Reviewerly/exHarmony huggingface dataset:
Peningkatan Keterampilan Menulis dengan Media Gambar Terhadap Siswa Kelas I Sekolah Dasar

This study aims to provide a depiction of the ptodrd improvement of paragraph writing skills of grade 1 students at SD Negeri 3 Kemranggon, Susukan Subdistrict, Banjarnegara Regency through learning media in the form of images or photos. The method applied is field research that uses case studies, in which students or students will be observed the development or improvement of their writing skills before and after carrying out learning using image media. Data collection techniques conducted in the form of field observations using instruments that have been created with the aim of observing the learning process that occurs in the classroom using learning media in the form of images or photos. The results of this study showed the existence of power levels with an average value of 56.87% at the beginning increased to 66.43% in the first cycle, and in the second cycle increased again to 80.17%, and the value obtained has exceeded the complete value limit of the average value above 65 (KKM)
LibGuides: LIS 511 (2019 Spring): Julius Caesar: Databases

A guide for resources about the Roman military general, dictator, politician and leader Gaius Julius Caesar
Best CPU For VR Gaming

CPU means Central Processing Unit. It is also called the central processor, main processor, or processor. It is the principal part of a computer and it ...
The Efficiency of Public Spending in Basic Education in School Districts in Ecuador

This paper assesses the efficiency of spending in basic education among school districts in Ecuador using Data Envelopment Analysis (DEA). The analysis presented here uses administrative data on educational inputs and per-pupil spending to quantify how much school districts in Ecuador can generate, with these inputs, education outputs; notably, student learning and progression rates. Results indicate that school districts in Ecuador are quite efficient at maintaining children in the education system, but not so much at ensuring their learning. Indeed, results indicate that school districts in Ecuador could improve their capacity to assure student learning (as proxied by results from their national learning assessment) by 10 to 15 percent without investing additional resources. These results are particularly relevant since fiscal savings in Ecuador started to deplete quickly since 2015, making it hard for the country to sustain its levels of social expenditure.
External Authentication, Part 1

In this chapter, I explain how external authentication works. This is a complex process, even by the high standard set by other ASP.NET Core Identity features, and there is a lot of detail to take in. The examples in this chapter use a simulated third-party service, which allows me to explain the interactions in a controlled way. In Chapter 23, I add support for working with real services from Google and Facebook. Table 22-1 puts external authentication in context.
Understanding Pixel-level 2D Image Semantics with 3D Keypoint Knowledge Engine

Pixel-level 2D object semantic understanding is an important topic in computer vision and could help machine deeply understand objects (e.g., functionality and affordance) in our daily life. However, most previous methods directly train on correspondences in 2D images, which is end-to-end but loses plenty of information in 3D spaces. In this paper, we propose a new method on predicting image corresponding semantics in 3D domain and then projecting them back onto 2D images to achieve pixel-level understanding. In order to obtain reliable 3D semantic labels that are absent in current image datasets, we build a large scale keypoint knowledge engine called KeypointNet, which contains 103,450 keypoints and 8,234 3D models from 16 object categories. Our method leverages the advantages in 3D vision and can explicitly reason about objects self-occlusion and visibility. We show that our method gives comparative and even superior results on standard semantic benchmarks.
UMP and Hebei University, a decade together

It has been a decade for Universiti Malaysia Pahang (UMP) to collaborate with Hebei Universitiy (HBU) since the establishment of Mandarin Language & Culture Centre (MLCC) in 2011. With the support of the Ministry of Higher Education (MOHE), Confucius Institute Headquarters (Hanban) and HBU, MLCC was upgraded to Confucius Institute in 2018.
LibGuides: SFCC Library Video Tutorials: Technology Tips

On this guide you'll find SFCC Library video tutorials on library services and research help, including how to log into databases, request books, search for articles, and more.
The Impact of Work Stress Sources in the Employees Performance in the Aqaba Special Economic Zone Authority Located in Jordan

The present study aimed to identify the impact of sources of work stress in the performance of employees in the authority of Aqaba Special Economic Zone, in order to achieve this goal, a questionnaire was designed as a measure of the Likert five-year, and distributed to a sample size (320) and female employees working in the Aqaba Special Economic Zone Authority. The researchers used statistical measures, which include frequencies, percentages and multiple regression analysis. The results of the study showed: &#x0D; &#x0D; There is significant effect of the sources of work stress combined in the performance of employees in the authority of Aqaba Special Economic Zone.&#x0D; No significant effect of role ambiguity in the performance of workers in the authority of Aqaba Special Economic Zone.&#x0D; There is significant effect of role conflict in the performance of workers in the authority of Aqaba Special Economic Zone.&#x0D; There is significant effect on the amount of work in the performance of employees in the authority of Aqaba Special Economic Zone.&#x0D; No significant effect of labor relations in the performance of employees in the authority of Aqaba Special Economic Zone.&#x0D; &#x0D; The study suggests a number of recommendations including:&#x0D; &#x0D; Reducing the negative effects of work pressures and by increasing the periods of rest and exercise, social activities and work as one team.&#x0D; Drawing the attention of senior management in the authority of Aqaba Special Economic Zone to adequate attention and sources of work stress and try to mitigate the aim of raising the morale of the human race.&#x0D; Authority to operate Aqaba Special Economic Zone on the appointment of new staff to reduce the staff veterans of the workload so that they can work in appropriate circumstances, and motivation to focus and creativity.&#x0D; Holding training sessions for subordinates and supervisors on the subject of pressure to try to avoid as much as possible, which would in the end, individual and organizational performance, avoid stress at work contribute significantly to improving individual and organizational performance.&#x0D; &#x0D; Conducting further scientific studies in the scope of this study to determine the causes and sources of work pressures.
International Student Exchange Management as Factor of Educational Services Development

Since the beginning of XXI century higher education internationalization trend has been intensifying around the world. The goal of the research is to study managing international education characteristics to promote educational services export. Levels of interconnection and interdependence between economic agents around the world are increasing; transnational forms of economic activity, information and communication technologies are developing. The market for higher education is developing intensively as educational services of the highest international level demand is increasing. The research goal is to analyze the main economic tendencies in modern students’ exchange programmes. The problems discussed are the need to increase educational services export by promoting it using various student exchange models, study of managing international education characteristics to promote educational services export and its development. For this purpose the methodology consisting of modeling educational policy processes and analyzing the results is used. The results of the study show that higher education takes on international socio-economic features, which are to be seen as an attractive investment target. Modern higher education is developing in the conditions of open access and mutual influence of conditions in different countries, depending on the basis of economic, political and cultural relations. The growing interest in education abroad leads to existing paradigm of educational services management review thus updating this study’s topic. In the conclusion we highlight that integrative processes in internationalization of education management can significantly enrich practical application in this sphere. Recommendations in this study can be used to increase educational services export and import.
AN ERROR ANALYSIS OF STUDENTS' ABILITY IN COMPOSING CONDITIONAL SENTENCES WRITTEN BY THE ELEVENTH GRADE STUDENTS OF MAN 2 BOYOLALI IN THE ACADEMIC YEAR 2020/2021

The aims of this research is to find out the following research questions such as what are the type and how many precentage of each errors, and what is most dominant error that made by students in composing conditional sentences. The reason of finding the most dominant error is to provide informations for the teacher, in order to solve the students’ problem and expected the teacher can improve the students’ knowledge in the English learning process. This study applied descriptive qualitative method. The techniques of collecting data was by means of the document method, interview and test. The result of this research showed that there were 4 types of error based on surface strategy taxonomy. They are omission, addition, misformation and misordering. Moreover, the recearcher found 58 errors made by students in composing conditional sentences. The errors consist of 30 items or 51.7% misformation error, 19 items or 32.7% omission error, 5 items or 8.7% addition error and 4 items or 6.9% misordering error. Therefore, this research shows that the most dominant error made by students is misformation error with has precentage 51.7%.Keywords: Conditional sentences, Error, Type, Dominant type
Joint Twin-Field-QKD-McEliece Cryptosystem

To overcome rate-distance-limit of QKD protocols, we propose a joint TF-QKD-McEliece scheme in which TF-QKD is used for raw-key transmission while McEliece-cryptosystem to transmit parity bits for information reconciliation. Proposed scheme is able to achieve record distance of 1127 km, thus significantly outperforming TF-QKD schemes.
t-SNE or not t-SNE, that is the question

T-distributed Stochastic Neighbor Embedding (t-SNE) has become an extremely popular algorithm for low- dimensional visualization of high dimensional data. While it is acknowledged that it is highly sensitive to its parameters, it continues to be used extensively by the machine learning community, with `intuition' an accepted basis for embedding selection. In this paper, we will illustrate and explain why t-SNE is not a distance preserving algorithm, but rather order preserving, with the cardinality of the order proportional to the perplexity parameter. We compare and contrast t-SNE with Sammon Nonlinear Mappings locally using Kruskal Stress and Spearman Rank Correlation measures.
An Online Lecture Concentration According to Online Learning Behavior, Learner Characteristics, Learning Satisfaction, and Teacher-student Interaction of University Students

Purpose: Because of the COVID-19 pandemic in 2020, colleges had no choice but to introduce online classes without sufficient preparation. As a result, various problems emerged in college students’ learning experience. To establish a strategy for improving online education quality, the necessity to understand university students’ learning behavior, learner characteristics, learning satisfaction, and the impact of professor-student interaction on online lecture concentricity. Methods: This study is a cross-sectional survey conducted on college students from nine universities who experienced online lectures in the first semester of 2020. Online lecture learning behavior, learner characteristics, learning satisfaction, and professor-student interaction were analyzed using t-test, ANOVA, and Pearson’s correlation coefficient to explore the difference in online lecture concentration. Results: The concentration in-class lectures and online lectures averaged 7.04 and 5.73, respectively. According to the analysis, online learning concentration was a significant positive correlation by learner characteristics (r=.40, p<.001), professor-student interaction (r=.30, p<.001), online lecture evaluation (r=.34, p<.001). Conclusion: Therefore, it is suggested that universities should establish guidance strategies related to lecture time zones, measures for managing and supervising learners, reflecting online lecture evaluation results, and strategies for improving student interaction.
Dynamic Ensembles in Named Entity Recognition for Historical Arabic Texts

The use of Named Entity Recognition (NER) over archaic Arabic texts is steadily increasing. However, most tools have been either developed for modern English or trained over English language documents and are limited over historical Arabic text. Even Arabic NER tools are often trained on modern web-sourced text, making their fit for a historical task questionable. To mitigate historic Arabic NER resource scarcity, we propose a dynamic ensemble model utilizing several learners. The dynamic aspect is achieved by utilizing predictors and features over NER algorithm results that identify which have performed better on a specific task in real-time. We evaluate our approach against state-of-the-art Arabic NER and static ensemble methods over a novel historical Arabic NER task we have created. Our results show that our approach improves upon the state-of-the-art and reaches a 0.8 F-score on this challenging task.
Efficient Learning across Multiple Domains with Deep Neural Networks

Author(s): Guo, Yunhui | Advisor(s): Rosing, Tajana S | Abstract: Learning with data from multiple domains is a longstanding topic in machine learning research. In recent years, deep neural networks (DNN) have shown remarkable performance on different machine learning tasks. However, how to efficiently utilize deep neural networks for learning with multiple domains is largely unexploited. A model aware of the relationships between different domains can be trained to work on new domains with fewer resources and achieve better performance. However, to identify and leverage the transferable structure is challenging.In this dissertation, we propose novel methods which allow efficient learning across multiple domains in several different scenarios. First, we address the problem of learning across two image domains with deep neural networks. We propose two adaptive methods which allow different images to fine-tune and reuse different residual blocks and convolutional filters of the pre-trained model. Experimental results show that the proposed SpotTune outperforms the standard fine-tuning on 12 out of 14 datasets. Second, we consider the case that the target domain only has few examples per category which is referred to as the cross-domain few-shot problem. We establish a new benchmark for cross-domain few-shot learning and propose a multi-model selection algorithm which achieves an average improvement of 2\% compared with the state-of-the-art approach on the proposed benchmark. Third, we consider learning with multiple domains simultaneously. We propose a multi-domain learning method based on depthwise separable convolution which achieves the highest score on the Visual Decathlon Challenge and reduces the number of parameters by 50% compared with the state-of-the-art approach. We further propose an efficient multi-domain learning method for distributed training in sensor networks. The proposed method can reduce the communication cost by up to 53% and energy consumption by up to 67% without accuracy degradation compared with conventional approaches. Finally, we address learning with multiple domains sequentially. We propose an algorithm called mixed stochastic gradient descent (MEGA) which allows the model to maintain the performance on old domains while being trained on a new domain. MEGA achieves an average accuracy of 91.21±0.10% on Permuted MNIST, which is 2% better than the previous state-of-the-art model. On Split CIFAR, the proposed MEGA achieves an average accuracy of 66.12±1.93%, which is about 5% better than the state-of-the-art method.
Signing In and Out and Managing Passwords

In this chapter, I describe the Identity API features for signing in and out of applications. Identity supports different ways of signing in, and I start with passwords in this chapter, including creating the workflows for managing passwords. In later chapters, I describe the other ways in which users can authenticate themselves. Table 8-1 puts the features for signing in and out and managing passwords in context.
Mitigating cybersecurity challenges in the financial sector with Artificial Intelligence

This paper investigates the evolving cyber threats landscape. This includes the phenomena of ransomware and the risks that such threats have to financial corporations. Cutting-edge technologies, such as Artificial Intelligence, could potentially have an influential impact on detecting cyber intrusions and protecting sensitive and critical business information, as well as ensure privacy. We comment on a framework that explains the causes of reported cyber incidents and the consequences of IT systems breakdown. This is done using fault tree and event tree risk analysis models. The results show that applying Artificial Intelligence techniques can increase the probability of the monitoring systems detecting anomalies and reducing threats. Although the cost benefit of implementing such techniques is still uncertain, they appear to be related to company characteristics such as size and maturity level. We therefore recommend that companies adopt such measures as part of their IT security protocols.
Generative Context Pair Selection for Multi-hop Question Answering

Compositional reasoning tasks such as multihop question answering require models to learn how to make latent decisions using only weak supervision from the final answer.Crowdsourced datasets gathered for these tasks, however, often contain only a slice of the underlying task distribution, which can induce unanticipated biases such as shallow word overlap between the question and context.Recent works have shown that discriminative training results in models that exploit these underlying biases to achieve a better held-out performance, without learning the right way to reason.We propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts.We show that on HotpotQA, our proposed generative passage selection model, while being comparable to the state-of-the-art answering performance, has a better performance (4.9% higher than baseline) on an adversarial held-out set that tests the robustness of model's multi-hop reasoning capabilities.
Evaluating and Comparing Ontology Alignment Systems: An Mcdm Approach

Ontology alignment is vital in Semantic Web technologies with numerous applications in diverse disciplines. Due to diversity and abundance of ontology alignment systems, a proper evaluation can portray the evolution of ontology alignment and depicts the efficiency of a system for a particular domain. Evaluation can help system designers recognize the strength and shortcomings of their systems, and aid application developers to select a proper alignment system. This article presents a new evaluation and comparison methodology based on multiple performance met-rics that accommodates experts' preferences via a multi-criteria decision-making (MCDM) method, i.e., Bayesian best-worst method (BWM). First, the importance of a performance metric for a specific task/application is determined according to experts' preferences. The alignment systems are then evaluated based on proposed expert-based collective performance (ECP) that takes into account multiple metrics as well as their calibrated importance. For comparison , the alignment systems are ranked based on a probabilistic scheme, where it includes the extent to which one alignment system is preferred over another. The proposed methodology is applied to six tracks from ontology alignment evaluation initiative (OAEI), where the importance of performance metrics are calibrated by designing a survey and eliciting the preferences of ontology alignment experts. Accordingly, the participating alignment systems in the OAEI 2018 are evaluated and ranked. While the proposed methodology is applied to six OAEI tracks to demonstrate its applicability, it can also be applied to any benchmark or application of ontology alignment.
The short practices

First, participants are guided in short practices as part of the assigned curriculum and are also given short practices as home practices. The second is by example, such as when short practices are spontaneously used by the teacher at appropriate moments during the sessions to support participants to reconnect to their direct experience. Short practices may be movement based or more commonly, short sitting practices using a chosen anchor as a focus of attention. MBCT regards the short practices, specifically the 3SBS, as a core element of the curriculum. In MBSR, equivalent short practices are generally a loosely structured 'pause', using a chosen anchor to encourage participants to drop into being mode or, a more structured practice based on the acronym STOP. Remember that the short practice is used in a given session should not be a surprise to participants in terms of skills or understanding.
WHAT VIRTUAL REALITY CAN DO FOR SPORT ? A NARRATIVE REVIEW OF THE LITERATURE

Background: The physical fitness of athletes is well above the average of normal people. In addition, the response time has to be much better to reach the championship level, therefore the training demands have changed greatly over the decades. Traditional exercise will always be a fundamental factor in training, however, new technologies have to be considered in order to maximize their effectiveness. An example of a technology is Virtual Reality (VR), where the athlete's awareness to a purpose-built world, where they can gain experience and skills similar to the conditions during the competition. The aim of this narrative review was to determine the utilization of VR in training of competitive athletes.Methods: A literature review using PubMed has been conducted to examine the current state of the art in the available literature using VR in the aspect of competitive sports. For the complete review 157 articles on the use of VR in sports were identified, but no article indicated competitive sports. After the individual sports search, 6 articles were adopted for this review of the literature.Results: The presented research results show positive results with the use of VR for athletes on academic level. Not only do they improve the response time in decision-making during competitions, but they also have the opportunity to learn new schemes for use in competitions more quickly. The presented articles describe the performance of competitive athletes at the academic championship level, so there is still a lack of confirmed research on top athletes in the available literature.Conclusions: Based on the limited number of scientific articles available, it can be assumed that the use of Virtual Reality has a positive impact even on the development of competitive athletes in order to achieve the best possible performance and new skills. It is not possible to make preliminary conclusions and further research is needed on the leading athletes in the respective sports.
LibGuides: A Teen’s Guide to Creative Writing: Home

This guide is created for teen creative writers and aspiring authors seeking to learn more about the writing and publishing industry