Reviewerly/exHarmony huggingface dataset:
BiStar: A Template-Based iStar Modeling Tool Combining Graphical and Textual Modeling

iStar modeling is an effective measure for requirements analysis, and researchers have proposed nearly thirty different modeling tools for this purpose. There are two types of existing modeling tools, i.e., graphical and textual. However, either type has its limitations. Graphical modeling tools suffer from the scalability issue. Textual tools rely on visual models when modeling. To overcome the limitations, we have developed the BiStar, a template-based iStar modeling tool combining graphical and textual modeling. The two main features of our BiStar tool are as follows. First, BiStar adopts textual templates to support the batch addition of four common iStar model elements, including Actors, Intentions, Dependencies, and Refinements. Second, BiStar supports the templated creation of iStar models using text models. In addition, BiStar has full support for graphical iStar modeling as a basic feature of a modeling tool. BiStar automatically visualizes the model elements and templates added in the text as described above. After visualization, modelers can freely choose between graphical and textual modeling to continue modeling. Our BiStar tool thus combines graphical modeling with textual modeling.
DESIGNING LESSONS USING ELECTRONIC LEARNING SYSTEMS: DIGITAL COMPETENCIES OF A VOCATIONAL EDUCATION TEACHER

The article examines the features of designing a modern lesson with the use of distance learning systems and compares the traditional form of a lesson and the modern one based on the Federal State Educational Standard. The article presents the competencies of secondary vocational education teachers, necessary for the training of future specialists in digital society.
Impact of Modern Education in The Light of Sanskrit Language Through CLT For Reading And Writing Skill

Milan MajiResearch Scholar, Dept. of Education National Sanskrit University, Tirupati, A.P. DOI: https://doi.org/10.52756/ieldps.2022.e01.004 Keywords: Sanskrit Learning, CLT, Reading and writing skill, Modern Education. Abstract:The usefulness and relevance of Sanskrit as the most excellent language in this scientific age are well known. If there is no language, it cannot be practical in the world. Language is … Impact of Modern Education in The Light of Sanskrit Language Through CLT For Reading And Writing Skill Read More »
AN EFFICIENT REVERSIBLE DATA HIDING BASED ON IMPROVED PIXEL VALUE ORDERING METHOD

Pixel value ordering (PVO) has been considered as an effective reversible data hiding method for high embedding capacity and good imperceptibility. In this paper, we propose a novel reversible data hiding based on four sorted pixels. This paper proposes a new reversible hiding scheme based on the arrangement of 4 pixels. Three bits will be embedded into each four pixels sub block without changing the order while the original PVO method only embeds 2 bits. In case the amount of payload is less than the embedding capacity, flatter blocks will be prioritized for embedding to improve image quality. To determine the flat of block, we use 12 neighborhood pixels of current block. Only blocks with satisfactory flatness are used for embedding. The proposed reversible data hiding not only gains high capacity but also gets good imperceptibility. Experimental results also show that the proposed reversible data hiding scheme outperforms several widely schemes using pixel value ordering method in terms of both image quality and embedding capacity.
Unsupervised Face Frontalization GAN Driven by 3D Rotation and Symmetric Filling

Face frontalization has been partially solved by deep learning methods, such as Generative Adversarial Networks (GANs). However, due to the lack of paired training datasets, current generative models are limited to specific poses. Similarly, current unsupervised frameworks do not utilize properties of human faces, which burdens the neural network training. To improve and overcome current challenges, we design a novel self-supervised method that takes full advantage of human face modeling and facial properties. With our proposed method, single-view images collected in the wild can be utilized in training and testing. Also, the synthesized images are robust to input faces with large variations. We utilize the symmetric properties of human face to texture unseen parts in a human face model. Then, a GAN is used to fix undesired artifacts. Experiments show that our method outperforms many existing methods.
A critical appraisal of the World Bank's structural adjustment programs and poverty reduction strategy papers in Africa

The official aims of the World Bank development programs - SAPs and PRSPs - in Africa and other developing countries are to promote local productivities, advance economic choices, improve living standards, maximise economic competencies, reduce government expenditures, and realise economic development. Based on these promising development policies, many African countries adopted those programs in the early 1980s as a panacea to their worst political and economic problems. Accordingly, this study critically examines the effectiveness of the World Bank's development programs - SAPs and PRSPs - in contemporary Africa. Moreover, the study rationally appraises the multi-dimensional consequences of SAPs and PRSPs in Africa. To this end, the study finds that these programs of the World Bank have brought massive socio-political and economic crises to contemporary Africa. Hence, rethinking the World Bank's development programs should be the prior duty of African states to escape from the existing chaos in almost every sector.
Neural Network Method for Detecting Textural Anomalies in a Digital Image

Modern computer vision systems often use neural networks to process images. But to use neural networks, you need to create databases to train these neural networks. In some cases, creating a training database takes the vast majority of the project's financial and human resources. Therefore, the actual task of finding methods to improve the quality of learning neural networks on small data is considered in this article. The ability to process data, which nature was not present in the original training database is relevant, also. To solve the problem of improving the quality of image segmentation by textural anomalies, this research is proposed to use as input to the neural network not only the image but also its local statistic data. It can increase the information content of the input information for the neural network. Therefore, neural networks do not need to learn to choose statistical features but simply use them. This investigation classifies the requirements for image segmentation systems to indicate atypical texture anomalies. The literature analysis revealed various methods and algorithms for solving such problems. As a result, in this science work, the process of finding features in the photo is summarized in stages. The division into stages of search for features allowed to choose arguments for methods and algorithms that can perform the task. At each stage, requirements were formed for methods, that allowed separate the transformation of image fragments into a vector of features by using an artificial neural network (trained on a separate image of the autoencoder). Statistical features supplement by the vector of features of the image fragment. Numerous experiments have shown that the generated feature vectors improve the classification result for an artificial Kohonen neural network, which is able to detect atypical image fragments.
Performance-Improved AdaBoost with Parameterized and Simple-Operation Genetic Algorithm

In this paper, we propose a design methodology for the AdaBoost with parameterized genetic algorithm (PGA) to improve the accuracy performance. It can provide 3 parameters to arbitrarily adjust. In addition, we develop the low-computing operations for crossover and mutation tasks, the essential processing in GA. In the performance analysis, our 6classifier AdaBoost with PGA method outperforms 10-classifier AdaBoost in terms of accuracy. While the number of classifiers survived is 2, 3, and 4, the according accuracy has a maximum increase of 5.74%, 7.65%, and 8.81%, respectively. Its benefits include a smaller number of classifiers required and accuracy increase.
Factors that Affect Elementary English Education in China in the 21st Century

With the development of globalization in the 21st century, English has gradually become one of the main tools for communication between countries and people in the world. With the increasing use and importance of the language, English education is becoming more necessary and experiencing development in China. Since it is believed that younger children have a stronger ability to learn new languages, English instruction in primary schools, is essential for students to become fluent in this language. However, in the past decade, primary school English instruction has been facing some difficulties, including the imbalance of English education quality in different regions caused by economic development, the barrier of learning English pronunciation brought by dialects in different parts of China, and the negative impact of excessive after-school tutoring institutions on the quality of English teaching in schools. These challenges hinder the development of English education.
The Efficacy of Using Digital Comics in Reading Class to Senior High School Students

This research aims at finding out the efficacy of using Digital Comics in teaching reading to senior high school students. The main question of this research: Is the use of Digital Comics effective in teaching reading to senior high school students? The research can be an addition to the persisting teaching reading of English particularly the use of the Digital Comics. This research was conducted at the tenth-grade students at SMA Kristen Mercusuar Kupang, an Indonesian senior High School. The total number of samples was 25 students. The research used a pre-experimental method with a pre-test and post-test design. The pre-test was given to find out the basic ability of the students in reading and the post-test given to find out the students’ improvement in reading after giving the treatment by using Digital Comics as media. The findings show that using the digital comics as media is effective in teaching reading to senior high school students. It is supported by the result of the significance test through SPSS 20 program that the P was 0.00. Therefore, it is recommended to use the digital comics in developing students’ competence in reading class of senior high school students.
Leveraging Distributional Bias For Reactive Collision Avoidance under Uncertainty: A Kernel Embedding Approach

Many commodity sensors that measure the robot and dynamic obstacle’s state have non-Gaussian noise characteristics. Yet, many current approaches treat the underlying uncertainty in motion and perception as Gaussian, primarily to ensure computational tractability. On the other hand, existing planners working with non-Gaussian uncertainty do not shed light on leveraging distributional characteristics of motion and perception noise, such as bias for efficient collision avoidance.This paper fills this gap by interpreting reactive collision avoidance as a distribution matching problem between the collision constraint violations and Dirac Delta distribution. To ensure fast reactivity in the planner, we embed each distribution in Reproducing Kernel Hilbert Space and reformulate the distribution matching as minimizing the Maximum Mean Discrepancy (MMD) between the two distributions. We show that evaluating the MMD for a given control input boils down to just matrix-matrix products. We leverage this insight to develop a simple control sampling approach for reactive collision avoidance with dynamic and uncertain obstacles.We advance the state-of-the-art in two respects. First, we conduct an extensive empirical study to show that our planner can infer distributional bias from sample-level information. Consequently, it uses this insight to guide the robot to good homotopy. We also highlight how a Gaussian approximation of the underlying uncertainty can lose the bias estimate and guide the robot to unfavorable states with a high collision probability. Second, we show tangible comparative advantages of the proposed distribution matching approach for collision avoidance with previous non-parametric and Gaussian approximated methods of reactive collision avoidance.
LEVERAGING CONFIDENTIAL COMPUTING TO ENABLE SECURE INFORMATION SHARING

The emergence of the RISC-V Instruction Set Architecture incentivizes the critical infrastructure protection community to consider the use of emerging open-source security mechanisms to facilitate secure information sharing. An exemplar is Keystone, a Confidential Computing Consortium project, that offers an accessible open-source framework for building trustworthy secure hardware enclaves based on the RISC-V Instruction Set Architecture. This chapter describes an attempt at extending Keystone to the HiFive Unmatched development platform and proposes enclave application development to effectively and affordably supplement deployed supervisory control and data acquisition devices with secure information sharing capabilities. Since the implementation of confidential computing principles axiomatically degrades real-time performance, the performance of supervisory control and data acquisition devices must be characterized to ensure that the devices enhanced with trusted execution environments meet operational requirements while supporting critical infrastructure operations with secure information sharing capabilities.
Application of Fuzzy Matter Element Model Based on Entropy Weight and GBDT Algorithm for Financial Fraud Behavior Identification

This study mainly focuses on the application of integrated entropy weight with GBDT algorithm on financial fraud identification. Based on fuzzy matter element analysis, this study used integrated entropy weight with GBDT algorithm to identify financial fraud behavior. Taking listed company as the research object, companies were classified as the fraud group and the non-fraud group according to the information disclosure evaluation results by Shenzhen Stock Exchange. The fuzzy matter element based on entropy weight was then used for further factor screening, and the fraud identification model was constructed based on GBDT algorithm. The overall recognition rate was 84.4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ; the identification rate of fraud group was 89.7%; and that of the non-fraud group was 83%. The inter-predictability and the accuracy of detection rate were significantly improved, and the results provided an efficient model to comprehend fraud behavior in financial markets.
Effect analysis of SPOC hybrid teaching based on deep learning in graphic creative course teaching

As a local applied undergraduate institution, the SPOC blended teaching approach needs to be carried out with the help of deep learning in classroom teaching.Through teaching preparation, knowledge construction, deepening migration, and teaching evaluation for design, combined with teaching practice research, we carry out learning in terms of online and offline teaching methods and learning practicality, the relevance of learning content, video-based learning, and knowledge systematization, respectively.Through the regression analysis, we found that for the graphic creativity course with strong practicality, we need to make full use of the respective advantages of online and offline to cultivate students' practical ability, innovation consciousness, innovation spirit, and other comprehensive abilities, and finally complete the purpose of systematic learning for students.
Knowledge of medical students about neuropathologist role

Background: Neuropathology involves pathological analysis of brain tumors and involvement of molecular mechanisms and requires analysis of substrate in terms of target therapeutic perspective. This study aimed to assess the knowledge of medical students related to the role of neuropathologists in healthcare services. Methods: This study was carried out among 298 medical students enrolled at Umm Al-Qura University, Makkah, Saudi Arabia, from March to June 2021 to observe their knowledge related to the role of neuropathologists in healthcare services. Results: The results demonstrated a significant difference in terms of knowledge related to brain autopsies by neuropathologists among both genders, as 112 (37.5%) female students were aware of details in comparison to 31 (10.4%) males (p = 0.014). The results related to reading and reporting of nerve biopsies also showed a statistically significant difference in the level of knowledge and awareness between female 166 (55.7%) and male 47 (15.7%) participants (p = 0.009). Considering neuropathology as a future career choice, the majority of female participants 99 (33.2%) agreed to opt for it in comparison to 24 (8%) male students (p = 0.025). Conclusion: The introduction of simulation and didactic methodology training programs addressing the significance of the neuropathologist&amp;apos;s role and basics of neuropathology in the medical universities&amp;apos; curricula could improve medical students&amp;apos; concept of neurology and proficiency to raise interest in the field of neurology.
Large Covariance Estimation by Bootstrapped Hierarchies and Shrinkage

This paper introduces a large-dimensional covariance estimator that exploits the hierarchical structure in financial returns. Prevailing techniques that filter the noise in a covariance matrix according to hierarchical agglomeration are fragile to data perturbations and inordinately suppress important cross-sectional information. By contrast, large-dimensional rotation-equivariant estimators ignore the orientational content shaped by the intrinsic group organization of financial returns. We integrate both modeling paradigms in order to yield an improved estimator that performs on real large-scale financial data.
2D Platformer Tutorial

For this chapter, you will now be shown how you can create the basics of a 2D platformer within Unity which can be used as the foundations for a future project or your first game. To set this project up, you will need to follow a similar process to the 3D first-person project from earlier in this book.
The Impact of Self-Efficacy, Mobility, and Benefits Awareness on adoption of mobile banking applications in Jordan

In addition to the pressure brought on by the rapid development of computer technology, the world faces a pandemic caused by covid-19.Digital applications and technologies, such as mobile banking applications, will help Jordan grow its usage of technology.In Jordan, mobile banking application adoption, digital laws and regulations, and implementations are still in their infancy.The fundamental objective of this study is to build a theoretical framework to analyze in depth the primary factors that may influence the adoption of mobile banking applications in Jordan.Self-Efficacy, Mobility, and Benefits Awareness were extracted from prior literatures that focused on identifying the impact of these variables on boosting perceived usefulness and intention to utilize mobile banking services.At the conclusion of the paper, the suggested research model is illustrated for future testing.
IMPLEMENTING CONTINUOUS CODE QUALITY FOR CODE QUALITY DEVELOPMENT IN THE SCRUM TEAM

There are challenges and drawbacks that will be faced in implementing a Scrum method. One of the problems is in Code Quality because the team only has a short time in each sprint. This causes the team to not be able to conduct a thorough code review, resulting in the team having to increase their workload and pressure. Code review is necessary to identify defects in the code before it goes into the core of the project. But the cumbersome, time-consuming and labor-intensive nature of code review causes a barrier in adopting code review practices properly. To simplify and speed up code review, the researcher will try to implement Continuous Code Quality (CCQ) with the Scrum process by conducting empirical experiments. By examining the effect on the speed of the process and knowing the point of view of the development team. Based on the experiments conducted, the CCQ automation system helps the development team in speeding up the code review process and maintaining good code quality. The biggest influence on the speed of the process remains in the complexity of the system built in a running sprint. Even with an automated code review system, a manual code review process is still needed to ensure there are no errors that cannot be detected by the automated system.
Blockchain Technology Adoption in Canadian Pharmaceutical Sectors: An empirical analysis for a future outlook

There are many calls in the literature to investigate the Blockchain technology adoption (BCT) in Canadian Organizations and its impact on boosting enterprises' competitive advantages.Although the literature requires more research cases, it is more timely and relevant that the analysis be done as early as today.Various empirical supports for Technology Acceptance Model (TAM) are available depending on situation specifics.TAM remains a widespread and convenient theoretical framework for examination of aspects contributing to technology acceptance.This study aims to find the driving forces that effectively illustrate the blockchain technology adoption in Canadian Pharmaceutical Organizations and to be able to face the challenges associated with the process of adoption.This study examined BCT application using contacts from Canadian Companies Capabilities directory (CCC) and applied SEM regression using AMOS software with 750 respondents from pharmaceutical businesses using TAM framework.Path analysis results were good: chi2 (4918.592),chi2 / DF (5.513), RMSEA (0.049), CFI (0.753), and TLI (0.804).Perceived ease of use, Perceived Usefulness, attitude towards use, and intention to use predicted BCT utilization, yet two relationships (i.e., PEOU->PU and PU->IU) were rejected in the tested model as they show negative conformity results.All components explain more than 50% of variation, hence presenting a reasonable fit between the data examined and the research model.These findings will help in understanding of pharmaceutical organizations' adoption of BCT for researchers, regulators and developers and providing supported evidence on factors contributing to the adoption of BCT in Canadian Organizations.
Creators of the New in Education

It is impossible to predict the future of education. It can be jointly created based on the emerging humanitarian, communication technologies and other achievements of modern civilization. 440 years after the great Jan Comenius, we are taking the next step by creating a new science of learning: digital mathetics and digital didactics. The authors of textbook with the connections to digital means aimed at mastering human activity and university professors start from the experience of their childhood are based on the achievements of mankind, realize and form the possibilities and needs of the future in the present. Science gets the opportunity to study the accelerating evolution of education and analyze big data displayed in a variety of digital platforms. The global educational community is becoming a source of experience for critical decisions and direct pedagogical interaction. Geographical and language barriers are easing. Leaders of society, state and business get the opportunity to realize their vision in a specific project and digital technologies open access to participate in the project to everyone who supports the humanistic idea in our transforming world.