Reviewerly/exHarmony huggingface dataset:
Make Your NonProfit a Welcoming Environment

Nonprofit Communications ReportVolume 20, Issue 12 p. 2-2 Articles Make Your NonProfit a Welcoming Environment First published: 03 November 2022 https://doi.org/10.1002/npcr.32065Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume20, Issue12December 2022Pages 2-2 RelatedInformation
Ways to Make Your LinkedIn Profile Shine

Nonprofit Communications ReportVolume 20, Issue 12 p. 1-1 Enhance Your Professional Profile Ways to Make Your LinkedIn Profile Shine Kim Pawlak, Kim PawlakSearch for more papers by this author Kim Pawlak, Kim PawlakSearch for more papers by this author First published: 03 November 2022 https://doi.org/10.1002/npcr.32064Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume20, Issue12December 2022Pages 1-1 RelatedInformation
A secure data fitting scheme based on CKKS homomorphic encryption for medical IoT

With the development of big data technology, medical data has become increasingly important. It not only contains personal privacy information, but also involves medical security issues. This paper proposes a secure data fitting scheme based on CKKS (Cheon-Kim-Kim-Song) homomorphic encryption algorithm for medical IoT. The scheme encrypts the KGGLE-HDP (Heart Disease Prediction) dataset through CKKS homomorphic encryption, calculates the data’s weight and deviation. By using the gradient descent method, it calculates the weight and bias of the data. The experimental results show that under the KAGGLE-HDP dataset,we select the threshold value is 0.7 and the parameter setting is (Poly_modulus_degree, Coeff_mod_bit_sizes, Scale) = (16384; 43, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 43; 23), the number of iteration is 3 and the recognition accuracy of this scheme can achieve 96.7%. The scheme shows that it has a high recognition accuracy and better privacy protection than other data fitting schemes.
A Study on Storytelling Education Using Legend

This paper studies about how student who have completed storytelling education plans storytelling content in the ‘9th Storytelling Contest’ using the legend of Gasaebawi(가새바위). First, student can use the original narrative as it is, or reconstruct the character/plot/background through modernization or adding new elements and changing the composition. And the theme of the legend of Gasaebawi(가새바위) is focused on a specific theme. These narrative and theme modulation patterns are due to the storytelling curriculum focused on genres and media. It was influenced by the genre trying to reconstruct in the process of storytelling. Therefore, this paper argues for the necessity of designing a storytelling curriculum that can revive the value of the original story.
The Journal of Korean Drama and Theatre and Television Drama: Focusing on Quotation of Lines/Scenes in Research on Television Drama

The Journal of Korean Drama and Theatre published by the Learned Society of Korean Drama and Theatre is the most representative journal for research on Korean television dramas as a kind of drama act. The number of theses that have been gathered by a total of 75 journal series is as many as 615, which have been issued until 2020, since 1991 when it was launched. Of them, the theses on television dramas began to be initially published by the 21th series issued in 2005, and then 76 theses investigated by 35 researchers are currently published. The scenes beyond/post sheet, which appear in television dramas as image literature, become main texts, due to the genre characteristics. Lines and scenes in screens are naturally mobilized for any quotations. The current research on television dramas, however, do not give any academic guidelines regarding the quotation of lines and scenes. This paper, therefore, attempts to provide a direction of examining the current conditions of the lines and scenes quoted by research on television dramas based on 76 theses gathered in The Journal of Korean Drama and Theatre and helping both the society and the journal improve them. The measure may be largely specified as five proposals. The key point of them lies in the essence of the quotation properly certified by research, in other words, the practice of it, which make it possible to realize scholarly conversations with academic value.
Signal Detection in MIMO Communications System with Non-Gaussian Noises based on Deep Learning and Maximum Correntropy Criterion

In this paper, we study signal detection in multi-input-multi output (MIMO) communications system with non-Gaussian noises such as Middleton Class A noise, Gaussian mixtures and alpha stable distributions, using several deep neural network-based detector models such as FULLYCONNECTED and DETNET detector. By applying information theoretic criterion of Maximum Correntropy , SVD analysis on the channel matrix and reducing network complexity, the suggested deep neural network detector performs well in environments with non-Gaussian noises and, compared to the deep neural network-based detector with MSE loss function, achieves better performance.
Types of Solutions in the Didactic Game “Logical Monsters”

The article presents the results of the approbation of the game “Logic Monsters”. The game is aimed at developing logic thinking and is applicable within various educational subjects. Several different implementations of the game’s basic idea have been proposed. Modifications encompass technological performance and cognitive goals directed at both directions of the task. The study covered a core group of students from four countries, aged 14 – 18. A survey, discussion and observation were conducted with them. Additionally, results from a group of teachers and a group of university students with whom discussions and observations were conducted are also presented. Conclusions are obtained and a classification of the types of “solutions” in the different game modifications is presented. A connection is made between the thinking style and the proposed solutions. Several authentic solutions are presented.
Projects Volunteers Can Do Virtually

The Volunteer Management ReportVolume 27, Issue 12 p. 5-5 Articles Projects Volunteers Can Do Virtually First published: 09 November 2022 https://doi.org/10.1002/vmr.31985Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume27, Issue12December 2022Pages 5-5 RelatedInformation
Infrastructure-Level Design: Serverless and Decentralized Machine Learning

This chapter presents the research topics in serverless machine learning. The model training procedure of Machine Learning (ML) usually consumes large amounts of time and cost, and need to be improved. Following the footsteps of conventional ML architecture, the emerging serverless computing paradigm brings new opportunities for ML to mitigate running time and operational overheads. Moreover, the ML developers then only need to define and manage the stateless functions, rather than the dedicated servers or middleboxes. Hence, serverless machine learning is envisioned by global researchers as a promising trend of ML techniques and applications.
Introduction

Modern machine learning applications are often deployed in the cloud environment to exploit the computational power of clusters. However, this in-cloud computing scheme cannot satisfy the demands of emerging edge intelligence scenarios, including providing personalized models, protecting user privacy, adapting to real-time tasks, and saving resource cost. To conquer the limitations of conventional in-cloud computing, it comes the rise of tiny machine learning (TinyML), which makes the end-to-end ML procedure totally on user devices, without unnecessary involvement of the cloud. Despite the promising advantages of TinyML, implementing a high-performance learning system still faces with many severe challenges, such as insufficient user training data, backward propagation blocking and limited peak processing speed. Observing the substantial improvement space in the implementation and acceleration of TinyML systems, it is necessary to present a comprehensive analysis of the latest research progress and point out potential optimization directions from the system perspective. This book will present a software and hardware synergy of TinyML techniques, covering the scope of model-level neural network design, algorithm-level training optimization and hardware-level instruction acceleration. These techniques could bring fruitful discussions and inspire the researchers to further promote the field of edge intelligence.
QUARANTEACHING AT A TRANSCARPATHIAN HIGHER EDUCATIONAL ESTABLISHMENT: STUDENT VIEWS

Distance learning (DL) has not been a common practice in Ukraine before the COVID-19 lockdown in March, 2020, so it meant real challenges for participants of education. This new reality triggered us to get insights into our students’ attitudes to DL. An online questionnaire was designed that was completed by 95 English, Hungarian and Ukrainian major BA and MA college students concerning the issue of DL. Results seem to prove that it has both positive and negative effects. From the empirical evidence it derives that introverted students can be winners of online education and they can continue their education in digital mode in the future with ease.&#x0D; Keywords: COVID-19; quarantine periods; quaranteaching; distance learning (DL); emergency remote teaching (ERT); higher education; student perspectives on DL; student attitudes to DL; Transcarpathia.
Multiple Target Tracking and Filtering Using Bayesian Diabatic Quantum Annealing

In this paper, we present a hybrid quantum/classical algorithm to solve an NP-hard combinatorial problem called the multiple target data association (MTDA) and tracking problem. We use diabatic quantum annealing (DQA) to enumerate the low energy, or high probability, feasible assignments, and we use a classical computer to find the Bayesian expected mean track estimate by summing over these assignments. We demonstrate our hybrid quantum/classical approach on a simple example. This may be the first demonstration of a Bayesian hybrid quantum-classical multiple target tracking filter. We contrast our DQA method with the adiabatic quantum computing (AQC) approach to MTDA. We give a theoretical overview of DQA and characterize some of the technical limitations of using quantum annealers in this novel diabatic modality.
A Privacy Protection based Countermeasure for E-commerce Price Discrimination

Price discrimination is a pricing strategy of differentiated prices for different customers usually employed by the provider of goods or services to obtain excess profits. Its essence is a monopolistic pricing behavior. In this paper, we propose a price discrimination countermeasure based on the Randomized Response (RR) mechanism of Local Differential Privacy (LDP). First, we select shopping records of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> customers from the database of the e-commerce platform as a large set P, and then add noise to randomly selected shopping records of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> customers. Due to the randomness, the merchant cannot deduce the shopping record of the customer from the randomized output, thereby protecting the shopping information of the customer. In addition, we use data desensitization technology to project the sensitive personal information of customers. The scheme evaluating results show that the scheme can help customers fight against the price discrimination well.
Spatio-Temporal Information for Action Recognition in Thermal Video Using Deep Learning Model

Researchers can evaluate numerous information to ensure automated monitoring due to the widespread use of surveillance cameras in smart cities. For the monitoring of violence or abnormal behaviors in smart cities, schools, hospitals, residences, and other observational domains, an enhanced safety and security system is required to prevent any injuries that might result in ecological, economic and social losses. Automatic detection for prompt actions is vital and may help the respective departments effectively. Based on thermal imaging, several researchers have concentrated on object detection, tracking, and action identification. Few studies have simultaneously extracted spatial-temporal information from a thermal image and utilized it to recognize human actions. This research provides a novelty based on frame-level and spatial and temporal features which combines richer context temporal information to address the issue of poor efficiency and less accuracy in detecting abnormal/violent behavior in thermal monitoring devices. The model can locate (bounded box) video frame areas involving different human activities and recognize (classify) the actions. The dataset on human behavior includes videos captured with infrared cameras in both indoor and outdoor environments. The experimental results using the publicly available benchmark datasets reveal the proposed model's efficiency. Our model achieves 98.5% and 94.85% accuracy on IITR Infrared Action Recognition (IITR-IAR) and Thermal Simulated Fall (TSF) datasets, respectively. In addition, the proposed method may be evaluated in more realistic conditions, such as zooming in and out etc.
Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance.
Multispectral Target Tracking with Robust Correlation and Optimal Position Prediction

Object tracking using only visible-light sensor is usually problematic when facing some challenging scenes related to a complex environment like darkness, strong light, rain, fog, etc. In this paper, we have taken images, both thermal and visible light spectrum, from RGBT234 dataset as inputs for our proposed tracker which works as follows: First, the single shot multibox detector (SSD) is proposed to produce automatically the initial position of the target for visible and infrared modes. Second, a switching fuzzy logic controller (FLC) has been proposed to ensure automatic selection and switching between tracking modes (visible-light or thermal infrared spectrum) when a new environment is detected using confidence measures on some relevant features of the frames for each mode. Finally, to improve the kernelized correlation filter as a base tracker and overcome the limitation of the multimodal visual tracking, especially during tracking mode switching when visual information is not available, robust optimal smooth variable structure filter (SVSF) is proposed. Experiments on the recently public benchmark RGB-T234 demonstrate the effectiveness of our proposed method when compared to other state-of-the-art trackers.
Detection of d <sub>1</sub> ⨂ d <sub>2</sub> dimensional bipartite entangled state: a graph theoretical approach

Abstract Braunstein et al have started the study of entanglement properties of the quantum states through graph theoretical approach. Their idea was to start from a simple unweighted graph G and then they have defined the quantum state from the Laplacian of the graph G . A lot of research had already been done using the similar idea. We ask here the opposite one i.e can we generate a graph from the density matrix? To investigate this question, we have constructed a unital map ϕ such that ϕ ( ρ ) = L ρ + ρ , where the quantum state is described by the density operator ρ . The entries of L ρ depends on the entries of the quantum state ρ and the entries are taken in such a way that L ρ satisfies all the properties of the Laplacian. This make possible to design a simple connected weighted graph from the Laplacian L ρ . We show that the constructed unital map ϕ characterize the quantum state with respect to its purity by showing that if the determinant of the matrix ϕ ( ρ ) − I is positive then the quantum state ρ represent a mixed state. Moreover, we study the positive partial transpose (PPT) criterion in terms of the spectrum of the density matrix under investigation and the spectrum of the Laplacian associated with the given density matrix. Furthermore, we derive the inequality between the minimum eigenvalue of the density matrix and the weight of the edges of the connected subgraph of a simple weighted graph to detect the entanglement of d 1 ⨂ d 2 dimensional bipartite quantum states. Lastly, we have illustrated our results with few examples.
Trends in the development of higher education: Analysis of problems in the context of strengthening national sovereignty

Aim. The presented study aims to analyze problems in the development of higher education in the context of strengthening the national sovereignty of the Russian Federation. Tasks. The authors consider the specifics of modern education; substantiate the need for the digital transformation of the educational process and the creation of a comprehensive digital electronic educational platform management system at universities; prove that the educational process should be student-centered, with the teachers constantly increasing their professional competence; analyze the current state of methodological support for the educational process; highlight the trend towards network interaction and universities’ participation in federal programs. Methods. This study uses general scientific methods of cognition, including analysis and synthesis, and the systems approach to identify major trends in the development of higher education. Results. One specific aspect of modern education is the changing relationship between the value of information and its accessibility. The activity of teachers at higher educaiton institutions requires constant self-improvement, i.e. improving professional competence and developing professionally significant qualities in accordance with modern requirements and a personal development program. An obvious trend in the development of universities engaged in vocational training is the expansion of their network interaction: universities join consortia of scientific and educational centers. Nowadays, a special role should be assigned to the university’s humanities courses, which have a unique set of functions that make it possible to efficiently respond to the challenges of the time. Conclusions. In fact, higher education institutions that train highly qualified professionals serve as drivers of technological breakthrough, strengthening the technological sovereignty and economic security of our country. They directly affect the future of the state and the stabilization of its national sovereignty. It is concluded that teachers should currently focus on helping students find the necessary information, analyze it, adequately evaluate it, form their own opinion and understanding. It is important to make complex changes in the content of teaching materials and in their usage context.
The Effectiveness of Path-Segmentation for Modeling Lasso Times in Width-Varying Paths

Models of lassoing time to select multiple square icons exist, but realistic lasso tasks also typically involve encircling non-rectangular objects. Thus, it is unclear if we can apply existing models to such conditions where, e.g., the width of the path that users want to steer through changes dynamically or step-wise. In this work, we conducted two experiments where the objects were non-rectangular, with path widths that narrowed or widened, smoothly or step-wise. The results showed that the baseline models for pen-steering movements (the steering and crossing law models) fitted the timing data well, but also that segmenting width-changing areas led to significant improvements. Our work enables the modeling of novel UIs requiring continuous strokes, e.g., for grouping icons.
Process Authentication through Blockchain: Three Case Studies

In this work, we elaborate on the concept of process authenticity, which intuitively corresponds to the validity of all process steps and their proper binding. It represents the most exciting forefront of distributed ledger technology research concerning the primary challenge of reliably connecting distributed ledger networks to the physical context it must operate. More in detail, the paper describes a novel methodological approach to ensure the authenticity of business processes through blockchain and several security mechanisms applied to the digital twins of the actual processes. We illustrate difficulties and opportunities deriving from implementing process authenticity in concrete case studies in which we were involved as software designers belonging to three critical application domains: document dematerialization, e-voting, and healthcare.