Reviewerly/exHarmony huggingface dataset:
Autonomy of Russian Universities: Historical Documentation Research of the 19th – 21st Century University Charters

Introduction. The article compares the charters of Russian universities and brings to light the principles of universities’ operation throughout their history from the 19th to the 21st century. The article describes the model of university autonomy in Russia and its influence on the development of the academia, including contemporary universities’ ambitions in terms of global rankings. Methods and Materials. The conceptual framework is based on the methods of documentary studies applied to analyze universities’ charters and the procedures of their development and use, including the preparation of the draft version, editing and further adjustments as well as the origin and characteristics of the document, that is, whether they resulted from ‘bottom-up’ or ‘top-down’ initiatives. The documentary analysis reveals not only the functions of charters but also the degree of universities’ autonomy as defined by these documents. Analysis. In terms of their history and functions, three types of university charters can be identified: general (unified), standard and individual. In the Soviet and post-Soviet periods, individual charters were mostly based on standard provisions or charters, that is, they were ‘top-down’ initiatives. General and standard charters were devised by the related governmental agencies and, therefore, corresponded to the goals pursued by the government at that stage. Individual university charters resulting from ‘bottom-up’ initiatives were created in the transition periods of 1918–1922 and the 1990s, which were characterized by massive socioeconomic change and search for new models of higher education institutions. Results. In the history of Russian higher education, there are several periods when universities had limited autonomy: early and mid-19th century (liberal reforms); 1920s (organizational and methodological experiments); 1960s (revival of limited autonomy of universities); 1990s (self-government and academic freedoms). Liberal cycles are directly reflected in the university charters, but the analysis of the procedural aspects of their development and functioning allows to conclude that autonomy should be considered as a temporary deviation from the basic model of a state university.
LibGuides: Teaching Banned Books in High School: Databases

The purpose of this guide is to support research by high school educators looking to incorporate banned and challenged books into their curriculum.
GraFS: Graph Analytics Fusion and Synthesis

Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics time-consuming and error-prone. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks. It features novel semantics-preserving fusion transformations that optimize the specifications and reduce them to three primitives: reduction over paths, mapping over vertices and reduction over vertices. Reductions over paths are commonly calculated based on push or pull models that iteratively apply kernel functions at the vertices. This paper presents conditions, parametric in terms of the kernel functions, for the correctness and termination of the iterative models, and uses these conditions as specifications to automatically synthesize the kernel functions. Experimental results show that the generated code matches or outperforms hand-optimized code, and that fusion accelerates execution.
Application of the "Internet +" model in the nursing management of esophageal cancer patients in chemotherapy

ObjectiveTo explore the application value of Internet + model in esophageal cancer patients in chemotherapy.MethodsA total of 90 patients with esophageal cancer treated in the First Affiliated Hospital of Zhengzhou University from January 2017 to January 2019 were selected by convenient sampling method, and all received chemotherapy. They were divided into observation group (n=45) and control group (n=45) according to the random number table method. The control group adopts routine nursing management, and the observation group adopts the Internet + management model. Blood samples were taken before the nursing intervention and 6 months after the intervention to measure the nutritional indicators of the two groups, including serum prealbumin (PA) , albumin (ALB) , and transferrin (TRF) levels. The Connor-Davidson Psychological Resilience Scale was used to evaluate the psychological resilience of the two groups of patients.ResultsThe levels of serum PA, ALB, and TRF in the two groups after intervention were higher than those before intervention, and the observation group were higher than the control group, the differences were statistically significant (P<0.05) . After intervention, the mental impact, acceptance changes, tolerance of negative emotions, ability, control scores, and total psychological elasticity scores of the two groups were higher than before the intervention, and the observation group were higher than the control group, and the differences were statistically significant (P<0.05) .ConclusionsCompared with traditional nursing management, the Internet + management model can further improve the nutritional level and mood of esophageal cancer patients in chemotherapy, and the overall application effect is good.Key words: Esophageal neoplasm; Internet +; Chemotherapy; Nutritional status; Psychological resilience
LibGuides: Accessing Library Resources Online During COVID-19: Online Teaching Resources

New videos, ebooks, and other resources made temporarily available to help institutions impacted by Coronavirus provide a better online learning experience for students.
What and How is BIPA Teaching through Online Media?

This study examines any online media that organizes Indonesian language teaching for foreign speakers.The research method used is a qualitative descriptive method.The results showed that teaching Bahasa for foreign speakers is very diverse.All Indonesian learning rides are paid, and some are not paid.As for the material aspect, there is a service provider that has made the rating as did the Indonesian Agency of the Republic of Indonesia, namely A1-C2 and there are also those who provide the material based on a particular thematic.These service providers are created by writers from various countries, from Indonesian writers themselves to American and European writers.The background of those who utilize this media of learning as diverse as going to travel to Indonesia, having a business in Indonesia, married to Indonesians, interested in Indonesian culture, or want to invest in Indonesia.The presence of online media in this BIPA lesson service is very beneficial for those who want to learn Indonesian from their own country.
Chinese Tone Recognition Based on 3D Dynamic Muscle Information

To advance the study of lip-reading recognition in accordance with Chinese pronunciation norms, we carefully investigated Mandarin tone recognition based on visual information, in contrast to that of the previous character-based Chinese lip reading technique. In this paper, we mainly studied the vowel tonal transformation in Chinese pronunciation and designed a lightweight skipping convolution network framework (SCNet). And, the experimental results showed that the SCNet was sensitive to the more detailed description of the pitch change than that of the traditional model and achieved a better tone recognition effect and outstanding antiinterference performance. In addition, we conducted a more detailed study on the assistance of the deep texture information in lip-reading recognition. We found that the deep texture information has a significant effect on tone recognition, and the possibility of multimodal lip reading in Chinese tone recognition was confirmed. Similarly, we verified the role of the SCNet syllable tone recognition and found that the vowel and syllable tone recognition accuracy of our model was as high as 97.3%, which also showed the robustness of our proposed method for Chinese tone recognition and it can be widely used for tone recognition.
Optimal route-finding system for Myanmar Country

Traffic congestion is becoming a serious problem in more and more modern cities. So, this system is proposed to solve the traveler’s facing problems in finding the shortest path from one place to another place. Encouraging the traveling salesperson is one of the most effective and economical ways to reduce the ever increasing congestion problem on the roads. This system is proposed as the optimal route finding system for Myanmar country. The proposed system is able to find the optimal route from the starting location to the intended location in short time by using Prim algorithm.
A Secondary Analysis of Panoramic Radiographs Reveals Hotspots in the Maxillofacial Region Associated with Diabetes.

Diabetes mellitus is the putative cause of a number of pathologies occurring in the bony and soft tissues of the maxillo-facial region and is known to exacerbate other oral diseases such as periodontitis.We present the first use of clinical panoramic radiographs for a secondary analysis of disease, with a focus on identifying hotspots in the maxillofacial region that are associated with diabetes. We developed a curated data set using Consensus Landmark Points (CLPs) and used that data to develop an analysis pipeline. This pipeline entailed automatic data cleansing, registration, and intensity normalization. The pipeline was used to process 7280 uncurated images that were subsequently analyzed using pixel-wise methods for a case/control study of patients with a history of diabetes. We detected statistically significant clusters of pixels that demarcated anatomical hotspots specific to the diabetic patients.
The 4 Most Common Compliance Risks & How to Avoid Them

Meet the four frameworks and practices designed to prevent the most common compliance risks - HIPAA, GDPR, PCI, and disaster recovery.
A Novel Activation Function in Convolutional Neural Network for Image Classification in Deep Learning

In deep learning, there are various parameters that helps to drive optimal results. One of those parameters is to use the correct activation function. The activation function must have ideal statistical characteristics. In this paper, a novel deep learning activation function has been proposed. Sigmoid activation function generally used in the output layer for bi-classification problem. Recently, swish activation used sigmoid function with hidden layers. Motivated by this, a new activation function is being proposed as (relu (x) + x * sigmoid (x)) to get the significance benefits of relu and sigmoid in a swish like flavour. The proposed function represents the desired statistical characteristics as unboundedness, monotonicity, zero centred and non-vanishing gradient. The experimental outcomes are also quite significant.
Ensemble Based Utterance Level Multimodal opinion Mining Framework Ensemble Ensemble

Ensemble based algorithms are widely used for eradicating the issue of overfitting in classification algorithms and the framework proposed is reducing the issue of overfitting by bootstrapping the random samples and feature samples derived from the YouTube trending videos. The utterance level preprocessing on the video frames are extracted using the label encoder algorithm and the preprocessed data is fed into the ensemble-based regression model for extracting the sentiment features using random sampling and feature sampling. Random forest algorithm is one of the most efficient ensemble algorithms for reducing overfitting and space effective out of bag error elimination prediction model used to construct the proposed opinion mining framework. The YouTube trending videos are extracted with its linguistic information and MFCC is used to interpret the audio signals extracted from the same using APP algorithms. The weighted utterance level fusion of audio and linguistic information is attained with an accuracy of 88.29%. The fusion level of utterances with the image frame set and linguistic information is preprocessed using label encoder and OpenCV commands which attained the accuracy of 72.33% of sentiment polarity obtained. The fusion at the utterance level of linguistic, acoustic and visual modalities achieved the sentiment classification accuracy of 90.06%. The proposed opinion mining framework works with 91% accuracy on sentiment polarity identification from the emotion expressed in the videos fed as input.
Assessing the methodological quality of empirical research on social media influencers

Empirical research on social media influencers provides useful insights about this new phenomenon for legal scholars, legislators and policymakers. Importantly, this research is conducted using different data collection methods and research designs. The goal of the chapter is to enhance drawing proper conclusions from this variety of empirical research as well as understanding its potential but also its limitations. We first introduce types of questions that can be relevant for legal scholars dealing with issues regarding social media influencers. In this respect, examples are provided of existing empirical research addressing some of these questions. Using the examples, we introduce basic criteria such as validity or reliability that are essential for assessing the methodological quality of empirical research. Finally, we discuss data collection methods used in empirical studies on social media influencers, pointing to their strengths and weaknesses regarding validity and reliability, but also highlighting certain ethical and technical aspects that might be crucial for methodological choices.
Learning Robust Feature Representations for Scene Text Detection

Scene text detection based on deep neural networks have progressed substantially over the past years. However, previous state-of-the-art methods may still fall short when dealing with challenging public benchmarks because the performances of algorithm are determined by the robust features extraction and components in network architecture. To address this issue, we will present a network architecture derived from the loss to maximize conditional log-likelihood by optimizing the lower bound with a proper approximate posterior that has shown impressive performance in several generative models. In addition, by extending the layer of latent variables to multiple layers, the network is able to learn robust features on scale with no task-specific regularization or data augmentation. We provide a detailed analysis and show the results on three public benchmark datasets to confirm the efficiency and reliability of the proposed algorithm. In experiments, the proposed algorithm significantly outperforms state-of-the-art methods in terms of both recall and precision. Specifically, it achieves an H-mean of 95.12 and 96.78 on ICDAR 2011 and ICDAR 2013, respectively.
Cuestiones existenciales en combinatoria y teoría de números: el método probabilístico

Probability is an indispensable branch of mathematics when it comes to formulating many physical phenomena and, in general, processes which contain some arbitrariness. A less-known aspect is the power it can achieve when tackling questions of discrete nature.The probabilistic method is a tool based on a neat and promising idea. With the aim of proving the existence of an object $C$ characterised by some property $P$, $C$ is embedded in a probability space and it is proved that the event corresponding to having property $P$ holds with positive probability.The objective of this paper is to set the theoretical background that rests behind this technique and to present some applications in problems with a combinatorial or number-theoretic flavour.
Automatic statistical evaluation of quality of unit selection speech synthesis with different prosody manipulations

Abstract Quality of speech synthesis is a crucial issue in comparison of various text-to-speech (TTS) systems. We proposed a system for automatic evaluation of speech quality by statistical analysis of temporal features (time duration, phrasing, and time structuring of an analysed sentence) together with standard spectral and prosodic features. This system was successfully tested on sentences produced by a unit selection speech synthesizer with a male as well as a female voice using two different approaches to prosody manipulation. Experiments have shown that for correct, sharp, and stable results all three types of speech features (spectral, prosodic, and temporal) are necessary. Furthermore, the number of used statistical parameters has a significant impact on the correctness and precision of the evaluated results. It was also demonstrated that the stability of the whole evaluation process is improved by enlarging the used speech material. Finally, the functionality of the proposed system was verified by comparison of the results with those of the standard listening test.
Gamification for Education of Deep Learning

In this study, we introduced a test platform of gamification, based on Unity-Agents, for education of deep learning. We determined to isolate the internal and external cause of deactivated Korean game-based education in comparison to European and drew requirements for educators, through the opinions of five educators by an open-ended discussion. Using the requirements, we drew design factors for the platform of gamification for education of deep learning. Our design will feature a complete learning based on game play and updated processes of ‘declaration of agents’, ‘process of player brains’, ‘training scenario’, ‘process of heuristic brains’, ‘processes of internal and external brains’, and ‘modifying action, reward, and status’. For the design, the learners could be able to keep up with their abilities and to endorse continued learning. We developed an executable prototype of gamification based on the design. The educators evaluated a learning sample that applied on the prototype and discussed that it was relevant, despite some technical kinks. We appreciated the feedback and our gamification would be modified to allow for their comments.
LibGuides: Guide to Searching: Research Questions

Develop a search strategy using keywords and boolean operators. Dig deeper to learn about different ways to search and locate information.
Iberian Cultures and Augmented Reality: Studies in Elementary School Education and Initial Teacher Training

Augmented reality (AR) allows an educational application that promotes and makes possible a series of interactions in the classroom. In this chapter, we explored the opportunities of integrating augmented reality applications into elementary school education in Spain. The process was centered on the exploration of figures through augmented reality devices—the group work was focused on pre-Roman artworks, trying to identify their characteristics. We also applied this approach to higher education, namely to initial teacher training in Castilla-La Mancha University, Spain. We concluded that our approach brings curiosity and satisfaction to the students, and also enhances their motivation because of its active and collaborative nature.
4 Ways to Fix WhatsApp Web Not Working Problem

WhatsApp Web isn’t a new thing as the company introduced it years ago to widen the possibility of using the platform. Nonetheless, not many use the ...
Processes of Formalization and Musical Pedagogical Education

The article raises the problem of search for new ways to develop musical pedagogical direction in modern conditions of higher education reformation. Based on his own experience and opinions of respected researchers, the author concludes about its crisis. Subsequent changes in higher education against the background of informatization and globalization can regarded as a series of formal procedures that regulate the relations of participants in the educational process, that is, as a process of formalization. The author considers the concept of “formalization”, shows the options for its use in modern science and practice, special attention is paid to issues of formalization in the field of humanitarian knowledge. Formalization as a way of knowing the surrounding reality and as mechanisms of perception and archiving social information has always existed. A striking example of this is the emergence and development of the art of music. Formalization occupies a special place in the culture of modern society, as it serves as a mechanism for limiting high information pressure. The general computerization observed today in society, as well as the tendency to create algorithms and technologies in all areas of knowledge, inevitably affect the organization system of the learning process in higher education institutions. As a result – the emergence of some formalized pedagogical systems, such as score-rating system of evaluation of the results of training of students. The musical pedagogical direction, which differs from all other areas of higher education primarily its focus on musical art, is most difficult to follow new trends. However, according to the author, it is precisely the understanding of what is happening, as well as a look at formalization from a position of creation that will help to find new opportunities for the development of musical and pedagogical education.
A General Deep Saliency Enhancement Framework

Identifying more accurate boundaries of salient objects has attracted broad attention in the field of saliency detection. To solve this problem, most of the previous works usually choose to train an extra edge detection branch or take a complicated loss to guide the training process. Different from these methods, this paper presents a general deep saliency enhancement framework which takes an unsupervised method to append the salient edge information. Firstly, trained deep-learning based saliency detection method works as top-down process, which is in charge of detecting main parts of salient objects. The obtained saliency region is further used as a rough mask to guide the following process. Then an unsupervised saliency edge detection method based on artificial characteristics works as bottom-up process, which is in charge of saliency map refinement. The bottom-up mechanism is used for saliency boundary estimation, while human visual mechanism is simulated to detect distinguishable saliency. After that, the rough saliency masks and saliency boundary detection results are fused by deep fusion method to output the final saliency maps. Experiments show that, not only this framework could achieve state-of-the-art performance on target detection, but also give more useful detailed inner information of salient objects.
STUDY OF DIFFERENT FPGA SYSTEMS IMPLEMENTED AES ALGORITHM: A REVIEW

With the rapid growth of multimedia technologies cryptography is considered as one of the most secured way for transmitting and receiving data. Now a days AES algorithm is not only limited to transmit video data but also for image and text transmission. In this paper we have reviewed four FPGA boards on which AES algorithm has been implemented.