Reviewerly/exHarmony huggingface dataset:
mSHAP: SHAP Values for Two-Part Models

Two-part models are important to and used throughout insurance and actuarial science. Since insurance is required for registering a car, obtaining a mortgage, and participating in certain businesses, it is especially important that the models which price insurance policies are fair and non-discriminatory. Black box models can make it very difficult to know which covariates are influencing the results. SHAP values enable interpretation of various black box models, but little progress has been made in two-part models. In this paper, we propose mSHAP (or multiplicative SHAP), a method for computing SHAP values of two-part models using the SHAP values of the individual models. This method will allow for the predictions of two-part models to be explained at an individual observation level. After developing mSHAP, we perform an in-depth simulation study. Although the kernelSHAP algorithm is also capable of computing approximate SHAP values for a two-part model, a comparison with our method demonstrates that mSHAP is exponentially faster. Ultimately, we apply mSHAP to a two-part ratemaking model for personal auto property damage insurance coverage. Additionally, an R package (mshap) is available to easily implement the method in a wide variety of applications.
Mining Intelligent Spatial Clustering Patterns: A Comparative Analysis of Different Approaches

Spatial data is a collection of information about the geospatial location and its events or characteristics. These spatial data are collected from the various positioning techniques viz. Global Positioning System (GPS), remote sensing, mobile devices, etc. A large amount of easily available spatial data drives the need to effectively uncover useful and interesting patterns using machine learning algorithms like Clustering. Clustering is a technique to group geospatial data possessing similar properties, characteristics to retrieve events or patterns of significance. This paper presents a comparative analysis of various algorithms on clustering and extensions of methods, conception, and their applications in various domains. The comparative analysis revels that the Density Peak Clustering algorithm has high accuracy on the IRIS dataset Finally, we present the research opportunities in spatial data clustering in the future enhancement section.
Estimation of the Global Solar Radiation Received on the Soil in Nalohou and Natitingou (Northern Benin)

The design of solar energy systems requires knowledge of the global solar radiation received by solar panels on the ground. However, for many developing countries such as the Benin Republic, there is a serious lack of radiometric stations everywhere in the country. The unavailability of these data, therefore, limits knowledge regarding the optimal functioning of solar installations. To cope with these difficulties, a method for estimating the global irradiation is developed at Nalohou AMMA CATCH station and Natintingou synoptic station, two sites in North-West Benin. This model depends on meteorological and astronomical parameters. Meteorological data such as sunshine duration, relative humidity, and air temperature have been collected on the synoptic station of Natitingou, and observed data of irradiation are collected from Nalohou. They are used to validate the model. The results show that the estimation of the mean square deviation value is around 10 -2 . Overall solar radiation is significant in this region and its annual average value varies between 9.80 kWh/m2 and 10.50 kWh/m2. This model can, therefore, be used by solar system operators to optimize energy production in the region.
Analysis of problem-based learning impact on academic performance according to the forgotten(fuzzy) effects theory

In a globalized, demanding, and virtualized environment, uncertainty is part of the academic performance. This condition affects all schools irrespective the education level, size, or location, and therefore the tendency to suffer the consequences of the volatility of phenomena, including the change rate in methodological strategies. In the present work, the factors that motivate the exposure of higher education centres are identified, using a model based on the principles of fuzzy or diffuse subsets, called the Forgotten (fuzzy) Effects. This methodology has allowed the gathering of qualitative information, derived from the appreciation of a group of experts, whose knowledge comes from daily experience. This characteristic gives it the empiricism necessary for a scientific contextualization. It has been found that factors such as a) lack of information, b) poor curricular planning, c) teacher's attitude, though other factors such as the hourly load, the lack of research culture, motivation and the constant changes of public policies, influence the exposure of schools to unforeseen variations of the methodological type. After determining the factors intervening in academic performance, the Problem-Based-Learning model was applied in the Engineering field; the average of the graduation subjects' examination (taken from 2017 to 2020), and the integration projects, showed 15% improvement in academic performance. This evidences that cause-effect factors such as the very theoretic management, or its contents, do not contribute to the enhancement of the students' graduation profile; neither they offer real solutions to the society.
간호학과 여학생의 절주의도 영향요인

Purpose: This study was performed to measure the influence of drinking motives, drinking refusal self-efficacy, and self-determination on the intention to control drinking among nursing students. Methods: A cross-sectional survey was done with the participation of 258 nursing students attending four universities in G city. Data collection was conducted from May to June, 2018, using a self-report questionnaire. Data were analyzed using the t-test, ANOVA, Scheffe test, Pearson’s correlation coefficients, and Hierarchical multiple regression. Results: The average scores for drinking motives, drinking refusal self-efficacy, self-determination, and intention to control drinking were 2.29, 4.54, 3.89, and 4.29 respectively. There were significant differences in the intention to control drinking according to grade, religion, and age of alcohol initiation. The factors influencing the intention to control drinking in nursing students were identified as self-determination (β=.24), drinking refusal self-efficacy (β=.17) and drinking motives (β=-.17). This factor explained 26% of the nursing students' intention to control drinking. Conclusion: Therefore, it is necessary to develop a alcohol education program to improve the drinking culture of nursing college students and to improve their self-regulation skills in order to help them form a desirable drinking habit, and it is also necessary to apply the developed intervention program and test the effect.
ENKRIPSI DATA COCKPIT VOICE RECORDER PADA PESAWAT MILITER DENGAN MENGGUNAKAN ALGORITMA SIMETRIS

Information confidentiality issues in the military institution is a must, so far the military aircraft should not be equipped with Blackbox as information storage media data plane (FDR, Flight Data Recorder) and information on the cockpit voice (CVR, Cockpit Voice Recorder). On the other hand, the information is needed for the investigation of an accident, but it also Blackbox can be used as guidance in detecting damage of the aircraft systems at the time of treatment. In this thesis was discussed about alternative solutions for problems that are opposite to each other, especially in the media Cockpit Voice Recording (CVR). Solutions offered in this thesis is the application of encryption on the cockpit voice recording systems. The model chosen is encrypted symmetric encryption streamchiper because its strength lies in the confidentiality of password used. After doing some experiments on the type of password that is used, optimal results obtained by applying the password in the form of sound files with audio frequencies. The encryption method became one of the methods chosen for the security of information on the Cockpit Voice Recorder (CVR) with the settings and key generation algorithm will determine the level of encryption security. Information security management system data on the Cockpit Voice Recorder (CVR) military aircraft can be applied in a manner keeping the password to decrypt. Decrypt the voice data is used to determine the cause of the accident and taken as a lesson to improve the safety of military aircraft.
Enseñanza remota durante la pandemia por COVID-19 en la Universidad Tecnológica del Norte de Aguascalientes

This article presents an exploratory study of the experiences of university teachers during Emergency Remote Teaching (ERE) in response to COVID-19. Teaching practices, conditions for remote teaching and teachers’ working conditions were investigated under a systematic approach. An internet survey was applied to all teachers at the Universidad Tecnológica del Norte de Aguascalientes. Results show an overload of academic activities among teachers, an increase in working hours due to planning and assessment in online environments, and other sources of stress among teachers. Some difficulties faced by the institution are hightlighted and conclusions emphasize the importance of ensuring inputs that contribute to improving teaching and learning environments, preserving the health of teachers and continuing the research on the influence of different teaching conditions on teaching practices.
Laws and Emerging Technologies

New technologies and so-called communication and information technologies are transforming our society, the way in which we relate to each other, and the way we understand the world. By a wider extension, they are also influencing the world of law. That is why technologies will have a huge impact on society in the coming years and will bring new challenges and legal challenges to the legal sector worldwide. On the other hand, the new communications era also brings many new legal issues such as those derived from e-commerce and payment services, intellectual property, or the problems derived from the use of new technologies by young people. This will undoubtedly affect the development, evolution, and understanding of law. This Special Issue has become this window into the new challenges of law in relation to new technologies.
ABOUT POSSIBILITY OF USING INTERNATIONAL SACERS RATING SCALE FOR THIS PURPOSE FOR ASSESSING EDUCATIONAL INSTITUTIONS OF THE REPUBLIC OF KAZAKHSTAN

The article emphasizes the relevance of the problem to improve the quality of education, as well as the possibility of using international SACERS rating scale for this purpose. In order to show the advantages of assessing the environment of educational institutions according to the SACERS scale, a comparative analysis of the criteria for assessing educational institutions of the Republic of Kazakhstan was carried out. At the same time, it was noted that the assessment of educational institutions with the SACERS scale has been carried out in Moscow schools since 2015. The evidence that Kazakhstan has begun to pay attention to this issue is the inclusion of the discipline "SACERS assessment of learning conditions" in the curriculum of the educational program 7M011 – Pedagogy and Psychology, Abay Kazakh Pedagogical University. Master students conduct their first studies in the discipline and provide their own conclusions and recommendations. The article also considers that the SACERS assessment scale can help to assess the educational environment of schools in the country identifying its strengths and weaknesses. Then it is possible to comply the construction of new educational institutions with international standard, so there is an oportunity to improve pupils' knowledge, involvement and creativity as well.
USING TOPIC MODELING FOR COMMUNITIES CLUSTERIZATION IN THE VKONTAKTE SOCIAL NETWORK

Topic modeling is one of the most widely used methods in text analysis. It can be used to select topics as well as to find the topics distributed in each document from the corpus. In this article, we present a method for clustering communities in the social network VKontakte (the most popular Russian social network) using topic modeling. As a communities sample a set of groups for which several students of Tomsk State University are subscribed was selected. There were about 7,000 of them in this set. The article describes the method by which the text corpus was formed, as well as mathematical modeling using two popular classical methods LDA and ARTM. A detailed description of these models, quality assessment criteria, and the main practical techniques used by the authors in training the models are given. The aggregated results of clustering communities by topic are also presented. There are also described a method for expert evaluation of community topics based on visualization of the words that make up the lexical core of the topic.
Issues of increasing the quality of master training based on a competence approach

The article reveals the essence and content of the pedagogical model of formation of readiness of undergraduates for research activities. The model consists of motivational-target, content-technological, diagnostic-evaluation and effective-final components.
Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking

Dialogue State Tracking (DST), which is the process of inferring user goals by estimating belief states given the dialogue history, plays a critical role in task-oriented dialogue systems. A coreference phenomenon observed in multi-turn conversations is not addressed by existing DST models, leading to sub-optimal performances. In this paper, we propose Coreference Dialogue State Tracker (CDST) that explicitly models the coreference feature. In particular, at each turn, the proposed model jointly predicts the coreferred domain-slot pair and extracts the coreference values from the dialogue context. Experimental results on MultiWOZ 2.1 dataset show that the proposed model achieves the state-of-the-art joint goal accuracy of 56.47%.
Learning Robot Exploration Strategy with 4D Point-Clouds-like Information as Observations

Being able to explore unknown environments is a requirement for fully autonomous robots. Many learning-based methods have been proposed to learn an exploration strategy. In the frontier-based exploration, learning algorithms tend to learn the optimal or near-optimal frontier to explore. Most of these methods represent the environments as fixed size images and take these as inputs to neural networks. However, the size of environments is usually unknown, which makes these methods fail to generalize to real world scenarios. To address this issue, we present a novel state representation method based on 4D point-clouds-like information, including the locations, frontier, and distance information. We also design a neural network that can process these 4D point-clouds-like information and generate the estimated value for each frontier. Then this neural network is trained using the typical reinforcement learning framework. We test the performance of our proposed method by comparing it with other five methods and test its scalability on a map that is much larger than maps in the training set. The experiment results demonstrate that our proposed method needs shorter average traveling distances to explore whole environments and can be adopted in maps with arbitrarily sizes.
LibGuides: OPEN FL: LibreTexts

Guide for the open and affordable learning community of Florida, OPEN FL. All Libretexts libraries are accessible to everyone via the internet, completely free.
Analysis of Film Budget and Profit using the Bisecting K-Means Algorithm

As the development of the film industry has become increasingly competitive in the last few decades, the film ecosystem needs to get more attention for stakeholders involved in it to continue to carry out various innovative actions and create more effective creative economy marketing strategies. By utilizing existing datasets, film content producers can build a recommendation system that can support the process of assessing business models and analyzing the concept of creative film products that will be launched in the existing film market so that later planning and design of more profitable film production concepts can be produced. And sustainability in terms of funding (budget) and projected revenue (gross profit). This research is a form of elaboration on the design of a recommendation system using the Bisecting K-Means Algorithm to be able to produce an analysis result in the form of classification of various film products contained in a dataset that has been collected as many as 5048 rows of data by taking 1000 lines of data. As a special data allocation for conducting training on the system. The data that has been allocated will then be carried out by the clustering process by dividing into 4 (four) different clusters, each of which is the result of regression based on predetermined parameters and forming a centroids which is the average value of all cluster nodes built.
Active Learning for Distributionally Robust Level-Set Estimation

Many cases exist in which a black-box function $f$ with high evaluation cost depends on two types of variables $\bm x$ and $\bm w$, where $\bm x$ is a controllable \emph{design} variable and $\bm w$ are uncontrollable \emph{environmental} variables that have random variation following a certain distribution $P$. In such cases, an important task is to find the range of design variables $\bm x$ such that the function $f(\bm x, \bm w)$ has the desired properties by incorporating the random variation of the environmental variables $\bm w$. A natural measure of robustness is the probability that $f(\bm x, \bm w)$ exceeds a given threshold $h$, which is known as the \emph{probability threshold robustness} (PTR) measure in the literature on robust optimization. However, this robustness measure cannot be correctly evaluated when the distribution $P$ is unknown. In this study, we addressed this problem by considering the \textit{distributionally robust PTR} (DRPTR) measure, which considers the worst-case PTR within given candidate distributions. Specifically, we studied the problem of efficiently identifying a reliable set $H$, which is defined as a region in which the DRPTR measure exceeds a certain desired probability $\alpha$, which can be interpreted as a level set estimation (LSE) problem for DRPTR. We propose a theoretically grounded and computationally efficient active learning method for this problem. We show that the proposed method has theoretical guarantees on convergence and accuracy, and confirmed through numerical experiments that the proposed method outperforms existing methods.
LibGuides: Diverse Reads: The Why, How, and Where: Home

A resource for youth services librarians and teachers on diverse read selections and programming.
Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation

In this work, we aim to enhance model-based face reconstruction by avoiding fitting the model to outliers, i.e. regions that cannot be well-expressed by the model such as occluders or make-up. The core challenge for localizing outliers is that they are highly variable and difficult to annotate. To overcome this challenging problem, we introduce a joint Face-autoencoder and outlier segmentation approach (FOCUS).In particular, we exploit the fact that the outliers cannot be fitted well by the face model and hence can be localized well given a high-quality model fitting. The main challenge is that the model fitting and the outlier segmentation are mutually dependent on each other, and need to be inferred jointly. We resolve this chicken-and-egg problem with an EM-type training strategy, where a face autoencoder is trained jointly with an outlier segmentation network. This leads to a synergistic effect, in which the segmentation network prevents the face encoder from fitting to the outliers, enhancing the reconstruction quality. The improved 3D face reconstruction, in turn, enables the segmentation network to better predict the outliers. To resolve the ambiguity between outliers and regions that are difficult to fit, such as eyebrows, we build a statistical prior from synthetic data that measures the systematic bias in model fitting. Experiments on the NoW testset demonstrate that FOCUS achieves SOTA 3D face reconstruction performance among all baselines that are trained without 3D annotation. Moreover, our results on CelebA-HQ and the AR database show that the segmentation network can localize occluders accurately despite being trained without any segmentation annotation.
Algorithmic Collusion: Fear of the Unknown or too Smart to Catch?

Algorithms, which businesses use more and more to set pricing strategies with each passing day, could be pro-competitive and provide significant efficiencies. Depending on how firms use them, however, they can also potentially restrict competition and harm consumers. Scholars and enforcers debate on the right scope of the theory of harm as to using algorithms in critical competition parameters, particularly in pricing strategies. Yet, one question remains open: if firms use algorithms that may tacitly collude through machine learning - particularly deep learning technologies, will that alone be sufficient to hold them liable for a competition law infringement? This article discusses the current limits of the collective knowledge on this subject and explores what guidance could still be provided to businesses. The article argues that the solution does not lie in taking premature regulatory actions without sufficient empirical evidence to justify any shift away from the traditional concepts of tacit collusion, or without proper guidance for companies to avoid the risk of legal exposure. The need is evident for further research on how self-learning algorithms operate in real-life settings, which could start defining a “red zone” for businesses to watch out and for enforcers to focus their energy and resources.
Smart Keyboard Using Internet of Things

A keyboard is a main input device for any communication or any computer device mainly the portable one. Our objective was to interact between a computer and human-being, a system of keyboard is represented by means of camera and Image Processing. Our physical keyboard consists of a camera, Paper, Raspberry-pi-3b+ and a stand (approx. 35cm from the surface). The camera is used to capture several frames of finger tip to determine the button pressed. Every Keystroke can be determined by the camera using Imager thresholding and contours. Our Experiment shows that it is a reliable sense of technology with high accuracy.
The Influence of Social Media on Students’ Reading Ability

The objectives of the research is to find out whether is any positive and significant influence of influence of the use social media on students’ reading ability at the eighth grade of SMP Negeri 4 Purworejo in the academic year of 2019/2020.. The sample involved 50  students by applying purposive sampling. The population of this study is the eighth grade students of SMP Negeri 4 Purworejo in the academic year of 2019/2020. The total of the eighth grade students is 256 students. formula.The instruments to take the data by questionnaire to observing the use of qqqqcategorized in sufficient level   with M = 74.56, the SD = 9.73 and S = 94,741 at the range 70-79. (2) the students’ reading ability can be categorized in excellent level with M = 77.8, the SD = 16.394and S = 268.776 at the range 80 – 100. (3) the result of hyphotesis test shows that the value of correlation is 0.312 which is higher  than 0.279 rtable 5%. It means that the use of social media influence the students’ reading ability as big as 9.8% and the residue is 90.2% influenced by other factor. The significant value of regression based on SPSS 16.0 is 0.027which is lower than 0.05. It means that there is a positive and significant influence between two variables