Reviewerly/exHarmony huggingface dataset:
Hilbert stereo reconstruction algorithm based on depth feature and stereo matching

This paper proposes a Hilbert stereo reconstruction algorithm based on depth feature and stereo matching to solve the problem of occlusive region matching errors, namely, the Hilbert stereo network. The traditional stereo network pays more attention to disparity itself, leading to the inaccuracy of disparity estimation. Our design network studies the effective disparity matching and refinement through reconstruction representation of Hilbert’s disparity coefficient. Since the Hilbert coefficient is not affected by the occlusion and texture in the image, stereo disparity matching can conducted effectively. Our network includes three sub-modules, namely, depth feature representation, Hilbert cost volume fusion, and Hilbert refinement reconstruction. Separately, texture features of different depth levels of the image were extracted through Hilbert filtering operation. Next, stereoscopic disparity fusion was performed, and then Hilbert designed to refine the difference regression stereo matching solution was used. Based on the end-to-end design, the structure is refined by combining the depth feature extraction module and Hilbert coefficient disparity. Finally, the Hilbert stereo matching algorithm achieves excellent performance on standard big data set and is compared with other advanced stereo networks. Experiments show that our network has high accuracy and high performance.
NOT NATURAL, NOR NEUTRAL: THE CULTURAL CONFIGURATIONS OF SOCIAL MEDIA AFFORDANCES WITHIN CHILEAN INFLUENCER INDUSTRY

This paper explores the configurations of social media’s affordances within the Chilean influencer industry. Chile has a growing number of professional social media influencers who blur global norms and local markets, working with both local brands and international campaigns. We argue for situating affordances within a wider context in which the features of platforms acquire particular meanings. Our analysis focuses on two dynamics. On the one hand, we examine how the Chilean influencer industry is shaped by a technological frame (Bijker, 1995) that structures the valence of affordances. We show that affordances are not “naturally” or “neutrally” imagined by actors but rather culturally located within technological frames that shape the discourses, values, and practices from which they obtain cultural meaning. On the other hand, we analyze how affordances provide a material support for the temporal and spatial expansion of technological frames. Thus, cultural contexts and platforms’ features mutually constitute each other in ways that have not always been recognized in the scholarly literature about affordances. We situate negotiations about what it means to be an influencer in Chile, the role of intermediaries (e.g. branding agencies), communication with followers, and the global influencer industry as part of this mutually constitutive relationship.
BAYESIAN PREDICTION FOR THE EXPONENTIATED GAMMA DISTRIBUTION BASED ON UNIFIED HYBRID CENSORED SCHEME

In this article, we will study the exponentiated Gamma distribution and the maximum likelihood function of it and we study one-and two-sample Bayesian prediction intervals based on unified hybrid censored data.Because one-and two-sample Bayesian predictive survival function can not be computed in closedform so we will used the Markov Chain Monte Carlo method to obtain the approximate predictive survival function.Lastly, we used a real data set to find Bayesian prediction intervals.
Joints in Random Forests

Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist. Due to their discriminative nature, however, they lack principled methods to process inputs with missing features or to detect outliers, which requires pairing them with imputation techniques or a separate generative model. In this paper, we demonstrate that DTs and RFs can naturally be interpreted as generative models, by drawing a connection to Probabilistic Circuits, a prominent class of tractable probabilistic models. This reinterpretation equips them with a full joint distribution over the feature space and leads to Generative Decision Trees (GeDTs) and Generative Forests (GeFs), a family of novel hybrid generative-discriminative models. This family of models retains the overall characteristics of DTs and RFs while additionally being able to handle missing features by means of marginalisation. Under certain assumptions, frequently made for Bayes consistency results, we show that consistency in GeDTs and GeFs extend to any pattern of missing input features, if missing at random. Empirically, we show that our models often outperform common routines to treat missing data, such as K-nearest neighbour imputation, and moreover, that our models can naturally detect outliers by monitoring the marginal probability of input features.
Teachers and burnout: Biodanza SRT as embodiment training in the development of emotional skills and soft skills

The exposure to high stress conditions has led to an increasing prevalence of Burnout among teachers; this is due to emotional exhaustion, depersonalization and reduced personal accomplishment.Soft Skills play an important role in working performance, representing the set of emotional skills capable of promoting personal well-being.Giving priority to emotional support enables the development of protective factors and positive skills to counteract work-related stress and the onset of Burnout.Biodanza SRT is a body-mediated system that, through an Embodiment Training, promotes the development of personal empowerment by strengthening emotional skills, Soft Skills and the acquisition of Best Practices making individuals able, capable and competent in the personal management of tools and strategies to improve their emotional, affective, relational, social and professional life.
Low-rank robust online distance/similarity learning based on the rescaled hinge loss

An important challenge in metric learning is scalability to both size and dimension of input data. Online metric learning algorithms are proposed to address this challenge. Existing methods are commonly based on Passive/Aggressive (PA) approach. Hence, they can rapidly process large volumes of data with an adaptive learning rate. However, these algorithms are based on the Hinge loss and so are not robust against outliers and label noise. We address the challenges by formulating the online Distance/Similarity learning problem with the robust Rescaled Hinge loss function. The proposed model is rather general and can be applied to any PA-based online Distance/Similarity algorithm. To achieve scalability to data dimension, we propose low-rank online Distance/Similarity methods that learn a rectangular projection matrix instead of a full Mahalanobis matrix. The low-rank approaches not only reduce the computational cost but also keep the discrimination power of the learned metrics. Also, current online methods usually assume training triplets or pairwise constraints exist in advance. However, this assumption does not hold, and generating triplets using available batch sampling methods is both time and space consuming. We address this issue by developing an efficient, yet effective robust one-pass triplet construction algorithm. We conduct several experiments on datasets from various applications. The results confirm that the proposed methods significantly outperform state-of-the-art online metric learning methods in the presence of label noise and outliers by a large margin.
PEDAGOGICAL REFLECTION IN THE MODELING OF THE PURPOSES OF THE PROFESSIONAL PREPARATION OF THE FUTURE SPECIALISTS

The problem of the use of the pedagogical reflection in the process of the professional preparation of the future specialists has been actualized in the article, in particular based on the analysis of the experience of its use in the realization of the educational-professional program of the future bachelors of the law.The new knowledge has been represented with the complementarity of the known methodological approaches to the use of the pedagogical reflection in the modeling of the purposes of the educational-professional program, the new interpretation of the pedagogical reflection and the generalization of the experience of the use and the approbation of the reflexive procedures of the value-semantic direction.In the research of the question from the point of view, the contradictions of the realization of the pedagogical reflection have been determined; the factors of the influence on the modern process of the building of the educational-professional models have been installed; at the level of the philosophical methodology, the new worldview positions of Homo educandus have been explained, which influence significantly on its value-motivational choice of the personal-educational trajectory.The role of the pedagogical reflection in the process of the general cultural and the professional development of the future bachelor of the law has been installed.The possibilities of the pedagogical reflection in the procedure of the formation of the purposes of the professional preparation, in particular the modeling of the process of the teaching have been characterized.The methodological references-points have been presented with the need of the consideration in the educational programs' changes that have been occurred in the public consciousness and the worldview of the human at the present stage of the development of the community (the post-metaphysical thinking, the linguistic turn, the refusal of the recognition of the advantage of the theory over the practice, the concretization of the mind); the methodology of the value-semantic sending of the pedagogical reflection, the conceptualization of the pedagogical reflection as the special philosophical-educational procedure of the "embedding" of the student in the general cultural meanings of the past, of the present and of the future of the common vital activity of the subjects of the single cultural-educational space.
DEVELOPING E-BOOK FOR PRE-INTERMEDIATE GRAMMAR IN EFL CLASSROOM

The current article attempts to develop teaching material for intermediate grammar in form of electronic book (e-book) which utilize epub format. E-book is assumed to be an interesting learning sources since it utilizes digital media, in which it is suitable with the studentsâ€™ condition in digital era. This research was carried out by using Research and Development method. In developing e-book, several stages were conducted; pre-liminary research, prototyping phase, and assessment phase. From the assessment result, it was found that three experts in; material, language used and media declared that this e-book is appropriate to be used. Moreover, in term of studentsâ€™ perception, it was revealed that from aspect relevance (4.0); consistency (3.8); practice (4.4); and effective (4.2) which hence it can be said that students have positive perception toward the use of this e-book.. Therefore, it can be concluded that generally this e-book is very good and recommended to be used in pre-intermediate grammar classroom.
Analysis of English Learning Difficulty of Students in Elementary School

The low student learning outcomes are caused by students' thought that English is a difficult and boring subject so that students have difficulties understanding it. The purpose of this study is to analyze the types of learning difficulties experienced by students, the factors that affect learning difficulties, and to find out the efforts to overcome the difficulties of learning English in grade V of Elementary School. This research method uses descriptive qualitative. The research subjects were 22 grade V students and grade V teachers. The data collection procedures used were interviews, questionnaires, and documentation. Data analysis used is a qualitative approach. The results of this study were 16 students experienced difficulty learning English, and also did not understand when the teacher delivered English materials. The factors that cause difficulty in learning English for grade V students are internal factors, including students are still less motivated and less interested in taking English lessons, while the external factor is that the teacher does not use teaching aids and the methods used are less varied and not innovative. Based on the above results, it is showed that grade V Elementary School students still have difficulty learning English lessons. Therefore, it can be concluded that students' learning concerns will have an impact on low learning outcomes
Open Hydrosystem Information Network: Greece&amp;#8217;s new research infrastructure for water

&amp;lt;p&amp;gt;The Open Hydrosystem Information Network (OpenHi.net) is a state-of-the-art information infrastructure for the collection, management and free dissemination of hydrological and environmental information related to Greece&amp;amp;#8217;s surface water resources. It was launched two years ago as part of a the national research infrastructure &amp;amp;#8220;Hellenic Integrated Marine Inland water Observing, Forecasting and offshore Technology System&amp;amp;#8221; (HIMIOFoTS), which also comprises a marine-related component (https://www.himiofots.gr/). The OpenHi.net system receives and processes real-time data from automatic telemetric stations that are connected to a common web environment (https://openhi.net/). In particular, for each monitoring site it accommodates stage measurements, raw and automatically post-processed. Furthermore, in some specially selected sites time series related to water quality characteristics (pH, water temperature, salinity, DO, electrical conductivity) are provided. The web platform also offers automatically-processed information in terms of discharge data, statistics, and graphs, alerts for extreme events, as well as geographical data associated with surface water bodies. At the present time, the network comprises about 20 stations. However, their number is continuously increasing, due to the open access policy of the system (the platform is fully accessible to third-parties uploading their data). In the long run, it is envisioned that a national-scale hydrometric infrastructure will be established, covering all important rivers, lakes and reservoirs of the country.&amp;lt;/p&amp;gt;
Face Presentation Attack Detection in Ultraviolet Spectrum via Local and Global Features

The security of the commonly used face recognition algorithms is often doubted, as they appear vulnerable to so-called presentation attacks. While there are a number of detection methods that are using different light spectra to detect these attacks this is the first work to explore skin properties using the ultraviolet spectrum. Our multi-sensor approach consists of learning features that appear in the comparison of two images, one in the visible and one in the ultraviolet spectrum. We use brightness and keypoints as features for training, experimenting with different learning strategies. We present the results of our evaluation on our novel Face UV PAD database. The results of our method are evaluated in an leave-one-out comparison, where we achieved an APCER/BPCER of 0%/0.2%. The results obtained indicate that UV images in presentation attack detection include useful information that are not easy to overcome.
Expert System For Cupping Treatment Using Case Based Reasoning Method

Cupping is one of the methods of treatment taught by Rasulullah SAW (thibbun nabawi) which can be believed to be a cure because it is sourced from revelations that are certain, divine powers with the perfection of reason. Rasulullah SAW said: "Every disease has a cure. So if the drug is about an illness, the disease will be cured with the permission of Allah SWT. (HR. Muslim), cupping is the best choice for treatment, because it is cheap, lawful, safe, practical and follows the sunnah of Rasulullah SAW. If cupping is done properly and correctly, according to medical rules. With the limited knowledge and memory of the therapist in determining the location of the cupping point can be an error because if there is a new symptom or with a new disease that has never been overcome by a therapist becomes one of the obstacles in determining the location of the cupping point quickly and accurately. so the need for the application of an expert system that can facilitate the cupping therapists to determine the location of the cupping point to patients precisely and quickly. Expert system that will be created using a case based reasoning method. The implementation of the system uses the PHP programming language and MySQL database. With an expert system that uses the case-based reasoning method of cupping treatment can determine the cupping point precisely in accordance with the symptoms that are inputted to be processed into output in the form of solutions to overcome the disease by displaying conclusions in the form of cupping points so as to facilitate the services performed by therapist.
Methodology of Designing the Pithy Components of Elective Courses on the Higher Mathematics of Pedagogical Profile

The significance of the problem under study is due to the fact that the methodology of teaching students -future mathematics teachers of scientific knowledge is based on the idea of projecting the activities of a mathematics teacher with scientific knowledge on the process of studying the educational material of a school mathematics course.It includes the target (learning outcomes), substantive (pithy component systems), as well as processural (teaching methods and means) components.In this case, the main means of teaching students -future mathematics teachers is a system of tasks, the execution of which contributes to the formation of scientific knowledge, models the work of a mathematics teacher in the study of mathematical allegations.This article identifies the problems of designing and developing the content of elective courses in the system of higher pedagogical education, the psychological and pedagogical factors of students' assimilation of the content of educational (scientific) material in the study of elective disciplines determined, the theoretical foundations of designing the content of educational materials are determined, pithy meaningful components of elective courses based on the principles and criteria of pedagogical didactics have been developed, complementing the existing system of pithy components of mathematical disciplines, a methodology for the development and use of substantial components is proposed, the possibility of realizing their basic functions to form professional knowledge and skills of students -future mathematics teachers is justified, the basic requirements for designing the content of the educational material of elective disciplines are determined.
Klasifikasi Argument Pada Teks dengan Menggunakan Metode Multinomial Logistic Regression Terhadap Kasus Pemindahan Ibu Kota Indonesia di Twitter

Information on moving the Indonesian capital from Jakarta to East Kalimantan certainly raises the pros and cons conveyed by the Indonesian people through the Twitter social network. However, the pros and cons comments are of course varied, accompanied or not accompanied by arguments or even completely unrelated to the topic under discussion. User limitations in filtering out that information will certainly make it difficult for the public or even the government to analyze the information contained in the tweet. Therefore, a system was built that could classify tweets automatically into three classes, namely non-arbitration, argument and unknown. The method used in this research is Multinomial Logistic Regression (MLR). MLR is a generalization method of Logistic Regression and is used to classify 3 or more classes. Before the classification process is carried out, the tweet must be preprocessed in order to make the tweet clear of all existing noise. Feature extractions used in this study include unigram, bigram and trigram. In this study, there are 12 test scenarios and comparison methods, namely Artificial Neural Network (ANN). Of all the test scenarios the best results for the MLR method are SRU with an accuracy of 41,30%, while for the ANN method namely the RU scenario with an accuracy of 45,10%.
Stochastic Simulation Method for Reasoning of Dynamic Uncertain Causality Graph (DUCG)

Abstract Dynamic Uncertain Causality Graph (DUCG) is an innovative model developed recently on the basis of dynamic causality diagram (DCD) model, which has been proved to be reliable for fault diagnosis of nuclear power plants. DUCG can represent complex uncertain causal relationship graphically, with both high efficient inference and support of incomplete expression. Therefore, DUCG is often built much larger than Bayesian Network (BN). However, as the scale of real problem is so large, DUCG still has the problem of combination explosion. Stochastic Simulation is a common solution for it. However, it is almost impossible to use traditional sampling algorithms for DUCG because the joint probability of evidences could be less than 10−20. In this paper, the algorithm based on conditional stochastic simulation for the inference of DUCG was proposed. It obtains the probability of evidences by calculating the expectation of the conditional probability in sampling process instead of using the sampling frequency, which overcomes the difficulty. What’s more, this algorithm uses recursive reasoning method of DUCG to calculate conditional probability distributions of node for sampling, which means this process only depends on its parent nodes’ states. As a result, the algorithm features in lower time complexity. In addition, it has the potential of parallelization like other sampling algorithms. In conclusion, this algorithm is promising to provide a new solution to the inference of the DUCG in large-scale and complex state situations.
Refining Statecharts while preserving the structure and behavior

Statecharts are one of the most popular formalisms to model the behavior of reactive systems and are especially used in cyber-physical systems. Following common step-wise refinement strategies, modelers often develop and evolve Statecharts models incrementally to satisfy requirements and changes. Typically, modelers start with a more abstract model that satisfies important properties. They then refine the model gradually by introducing detail in steps that are guaranteed to preserve the important properties. The literature is rich with Statecharts refinement approaches, where the main concern is to preserve the behavior of the original model. However, they provide insufficient guarantees that the structure of the original model is preserved after refinement. This reduces the practicality of applying refinements: the structure of a Statecharts model encodes design decisions that developers must properly maintain.