Reviewerly/exHarmony huggingface dataset:
Interactive Media Development Using Microsoft Sway in Elementary Level Learning Circle

Education process and technology go hand in hand and adapt to each other for teachers in carrying out their duties. The Microsoft sway application is the best choice for learning materials for students when carrying out distance learning (PJJ). By office 365 account, teachers can develop learning materials designed with existing Microsoft Sway features. There are titles, text, videos, images that are integrated directly with the internet network. Microsoft Sway is a website-based application that provides more interesting, interactive, online learning content and is integrated with the LMS (Learning Management System). The sway application design is designed with the Deck and Lou Carey development model. The development stage starts with planning, design, and development. The resulting validation shows a value of 95.55% on learning materials assessed by experts in the field of mathematics the media expert validation shows a value of 93.33% assessed by experts in Educational technology. Furthermore, student trials with a questionnaire model showed 84.59% very well in learning media, So the score obtained shows that the learning media using Microsoft Sway is feasible for students to use
Experimental Comparison of Pre-Trained Word Embedding Vectors of Word2Vec, Glove, FastText for Word Level Semantic Text Similarity Measurement in Turkish

This study aims to evaluate experimentally the word vectors produced by three widely used embedding methods for the word-level semantic text similarity in Turkish.Three benchmark datasets SimTurk, AnlamVer, and RG65_Turkce are used in this study to evaluate the word embedding vectors produced by three different methods namely Word2Vec, Glove, and FastText.As a result of the comparative analysis, Turkish word vectors produced with Glove and FastText gained better correlation in the word level semantic similarity.It is also found that The Turkish word coverage of FastText is ahead of the other two methods because the limited number of Out of Vocabulary (OOV) words have been observed in the experiments conducted for FastText.Another observation is that FastText and Glove vectors showed great success in terms of Spearman correlation value in the SimTurk and AnlamVer datasets both of which are purely prepared and evaluated by local Turkish individuals.This is another indicator showing that these aforementioned datasets are better representing the Turkish language in terms of morphology and inflections.
GANs Unplugged

With the influx of deepfake and style transfer technology in today's news and social media, everyone is told that these applications are powered by artificial intelligence and deep learning, but too often the explanation of how it works goes no further. Rather than waiting until second semester of computer science grad school to learn about generative adversarial networks (GANs), we propose a classroom activity to introduce GANs to secondary school students. Our GANs Unplugged activity steps outside a traditional classroom environment and forms groups of students that physically act as the various components of a GAN. Students in the Real and Generator (i.e. Fake) groups both draw sketches of animals on index cards, but only the Real group has the secret, specific instructions on how to draw their animals. A third Discriminator group tries to determine if each card it receives came from the Real group or the Fake group. The Discriminator group also makes a single feedback mark on the drawing to suggest how that drawing could be more "real" before passing the card back to the group that drew it. As this game progresses, the Discriminators get better at determining real and fake, that is until the Generators adapt and learn to draw more and more realistic "fake" images. We describe the details of GANs Unplugged and present our experience running this activity at three different artificial intelligence summer camps for high school students.
A Novel Hyperchaotic 2D-SFCF with Simple Structure and Its Application in Image Encryption

In this paper, a novel image encryption algorithm is proposed based on hyperchaotic two-dimensional sin-fractional-cos-fractional (2D-SFCF), called sin-fractional-cos-fractional image-encryption (SFCF-IE). The 2D-SFCF is constructed from two one-dimensional cosine fractional (1-DCFs), and it has a more complex chaotic behavior with a larger parameter space than one-dimensional chaotic systems. Compared with the two-dimensional (2D) chaotic system, the 2D-SFCF has a simple structure, and the parameter space in the chaotic state is continuous, which is beneficial to generating the keystream in the cryptosystem. Therefore, in the novel image encryption algorithm, we use the 2D-SFCF to generate the keystream of the cryptosystem. The encryption algorithm is a process of scrambling and diffusion. Different from common diffusion methods, the diffusion starting position of the SFCF-IE is randomly generated, enhancing the algorithm's security. Simulation experiments show that the image encrypted by this algorithm has better distribution characteristics and can resist common attack methods.
MIDS: The digitisation standard for Natural Science collections

The digitisation standard, MIDS (Minimum Information about a Digital Specimen), has been developed to provide a clear definition for the level of digitisation of the specimens held in natural science collections. The standard comprises three levels of digitisation: Level 1 (Basic) is equivalent to a virtual cabinet or drawer in the collection; Level 2 (Regular) provides information needed for most research; Level 3 (Extended) provides all information on the specimen itself as well as links to associated resources. Level 1 (Basic) is equivalent to a virtual cabinet or drawer in the collection; Level 2 (Regular) provides information needed for most research; Level 3 (Extended) provides all information on the specimen itself as well as links to associated resources. A pre-digitisation Level 0 (Bare) allows for the citation of the specimen and the attachment of additional information. The standard has been developed through the collaboration of people representing digitisation, curation, data management, software development and research. The development has been coordinated within the TDWG MIDS Task Group. The aim has been to develop a standard that will fulfill the needs of those managing digitisation programmes (e.g., estimating costs, priorities) as well as the users of the digitised specimens (e.g., informing potential data users of a collection’s data richness).
Dual Auto-Encoder Based Rating Prediction Recommendation Algorithm

Collaborative filtering is the most widely used method in recommendation algorithms, but it still faces the serious problem of data sparsity. Traditional collaborative filtering uses matrix decomposition to learn the latent features of users and items. As an extension model of matrix decomposition, Funk-SVD model has attracted wide attention due to its good scalability and easy implementation, but it is difficult to extract the latent features of users and items from sparse rating information because it essentially learns the linear relationship between users and items. To solve this problem, we propose a Dual auto-encoder based Rating Prediction Recommendation Algorithm (DRPRA) model. The DRPRA model uses the strong ability of deep learning in feature learning, which combines double auto-encoders with Funk-SVD. First, the auto-encoder captures the latent features of users and items respectively. Then, the Funk-SVD combines the user features with item features to reconstruct the rating matrix. After that, we minimize the error between original rating matrix and reconstructed rating matrix, and to alleviate the problem of data sparsity and improve the accuracy of rating prediction effectively. We conducted extensive experiments on Movielens-100K, Movie Tweeting-10k, and Film Trust datasets, and the results show that the rating prediction model based on dual auto-encoders has a superior recommendation performance.
The Image of the Siberian Territories of the Russian Empire in the Journal “Sibirskii Nablyudatel'” (1901–1906)

The article is devoted to the analysis of the image of the Siberian territories of the Russian Empire in the XIX -early XX centuries through consideration of the management system, industry, agriculture, education and population of Siberia.These are the areas that received the most extensive coverage on the pages of the Tomsk historical edition "Sibirskii nablyudatel '" published in the 1901-1906.The analyze results of the content of the magazine articles show that the image of the Siberian territories in the field of their management and education is characterized by significant transformations, and their role is defined as the leading one.Thanks to well-established office work, regular reporting, redistribution of financial income and expenses, a management system arises that functions in cities and still remains unchanged.Significant transformations that took place in the field of education in Siberia by the end of the 19th-beginning of the 20th centuries made it possible to determine the leading role of Siberian cities, headed by Tomsk, in the all-Russian education system of that time.The ethnic image of the Siberian territories is defined as multinational and multicultural, characterized by a tendency for the synthesis of cultures, manifested in some customs and in linguistic borrowing.
Students’ Affective Learning Outcomes and Academic Performance in the Blended Environment at University: Comparative Study

This study examines how the gender of students and their level of education (undergraduate or postgraduate students) influence affective learning outcomes in a mixed environment. The research model is based on four key indicators: satisfaction, perception of experience (perceived usefulness, ease of use, and perceived behavioral control), perception of benefits (general learning effectiveness, knowledge sharing and increasing, study skills improvement, and sense of progress), and academic performance. Moderating factors, including gender and education level, were used to define the distinctions in the measured results. The study was conducted in the fall semester of 2021. The target samples were students of undergraduate and postgraduate levels studying during the semester in the blended environment. A total of 513 students from Peter the Great Polytechnic University took part in the research. The online questionnaire was conducted to define the affective learning outcomes of students in the blended environment and the influence of affective outcome factors on academic performance. The Likert-type five-point scale was used to determine all the variables. For our statistical analysis, we used SPSS 24.0 and SmartPLS 3.0 programs. Two tests were implemented to determine the differences between gender and education level in relation to students’ affective learning outcomes. Finally, our study revealed how distinctive aspects of affective learning outcomes impact academic learning outcomes in a blended format using linear regression analysis. According to the results obtained, the results between males and females were similar and academic performance results were mostly predicted by satisfaction level. A difference was revealed between undergraduate students’ results and postgraduate students’ results. Perceived benefit has a greater effect on academic performance for postgraduate students, while satisfaction level has a greater effect on academic performance results for undergraduate students than for postgraduate students.
Deep Learning Approach for Aspect-Based Sentiment Analysis of Restaurants Reviews in Spanish

Online reviews of products and services have become important for customers and enterprises. Recent research focuses on analyzing and managing those kinds of reviews using natural language processing. This paper focuses on aspect-based sentiment analysis for reviews in Spanish. First, the reviews data sets are normalized into different inputs of the neural networks. Then, our approach combines two deep learning models architectures to determine a positive or negative assessment and identify the most important characteristics or aspects of the text. We develop two architectures for aspect detection and three architectures for sentiment analysis. Merging the deep learning models, we tested our approach in restaurant reviews and compared them with state-of-the-art methods.
CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims

We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to facilitate and encourage work on improving algorithms for retrieving evidential support for climate-specific claims, addressing the underlying language understanding challenges, and ultimately help alleviate the impact of misinformation on climate change. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. While during this process, we could rely on the expertise of renowned climate scientists, it turned out to be no easy task. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the \textsc{fever} framework, which we believe provides a valuable challenge for general natural language understanding. We hope that our work will mark the beginning of a new exciting long-term joint effort by the climate science and AI community.
Mapping of gold-related alteration minerals and linear structures using ASTER data in the Giyani Greenstone Belt, South Africa

This paper focuses on the mapping of alteration minerals and linear structures associated with gold mineralization in the Giyani Greenstone Belt (GGB). Spectral Information Divergence (SID) and Linear Spectral Unmixing (LSU) classification algorithms were applied to the ASTER data using image extracted endmember spectra which match the spectral profiles of predominant alteration minerals (biotite and calcite) related to gold mineralization in the GGB. Directional convolution filters were also applied to the ASTER data to extract structural lineaments that may be related to gold mineralization. In addition, Fry analysis of known gold occurrences in the GGB was conducted to determine the major distribution patterns along which gold mineralization have occurred. LSU and SID results show several pixels classified as alteration minerals corresponding to known gold occurrences. SID results also show minerals overlapping into the gneissic rock, which is a rare host of gold mineralization in the study area. On the other hand, LSU results show high abundances of calcite and biotite generally confined within the mafic-ultramafic greenstone rocks, which have been hypothesized as the source of gold-mineralized fluids in the GGB. In general, LSU show distinct broad regions classified as alteration minerals corresponding to known gold occurrences that have been previously reported to host these alteration minerals. Directional convolution filters enabled the extraction of predominately NE-SW oriented linear structures, including the major shear zone which is associated with gold mineralization in the GGB. Fry analysis revealed three major distribution patterns: N-S, NE-SW, ENE-WSW along which gold mineralization have occurred. These patterns correspond to major lineaments associated with gold mineralization reported in the previous structural studies of the GGB.
Application of BP Neural Network in Teaching Quality Evaluation of Higher Vocational Education

Higher vocational education has developed rapidly and attracted wide attention. Teaching quality evaluation is an important part of teaching management in classroom teaching and is an important part of teaching management. Based on the theory of teaching quality, 16 evaluation indicators are identified from the four aspects of teaching attitude, teaching content, teaching process, and teaching results and an HVE quality evaluation index system is constructed. The evaluation model is established by BP neural network algorithm. Finally, five higher vocational colleges in Henan Province are selected for empirical research. The results show that the evaluation model constructed in this paper has certain applicability and can provide a reference for HVE quality evaluation and improvement. Although the evaluation models herein have certain applicable values, further improvements are still needed in terms of indicator identification and correction.
PELATIHAN PENULISAN ARTIKEL POPULER UNTUK MENUNJANG KENAIKAN PANGKAT BAGI GURU DI SMAN 4 BANDAR LAMPUNG

Popular scientific work is often interpreted as a written work made based on a scientific rule with the aim that all discussions can be understood by various general groups or not only academics. There are so many benefits that can be felt by teachers from writing popular articles, by writing popular articles the teacher can share knowledge with anyone through his writings, can motivate students. And to take care of promotions for teachers, one of them is promotion. The promotion for teachers is one way to improve achievement and ensure welfare. one of the elements of promotion of teachers from certain groups to a higher class one of the supporters is to make written works published in mass media such as newspapers, magazines, or online media on official websites. According to data released by Kompas, the number of teachers in Indonesia currently reaches 2,698,103. Unfortunately, of the number of teachers who are able to produce a written work is very minimal. This can be proven by the number of written works published by the teacher. This problem is also experienced by SMKN 4 Bandar Lampung, teachers who do not teach Indonesian still find it difficult and confused to determine ideas from popular scientific articles. Based on the partner problems above, our team agreed to provide solutions to partner problems, namely by providing opinion writing training especially for teachers at SMKN 4 Bandar Lampung. At the time of training, teachers are asked to think of ideas that will be used as popular scientific works and to jointly correct the results of scientific writings from each teacher.
PF-HIN:Pre-Training for Heterogeneous Information Networks

In network representation learning we learn how to represent heterogeneous information networks in a low-dimensional space so as to facilitate effective search, classification, and prediction solutions. Previous network representation learning methods typically require sufficient task-specific labeled data to address domain-specific problems. The trained model usually cannot be transferred to out-of-domain datasets. We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network. Unlike traditional network representation learning models that have to train the entire model all over again for every downstream task and dataset, PF-HIN only needs to fine-tune the model and a small number of extra task-specific parameters, thus improving model efficiency and effectiveness. During pre-training, we first transform the neighborhood of a given node into a sequence. PF-HIN is pre-trained based on two self-supervised tasks, masked node modeling and adjacent node prediction. We adopt deep bi-directional transformer encoders to train the model, and leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In the fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification, and node clustering. PF-HIN outperforms state-of-the-art alternatives on each of these tasks, on four datasets.
A Survey on Text-Dependent and Text-Independent Speaker Verification

Speaker verification (SV) aims to detect an individual’s identity from his/her voice. SV has been successfully applied in various areas such as access control, remote service customization, financial transactions, etc. Depending on whether the text content is pre-defined or not, SV can be text-dependent or text-independent. This paper reviews recent research on text-dependent SV (TD-SV) and text-independent SV (TI-SV). Because most modern SV systems apply deep learning methods to boost performance, we focus on the studies that use deep speaker embedding, a technique representing a person’s identity via a fixed-dimensional vector encoded from a variable-length utterance. Rather than detailing every existing SV system, we make an overview of the representative SV systems that have attracted wide attention. Furthermore, an increasing number of SV systems have been devoted to addressing real-world challenges such as reverberation and noise, and this has driven a large number of studies on practical SV. Therefore, the survey compares the existing SV systems in the Far-Field Speaker Verification Challenge 2020 (FFSVC 2020) to illustrate the most effective techniques for both TD-SV and TI-SV.
Study of methods for endpoint aware inspection in a next generation firewall

Abstract Given the global increase in remote work with the COVID-19 pandemic and deperimeterization due to cloud deployment of next generation firewalls, the concept of a next generation firewall is at a breaking point. It is becoming more difficult to define the barrier between the good and the bad. To provide the best security for an endpoint with minimal false positives or false negatives it is often necessary to identify the communicating endpoint application. In this study, we present an analysis of key research and methods for providing endpoint aware protection in the context of a next generation firewall. We examine both academic research as well as state-of-the-art of the existing next generation firewall implementations. We divide endpoint application identification into passive and active methods. For passive endpoint application identification, we study several traffic fingerprinting methods for different protocols. For active methods we consider active scanning, endpoint metadata analysis and content injection and reference existing implementations. We conclude that there are several open areas for future research, and that none of the considered methods is a silver bullet solution for endpoint aware inspection in the context of a next generation firewall. To our best knowledge, this is the first study to examine current research and existing implementations of endpoint aware inspection.
Homodyne Detection of Non-Gaussian Quantum Steering

Quantum correlations are at the core of current developments in quantum technologies. Certification protocols of entanglement and steering, suitable for continuous-variable non-Gaussian states are scarce and generally highly demanding from an experimental point of view. We propose a protocol based on Fisher information for witnessing steering in general continuous-variable bipartite states, through homodyne detection. It proves to be relevant for the detection of non-Gaussian steering in scenarios where witnesses based on Gaussian features like the covariance matrix are shown to fail.