Reviewerly/exHarmony huggingface dataset:
PENINGKATAN HASIL BELAJAR MATEMATIKA MENGGUNAKAN MODEL PEMBELAJARAN TALKING STICK

The results of mathematics lessons of grade 4 students of MI Maarif Kapulogo Village, Kepil Wonosobo subdistrict can be said to be renddah. Based on the results of the interview obtained data on the average daily repeat value of students multiplication material and the division of numbers is 67.00 and students who can be said to complete is 6 students out of 10 students, then the percentage of completion of student learning is 62%.&#x0D; The research aims to know the process of mathematics learning and to know the improvement of learning outcomes in grade 4 students of MI Ma`arif Kapulogo Kepil Wonosobo in the 2020/2021 school year after learning using Talking stick learning models.&#x0D; This type of research is a class action research with 2 cycles and each cycle consists of 4 stages namely planning, implementation, observation and reflection. The subject of the study was a grade 4 student of MI Ma'arif Kapulogo with a total of 10 people. Collection using observation and test methods. Data analysis using descriptive analysis.&#x0D; The results showed that the mathematics learning process using Talking stick learning model in grade 4 students of MI Ma'Arif Kapulogo Kepil Wonosobo in the 2020/2021 school year was carried out as many as two cycles and each cycle consisted of 2 meetings. Each cycle consists of four stages, namely planning, implementation, observation and reflection. The results showed that the average results of learning mathematics in pre-cycle was 57 with 50% completedness. Then the average results of learning mathematics in the cycle increased to 70 with 70% completeness and in the second cycle the average results of mathematics learning increased to 85 with 90% completeness. Thus, the learning model of Talking stick approach can improve the results of mathematics learning students in grade IV MI Ma'arif Kapulogo District Kepil Wonosono Regency
Model selection-inspired coefficients optimization for polynomial-kernel graph learning

Graph learning has been extensively investigated for over a decade, in which the graph structure can be learnt from multiple graph signals (e.g., graphical Lasso) or node features (e.g., graph metric learning). Given partial graph signals, existing node feature-based graph learning approaches learn a pair-wise distance metric with gradient descent, where the number of optimization variables dramatically scale with the node feature size. To address this issue, in this paper, we propose a low-complexity model selection-inspired graph learning (MSGL) method with very few optimization variables independent with feature size, via leveraging on recent advances in graph spectral signal processing (GSP). We achieve this by 1) interpreting a finite-degree polynomial function of the graph Laplacian as a positive-definite precision matrix, 2) formulating a convex optimization problem with variables being the polynomial coefficients, 3) replacing the positive-definite cone constraint for the precision matrix with a set of linear constraints, and 4) solving efficiently the objective using the Frank-Wolfe algorithm. Using binary classification as an application example, our simulation results show that our proposed MSGL method achieves competitive performance with significant speed gains against existing node feature-based graph learning methods.
Os estudantes chineses e os hábitos de uso da internet ao encontro da língua portuguesa e do mundo lusófono (Chinese Students and Their Internet Habits as a Window to the Portuguese-speaking World)

Portuguese Abstract: Trata-se de um estudo descritivo do uso da internet por parte dos jovens estudantes chineses. Tal abordagem e particularmente importante para professores e alunos de PL2, para quem a internet pode representar o unico meio de contacto com a lingua fora da sala de aula. O artigo analisa a utilizacao da internet pelos universitarios chineses como ferramenta para adquirirem ou aprofundarem os seus conhecimentos sobre o mundo lusofono. A analise baseia-se num questionario realizado a uma turma de 32 estudantes do segundo ano de Portugues da Universidade de Sun Yat-sen, na provincia de Guangdong. As conclusoes revelam o conjunto de interesses dos estudantes no contacto com o portugues fora da sala de aula e os fatores que os influenciam. A investigacao relaciona-se ao uso da internet como meio de aprendizagem da lingua e como fonte de conhecimento do perfil dos estudantes chineses em termos das suas escolhas recreativas, culturais ou comerciais relativas ao mundo lusofono. English Abstract: We propose a descriptive study on the use of the internet by young Chinese students. Addressing this issue is of particular importance for teachers and students of Portuguese as a Second Language for whom the internet may be the only means of contact with the foreign language outside the classroom. We analyze the way Chinese university students use the internet as a tool to acquire or deepen their knowledge of the Portuguese-speaking world. This analysis is based on a survey conducted with a group of second year students in Portuguese at Sun Yat-sen University, in the province of Guangdong. The conclusions reveal the set of interests guiding the students’ contact with Portuguese outside the classroom and the factors that may have an influence in it. This research is related to the use of the internet as a means of learning a language, as well as a source of knowledge regarding the profile of Chinese students in terms of their recreational, cultural, or commercial choices related to the Portuguese-speaking world.
Mediating Effects of Major Suitability on the Relation of Tourism Related Students' Job-seeking Anxiety on Career Preparation Behavior

This study examined the effects of Job-seeking Anxiety on major suitability and career preparation behavior, focusing on two-year college students in the metropolitan area who major in tourism. The survey period for this study was 230 questionnaires distributed from March to June 2021, and the final 166 questionnaires were used for analysis. The statistical analysis was conducted by multiple regression analysis, three-step regression analysis by Baron & Kenny (1986), and Sobel's Z test. The main results are as follows. First, the result of verifying that Job-seeking Anxiety(Job-seeking stress) will have a significant effect on major suitability showed that it has a significant negative effect. Second, the hypothesis that Job-seeking Anxiety will have a significant effect on career preparation behavior showed that it has a significant positive effect. The results of verifying that major suitability will have a significant effect on career preparation behavior showed that it is not significant. Fourth, the major suitability had a partial mediating effect on the effect of job insecurity on career preparation behavior. As a practical implication, non-face-to-face classes due to COVID-19 are a way to relieve the deterioration of learning ability and anxiety about the future. It should be encouraged to provide various opportunities outside of class to promote the improvement of practical skills. As a theoretical implication, job insecurity and major suitability of tourism majors at two-year colleges in the metropolitan area were meaningfully derived to increase their career preparation behavior. In particular, two-year universities are considered to be basic research for effective career education and counseling despite the characteristics of admission and graduation during the COVID-19 outbreak.
An Attention-Based Deep Learning Model with Interpretable Patch-Weight Sharing for Diagnosing Cervical Dysplasia

Diagnosis of cervical dysplasia by visual inspection is a difficult problem. Most of the recent approaches use deep learning techniques to extract features and detect a region of interest (RoI) in the image. Such approaches can lead to loss of visual detail that appears weak and local within the cervical image. Also, it requires manual annotation to extract the RoI. Moreover, there are not many labeled data due to the medical image’s characteristics. To mitigate the problem, we present an approach that extracts global and local features in the image without manual annotation when there is a shortage of data. The proposed approach is applied to classify cervix cancer, and the results are demonstrated. First of all, we divide the cervix image into nine patches to extract visual features when high-resolution images are unavailable. Second, we generate a deep learning model sharing a weight between patches of the image by considering the patch-patch and patch-image relationship. We also apply an attention mechanism to the model to train the visual features of the image and show an interpretable result. Finally, we add a loss weighting inspired by the domain knowledge to the training process, which guides the learning better while preventing overfitting. The evaluation results indicate improvements over the state-of-the-art methods in sensitivity.
AtoX: Take Technology as a Sword and Forge Ahead

In the eighth month of the bear market, the disappearance of the coin circle gradually spread, and the downturn became the main theme of the digital currency market. The confused and awkward atmosphere surrounded the major institutions. In a bull market, a project can take only a few months from start-up to landing on the exchange, which means that the exit cycle of the investment institution is very short. Now, as the leek has been cut to the end, the investment institutions slowly discover that they have become the last takers.2018 is the cold winter of the coin circle, and it is also the winter of the organization. When you ask the CEO of top institutions, what is the value of the digital currency assets in their hands? As the currency bubble recedes, the capital pattern of 2019 will inevitably usher in a big reshuffle. It remains to be seen whether institutions that focus only on speculation will live through the winter.However, in my opinion, projects such as Atox, which are more focused on blockchain technology itself, are bound to thrive in a healthy regulatory environment!AtoX is a public blockchain system based on the fourth-generation blockchain technology created by a team from the Jewish nation. Based on this, AtoX Swap cross-chain wallet and decentralized exchange were established.Compared with previous systems, it has the following characteristics:1. Different design ideas: The most important difference between AtoX and Ethereum, EOS and Bitcoin is that the design idea is different. Bitcoin can only be used as electronic money. Ethereum is just a technology that increases the smart contract to blockchain 2.0. Based on the fourth-generation blockchain technology, AtoX is committed to the global blockchain landing applications. It is a fourth-generation blockchain platform for the world, allowing developers to implement blockchain applications with high efficiency, high performance and fit for business.2, The consensus mechanism is different: AtoX uses the RPCA consensus mechanism, compared with POW, POS, etc., can achieve higher TPS in performance to apply to more scenarios, and can save more computing performance.3, The technical realization is different: AtoX is based on the fourth-generation blockchain technology, which breaks through the chain expansion problem caused by the low performance of the traditional blockchain technology. Through the technology of chain compression, the application construction can be realized more flexibly. Therefore, AtoX skills retain the advantages of a centralized platform, and can achieve the security and reliability of decentralization.2018 has been passed, and the coin circle has also passed an unusually rich year. We may be involved in the tuyere of 2018, or perhaps as a bystander. We can only look forward to 2019, and we can do nothing but to cut through the fog and continue to forge ahead!
LibGuides: Academic Sources: Empirical Research

Describes empirical research and the format used in publishing this kind of scholarly article.
Together towards campus digitization

Pekan, 6 November 2020 - Universities need to make comprehensive preparations in the era of the digital revolution to remain competitive and accelerate progress as well as shape the glory in the national education landscape.
Capacity and Allocation across Sensory and Short-Term Memories

Human memory consists of Sensory Memory (SM), Short-Term Memory (STM), and Long-Term Memory. SM enables a large capacity but decays rapidly. STM has limited capacity but lasts longer. Previously, it was shown that major bottlenecks for motion processing exist prior to STM (Ogmen et al, 2013. PLoS ONE) and that the contents of SM are allocated exclusively to the current event segment (Tripathy & Ogmen, 2018. Frontiers in Psychology). Here we used mixture modeling to test if the modeling results obtained in Ogmen et al., (2013) also hold for the dataset from Tripathy & Ogmen (2018) in which stimuli contained one to four disks moving in different directions. Each disk changed its direction at the mid-point of its trajectory. The synchronized deviations indicated an event boundary. Observers were instructed to partially or fully report the directions of disks, randomly selected from one of the two event-segments. Experiment 1 varied set-size with cue-delay set to 0 ms. A mixture model consisting of a Gaussian (Memory) and a Uniform (Guessing) distributions found that intake (fraction of target items retained in memory) and precision (1/Standard-Deviation) decreased with increasing set-size. In Experiment 2, set-size was fixed at 3, with a varying cue-delay. Intake dropped gradually and substantially from stimulus encoding to SM and STM, but precision dropped only moderately across memory stages. These results are in agreement with the mixture analysis in Ogmen et al., (2013). We also asked if a mixture model containing two, as opposed to one, Gaussians would capture better the data. Model comparison based on the Akaike Information Criterion favored the two Gaussian model. In summary, we generalized previous findings about memory capacity and bottlenecks using a different data set and propose a mixture model with two Gaussians as a better statistical model than a single Gaussian version.
Il dizionarietto di greco: le parole dei nostri pensieri. Nuova edizione rivista e ampliata

The book shows how the Greek language universe is the conceptual tank of 3000 years of Western culture, not only in the classical and early Christian times but also in the modern era, as the neologisms that have characterized the sciences in recent centuries (from physics to cybernetics, from economics to psychoanalysis) show. The book after a general Introduction is developed through more than 500 entries in alphabetical order (from Abyss to Zoology) with special attention to ethics, cultural fortune, common language outcomes, including common or curious uses, accompanied by short citations of Greek passages proposed in the original, and translated and transcribed (for the benefit of of those who, without knowing the language yet, would at least try the sound between them).
Two-Line Defense Ontology-Based Trust Management Model

Heterogeneity, dynamicity, and variability that are inherited in dynamic distributed networks have come with their security challenges. First, all devices on the network must be trusted to operate according to their designated security level. Hence, any accidental or suspicious access may breach the security of the protected resources. Second, the architecture of the dynamic networks, if not designed very carefully, can make sharing appropriate information among devices difficult or impossible. In this paper, we present an approach for trust management in dynamic distributed networks. Our approach is based on establishing two defense lines to protect the resources of the dynamic distributed networks from suspicious access and obtrusive penetrating trials. For the first line, we employ a machine learning approach that serves as an outer shield which faces the penetrating trials. If this shield is penetrated, then the inner strong semantic-based shield defends against the penetrating attacks and prevents them from reaching the protected resources. We show, in this paper, how machine learning models can be combined with semantic technologies to support flexible and adaptive trust management decisions. We develop a proof-of-concept implementation and give the complexity analysis for our approach.
Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

Colormapping is an effective and popular visualization technique for analyzing patterns in scalar fields. Scientists usually adjust a default colormap to show hidden patterns by shifting the colors in a trial-and-error process. To improve efficiency, efforts have been made to automate the colormap adjustment process based on data properties (e.g., statistical data value or histogram distribution). However, as the data properties have no direct correlation to the spatial variations, previous methods may be insufficient to reveal the dynamic range of spatial variations hidden in the data. To address the above issues, we conduct a pilot analysis with domain experts and summarize three requirements for the colormap adjustment process. Based on the requirements, we formulate colormap adjustment as an objective function, composed of a boundary term and a fidelity term, which is flexible enough to support interactive functionalities. We compare our approach with alternative methods under a quantitative measure and a qualitative user study (25 participants), based on a set of data with broad distribution diversity. We further evaluate our approach via three case studies with six domain experts. Our method is not necessarily more optimal than alternative methods of revealing patterns, but rather is an additional color adjustment option for exploring data with a dynamic range of spatial variations.
LibGuides: Teaching Elementary- For Beginners: In need of some inspiration?

In this lib guide, you will discover helpful tips, tricks, and information for beginning teachers. There is no need to reinvent the wheel. I have provided resources for classroom management, movement, Math websites, ELA websites, articles, Ted Talks, and, Teaching is exhausting. Listen to a Ted Talk or podcast to get your spark back.
Understanding Peoples' Sentiment During Different Phases of COVID-19 Lockdown in India

During a global pandemic like COVID-19, the success of governmental policies depends on the people's sentiments and extended cooperation towards these policies. Therefore, this study explores the prevalent discourse in social media about different aspects of the COVID-19 pandemic and the policies to manage and control it. Data from Twitter collected between 25 March 2020 and 1 July 2020 was used for topic modelling and sentiment analysis. Natural language processing-based text mining techniques were used for analysis. This study first identified different frequent COVID-19-related topics and then analyzed how the sentiments towards these topics differ across different phases of lockdown. Further, insights into how different topics were perceived by gender and age group are also discussed in this study. Finally, this study also analyzed how daily casualty due to COVID-19 influenced the public sentiments and number of daily tweets. The study provides a robust NLP-based text mining framework to predict the people's sentiment during COVID-19 lockdown in India. The insights presented in this study can help the authorities mitigate the COVID-19 pandemic effectively and help different agencies in the face of similar pandemics in the future.
Synthetic Series of Electricity Generation through a Photovoltaic System by using Different Panel Temperature Models

The electric energy produced by photovoltaic systems is a function of several variables that present random behavior, such as solar irradiance, ambient temperature, wind speed, and relative humidity. Several models describe the energy produced by these systems, which reflect the various factors that influence the generation and how significant these influences are. In this way, the present work aims to compare some panel temperature models and the electrical energy produced from a photovoltaic system located in the Midwest of Brazil. The applied methodology aims to evaluate the expected averages and standard deviations of the monthly synthetic series of generated energy to identify the main random variables that influence the electric generation. The results indicate the model that best describes the physical system concerning the randomness of the variables involved, the photovoltaic technology, the location, and the measurement period.
Problems of On-line Processing of EOG-Data in Reading

Eye movement monitoring is partly physics not only with regard to its techniques but also with regard to its self awareness. Considering this, the high variance of data occurring in eye movement investigations is astonishing. But from a methodological point of view, the real problematic variance results from apparatus, procedures, identification programs, or from insufficiently trained or instructed subjects (the variance resulting from a reader’s reading intention, from the way in which a text is read, or from reading skill is higher than even the worst apparatus can produce). Observations repeatedly made under different experimental conditions convinced us that bioelectrical influences as well as micro-movements of the eyes depend very much on subjects’ actual and habitual activation level, and that applies also to eyelid blink-rate. Precautions actually taken to handle the problem are as well heterogeneous and consist mainly in: formulating very restricted hypothetical models and looking at their proof as a result; and enlarging the number of subjects.
Secret keys Production Using Smart Keychain Generator

This paper presents a new method of generating random keys using the genetic algorithm to get more secure and less predictable. The solution depends on searching for a large population (initial inputs) and testing the randomness and strength of these keys according to the National Institute of Standards and Technology NIST. The experimental result show that generated keys cannot be guessed and have high degree of randomness with the broad area of use in different services and applications.
Nexus among Job Satisfaction, Organizational Commitment, Change and Culture: Evidence from Lebanon

It's a difficult task to describe the organizational transition because of the many contributions present in literature. To generate impact in an enterprise, we should define organizational change as an economic, financial, cultural, human or technical shift. To stimulate corporate growth, we can also view organizational change as several theories, values, strategies and techniques which aim to change the workplace. Although the concepts accessible for characterizing organizational transition are heterogeneous, we may describe them broadly as a mechanism initiated by an organization to satisfy a resolute need for growth. Personnel engagement in decision-making not only builds confidence but also helps to deter negative and harmful attitudes and behavior. The research implemented a mixed approach methodology throughout distributing a questionnaire over 400 respondents and maintaining 370 answers, and the throughout conducting in-depth interviews in Small Medium Enterprises in Lebanon. The results revealed that change management tends to affect job satisfaction in a positive way but this change should be managed effectively and efficiently. Commitment and Culture tends to play an important role in implementing change management practices.