Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF UTILIZING YOUTUBE VIDEO IN IMPROVING MATHEMATIC COMMUNICATION SKILLS IN BASIC TEACHER EDUCATION

This research is motivated by the low mathematical communication skills of prospective elementary school teacher students. The purpose of this study is to see the achievement and improvement of students' mathematical communication skills using YouTube videos and those using conventional learning. This study used a quasi-experimental method with a non-equivalent pretest and posttest control group design. The population in this study were all students of second semester elementary school teacher candidates at one of the private tertiary institutions in Cimahi City. While the sample in this study were 60 students of second semester elementary school teacher candidates. The instrument used in this study was a test of mathematical communication skills. The results showed: 1) achievement and improvement of mathematical communication skills using YouTube videos was better than conventional learning; 2) Students can be confident, active and motivated in learning mathematics with YouTube videos.
Probabilistic Programs with Stochastic Conditioning

We tackle the problem of conditioning probabilistic programs on distributions of observable variables. Probabilistic programs are usually conditioned on samples from the joint data distribution, which we refer to as deterministic conditioning. However, in many real-life scenarios, the observations are given as marginal distributions, summary statistics, or samplers. Conventional probabilistic programming systems lack adequate means for modeling and inference in such scenarios. We propose a generalization of deterministic conditioning to stochastic conditioning, that is, conditioning on the marginal distribution of a variable taking a particular form. To this end, we first define the formal notion of stochastic conditioning and discuss its key properties. We then show how to perform inference in the presence of stochastic conditioning. We demonstrate potential usage of stochastic conditioning on several case studies which involve various kinds of stochastic conditioning and are difficult to solve otherwise. Although we present stochastic conditioning in the context of probabilistic programming, our formalization is general and applicable to other settings.
Cloud Query Processing with Reinforcement Learning-Based Multi-objective Re-optimization

Query processing on cloud database systems is a challenging problem due to the dynamic cloud environment. The configuration and utilization of the distributed hardware used to process queries change continuously. A query optimizer aims to generate query execution plans (QEPs) that are optimal meet user requirements. In order to achieve such QEPs under dynamic environments, performing query re-optimizations during query execution has been proposed in the literature. In cloud database systems, besides query execution time, users also consider the monetary cost to be paid to the cloud provider for executing queries. Thus, such query re-optimizations are multi-objective optimizations which take both time and monetary costs into consideration. However, traditional re-optimization requires accurate cost estimations, and obtaining these estimations adds overhead to the system, and thus causes negative impacts on query performance. To fill this gap, in this paper, we introduce ReOptRL, a novel query processing algorithm based on deep reinforcement learning. It bootstraps a QEP generated by an existing query optimizer and dynamically changes the QEP during the query execution. It also keeps learning from incoming queries to build a more accurate optimization model. In this algorithm, the QEP of a query is adjusted based on the recent performance of the same query so that the algorithm does not rely on cost estimations. Our experiments show that the proposed algorithm performs better than existing query optimization algorithms in terms of query execution time and query execution monetary costs.
Essential repairs to section of A72 near Lanark

Everyone in the Lanark area is being urged to plan ahead while the A72 Kirkfieldbank Brae is closed for essential repairs.
Reliable Medchain Management System

The internet of things, popularly known as IoT, is intelligent sensors working together to accomplish a task. These devices are used in innumerable applications, and they are aiming to minimize human efforts. These unsupervised devices require some amount of security. Hence, the previous systems use blockchains to enhance security. The hashing system used in this technology preserves the security and confidentiality among the stakeholders. Medchain is a system that communicates between the personnel and insurance dealings securely. To enhance reliability of the previous work, the proposal is proposed. The methodology used in the contribution enhances the reliability by 2.89% compared to the previous proposal.
How to Conduct Experiments with a Real Car? Experiences and Practical Guidelines.

Higher computational power, new dimensions of interconnectivity and modern machine learning techniques are necessary for building a fully autonomous car, but exhibit an enormous technical complexity. Research about new approaches and technology for handling this complexity raises a problem: On the one side, researchers advocate transitions and replacements for the current systems mainly without deploying them in real cars on the streets. On the other side applying theoretical approaches without clear evidence of their practical benefits is risky for the practitioners. As a solution to close this gap, researchers should bring their ideas more often into physical cars and support their proposals with measurements from realistic experiments.
Hybrid modeling using multivariate, discrete probability distributions&amp;#160;

&amp;lt;p&amp;gt;In this contribution, I will suggest an approach to build models as ordered and connected collections of multivariate, discrete probability distributions (dpd's). This approach can be seen as a Machine-Learning (ML) approach as it allows very flexible learning from data (almost) without prior constraints. Models can be built on dpd's only (fully data-based model), but they can also be included into existing process-based models at places where relations among data are not well-known (hybrid model). This provides flexibility for learning similar to including other ML approaches - e.g. Neural Networks - into process-based models, with the advantage that the dpd's can be investigated and interpreted by the modeler as long as their dimensionality remains low. Models based on dpd's are fundamentally probabilistic, and model responses for out-of-sample situations can be assured by dynamically coarse-graining the dpd's: The farther a predictive situation is from the learning situations, the coarser/more uncertain the prediction will be, and vice versa.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;I will present the main elements and steps of such dpd-based modeling at the example of several systems, ranging from simple deterministic (ideal spring) to complex (hydrological system), and will discuss the influence of i) the size of the available training data set, ii) choice of the dpd priors, and iii) binning choices on the models' predictive power.&amp;lt;/p&amp;gt;
Scale Development for measuring the level of Happiness of Uni3versity Students (HUS) : Implication for solving the Second-year crisis in South Korea

The purpose of this study is to find the factors that impact university students'' happiness and makes tangible the tools to quantify such factors. In order to do this, we first conducted group interview to identify key words and using this we created a 1st round of questionnaires. Next we performed factor analysis on the results collected to separate different happiness factors, and conducted a second round of survey to finalize the items/questions in the scale. As a result, we found that the five lower level factors include goal achievement, thankfulness, relationship, positivity, and forgiveness. This study contribution can be summarized as the following. We have to a way to measure students'' happiness which can be utilized in university policies and it can also be used to present solutions to our emerging students adaptability problem.
Controlador difuso de velocidad para un motor DC con escobillas

The objective of this article is to design a diffuse speed controller for a brushed DC motor withapplication in mobile robotics. Good performance in tracking the calculated optimal path involves precise control of the locomotion actuators in a mobile robot. Thus, we designed a fuzzy, functional and low computational cost controller for the mentioned actuators; the calculations are obtained from the various tools that the Matlab software offers us in the field of fuzzy logic. Therefore, this article provides simulations that corroborate fuzzy control as a viable alternative for this type of application.
Subject Guides: Environmental Science: Earth Sciences Data and Organizations

A guide to library resources and tools for the Environmental Science students, faculty, and staff at Northeastern University
Framework for an Integrated EhealthPlatform with Smart Diagnostic Engine forImproved Healthcare Access for RuralDwellers

The healthcare challenges faced by rural dwellers in developing nations, especiallythose in the African continent include a lack of sufficient workforce, especially doctors,lack of healthcare facilities and access to the required medications. This paper presentsa framework for the development of an integrated Ehealth platform which will providethe rural dwellers with access to high-quality medical personnel located in the urbanareas. The unique advantages of this platform range from the inclusion of reputablepharmacies and medical diagnostic labs to minimize the risk of fake drugs and wrongdiagnostic results coupled with integrated drug delivery and sample collection systemwhich can be implemented using taxis, motorbikes or drones. The system alsoincorporates an Artificial Intelligence-based disease diagnostic engine which isdesigned to learn continuously from the patient/doctor interactions and support thedoctor in the patient diagnosis. This system presents a robust solution for providingrural dwellers with access to qualified medical service and also enable the doctors toprescribe high-quality drugs from reputable pharmacies close to the patient for thepatient’s use. Governments and donor agencies can utilize this platform to provide andpay for quality medical service to rural areas while at the same time, being able to trackthe spread of diseases.
Is Municipal Solid Waste composition affected by Demographics or Seasons

Residual wastes, material remaining after recycling and organics diversion, require disposal. One alternative is to create Solid Recovered Fuels [SRF] for energy intensive industries. Frequently, potential users of SRF require that the material be fully characterised come with guarantees of energy values and contamination levels. With limited data on compositional variations of residual wastes, particularly as it may pertain to an SRF product, a municipality in southern Ontario, Canada, required a detailed study of the composition and quality of their residual wastes. The wastes are collected separately from both single-family [CS] and high-rise residential [MR] properties and the study afforded an opportunity to distinguish between these waste streams, as well as addressing seasonal differences by sorting materials at different times of the year. Changing priorities during the study resulted in differences in the sorting categories for the 2 phases reported here. Moreover, components that were common to both phases showed different quantities by source and season. The question arose, were the differences significant? Not unexpectedly, the sort data was not normally distributed and applying classical statistical techniques was not successful. Enter, compositional data analysis techniques. When applying these techniques, it became evident that working with high dimensional data increases the complexity of the analyses and the difficulty of interpreting the results. This extended abstract covers some of the methods used to date. From these it was concluded that the dimensionality of the data should be further reduced to attempt to address the question raised above. The final presentation will include the analysis of the less complex data set.
Afectaciones en sistemas relacionales de adultos jóvenes frente a videojuegos en pandemia

Due to the present COVID-19 pandemic, people's ordinary lives have undoubtedly changed in many ways, the various affectations of relational systems among those changes. This investigation aims to precisely define and comprehend how the systems have changed and the possible influence of video games in these processes. For the purpose of this research, a qualitative approach was applied, focused on the analysis of meanings to comprehend the realities constructed by the narratives of the subjects of the study. As such, a series of semi structured interviews were performed, mediated by convenience sampling, choosing 4 young colombian adults. Findings show as a main result, the adaptation and inclusion of video games in the different relational systems by the interviewed.
Sound, Touch and Movement

Keywords: haptics, audiotactility, Aristotle, the world of touch, size-weight illusion, contact microphones, John Cage, phonographic needles, Cartridge Music, Bill Fontana, Harmonic Bridge, Jodi Rose, singing bridges,German research, bass shakers and butt kickers, audio-tactile loudness illusion, music and tactile perception, 5.1 audio, subwoofers, tactile audio mixers, infrasound, unsound, low end theory, sound induced flash illusion
Study Guides: Information Technology: Finding Journal Articles and Conference Papers

This guide contains resources and guidance to help students studying Computer Science and Information Systems.
Reliability Analysis of Compressed CNNs

The use of artificial intelligence, Machine Learning and in particular Deep Learning (DL), have recently become a effective and standard de-facto solution for complex problems like image classification, sentiment analysis or natural language processing. In order to address the growing demand of performance of ML applications, research has focused on techniques for compressing the large amount of the parameters required by the Deep Neural Networks (DNN) used in DL. Some of these techniques include parameter pruning, weight-sharing, i.e. clustering of the weights, and parameter quantization. However, reducing the amount of parameters can lower the fault tolerance of DNNs, already sensitive to software and hardware faults caused by, among others, high particles strikes, row hammer or gradient descent attacks, et cetera. In this work we analyze the sensitivity to faults of widely used DNNs, in particular Convolutional Neural Networks (CNN), that have been compressed with the use of pruning, weight clustering and quantization. Our analysis shows that in DNNs that employ all such compression mechanisms, i.e. with their memory footprint reduced up to 86.3x, random single bit faults can result in accuracy drops up to 13.56%.
Fairness for Image Generation with Uncertain Sensitive Attributes

This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification setting. Moreover, while traditional group fairness definitions are typically defined with respect to specified protected groups -- camouflaging the fact that these groupings are artificial and carry historical and political motivations -- we emphasize that there are no ground truth identities. For instance, should South and East Asians be viewed as a single group or separate groups? Should we consider one race as a whole or further split by gender? Choosing which groups are valid and who belongs in them is an impossible dilemma and being with respect to Asians may require being unfair with respect to South Asians. This motivates the introduction of definitions that allow algorithms to be \emph{oblivious} to the relevant groupings. We define several intuitive notions of group fairness and study their incompatibilities and trade-offs. We show that the natural extension of demographic parity is strongly dependent on the grouping, and \emph{impossible} to achieve obliviously. On the other hand, the conceptually new definition we introduce, Conditional Proportional Representation, can be achieved obliviously through Posterior Sampling. Our experiments validate our theoretical results and achieve fair image reconstruction using state-of-the-art generative models.
Evaluating the Levels of Innovation Seen in Selected countries in the 1404 Document and its Global Impact on the Performance of Cities

Innovations in the developing economies of developing countries such as Iran, are increasingly important as they seek to find solutions to complex challenges, gain competitive advantages in key industries and technologies, and create better living standards for citizens. Therefore, the present study was conducted with the aim of analyzing the levels of innovation seen in the selected countries in the 1404 document and the effect of innovation on urban performance. The data used in the research is derived from the 2017 Global Innovation Index (GII) and the 2017 Global Cities Report (GCR). The statistical population of the study was the UAE, Armenia, Iran, Azerbaijan, Bahrain, Pakistan, Turkey, Saudi Arabia, Oman, Qatar and Kuwait and all their capital cities. In this regard, the Prometheus model and Pearson Correlation Analysis were employed. The results of the study showed that the UAE and Turkey have the best innovation performance among other countries. Also, the levels of Iranian innovation in institutional components, human capital and research, infrastructure, market, business complexity, knowledge and technology, and creative output compared to the selected countries, were respectively, very low, moderate, very low, moderate, low, medium, and low. The results also showed, there is a significant correlation between the rankings of countries and the rankings of cities within them, which had a score of 0.907. This was such that, Istanbul, the capital city of Turkey, and Dubai, the capital city of UAE, were respectively ranked 26th and 28th among other capital cities, and were found to be among the world's top cities. After these results were obtained, suggestions were offered in this paper.
Fine Grained Sentimental Analysis of Social Network Chat Using R

Abstract In current years, sentiment examination has expanded much consideration in, analyzing and monitoring data of social media, public relations, data mining, market research, medical data, political analysis, cyberbullying etc., in all areas extended the utilization of sentiment analysis. Sentiment exploration are classified into three categories in which Rule-based frameworks accomplish sentiment analysis based on a set of physically created procedures. Programmed frameworks rest on machine learning procedures to learn from information though Hybrid frameworks combine both procedure based and programmed approaches. In this paper we apply rule based characteristic language processing and content analysis procedures to recognize and extricate subjective data from content of social media and centering on extremity such as positive, negative, unbiased conjointly on sentiments and feelings as irate, upbeat, pitiful.. By periodically sorting the assumption behind surveys, social media conversations and more ready to make quicker and more precise decisions.
LibGuides: Library Migration Information: Library Services & Resources

A guide to requesting and other service changes during our consortial migration in June and July of 2021.
Cost Optimization: Return on Your Cloud Investment

With an increase in cloud real estate, resource sprawl can become a reality if environments are not managed effectively. Though converting capex to opex is one of the major advantages of cloud computing, you should have a laser focus on optimizing your cloud costs before you even deploy the first resource to ensure the optimal return on investment (ROI). It is a misnomer that monitoring your cloud usage will help with optimizing the cost; it is only one aspect of the solution. In this chapter, we will explore the considerations for cost optimization while deploying your workloads to Azure.
Security: Protect Your Workloads in the Cloud

Security paradigms in the cloud are different from traditional on-premises deployments where the focus was mostly on securing the network perimeter. In the cloud, the threat vectors are much more advanced, and so your security strategy has to be too. Security breaches would negatively impact the customer trust as well as your revenue. Hence, security is a non-negotiable aspect of any application architecture. This chapter will cover the practical aspects of enabling security to ensure the confidentiality and integrity of workloads in Azure.
сomparative analysis of volatility of cryptocurrencies and fiat money

The object of the research is the leading national fiat currencies and transnational anonymous analogues of the currency. They have received the slang name of cryptocurrencies. The subject of the study is volatility. It is the most important financial indicator in the management of financial flows, as a measure of risk of using a financial instrument at a specified time interval. The subject of the research requires consideration of digital goods of limited emission, which are the product of energy conversion into information in the form of a program code on a tangible medium. The limited amount of the emission for these goods without the syndrome of “printing press” has served as a basis for the name “cryptocurrency” and “digital gold”. The issue of cryptocurrency is a reward in the competitive procedure of checking transactions in peer-to-peer networks that implement the technology of distributed registries, and it is essentially a by-product of their functioning. The significant feature of such a product is anonymity and cross-border. They give rise to fundamental legal issues. Cryptocurrency obtaining technology is available to general public and has become an innovative phenomenon. The latest financial phenomena need to be investigated, so it is necessary to conduct a multilateral scientific analysis, identification and comparison of cryptocurrency with the fiat currency. The article compares the volatility of different currencies such as fiat (US dollar, euro, Chinese yuan and Japanese yen) and the most popular cryptocurrencies (Bicoin, Litecoin, Ethereum and Monero) at the present time. The aim of the study is to obtain new estimates of cryptocurrency based on the use of tools such as GARCH model, simple historical volatility (SHV) and developed by the authoring tool, which is based on the Chaikin method.
VIDEO ANALYSIS AND 3D DETECTION SIMULATION OF JUMP SHOT PRECISION FOR BASKETBALL PLAYERS

ABSTRACT With the rapid development and application of computer technology, the application of computer science knowledge in basketball is also more and more extensive. Based on genetic algorithm and the background subtraction method, video analysis and 3D detection simulation model of shot jump action precision were constructed in this study. According to the genetic algorithm search method, jump shot precision was analyzed, and the problems encountered in the actual shooting process of basketball players were studied and solved. The results show that this study is necessary and feasible.