Reviewerly/exHarmony huggingface dataset:
A Data-Driven Approach for Electric Energy Equipment Using Wireless Sensing Technology in the Context of Carbon Neutrality

In this paper, we use wireless sensing technology to conduct an in-depth study and analysis of data-driven power energy equipment in the context of carbon neutrality. For the high-order uncertainty of renewable energy power generation and the nonlinearity of the tidal equation, a set of orthogonal bases under arbitrary probability space can be constructed by itself using the high-order information of renewable energy power generation statistics, and then, polynomials fit the state variables such as voltage in the tidal equation using the orthogonal bases and calculate the fitting parameters using the stochastic Gallatin integration method. Based on the analysis of the online monitoring project of the main power equipment in the substation, a substation power equipment condition monitoring system is designed to realize the real-time monitoring of the temperature status of power equipment, substation smoke and temperature, and humidity environment, and the feasibility and advanced of the system are verified by elaborating the analysis. Using wireless transmission to send real-time temperature information to the monitoring background, the infrared thermal imaging online monitoring system is designed around the front-end data acquisition system, transmission network, background data processing, and display module. Saving-investment equilibrium means that total investment equals total savings; government budget balance can be achieved through government savings or deficits. When both supply chain approaches enter into the same environmental competition, the added value that consumers value the product because of the carbon-neutral approach becomes smaller in the coefficient constraint of green effort investment cost due to the competition between the two supply chains, which in turn increases the green investment cost of the supply chain with the green effort carbon-neutral approach, indicating that in the case of competition, consumers, when faced with two products produced by two supply chains, are interested in the product with the carbon-neutral approach has less value-added, and some of the demand is shifted to the product not produced with the carbon-neutral approach.
Lighting-and Personal Characteristic-Aware Markov Random Field Model for Facial Image Relighting System

A learning-based image relighting framework is proposed for automatically changing the lighting conditions of facial images from one lighting source to another.Given only a 2D unseen facial testing image, the framework automatically infers the highlight or shadow areas in the relighted image in accordance with the specific facial characteristics of the input image using a learned non-parametric Markov random field model of the facial appearance correlation between the source and target lighting conditions.The proposed framework first decomposes the input image into its global and local components, where these components relate mainly to the lighting and detailed facial appearance characteristics of the image, respectively.The two components are then processed independently to ease the problem of insufficient training samples and to properly analyze the local contrast, overall lighting direction effects, and personal feature characteristics of the unseen subject.Specifically, the global and local components are processed by a lighting-aware classifier and a personal characteristic-aware classifier, respectively, in order to determine the semantic factors of the facial region.The semantic factors of the facial region are then used to update the Markov random field model of the facial appearance correlation between the source and target lighting conditions and to produce lighting enhancement matrices for the lighting components and facial characteristic components, respectively.Finally, the lighting enhancement matrices are applied to the original decomposition images, which are then integrated to obtain the final relighted result.The experimental results show that the proposed image relighting framework generates vivid and recognizable results despite the scarcity of training samples.Furthermore, the relighted results successfully simulate the individual lighting effects produced by the specific personal characteristics of the input image, such as the nose and cheek shadows.The effectiveness of the proposed framework is demonstrated by means of face verification tests, in which input images taken under side-lighting conditions are transformed to normal lighting conditions and are then matched with a dataset of ground truth images also taken under normal lighting conditions.
Helping researchers to solve their structures: automation and user guidance in <i>CCP4 Cloud</i>

Structure solution in macromolecular crystallography is not always a straightforward process and it may be rather difficult for structural biologists without advanced training.A trained crystallographer exploits an extended set of approaches and tricks, based on the analysis of several indicators, general assessment of the case, and developed strategies for dealing with a particular class of problems.If such an approach, used by an expert, can be formalised in terms of an algorithm, then it can be implemented as a computer program or automated user advice system to help users solving structures quicker and with a higher success rate.It is not surprising then, that programs for macromolecular crystallography are moving towards full automation, taking off burden from researchers and lowering the entry barriers for novice users.During the last few years, substantial progress has been made towards automation of the whole macromolecular structure determination process.There are a number of examples of successful automatic solutions for various stages of structure determination, such as molecular replacement, experimental phasing, and refinement [1][2][3][4][5].In this communication, we report two novel automation features implemented in CCP4 Cloud [6], the new system for solving macromolecular structures online, released with CCP4 Software Suite 7.1 in 2020.Automated user advice framework, named Verdicts, provides simple graphical representation of results quality with detailed analysis of points for improvement as part of every task (e.g., refinement) report.The analysis includes suggestions on what could be done in order to improve the result (i.e., which parameters could be optimised).Then, the task can be re-run with the suggested parameters, which can be further adjusted by the user as appropriate.Another automation feature, Workflows, was designed for unfolding structure solution Projects, or their parts, automatically using user-supplied data.Such automatically initiated and unfolded Projects may include a number of tasks, arranged in branching Project Trees as if this were done by the user themselves.In common cases without complications, this may result in structure solved, and if not, then a starting Project is offered to the user for analysis and further manipulations, where simple, first-order structure solution attempts are already performed.Any task or branch of the starting Project may be cloned and re-run with optimized parameters, and new tasks may be added as needed.Workflows combine automation and human expert skills, and, therefore, represent an excellent starting point for users with different level of expertise, ranging from novices to experienced crystallographers.Workflows are particularly useful in a common case of processing large sets of isomorphous crystals, because, once structure is solved in one crystal, the process is well-repeatable in systems with moderate modifications.
A Heuristic Sequencing Method for Time Optimal Tracking of Open and Closed Paths

Tracking sequences of predefined open and closed paths is of crucial interest for applications like laser cutting and similar production processes.These distinct paths are connected by non-productive, four times continuously differentiable trajectories, which also account for the overall process time.Heuristic methods are applied in order to find a proper sequencing of the open and closed path and thereby minimize the overall process time subject to constraints given by the system limits.To this end the exact traversing times of the non-productive linking trajectories are computed, which also have to be time optimal subject to the system limits.Finally two heuristic algorithms are presented and compared with respect to solution quality and calculation time using randomly generated problems.
Pengaruh Media Pembelajaran Crosword Puzzle Dengan Model Discovery Learning Terhadap Hasil Belajar Siswa Pada Konsep Gelombang Bunyi Di SMAN 1 Boliyohuto

This study aims to determine the effect of crossword puzzle learning media with discovery learning models on student learning outcomes on the concept of sound waves. This research is a quasi-experimental type of research using one class (one samples) only the experimental class using the One Group pretest posttest design. In this study, learning was conducted three times online with the discovery learning model. The instrument used in data collection is an essay test of learning outcomes. The method used to analyze the data is descriptive analysis technique, namely normality test, hypothesis testing with a significance level of 5%. Based on hypothesis testing using parametric statistics t test obtained tCalculate (5.789 &gt; ttabel (2.024) with a significant level = 0.05. where hypothesis one (H_1) is accepted. This shows that there are differences in student learning outcomes before and after learning using learning media crossword puzzle with the discovery learning model. This result is also shown from the increase in student learning outcomes on the posttest test which is higher than in the pretest test with an average value of 76.03% and 65.71%. Thus the use of crossword puzzle media with the discovery learning model affects the results student learning
An Exploratory Study on the Difficulties Faced by First-Generation Learners in Writing Skills

Language is a complex process, especially its written mode. To write better, one must be well acquainted with vocabulary and contextual expressions. The difficulties faced by the first generation learners of English in India are numerous because exposure is a bigger issue. The remedies for their difficulties are elaborately discussed in this paper. The aim is to throw light on the obstacles of the first generation learners at the tertiary level who had studied English as the second language or as a medium of instruction at the school level. Eighty such participants were selected using random sampling technique and a semi￾structured interview was conducted. A mixed-method approach was used and a questionnaire was administered. The present study identifies difficulties in writing skills faced by first generation learners. And also, this paper focuses on the impact of difficulties in writing skills on them. Additionally, remedial measures to enhance writing skills are also discussed.
Fair Outlier Detection Based on Adversarial Representation Learning

Outlier detection aims to identify rare, minority objects in a dataset that are significantly different from the majority. When a minority group (defined by sensitive attributes, such as gender, race, age, etc.) does not represent the target group for outlier detection, outlier detection methods are likely to propagate statistical biases in the data and generate unfair results. Our work focuses on studying the fairness of outlier detection. We characterize the properties of fair outlier detection and propose an appropriate outlier detection method that combines adversarial representation learning and the LOF algorithm (AFLOF). Unlike the FairLOF method that adds fairness constraints to the LOF algorithm, AFLOF uses adversarial networks to learn the optimal representation of the original data while hiding the sensitive attribute in the data. We introduce a dynamic weighting module that assigns lower weight values to data objects with higher local outlier factors to eliminate the influence of outliers on representation learning. Lastly, we conduct comparative experiments on six publicly available datasets. The results demonstrate that compared to the density-based LOF method and the recently proposed FairLOF method, our proposed AFLOF method has a significant advantage in both the outlier detection performance and fairness.
Embodied Gambling—Investigating the Influence of Level of Embodiment, Avatar Appearance, and Virtual Environment Design on an Online VR Slot Machine

Slot machines are one of the most played games by players suffering from gambling disorder. New technologies like immersive Virtual Reality (VR) offer more possibilities to exploit erroneous beliefs in the context of gambling. Recent research indicates a higher risk potential when playing a slot machine in VR than on desktop. To continue this investigation, we evaluate the effects of providing different degrees of embodiment, i.e., minimal and full embodiment. The avatars used for the full embodiment further differ in their appearance, i.e., they elicit a high or a low socio-economic status. The virtual environment (VE) design can cause a potential influence on the overall gambling behavior. Thus, we also embed the slot machine in two different VEs that differ in their emotional design: a colorful underwater playground environment and a virtual counterpart of our lab. These design considerations resulted in four different versions of the same VR slot machine: 1) full embodiment with high socio-economic status, 2) full embodiment with low socio-economic status, 3) minimal embodiment playground VE, and 4) minimal embodiment laboratory VE. Both full embodiment versions also used the playground VE. We determine the risk potential by logging gambling frequency as well as stake size, and measuring harm-inducing factors, i.e., dissociation, urge to gamble, dark flow, and illusion of control, using questionnaires. Following a between groups experimental design, 82 participants played for 20 game rounds one of the four versions. We recruited our sample from the students enrolled at the University of Würzburg. Our safety protocol ensured that only participants without any recent gambling activity took part in the experiment. In this comparative user study, we found no effect of the embodiment nor VE design on neither the gambling frequency, stake sizes, nor risk potential. However, our results provide further support for the hypothesis of the higher visual angle on gambling stimuli and hence the increased emotional response being the true cause for the higher risk potential.
Implementation of Logistic Regression Classification Algorithm and Support Vector Machine for Credit Eligibility Prediction

Credit is a provision of money or bills that can be equated with it, the provision of loans or credit. A good credit analysis is very necessary, because it is one of the most important processes in the form of an investigation regarding the smooth or substandard credit repayments. The stages of identifying and predicting customers properly and correctly can be done before the loan process. This is done by examining the historical data of the customer's loan. At this time this activity is an effort made by the banking industry in dealing with credit risk problems. In this research, researchers will apply several data mining classification methods, including Logistic Regression algorithms and Support Vector Machines to predict creditworthiness. The dataset used 481 record motorized vehicle loan data, both problematic and non-problematic. The input variables in this study consisted of thirteen variables, including marital status, number of dependents, age, residence status, home ownership, occupation, employment status, company status, income, down payment, education, length of stay, and housing conditions. From the results of research and testing, the performance of the Logistic Regression model for predicting creditworthiness provided an accuracy rate of 94.81% with an area under the curve (AUC) value of 0.987. While the performance of the Support Vector Machine model provides an accuracy of 94.19% with an area under the curve (AUC) value of 0.978. Based on the T-Test test, the Logistic Regression method has the same performance compared to the Support Vector Machine.
Blockchain based Alimentary Agricultural Product Provenance: A Manufacturing Synthesis

The research proposes a blockchain based traceability system on agricultural products supply chain, where each exchange is rearranged as a block in the chain and is noticeable to the authorized members concerned. The Paper is based on a methodology in which an effective method for recognizing a guaranteed product using blockchain innovation is used; that is, to store information from chemical lab test in sequence and chronological order, so that they are difficult to control a short time later. Here we try to understand how blockchain technology is applied in tea powder production and delivery. The data blocks are permanent as any change to the recorded data, breaks the link. In the general process of converting tea leaves to the tea powder bundles, the traceability framework proposes security, transparency and safety. The need for a traceability system for the food supply chain is unavoidable when irrational chemicals and hazardous synthetic substances are manufactured, adulterated, processed and used. Adulterants are to a great extent added to food products to diminish production costs, sell it at a more significant cost or to attract the purchaser in some other way. To beat these issues, food supply industry needs a transparent system which empowers a purchaser to confirm the structure of each food product from the farm to the retailer.
Training Future Primary School Teachers to Organize Game-Based Music Activities

The article analyzes the current role of a game as a universal cognitive method based on relevant literary sources to justify the need to reform the system of professional training for primary school teachers. The article aims to develop, theoretically justify and experimentally verify the newly modelled pedagogical conditions for training future primary school teachers to organize game-based music activities of primary school pupils, as well as methods of implementing these conditions in teacher education. Importantly, the article demonstrates how the author’s methodology for implementing these pedagogical conditions has been developed, implemented and quasiexperimentally verified. As part of preliminary, technological-practical, and creative-organizational stages of such professional training, the new model involves the consistent use of the author’s techniques and algorithms to organize students’ activities in the framework of professional-oriented courses, research projects, as well as an optional course “Practice of Organizing Game-Based Music Activities for Primary School Pupils”. The international relevance of the article lies in developing and implementing universal educational conditions to train future primary school teachers to organize game-based music activities under today’s conditions of globalization and informatization. The obtained data indicate some positive changes in quasi-experimental groups due to the implemented pedagogical conditions at the final stage of the research.
Research and forecasting of educational process parameters by using artificial intelligence

In this paper we present the results of an interdisciplinary research based on the application of big data, date science, artificial intelligence and machine learning methods in educational analytics. Artificial intelligence techniques applied for the analysis of depersonalized data stored in the information and analytical system "E-education in the Republic of Tatarstan" from 2015 to 2020. BigData technologies were used in this work to perform high-performance computing related to initial preprocessing of raw data in computation cluster. By using the methods of artificial intelligence, we modelled one of the most important stages in the formation of the educational trajectories of schoolchildren, associated with the fact that after the 9th grade, schoolchildren either continue their studies in high school (grades 10-11), or move to the professional educational organizations. As the input data for neural network training, we used a vector containing the average marks for all quarters of pupils, obtained by using high-performance Dask-based cluster data processing system from initial raw data. We concluded that multi-layer neural network with two hidden layers was able to predict the pupil’s pass to 10th grade, and achieved the best performance with classification accuracy exceeding 70%. Also, the performance of trained neural network had been analyzed by visualization of Receiver Operator Characteristic (ROC)-curve and by calculation of recall, precision, specificity and area covered by the ROC-curve (AUX) parameters.
Answer Classification via Machine Learning in Community Question Answering

As a new type of knowledge sharing platform, the community question answer website realizes the acquisition and sharing of knowledge, and is loved and sought after by the majority of users. But for multi-answer questions, answer quality assessment becomes a challenge. The answer selection in CQA (Community Question Answer) was proposed as a challenge task in the SemEval competition, which gave a data set and proposed two subtasks. Task-A is to give a question (including short title and extended description) and its answers, and divide each answer into absolutely relevant (good), potentially relevant (potential) and bad or irrelevant (bad, dialog, non-English, other). Task-B is to give a YES/NO type question (including short title and extended description) and some answers. Based on the answer of the absolute correlation type (good), judge whether the answer to the whole question should be yes, no or uncertain. This paper first preprocesses this data set, and then uses natural language processing technology to perform word segmentation, part-of-speech tagging and named entity recognition on the data set, and then perform feature extraction on the preprocessed data set. Finally, SVM and random forest are used to classify on the basis of feature extraction, and the classification results are analyzed and compared. The experiments in this paper show that SVM and random forest methods have good results on the data set, and exceed the multi-classifier ensemble learning method and hierarchical classification method proposed by the predecessors.
Mapping of Landslide Areas Using Geographic Information Systems In Kebonharjo Kulon Progo Regency

Indonesia is a country that is surrounded by a ring of volcanoes or commonly known as a ring of fire, the implication is there are many disaster caused by tectonic processes such as collisions between plates, earthquakes, and landslides. Landslides are disaster that often occur in Indonesia, especially the study area in Kebonharjo Village. Disaster events cause damage to both the physical and non-physical environment. The mapping of the area used the Geographic Information System method based on actual data provided by the Regional Disaster Management Agency of Kulon Progo Regency in the form of hamlet areas that are prone to landslides and areas that occur landslide disaster. Other data is data from the Geospatial Information Agency to facilitate direct observation and mapping in the research field. The method used is a survey method with a quantitative and qualitative analysis approach. Qualitative analysis was carried out using the method of analyzing the topography and geomorphology of the research area. Quantitative analysis was carried out based on landforms classified with aspects of slope, rock type, soil type, and rainfall intensity. The results of the analysis of these data will produce a landslide hazard map in the research area as a reference that can be used by the local government to determine appropriate environmental management strategies and policies in Kebonharjo Village.