Reviewerly/exHarmony huggingface dataset:
Design and technological approach when forming readiness for autonomous learning activities

ntroduction. The need for the formation of students’ readiness for autonomous learning activities is caused by modern trends in the economy such as the rapid change of technology, self-employment of population, and the development of entrepreneurship. A modern specialist needs to possess skills such as knowing how to work with big data, project planning, and the ability to quickly acquire new knowledge and professions. The purpose of the present article is to substantiate the relevance and the possibility of forming students' readiness for autonomous learning activities in order to create their own educational product based on the skills of goal-setting, goal-fulfillment, selfreflection, and self-management. One of the methodological approaches to the formation of readiness for autonomous learning activities is the design and technological approach. Materials and methods. To carry out the study, the authors used content analysis of psychological and pedagogical research of domestic and foreign authors, methods of assessing the level of readiness of students for autonomous learning activities, as well as tested the developed method of autonomous learning activities formation in students. Results. Autonomous learning activities, as a process, are a form of organization of educational activity, aimed at developing in students the skills of goal-setting and target-fulfillment, self-reflection, and self-management, and as a result, the ability to create their own educational product based on the formed skills. Students experience the greatest difficulties when performing tasks requiring the skills of goal-setting and self-reflection. The authors’ method based on self-monitoring and educational strategies has shown effectiveness when forming readiness for autonomous learning activities. Discussion. The results of the application of the authors’ methodology when forming in students the readiness for autonomous learning activities indicate its effectiveness. The practical significance of the study is provided by the obtained results, which gives grounds for its implementation in the educational process of the university, as well as for further research in this direction.
Transformational Leadership and its Impact on the Job Performance at Great Municipalities in Jordan

This study aimed to identify the effect of transformational leadership on the job performance of workers in major municipalities in the North of Jordan. To achieve the results of the study, a questionnaire consisted of (40) paragraphs was developed to measure the variables of the study and its dimensions, achieve its goals, and test its hypotheses. The study revealed that there is an important and statistically significant positive effect of the practice of transformational leadership on job performance with a determination factor of (R2) of (221). The study also showed that there was an effect of the four variables of transformational leadership, the most influential of which was after inspiring motivation with an explanatory capacity of (22). (50%), while it showed that the least impact on job performance is after intellectual stimulation with an explanatory capacity amounting to (17.6%). The study recommended increasing more interest on the transformational leadership practices, by conducting training programs and workshops for mayors that would motivate the concept and practice of transformational leadership, and the necessity for municipal leaders to give more importance caring for employees and meeting their needs through tools of motivation.
Minecraft Altered Skin Channel (MASC)

Governments across the world are increasingly pushing for encryption backdoors and other privacy breaking features. One possible method to maintain privacy in a post-encryption environment is to utilize covert channels as a means to hide traffic from monitoring authorities. Covert channels have often been used to hide unwanted or prohibited web traffic, however, the traffic need not be malicious for there to be a necessity for privacy. In this paper we explore a novel covert channel named Minecraft Altered Skin Channel (MASC) that exploits the inherent inefficiencies within Minecraft skin images. Data is transferred by reflecting a modified skin image off of the publicly accessible Minecraft Skin servers. We utilize a custom steganographic algorithm and a public Application Programming Interface (API), which when combined, grant the ability to quickly and asynchronously send and receive encoded messages without any direct communication between the two or more participating parties. We demonstrate the effectiveness of the scheme by implementing an efficient sender-receiver proof of concept. Finally, we go on to empirically analyze the program’s results and show the comparatively high bitrate of the channel.
Cross-Lingual Approaches for Task-Specific Dialogue Act Recognition

In this paper we exploit cross-lingual models to enable dialogue act recognition for specific tasks with a small number of annotations. We design a transfer learning approach for dialogue act recognition and validate it on two different target languages and domains. We compute dialogue turn embeddings with both a CNN and multi-head self-attention model and show that the best results are obtained by combining all sources of transferred information. We further demonstrate that the proposed methods significantly outperform related cross-lingual DA recognition approaches.
Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

Leukemia refers to a disease that affects the white blood cells (WBC) in the bone marrow and/or blood. Blood cell disorders are often detected in advanced stages as the number of cancer cells is much higher than the number of normal blood cells. Identifying malignant cells is critical for diagnosing leukemia and determining its progression. This paper used machine learning with classifiers to detect leukemia types as a result, it can save both patients and physicians time and money. The primary objective of this paper is to determine the most effective methods for leukemia detection. The WEKA application was used to evaluate and analyze five classifiers (J48, KNN, SVM, Random Forest, and Naïve Bayes classifiers). The results were respectively as follows: 83.33%, 87.5%, 95.83%, 88.88%, and 98.61%, with the Naïve Bayes classifier achieving the highest accuracy; however, accuracy varies according to the shape and size of the sample and the algorithm used to classify the leukemia types.
Subject Guides: Marine Mechanical Technician: Find Internet Resources

This guide provides quick access to quality resources relevant to the Marine Mechanical Technician program
Leveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval

This paper describes the participation of UvA.ILPS group at the TREC CAsT 2020 track. Our passage retrieval pipeline consists of (i) an initial retrieval module that uses BM25, and (ii) a re-ranking module that combines the score of a BERT ranking model with the score of a machine comprehension model adjusted for passage retrieval. An important challenge in conversational passage retrieval is that queries are often under-specified. Thus, we perform query resolution, that is, add missing context from the conversation history to the current turn query using QuReTeC, a term classification query resolution model. We show that our best automatic and manual runs outperform the corresponding median runs by a large margin.
On a poset of quantum exact promise problems

Abstract Two of the most well-known quantum algorithms, those introduced by Deutsch–Jozsa and Bernstein–Vazirani, can solve promise problems with just one function query, showing an oracular separation with deterministic classical algorithms. In this work, we generalise those methods to study a family of quantum algorithms that can, with just one query, exactly solve promise problems stated over Boolean functions. We also show that these problems can be naturally ordered, inducing a partially ordered set of promise problems. We study the properties of such a poset, showing that the Deutsch–Jozsa and Bernstein–Vazirani problems are, in a certain sense, extremal problems in it, determining some of its automorphisms and proving that it is connected. We also prove that, for the problems in the poset, the corresponding classical query complexities can take any value between 1 and $$2^{n-1}+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> .
Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function

Recently further developed Deep Neural Network sheds light on automatically learning high-level image representation from raw pixels. Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach to arrange high dimensional vectors in a compact image form conductible for CNN-based deep learning. This paper demonstrates the use of deep neural networks for developing a system that can recognize various texture features.
Designing for Usability: A Statistical Disclosure Control Tool for Microdata Sets

Governments across the world looking to implement Open Government Data (OGD) initiatives undergo many problems. One such problem is the risk to privacy from opening data sets as most of the data is at a microdata level which corresponds to specific individuals. A solution to such a predicament is the application of Statistical Disclosure Control (SDC) techniques on microdata. SDC methods anonymize microdata that reduces the risk of disclosure while also maintaining the value of the data. SDC methods can be applied by using software tools, however, these tools are designed from the perspective of experts or for the purpose of demonstration. Moreover, ongoing research has led to the slow progress in not only the development of these tools, but also their adoption. Resulting in limited support material and even smaller user base. As a consequence, individuals or organizations looking to adopt these tools to satisfy their data privacy objectives cannot use them. Out of many SDC tools, ARX is a stable application that equips its users with an arsenal of techniques to anonymize microdata sets. It also undergoes regular updates, thus keeping pace with the current developments in the field of SDC techniques. Despite this, ARX is not widely used due to its perceived complexity. This thesis addresses the problem of the complexity that is associated with ARX which makes it difficult to adopt them to anonymize their data sets. The thesis provides a solution to this problem by developing a prototype tool which reduces the complexity of SDC techniques through a simplified, user-friendly approach to data anonymization. The thesis does not aim to enhance privacy methods or improve the functionalities of already existing tools by proposing a replacement. The thesis tries to bridge the gap which implicitly occurs when privacy tools are designed from the perspective of experts. It is understood that the protection of private data should only be handled by experts. However, to build that expertise, people have to be introduced to simpler tools without being overwhelmed by the complexity that is immanent with concepts of SDC.
A Common Steganalysis Method of Low Embedding Rate Steganography in Compressed Speech Based on Hierarchy Feature Extraction and Fusion

The current steganalysis researches have not specifically explored the compressed speech stream with 1%–9% embedding rates. In this paper, we propose a common steganalysis method of low embedding rate steganography based on hierarchy feature extraction and fusion. Firstly, codewords in each frame are converted to a multi-hot vector. And each multi-hot vector will be mapped into a fixed length embedding vector to get a more compact representation by utilizing the pre-trained dictionaries. Then, a hierarchy feature extraction and fusion framework is employed to perform the extraction and fusion of different levels of correlation features. Specifically, a 5-layer convolutional neural network is used to extract correlation feature information from local to global. The features of different local scales are restored to the same size by the transposed convolution. In addition, the attention mechanism is introduced in different layers of the network to assign different importance weights to the output feature within each layer. Finally, the prediction results can be generated by the fully connected layer. Experimental results show that our method performs better than the existing steganalysis methods for detecting multiple steganographies in the low bit-rate compressed speech streams. On the mixed dataset of multiple steganography methods, the proposed method can reach 73.56% on the speech stream under 5% embedding rate. And the accuracy can exceed 83% on the dataset under 9% embedding rate.
Waves of Connectivism during Pandemic for Teaching English to Undergraduate Deaf Students

Covid-19 and the sudden lockdown have created formidable challenges all around the world. It has morphed the comprehensive schooling system switching to digital platforms. Teachers and students have explored the possibilities of teaching and learning through online platforms where the 20th-century learning theory, ‘connectivism’ captures the attention which delineates how the e-learning platforms have created new opportunities for people to teach, learn and share knowledge all over the world. This study aims to check the effectiveness of virtual teaching to deaf and hard-of-hearing students during the lockdown period. As the primary language of deaf students, the medium of instruction should be sign language. Various online teaching tools are used to make the session more effective for the study.
Impact of Enterprise Financing Constraints on Labor Income Share Based on Internet of Things Data Analysis Technology

Although the Chinese economy has developed rapidly since the reform and opening up, the income distribution gap is widening year by year. The final social income distribution pattern is highly dependent on the primary distribution pattern. Therefore, the changing trend and influencing factors of labor income share have become the focus of academic research and the focus of government attention. Based on this, this article proposes enterprise financing based on Internet of Things data analysis technology. Studies on the impact of restraints on labor income shares will help further research on the impact of corporate financial restraints on future labor income shares. Based on the financial data reports published in the CCER database and the company’s IPO prospectus and annual report, this paper discusses whether the company’s key business products belong to the Internet of Things’ key technology application categories. Take 226 IoT companies as the research objects of this article, and conduct a secondary screening. The final survey sample is used to investigate the impact of corporate funding constraints on labor income share. Tests have proved that among the 179 resource-based enterprises undergoing transformation, 109 enterprises have undergone intraindustry transformation, accounting for 48.23% of the overall sample and 60.89% of the sample of transformed enterprises. Downstream expansion makes the business industry expand. This shows that funding constraints have a negative impact on labor income share. This is primarily the result of the impact of the long-term debt-to-asset ratio on labor income share.
REST-i: Sleep Monitor Device: a study of self-efficacy in the use of health tech [Design]

REST-i is a lightweight stand-alone sleep monitor device to be positioned next to a bed. The functionalities of the device are to capture and display contextual data while the person sleeps overnight. Enabling this functionality are two in-built sensors: an infra-red sensor and camera to capture body heat and movement, a humidity sensor to capture ambient humidity and levels of body sweat throughout the night. Displaying contextual data is enabled by a screen and a LED light. The screen shows and record a graph of the temperature-humidity-movement captured during the night. The LED light is a real-time display of any drastic changes of temperature-humidity-movement levels; it utilises a change of colour from light blue to red to display such changes. Data management capabilities comprises: the set-up of a personal benchmark (temperature-humidity-movement individual data), storage and exporting of data.
Do Healthcare Metadata Models Designed for Web Publishing Meet the Accreditation Standards? A Case Study in the Healthcare and Medical Education

This article reports comparison results from studying the alignment of selected metadata models, used to manage E-Learning Materials (ELM) in the medicine and healthcare education, against the accreditation standards of the European Accreditation Council for Continuing Medical Education (EACCME).The study aims to (1) illustrate the variety of application profiles developed as metadata models in that field, (2) identify the alignment of the metadata models with the criteria set by the EACCME.This study compared several application profiles, developed based on established metadata standards, using an evaluation matrix created using the accreditation criteria set by the EACCME.Further technical criteria were added to compare between the metadata models and their alignment with the new techniques used for publishing data online.Such techniques aid in the searchabililty and discovery of online content via the internet.Therefore, the use of XML and RDF technologies combined with the use of ontologies and controlled vocabulary was emphasized in the implementation of many metadata models.One limitation faced during conducting this research was the poor documentation of the methodology for designing the metadata models.Despite this, it is apparent that the EACCME criteria can work as best practices guidelines for describing the content of digital libraries with medical and healthcare content in order to be accredited later on.The study showed that the medicine and healthcare educational content requires specialized metadata models that consider having specific properties such as clinical history and expiry time of its material shown in some of the application profiles.Further research should be conducted to evaluate the completeness and conformity of applying the application profiles in digital libraries and online repositories to the standards' guidelines.
Hire a Service App: An Android-based Home Service Mobile Application with Geomapping using Dijkstra’s Algorithm

Current technological advances have led to the creation of smartphones and mobile applications. In addition,different tasks are accomplished by using a mobile application, where each app has its own function and purpose.There are many skilled workers in the Philippines who offer a variety of services to the local community. However,for the local community, the manual search for the services offered is a tedious task. Thus, this work has proposedthe development of “Hire a Service App”, a mobile home service application. In this work, the application wasdeveloped using Dijkstra’s Algorithm and Bubble Sort Algorithm. In addition, the proposed application wasdeveloped using Android Studio as a platform and used the Google Map Kit extension for mapping purposes.Likewise, the application was designed to run on Android 4.1 up to Android 7.0. The application was tested in termsof functionality, reliability, usability, efficiency, maintainability, and portability The test result have shown that thedeveloped application has exhibited mean value of 4.66 for functionality, 4.69 for efficiency, 4.67 formaintainability, 4.68 for reliability, and 4.76 for both usability and portability. Overall, the developed applicationexhibited excellent performance with total mean score of 4.72.
Необходимость реформирования системы отечественного высшего образования как ответ на вызовы современного общества

The article considers problems of developing the domestic higher education system in modern conditions, which determine the need for its reforming. It shows that these problems, as in many areas of national socio-economic development, are complex in nature, due to a serious loss of the best traditions of Soviet higher education, illegal copying non-best examples of foreign experience, a significant imbalance in the system of science, higher education and production (economic activity) relationships in the domestic space. The paper highlights key problems of developing the domestic higher education system, and the main trends of its reforming, which meet the challenge of modern society and the needs of the future society. It demonstrates that the need to reform the system of domestic higher education as a response to the modern society challenges today is determined by the urgency of simultaneous implementation of all innovative changes inherent in innovations according to J. Schumpeter in the system of domestic higher education, including the need to change the approach  to selecting the student contingent contributing to choosing the most capable and talented youth for universities; a radial shift as a result of changing the technological structures of the product (goods, works, services) composition, which requires an adequate change in training the university personnel; intensive development of educational  technologies due to new opportunities provided by information technologies; expedient modification of organizational structures in the  higher education system due to the possibilities of digitalization; changing the nature of the “state-university” link interaction forcing universities to prepare universal graduates as the opportunities of employment in a particular profession for graduates of Russian  universities have become probabilistic due to the changed market  conditions. The authors propose an algorithm to form a new methodological approach for adaptive transformations of the higher  education system using end-to-end digital technologies.
Reducing Cybersecurity Vulnerabilities Through the Use of 12N QR Codes

The following sections are included:IntroductionGrowing Cybersecurity BreachesRetail Supply Chain Vulnerabilities — Target and Home DepotCyberattack StrategiesDefending Against CyberattacksThe Importance of Up-To-Date TechnologySupply Chain Cybersecurity Challenges12N Codes: One Label Does It AllMore Secure 2-FA Using 12N QR CodesAvoiding the Need for User LoginsSummaryReferences
LibGuides: Voluntary Education Center: Social Media

Providing best advice to servicemembers and their families regarding their educational options.
LibGuides: . Information Commons Resource Guide: System Material Circulation

An all inclusive guide to how to utilize Sarasota-Manatee campus Library Services resources. Learn how to use each of USF's book/material circulation tools.