791° (orientation a), 0.789° (orientation b), and 0.910° (orientation c). The orientation calculation errors are reduced by 60, 71, and 58%, respectively. The accuracy of the orientation calculation is greatly improved, thus avoiding the appearance of orientation solutions with large errors.
Evaluation of the Global Horizontal Irradiation (GHI) on the Ground from the Images of the Second Generation European Meteorological Satellites MSG

The measurement of solar irradiation is still a necessary basis for planning the installation of photovoltaic parks and concentrating solar power systems.The meteorological stations for the measurement of the solar flux at any point of the earth's surface are still insufficient worldwide; moreover, these measurements on the ground are expensive, and rare.To overcome this shortcoming, the exploitation of images from the European meteorological satellites of the second generation MSG is a reliable solution to estimate the global horizontal irradiance GHI on the ground with a good spatial and temporal coverage.Since 2004, the new generation MSG satellites provide images of Africa and Europe every 15 minutes with a spatial resolution of about 1 km × 1 km at the sub-satellite point.The objective of this work was to apply the Brazil-SR method to evaluate the global horizontal GHI irradiance for the entire Moroccan national territory from the European Meteosat Second Generation MSG satellite images.This bibliographic review also exposed the standard model of calculation of GHI in clear sky by exploiting the terrestrial meteorological measurements.