Reviewerly/exHarmony huggingface dataset:
Cyber Security Deficiencies in the Education Environment

Information technologies have already radically transformed the communications and information technology sectors, trade, media, and the education sector, especially education, which is on the top priority. Digital technologies will radically change the way data is sent and retrieved, will allow immediate and real-time feedback from students, will facilitate access to education through distance learning, will allow new service providers to enter traditional markets education. Thus, a diligent and well planned implementation of cyber security, as well as information security of information networks of educational institutions will provide a definite competitive advantage in the information environment. In the context of a dynamic information environment prone to large-scale cyber-attacks, in which cybercriminals are using increasingly advanced methods to implement attack vectors that are undetectable and difficult to neutralize, this scientific study is designed to protect data and security of your institutions as well as to ensure the confidentiality of your students and teachers. Whether we are talking about large or small educational institutions, this study provides a series of recommendations on the secure use of social networks with a minimum set of measures needed to prevent cyber-attacks as well as reduce the damage caused in case of attacks. At the same time, it will examine the best practices in the online environment, effectively analyze the privacy settings of mobile devices and present some top tips for the secure use of social networks. The study aims to comply with the following minimum set of measures in order to prevent cyber-attacks in educational institutions, and reduce the damage caused in the event of attacks. Cyber-bezpieczeństwo w obszarze edukacji Technologie informacyjne radykalnie przekształciły sektory komunikacji, handlu, mediów i edukacji. Technologie cyfrowe zmienią sposób przesyłania i wyszukiwania danych, umożliwią natychmiastową informację zwrotną od uczniów w czasie rzeczywistym oraz ułatwiają dostęp do edukacji poprzez kształcenie na odległość. Zatem rzetelne i dobrze zaplanowane wdrożenie cyberbezpieczeństwa, a także bezpieczeństwa informacyjnego sieci informacyjnych placówek oświatowych zapewni zdecydowaną przewagę konkurencyjną w środowisku informacyjnym. Artykuł ma na celu przedstawienie środków zapobiegawczych przeciw cyberatakom w placówkach edukacyjnych i ograniczania szkód wyrządzanych w przypadku takich ataków.
Exploration of Obesity Status of Indonesia Basic Health Research 2013 With Synthetic Minority Over-Sampling Techniques

The accuracy of the data class is very important in classification with a machine learning approach. The more accurate the existing data sets and classes, the better the output generated by machine learning. In fact, classification can experience imbalance class data in which each class does not have the same portion of the data set it has. The existence of data imbalance will affect the classification accuracy. One of the easiest ways to correct imbalanced data classes is to balance it. This study aims to explore the problem of data class imbalance in the medium case dataset and to address the imbalance of data classes as well. The Synthetic Minority Over-Sampling Technique (SMOTE) method is used to overcome the problem of class imbalance in obesity status in Indonesia 2013 Basic Health Research (RISKESDAS). The results show that the number of obese class (13.9%) and non-obese class (84.6%). This means that there is an imbalance in the data class with moderate criteria. Moreover, SMOTE with over-sampling 600% can improve the level of minor classes (obesity). As consequence, the classes of obesity status balanced. Therefore, SMOTE technique was better compared to without SMOTE in exploring the obesity status of Indonesia RISKESDAS 2013.
Live Chat Analysis Using Machine Learning

The World Wide Web such as social networking sites and blog comments forum has huge user comments emotion data from different social events and product brand and arguments in the form of political views. Generate a heap. Reflects the user's mood on the network, the reader, has a huge impact on product suppliers and politicians. The challenge for the credibility of the analysis is the lack of sufficient tag data in the Natural Language Processing (NLP) field. Positive and negative classify content based on user feedback, live chat, whether the user is used as the base for a wide range of tasks related to the text content of a meaningful assessment. Data collection, and function number for all variants. A recurrent neural network is very good text classification. Analyzing unstructured form from social media data, reasonable structure, and analyzes attach great importance to note for this emotion. Emotional rewiring can use natural language processing sentiment analysis to predict. In the method by the Recurrent Neural Networks (RNNs) of the proposed prediction chat live chat into sentiment analysis. Sentiment analysis and in-depth learning technology have been integrated into the solution to this problem, with their deep learning model automatic learning function is active. Using a Recurrent Neural Networks (RNNs) reputation analysis to solve various problems and language problems of text analysis and visualization product retrospective sentiment classifier cross-depth analysis of the learning model implementation.
Visual Cryptography Using Hill Cipher and Advanced Hill Cipher Techniques

Visual cryptography is a method in cryptography which deals with encryption of visual information such as pictures or text in such a way that the decrypted information appears as an original image. Cryptography is defined as the study of mathematical and scientific methods and allied facets of information security like data integrity, data confidentiality and data authentication. The main focus of the work is to present a method of image encryption and decryption which focusses on image security. The work proposes Hill Cipher and advanced Hill Cipher algorithms for image encryption and decryption. The algorithms work on both gray scale and color images effectively. Histogram, entropy and correlation coefficients are calculated. The correlation values of encrypted images using advanced Hill Cipher algorithm are less than that of Hill Cipher algorithm. Entropy of encrypted image using advanced Hill Cipher algorithm is greater than that of Hill Cipher and original image. The results show that advanced Hill Cipher algorithm is better than Hill Cipher algorithm for image cryptography.
Setting Up Spark

So that we can develop a .NET for Apache Spark application, we need to install Apache Spark on our development machines and then configure .NET for Apache Spark so that our application executes correctly. When we run our Apache Spark application in production, we will use a cluster, either something like a YARN cluster or using a fully managed environment such as Databricks. When we develop applications, we use the same version of Apache Spark locally as we would when we run against a cluster of many machines. Having the same version on our development machines means that when we develop and test the code, we can be confident that the code that runs in production is the same.
Benchmarking Neural Embeddings for Link Prediction in Knowledge Graphs Under Semantic and Structural Changes

Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. While algorithmic advances have strongly focused on efficient ways of learning embeddings, fewer attention has been drawn to the different ways their performance and robustness can be evaluated. In this work we propose an open-source evaluation pipeline, which benchmarks the accuracy of neural embeddings in situations where knowledge graphs may experience semantic and structural changes. We define relation-centric connectivity measures that allow us to connect the link prediction capacity to the structure of the knowledge graph. Such an evaluation pipeline is especially important to simulate the accuracy of embeddings for knowledge graphs that are expected to be frequently updated.
A formação de pessoal em arquivologia na Bahia: marcos históricos e institucionais

This article aims to contribute to the historiography of training in archival sciences in Bahia state, with an emphasis on the historical landmarks of the administration of L. H. D. Tavares (1959-1969) at the state public archive. Based on documentary research in the Apeb fond, it seeks to demonstrate a similar role to that exercised by the Arquivo Nacional in the institutionalization of education in archival sciences in Brazil.
Cryptanalysis and Security Evaluation Using Artificial Neural Networks

This chapter introduces a novel methodology for determining mutual information between the secret key and cypher message. The methodology is based on using artificial intelligence, specifically neural networks, to determine correlations that can be found in the weak and medium encryption algorithms. Furthermore, this chapter presents a relevant survey in the field of research with a similar purpose, a detailed description of the developing environment, a theoretical overview of the matter, as well as a detailed methodology description with additional results. The final results show the performance of the proposed methodology, as well as the results of applying it to today’s most-secured algorithms. Based on the obtained results, this chapter proves the efficiency of the proposed methodology. Besides having Advanced Encryption Standard (AES) and Data Encryption Standard (DES) as reference algorithms, this methodology could be applied to real-world algorithms. Absolute proof of this methodology represents the simple crypt algorithm that scored over 99.9% accuracy in most dataset cases. In the end, results on AES do not show any significant accuracy of the model, but when DES comes in a case, the model scores about 70% accuracy. This result proves the real-world applications of this methodology.
LibGuides: Market Research: Home

Sources of information about the potential consumers, competitors and/or industry information.
DropLoss for Long-Tail Instance Segmentation

Long-tailed class distributions are prevalent among the practical applications of object detection and instance segmentation. Prior work in long-tail instance segmentation addresses the imbalance of losses between rare and frequent categories by reducing the penalty for a model incorrectly predicting a rare class label. We demonstrate that the rare categories are heavily suppressed by correct background predictions, which reduces the probability for all foreground categories with equal weight. Due to the relative infrequency of rare categories, this leads to an imbalance that biases towards predicting more frequent categories. Based on this insight, we develop DropLoss -- a novel adaptive loss to compensate for this imbalance without a trade-off between rare and frequent categories. With this loss, we show state-of-the-art mAP across rare, common, and frequent categories on the LVIS dataset.
Unsupervised Learning and Other Tools for Data Stream Mining

In Chapter 3, you learned supervised machine learning techniques for both regression and classification problems in a streaming data context. This chapter starts with unsupervised learning for streaming data and then overviews some of the other software environments available for data stream mining.
Towards an explainable AI adjunct to deep network obstacle detection for multisensor vehicle maneuverability assessment

Advancement in remote sensing capabilities have led to unprecedented quantity and quality of data across a number of sensing modalities. It is now possible to outfit nearly any mobile platform not only with high resolution cameras, but also with inexpensive infrared and LIDAR sensors. For the specific goal of providing a comprehensive assessment of vehicle maneuverability, we address the problem of co-registering multiple sensor phenomenologies, such as visual, infrared, and LIDAR imagery collected from vehicle-mounted sensors. We show that a data fusion across these sensors provides invaluable information in hazard detection, localization, and classification. In addition, the co-registered measurements lead to the feasibility of enhanced heterogeneous data machine learning methods. This approach is verified on a dataset collected by the U.S. Army ERDC.
A design and development of a building environment 3D-walkthrough

- This paper reports an ongoing study that uses 3D walkthrough as an alternative promotional platform. A user centered design methodology was adopted. The design and development process that combines the integration of 3D virtual environment and multimedia development is the focus. A pilot study with experts in 3D and multimedia was done. Result showsa positive feedback.
Operationalizing autonomy and AI for the Air Force

Autonomy and AI have made tremendous progress in recent years to the point where operational applications of these technologies can provide decisive advantages. This presentation will discuss the approach recommended to rapidly field autonomy and AI capabilities at scale including the development of a common platform, addressing trust issues, and agile methodology. Examples in sensor exploitation and business processes will be used to demonstrate the operational value of current generation of AI. However, this generation has limitations, and the talk will conclude with future research required to expand the safe, ethical, and effective use of these technologies.
Development of a Web-Based Online Library Information System at the National University Using the Waterfall Method: Development of a Web-Based Online Library Information System at the National University Using the Waterfall Method

The definitions of Pustaka places or libraries are: Several source charts from schools to lectures have not escaped the info-info/knowledge from the library. In relation to the advancement of this technology makes: people to be able to mind effectively also efficient work. For example: conventional systematic generation is more computerized.From the utilization of a feature of the web connected online, the de also: increased levels of efficiency and effectiveness in the book Search and booking. This research designed a website based information system with PHP.  From the results of research that has been implemented authors implement the results of the research on: Development of information Systems Web based Online Library of national universities using Waterfall method
Do We Really Need Dice? The Hidden Region-Size Biases of Segmentation Losses

Most segmentation losses are arguably variants of the Cross-Entropy (CE) or Dice losses. On the surface, these two categories of losses seem unrelated, and there is no clear consensus as to which category is a better choice, with varying performances for each across different benchmarks and applications. Furthermore, it is widely argued within the medical-imaging community that Dice and CE are complementary, which has motivated the use of compound CE-Dice losses. In this work, we provide a theoretical analysis, which shows that CE and Dice share a much deeper connection than previously thought. First, we show that, from a constrained-optimization perspective, they both decompose into two components, i.e., a similar ground-truth matching term, which pushes the predicted foreground regions towards the ground-truth, and a region-size penalty term imposing different biases on the size of the predicted regions. Then, we provide bound relationships and an information-theoretic analysis, which uncover hidden region-size biases: Dice has an intrinsic bias towards specific extremely imbalanced solutions, whereas CE implicitly encourages the ground-truth region proportions. Our theoretical results explain the wide experimental evidence in the medical-imaging literature, whereby Dice losses bring improvements for imbalanced segmentation. Based on our theoretical analysis, we propose a principled and simple solution, which enables to control explicitly the region-size bias. The proposed method integrates CE with explicit terms based on L1 or the KL divergence, which encourage segmenting region proportions to match target class proportions, thereby mitigating class imbalance but without losing generality. Comprehensive experiments and ablation studies over different losses and applications validate our theoretical analysis, as well as the effectiveness of explicit and simple region-size terms.
Frequency Characteristics of a Quantum Motherboard in Preprocessor and Distributed Sensor Mode

In this work, we describe a quantum motherboard based on a system of five interacting resonators connected with two-level atoms via switched couplings. Based on algebraic methods, the frequency spectrum of the atomic-resonator platform was optimized and the dynamics in the preprocessor and distributed sensor mode were numerically studied. A comparative analysis of several optimization methods for the proposed scheme.
Risk score learning for COVID-19 contact tracing apps

Digital contact tracing apps for COVID, such as the one developed by Google and Apple, need to estimate the risk that a user was infected during a particular exposure, in order to decide whether to notify the user to take precautions, such as entering into quarantine, or requesting a test. Such risk score models contain numerous parameters that must be set by the public health authority. In this paper, we show how to automatically learn these parameters from data. Our method needs access to exposure and outcome data. Although this data is already being collected (in an aggregated, privacy-preserving way) by several health authorities, in this paper we limit ourselves to simulated data, so that we can systematically study the different factors that affect the feasibility of the approach. In particular, we show that the parameters become harder to estimate when there is more missing data (e.g., due to infections which were not recorded by the app), and when there is model misspecification. Nevertheless, the learning approach outperforms a strong manually designed baseline. Furthermore, the learning approach can adapt even when the risk factors of the disease change, e.g., due to the evolution of new variants, or the adoption of vaccines.
THE PRINCIPLES OF ORGANIZING THE SCHOOL MUSEUM OF NATURAL DIRECTION

The article briefly outlines the history of development and formation of museum pedagogy as a direction in education and outlines its relationship with culture and art. The main urgent tasks of museum pedagogy are outlined and the main directions in which research is carried out today are identified, including general historical approaches to the development of museum pedagogy, the organization and implementation of museum pedagogical projects, the training of future teachers by means of museum pedagogy, museum pedagogy in the work of educational and extracurricular educational institutions, “museum pedagogy” for students of higher educational institutions, etc. The differences between children’s museums and museums at educational institutions (school museums) from a historical perspective are determined. The main directions of the school museum’s activities are noted, including: excursion, cultural and educational, exposition, search and research. The forms of implementation for certain areas of activity are indicated as being combined into three groups: mass, group, individual. According to the theoretical conclusions, the principles of organizing the school science museum of the natural direction have been established, such as: scientific, integration, continuity, visibility, systematicity, dynamism, practicality, humanity, creativity, etc. It has been established that compliance with certain principles of organizing a school science museum of a natural direction will effectively implement the practice of creating science and educational museums on the basis of formal and non-formal education institutions with the participation of teachers and education seekers. The organization of a school science museum in an educational institution will allow to reveal the artistic and aesthetic, cognitive, scientific, cultural, potential of the individual and will allow to fully perceive the natural and cultural heritage of mankind.
Concept Drift Detection in Data Streams

In the last chapter, you were introduced to streaming data, its applications, windowing techniques, and incremental and online learning algorithms. You were also introduced to the scikit-multiflow framework in Python and the various streaming data generators. This chapter discusses algorithms for concept drift detection in a streaming data context. It explores relevant techniques and how to implement them using the scikit-multiflow framework in Python.
Quantum fisher information matrix of a single qutrit in lambda configuration

Quantum sensing is an important application of Quantum Information techniques. In this work, we present a mathematical model for a single Qutrit in &Lambda; (Lambda) configuration and its quantum Fisher information.
NLP-CUET@DravidianLangTech-EACL2021: Investigating Visual and Textual Features to Identify Trolls from Multimodal Social Media Memes

In the past few years, the meme has become a new way of communication on the Internet. As memes are in images forms with embedded text, it can quickly spread hate, offence and violence. Classifying memes are very challenging because of their multimodal nature and region-specific interpretation. A shared task is organized to develop models that can identify trolls from multimodal social media memes. This work presents a computational model that we developed as part of our participation in the task. Training data comes in two forms: an image with embedded Tamil code-mixed text and an associated caption. We investigated the visual and textual features using CNN, VGG16, Inception, m-BERT, XLM-R, XLNet algorithms. Multimodal features are extracted by combining image (CNN, ResNet50, Inception) and text (Bi-LSTM) features via early fusion approach. Results indicate that the textual approach with XLNet achieved the highest weighted f_1-score of 0.58, which enable our model to secure 3rd rank in this task.
Nikolai Myaskovsky on the Pages of the Journal “Sovetskaya Muzyka” During the Years 1933–1951: Content Analysis

Nikolai Yakovlevich Myaskovsky enjoyed the leading position in the pantheon of Soviet classics. At the center of the author’s attention is the image of Myaskovsky the composer appearing on the pages of the journal “Sovetskaya muzyka” (the official edition of the Soviet Composers’ Union). The carried out content analysis of articles dating from the year 1933–1951 makes it possible for us to assert that Myaskovsky is presented in the press as a Soviet artist “who overcame independently the path of correctional education.” The materials confirm Myaskovsky’s high professional status and authority, as the result of which in the years 1936 and 1948 the critique of his musical output did not acquire harsh forms. Carrying out the task of interpreting his compositions in the vein of the aesthetics of socialist realism, musical critics have marked the national and ideal foundation of his music through the category of words which indicated at the democratically accessible and optimist qualities of his music. The chief attribute of Myaskovsky the composer – the subjective directedness of his music (individualism, pessimism) – was equated to non-conformism through the perspective of the binary mythological system of Soviet aesthetics. In connection with this the motive of struggle and overcoming became the leading one in the description of the composer’s creative path, endowing his image with heroic features. Keywords: Nikolai Myaskovsky, journal “Sovetskaya muzyka,” Soviet composer, socialist realism in music, receptive research, content analysis.