Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: GPGPU-Based Fast Calculation Of Large Matrices And Dynamic Prediction Of Sports Injury Images

Sports injuries are usually direct impact and application of force than the body is structurally acceptable, is caused by excessive use. Common injuries, bruises, sprains, strains will include joint injury and nose bleeding. General-Purpose Computation on Graphics Processing Units (GPGPU) is Central Processing Units (CPU) usually using a graphics processing a processing unit a computer graphics calculations Graphics Processing Units (GPU), traditionally the central processing unit run the calculation of processing applications. Dynamic prediction, the patient is assumed to have been exposed to danger at a predictable point in time, has an intuitive representation associated with the estimated probability of the occurrence. The Drawback of the existing method does not give proper sports injury images and calculation. The proposed method of the Binary Support Vector System (BSVS) algorithm is to give the proper classification, result, and prediction.
ON THE TRUTH-CONVERGENCE OF OPEN-MINDED BAYESIANISM

Abstract Wenmackers and Romeijn [38] formalize ideas going back to Shimony [33] and Putnam [28] into an open-minded Bayesian inductive logic, that can dynamically incorporate statistical hypotheses proposed in the course of the learning process. In this paper, we show that Wenmackers and Romeijn’s proposal does not preserve the classical Bayesian consistency guarantee of merger with the true hypothesis. We diagnose the problem, and offer a forward-looking open-minded Bayesians that does preserve a version of this guarantee.
Comparative Analysis of Various Software Development Approaches

This research paper deals with the software management processes concern with the software development. Varity of SDLC models have been designed accepted and used for software development. This model give a theoretical guideline regarding development of the software. Each SDLC has its own pros and cons. The aim of this paper is to analysis some methodologies that gives comparison on SDLC models by studied available, tools, techniques and methodologies of SDLC models.
Construction of Employment Ability Simulation Training System in Artificial Intelligence Era

Abstract Artificial intelligence has brought a short-term destructive substitution effect on the employment of college graduates. At present, the lack of employability development in colleges and universities is mainly manifested in four aspects: two-way choice of supply and demand, individual’s growth, vocational education and artificial intelligence environment demand. On the basis of employment intelligent module and as the guidance of individual’s growth needs, this paper constructs a graduate employment simulation training system for different levels, different jobs “work environment”, with the interaction of professional assessment and planning system, employment information intelligent system, career counseling.
Research on Water Environment Monitoring Technology Based on Computer Hydrochemical Model

The following topics are dealt with: Internet; feature extraction; neural nets; data mining; learning (artificial intelligence); artificial intelligence; cloud computing; computer vision; object detection; organisational aspects.
On the Development of a Classification Based Automated Motion Imagery Interpretability Prediction

Motion imagery interpretability is commonly represented by the Video National Imagery Interpretability Rating Scale (VNIIRS), which is a subjective metric based on human analysts’ visual assessment. Therefore, VNIIRS is a very time-consuming task. This paper presents the development of a fully automated motion imagery interpretability prediction, called AMIIP. AMIIP employs a three-dimensional convolutional neural network (3D-CNN) that accepts as inputs many video blocks (small image sequences) extracted from motion imagery, and outputs the label classification for each video block. The result is a histogram of the labels/categories that is then used to estimate the interpretability of the motion imagery. For each training video clip, it is labeled based on its subjectively rated VNIIRS level; thus, the required human annotation of imagery for training data is minimized. By using a collection of 76 high definition aerial video clips, three preliminary experimental results indicate that the estimation error is within 0.5 VNIIRS rating scale.
Quality Control of Phase Change Energy Storage and Thermal Insulation Building Materials based on Optical Fiber Sensing Technology

In recent years, the use of phase change energy storage and thermal insulation building materials has gradually expanded. In order to effectively control the use effect of phase change energy storage and thermal insulation building materials, a quality control method for phase change energy storage and thermal insulation building materials based on optical fiber sensing technology is proposed. Complete the selection of optical fiber sensor components to achieve high-precision collection and processing of building material data. The main evaluation basis is obtained by using extension set theory, and the data association degree of building material quality is studied. The comprehensive evaluation analysis method is used to complete the quality evaluation and control of building materials. So far, the design of the quality control method for phase change energy storage thermal insulation building materials based on optical fiber sensing technology is completed. Through the comparison of experiments, it can be seen that this method has a better quality control effect than the traditional method of phase change energy storage insulation building materials.
Organization of the interface of games and applications, developed for children with intellectual disability

Abstract In this article will be discussed the issues related to the requirements for the development of digital games for children with intellectual disability. We proposed structured requirements for the design of different types of games in terms of interface, design technologies, color models, object shape and brightness, scope in applications of sound, game duration, and so on.
Research of Hashing Algorithms in Image Retrieval

The rapid development of information technology has resulted in an increasing body of image data generated on a daily basis. How to retrieve needed images from massive image data has become one of the most intensively researched questions. A variety of hash learning approaches taking into account of problems associated with image retrieval from different perspectives have emerged in recent years. Drawing upon feature learning, this paper maps data to binary hash codes using machine learning mechanisms to allow them retain, to the greatest extent possible, similar information with raw data. With respect to the drawbacks of hash learning algorithms, an improvement method that enable better retrieval accuracy and faster search results are proposed. Such a method has exhibited high accuracy and efficiency in image retrieval and is of great research significance.
Research on Sentiment Analysis of Two-way Long and Short Memory Network Based on Multi-Channel Data

In recent years, with the rapid development of deep learning and the Internet, Weibo and e-commerce platforms play an important role in people's daily lives. User data and Weibo comments on e-commerce platforms hide rich emotional information. At the same time, these massive amounts of data also attract more researchers to participate in the research of short text data such as online shopping and Weibo comments, researchers can analyze these data to help businesses increase sales and improve product quality. On the other hand, it also provides a reference for consumers to fully understand the attributes of commodities. How to dig deeper into the potential emotional information of short texts is the most important thing. The emergence of neural networks has pushed natural language processing sentiment analysis to a hot topic, this article uses a combination of convolutional neural network and attention mechanism to solve this problem. This paper proposes a two-way long and short memory network model based on multichannel data. The model first uses shallow learning features such as emotional part of speech extracted from short texts to map to high-dimensional space to form 3 high-dimensional feature vectors, combine the three feature vectors with word embedding and input them into BiLSTM form three channels, the attention mechanism is added to the three fully connected layers, and finally input to the SoftMax classification layer. The experimental results show that compared with the traditional text convolutional neural network, the method proposed in this paper has a significant improvement in the accuracy of short text sentiment analysis tasks.
Virtual ACM UMAP 2020

This year, the 28th (and the first virtual) ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2020, virtually in Genoa, Italy, July 12-18, 2020) was held virtually due to Covid-19 pandemic. UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users or to groups of users. The conference spans a wide scope of topics related to user modeling, adaptation, and personalization.
Automatic Identification of Watermarks and Watermarking Robustness Using Machine Learning Techniques

The goal of this article is to propose a framework for automatic identification of watermarks from modified host images. The framework can be used with any watermark embedding/extraction system and is based on models built using machine learning (ML) techniques. Any supervised ML approach can be theoretically chosen. An important part of our framework consists in building a stand-alone module, independent of the watermarking system, for generating two types of watermarks datasets. The first type of datasets, that we will name artificially datasets, is generated from the original images by adding noise with an imposed maximum level of noise. The second type contains altered watermarked images obtained from the original ones by using different transformations. The module also performs an automatic labeling process of these data, building watermarks’ containers. Then, many models can be built using the watermarks containers and different ML techniques. Comparing the performances of all the obtained models allows the choice of the best model, or provides details for building ensemble learning. To validate the proposed framework, we conducted experiments using a particular watermarking system, built by us and many models based on artificial neural networks (ANN) and support vector machines (SVM). As a side result we identified a possible methodology for evaluating the robustness of a watermarking system, by using ANN and the two types of datasets generated in our proposed methodology.
Application of Analytical Hierarchy Process Method for SQM on Customer Satisfaction

Abstract The research objective was to analyze service quality management on customer satisfaction by utilizing decision support system (DSS) techniques in the completion process. The research data source is the Regional Drinking Water Company (abbreviated as PDAM) “Tirtauli” in Pematangsiantar city. The object of the research is the customers / users of PDAM services which are carried out by means of random observations and questionnaires. The DSS technique used is the analytic hierarchy process (AHP) method. The result of the method is a recommendation in the form of a ranking of service quality management on customer satisfaction. The criteria used to measure customer satisfaction in research are physical evidence (K1), reliability (K2), responsiveness (K3), assurance (K4), care (K5). The calculation results of the AHP method state that two very influential criteria in improving service quality management on customer satisfaction are assurance (K4) with a final value of 0.1929 and reliability (K2) with a final value of 0.1912. The results of the research are expected to provide information and input to the management of PDAM Tirtauli in improving service quality management in order to achieve customer satisfaction.
CNN Based Predictor of Face Image Quality

We propose a novel method for training Convolution Neural Network, named CNN-FQ, which takes a face image and outputs a scalar summary of the image quality. The CNN-FQ is trained from triplets of faces that are automatically labeled based on responses of a pre-trained face matcher. The quality scores extracted by the CNN-FQ are directly linked to the probability that the face matcher incorrectly ranks a randomly selected triplet of faces. We applied the proposed CNN-FQ, trained on CASIA database, for selection of the best quality image from a collection of face images capturing the same identity. The quality of the single face representation was evaluated on 1:1 Verification and 1:N Identification tasks defined by the challenging IJB-B protocol. We show that the recognition performance obtained when using faces selected based on the CNN-FQ scores is significantly higher than what can be achieved by competing state-of-the-art image quality extractors.
Towards the automatic generation of Arabic Lexical Recognition Tests using orthographic and phonological similarity maps

Lexical Recognition Test (LRT) themes are one of the main methods that are widely used to measure language proficiency of some common languages such as English, German and Spanish. However, similar research for Arabic is still at development stages, and existing proposals mainly use human-crafted methods. In this paper, a new methodology, based on a newly developed algorithm, was proposed with the aim of automatically constructing high quality nonwords associated with a real quick measurement of Arabic proficiency levels (Arabic LRT). The suggested algorithm will automatically generate nonwords based on Arabic special characteristics they are orthography (spelling), phonology (pronunciation), n-grams and the word frequency map, which is an important factor to create a multi-level test. With the help of a large dataset of Arabic vocabulary, the proposed algorithm was experimented. For this purpose, a Web-based application, following the suggested methodology, was designed and implemented to facilitate the process of collecting and analyzing learners’ responses. The experimental results have shown that the LRT questions that were automatically generated by the proposed system had confused the learners, this is clear from the output of the confusion matrix which showed that (1/3) of the generated nonwords were able to distract the learners (with accuracy 65%). Consequentially, the results of recall and precision have smaller values, 0.52 and 0.48, respectively.
Estimation of Daily Average Global Solar Radiance Using Ensemble Models: A Case Study of Bhopal, Madhya Pradesh Meteorological Dataset

The paper presents an ensemble model to calculate the daily averaged global solar radiation prediction for the City of lakes Bhopal, in the Central region of India. Bhopal has a very diverse climate. During the wet season (commonly called Monsoon), the weather is mostly cloudy with ample rains and thunderstorms, whereas in the dry seasons of winter and summer, the sky is clear, and the sun shines brightly for most part of the day leading to solar radiation in a finite range every season. Thus, the climate of this city depicts the climate of the country and is suitable as solar radiation-based case study for further analysis of renewable energy source. We use meteorological variables like day of the year, sunrise and sunset time, maximum and minimum temperature, solar irradiance, precipitation, humidity, wind direction and wind speed to train the machine learning ensemble model which is built using Python scilearn kit code and trained over the dataset. With good correlation factors of independent variables which have strong influence on local weather, the experimental setup demonstrated good accuracy, and smaller values of root-mean-square error (RMSE), and good relative to mean absolute percentage error (MAPE) of the predictions with normalization of values for the given case study. To the best of our knowledge, this is the first attempt at analyzing the numerical weather prediction data for India where the only factors considered before setting up solar panels include inclination angle. With these set up prerequisite, solar energy estimation is less accurate as compared to that using ML and weather-related information, which can be acquired from reliable sources. The execution of this project will result in a relatively easier and less expensive way to solar radiance estimation and setup of solar panels across the country.
Near-Optimal Graph Signal Sampling by Pareto Optimization

In this paper, we focus on the bandlimited graph signal sampling problem. To sample graph signals, we need to find small-sized subset of nodes with the minimal optimal reconstruction error. We formulate this problem as a subset selection problem, and propose an efficient Pareto Optimization for Graph Signal Sampling (POGSS) algorithm. Since the evaluation of the objective function is very time-consuming, a novel acceleration algorithm is proposed in this paper as well, which accelerates the evaluation of any solution. Theoretical analysis shows that POGSS finds the desired solution in quadratic time while guaranteeing nearly the best known approximation bound. Empirical studies on both Erdos-Renyi graphs and Gaussian graphs demonstrate that our method outperforms the state-of-the-art greedy algorithms.
Face Sketch Recognition: Gender Classification Using Eyebrow Features and Bayes Classifier

Machine Learning is a subset of artificial intelligence which focuses on the development of computer programs that can access data and use it learn for themselves. Bayes Theorem is widely used in machine learning. The main objective of this paper is to classify the gender of the human being based on their face sketch images by using eyebrows features and Bayes Classifier. This paper presents a method for human face sketch gender classification and recognition. It is inspired in our other model which was pre-trained on the same task, but with sixteen features and fuzzy approach. Toward this end, just three features will be extract from the input face sketch image based on eyebrow face golden ratio and two other measurements. The face detection stage passes by Viola and Jones algorithm. The classification task is evaluated through Bayes classifier. An experimental evaluation demonstrates the satisfactory performance of our approach on CUFS database with 80% for training, 20% for testing. The proposed machine learning algorithm will be a competitor of the proposed relative the stat of the art approaches. The estimate rate reaches more than 98.96% for male gender and 97.38% for female gender.
Professional Development and Self-fulfilment of the Teacher in the Current Environment

The article examines core trends in the development of modern society and education. The aim of national educational project recently created in Russia is to enhance its entire system of education. In this regard, the objectives of the project have been identified which include, among others, the training of current teaching staff, their retraining and advanced training programs. Since the changes entail teachers and their professional development, we have highlighted a number of relevant problems. We find the solution to these problems in studying the process of the teacher's professional self-fulfilment. In this paper, professional self-fulfilment of the teacher is understood as an integral dynamic characteristic, reflecting the teacher's personal qualities in pedagogical activity. The idea of personal self-fulfilment is not limited to one idea of constant self-development and striving for a certain ideal, but invites a person to understand themselves, their ambitions, uniqueness and inner potential that can be realized either in professional or any other course of development. The article pays special attention to the formation of the teacher’s “competences of the future” as one of the objectives and conditions for improving professional development and professional self-fulfilment of the teacher. On the basis of individual and motivational and performance-oriented approaches we have proposed the structure of the teacher's activities aimed at professional development and self-fulfilment.
Research on Hexagonal Remote Sensing Image Sampling

Hexagonal discrete global grids can provide an excellent solution for the massive multi-source, multi-temporal, and multi-resolution raster data integration and management. Traditional images are rectangular pixels, and cannot be expressed on a hexagonal grid. Therefore, how to obtain images based on hexagonal pixels has attracted widespread academic attention. Combining current hexagonal sampling methods, this paper studies the evaluation criteria of hexagonal sampling accuracy, summarizes the previous research, and proposes a more general hexagonal sampling method for remote sensing images. This method mainly involves signal preprocessing, spectrum analysis, calculation of sampling interval, and establishment of accuracy evaluation standards. Finally, we verify the feasibility of the proposed hexagon algorithm to provide a reference for hexagon sampling.
Geonote: A Field Notebook and Database for Geology

Field studies in Geology typically require taking notes in a paper based field notebook that is then stockpiled for future references. The so stored data is hardly accessible over the years, and information sharing is also difficult because of searching time, and the particularities in notation from different experts. Although organizations like the Open Geospatial Consortium favor the standardization, the aforementioned issues exhibit the need for information systems to store geologic data that enable further analysis. In this paper, a system is proposed for geological data capture and storage, based on requirements specified by Geology experts in Peru. The resulting system maintains a hierarchical organization of geologic information, and suggests an architecture based on a normalized relational database. Finally, the system is capable to retrieve information from the geologic database, and generate a set of user defined reports.
Augmented Reality in Research and Practice

The continued development of mobile technologies has provided individuals the opportunity to work, learn, and play across a variety of contexts. The growth of these mobile devices has afforded an equivalent rise in popularity in augmented reality (AR) technologies. Though it has seen widespread adoption across a number of industries, AR has yet to see consistent integration in the realm of education. Several individuals and organizations have cited AR as being poised for mainstream educational adoption despite continued setbacks. The purpose of this chapter is to examine the claims surrounding AR for teaching and learning to understand what is being done and what is possible with the technology right now. This chapter provides a review of a variety of academic and non-academic sources towards the construction of a framework for AR for teaching and learning. Recommendations for research and practice are discussed.