Reviewerly/exHarmony huggingface dataset:
Optimization of Web Services and Semantic Web Performance using different techniques provided by the QoS

Current Web services research is increasingly using semantic models to extract useful information from the Web according to different principles of the Semantic Web which allowed to take advantage of the consideration of the meaning and the meaning of the exchanged data to improve the feasibility of the different tasks such as the description, the pairing, and the discovery. This work focuses on semantic-service-oriented architectures in which providers of different Web services can describe, publish, and discover Semantic Web Services, whose purpose is to solve problems such as information overload and the navigation problem, which concern the users of the Web. In this paper, a solution to performance and optimization problems is proposed for the selection of web services at the QoS quality of service level taking into account the quality of service using different techniques provided by the QoS.
Computer Assisted Design and Practice of GNSS Survey Course of Typical Tasks through Cloud Computing and Big Data Technology

Under the background of typical task orientation in higher vocational education, it is an inevitable trend to carry out teaching reform of GNSS Survey for engineering survey major in higher vocational education. This paper breaks the traditional theoretical teaching system, reconstructs it from the aspects of teaching content selection, combination of theory and practice teaching system, considers the actual situation of students' weak theoretical foundation in higher vocational colleges, and the core technical ability needed for employment and post, optimizes the curriculum structure, integrates the theoretical knowledge system and practice teaching links, and meets the requirements of project-oriented curriculum standards.
Technology Predicting the Future using Data Mining Techniques

The technologies of the future society can make a super connective society where everything is connected and organically interacted, and it promotes “Hyper-Connected”, “Hyper-Intelligent”, "Hyper-Real" as it is possible to become intellectual by techniques such as artificial intelligence and big data analysis. To find out the key triggers by extracting the technologies with these characteristics and identifying what factors and forms are used allows us to grasp changes in the industry due to the rapidly changing domestic environment and the influence of innovation technologies. According to the analysis, the key triggers of major technologies in information society are keywords such as security, technology, service, virtual, and finance which have a great chance to converge with other technologies. The implications of this study are as follows. First, key technologies of domestic information society were identified based on a huge amount of reliable news data. In addition, Network Analysis based on Data, which has increased the frequency of studies all over the field including computer science, sociology and business administration, is applied to predicting the future. Based on this, the methodology has been proposed that can be used to predict the pattern of technology development or notable technologies in the future. This study wasn’t conducted only on one detailed technology, but comprehensively on major technologies extracted in the era of 4th Industrial Revolution and presents Black Swan Prediction to enhance competitiveness of domestic industry in related technologies and create new added value.
A Method for Expressing Integrity Constraints in Database Conceptual Modeling

Traditional methods lack the necessary or appropriate means for expressing the integrity constraints during the database conceptual modeling stage. At most, integrity constraints are informally documented and then, coded in the application. This leads to late error detection and database inconsistencies due to the incapacity of the domain expert to validate the program code. Thus, it is necessary to express such constraints in a natural and formal way in order to close the gap between modelers and domain experts, and to support the transformation to other languages and models. As a result, we propose a controlled natural language based on Semantics of Business Vocabulary and Business Rules (SBVR) to help modelers and domain experts in the process of writing and validating the constraints that cannot be represented in an Entity-Relationship schema; and the Alloy language to allow a formal specification. Inaddition, all the correspondences between the models and languages are described in order to consistently express the constraints and to lay the foundations of the automatic transformation. Finally, a case study and a usability survey show that the proposal is feasible, without abandoning a traditional and popular approach such as the Entity-Relationship model.
AWWA Library

This is a description of AWWA's online video channel platform, Safety First on Video Streaming. The video channel is available from the AWWA Store ( www.awwa.org/streaming ).
Noninvasive methods for quantifying sound post placement in a cello

The position and orientation of the sound post in violin family instruments has long been known to color the sound of the instrument. Small changes in the position of the sound post relative to the top and back plates of the instrument can result in clearly audible changes in the instrument's tone. Quantifying the effect of the sound post's placement on tone requires precise localization of the sound post. Luthiers have developed simple techniques to localize the post relative to the top of the instrument. Precisely localizing the post relative to the back, however, typically requires disassembly of the endpin. This talk will explore nondestructive and noninvasive techniques to localize the sound post relative to the back of the instrument. Results of these techniques will be compared for two cellos – one with a laminate construction and the other carved. Finally, the effects of differences in material properties on these localization techniques will be discussed.
A 2-Level Iterated Tabu Search Algorithm for the Quadratic Assignment Problem

In this paper, a 2-level iterated tabu search (ITS) algorithm for the solution of the quadratic assignment problem (QAP) is considered. The novelty of the proposed ITS algorithm is that the solution mutation procedures are incorporated within the algorithm, which enable to diversify the search process and eliminate the search stagnation, thus increasing the algorithm’s efficiency. In the computational experiments, the algorithm is examined with various implemented variants of the mutation procedures using the QAP test (sample) instances from the library of the QAP instances – QAPLIB. The results of these experiments demonstrate how the different mutation procedures affect and possibly improve the overall performance of the ITS algorithm.
Mispronunciation Detection for Spoken Isolated Words using Segmentation and Classification under Low Resource Conditions for Kannada Language

Relocation makes it inevitable for a person to learn the local pronunciations correctly. With the advent of mobile phones, language learning can be made easy and flexible. Our research involves Kannada Kali, a mobile and cloud based application that is being developed to assist people in learning the correct pronunciations of Kannada (a language spoken in India). Automatic Speech Recognition systems which are used to aid pronunciation training require to be trained on sufficient amount of spoken target language data. Since collecting such data in not easy, the objective of our research is to detect mispronounced segments of words with minimal data. When there is scarcity of data, a comparative approach where spoken word segments are compared with the canonical pronunciations is more effective for detecting anomaly in pronunciation. Since syllables are basic independent units of pronunciation, the spoken words are segmented into syllables for effective comparison and feedback. We propose an unsupervised segmentation method called Spectrogram Formant Contour Analysis that detects syllable boundaries by analysing the change in contours of the formants in the spoken word spectrograms. The task of mispronunciation detection is more effective when the application can identify the actual syllable pronounced and communicate the correct pronunciation to the user. For the purpose of syllable classification, our method employs a novel approach where a model is trained on phonemes and given syllables as input for identification. Our study includes comparing the performance of three machine learning algorithms, namely, Convolution Neural Network, Support Vector Machines and K-Nearest Neighbours on the task of identifying phonemes when they are trained on minimal data. The accuracy of KNN on phoneme classification was the best with 80% for clean and 60% for noisy data. In case of our initial results on syllable classification for Kannada Kali, Support Vector Machines gave the highest accuracy of almost 30%.
Visualization of uncertainty in air quality ensemble forecasts

&amp;lt;p&amp;gt;Air quality forecasts help decision-makers to respond to air pollution episodes and to improve air quality management. In recent years, the public increasingly uses mobile apps to check forecasted air pollution levels and then adjusts outdoor activities accordingly. For Europe, state-of-the-art daily air quality forecasts are provided by the regional Copernicus Atmosphere Monitoring System (CAMS). The system integrates forecasts from 9 individual models. This ensemble approach not only achieves better predictive performance compared to a single model, but also allows a better quantification of forecast uncertainty. How to best communicate this uncertainty to a broad audience is by no means a trivial task, but yet essential to maintain trust in the forecasts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We developed innovative visualizations to convey CAMS forecast uncertainties in time series and maps. The development is strongly user-driven and involves iterative consultation with a wide range of expert and non-expert users. We investigate the feasibility of different bivariate techniques to communicate the ensemble's best estimate and its uncertainty in a single map. We explore user preferences for a variety of time-series graphs, including boxplots, violinplots, and fancharts. Whilst preferences are largely driven by the data and visualization literacy of the users, we identify some generally valid best practices in terms of graph types, choices of colors and labels, and accompanying textual explanations. Finally, we present our candidate designs for the public display of air quality forecasts on the regional CAMS webpage.&amp;lt;/p&amp;gt;
An Efficient Way to Detect and Remove Shadows Based on Multiple Light Sources

A shadow is shaped when a light is blocked by an opaque object. They are sometimes unwanted as they may cause failure of image analysis and can also cause poor eminence of information. This paper first describes some important techniques that are used for shadow detection such as segmentation, Histogram Matching and some techniques that are used for shadow removal such as Morphological operations. The proposed methodology is to design a system based on three basic aims-the first goal is shadow detection of single and multiple images, second goal is to remove the shadow from the single and multiple images and third is to calculate the different parameter for measuring the quality of shadow removal method. Once the shadows are detected it becomes simple to detect a non shadow area which is estimated using morphological operators but sometimes when the shadow of any image is merged with the foreground object then the detection process becomes more complex.
The organization of knowledge control during differentiated education at mathematics lessons

The article is devoted to the organization of knowledge control during differentiated education at mathematics lessons in general secondary school classes with the average number of thirty pupils in class. The article contains several steps for the improvement of knowledge control methods which are designed to help the teachers to increase the objectiveness of their own assessments and judgments as well as to help the pupils to obtain the skill of making conscious decisions and to learn to obtain self-evaluation and self-refection. These methods are aimed at the improvement of the level of the studied material as well as the quality of its acquisition. It is mentioned that the appropriate choice of the methods may improve the situation concerning the knowledge acquisition under the circumstances of general secondary school where a teacher is not able to refuse the traditional methods of education and to shift to a completely new experimental practice. However, under the mentioned circumstances there is a possibility to make changes in the teaching process in order to improve it. The article offers the version of an individual approach which implies the usage of an «intermediate» variant based on the division of the class on groups according to the pupils’ ability to acquire new material. The article contains the key stages of the realization of the practice in question such as everyday control, oral questioning, pedagogical control, tests etc. The description of the practice is based on the practical experience of the implementation of such a personally-based approach under the conditions of the class-lesson’s system applied in the secondary school. The organization of knowledge control in practice showed its success.
CodingTM: Development Task Visualization for SW Code Comprehension

The ninth IEEE Working Conference on Software Visualization (VISSOFT 2021) builds upon the success of the previous eight editions of VISSOFT, which in turn followed after six editions of the IEEE International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT) and five editions of the ACM Sympo0sium on Software Visualization (SOFTVIS). In 2021, VISSOFT will take place and be co-located with ICSME.
Independent Task Scheduling in Heterogeneous System

Recently, the rapid development in processing speeds, fast storage devices and better network connectivity, hasaccelerated the popularization of cloud computing. Cloud computing is an on-demand-servicewhich provides users with high end servers,storage and processing capabilities where the user need not be concerned with its infrastructure.Although, there are abundant resources in the cloud infrastructure, for the efficient working and execution of tasks, task scheduling plays a crucial role. Task scheduling results in better performance (throughput) of the system along with better resource utilization which ultimately results inreduced energy consumption. At any given time, a processor should never be in idle state, as it still consumes some amount of energy. In this paper, the use of Quantum Genetic Algorithm has led to the reduction in energy consumption. The objective is to find a scheduling sequencewhich can be implemented ina cloud computing environment. Along with minimizing energy consumption, the algorithm helps reduce makespan time of a processor as well.The results show a decrease in energy consumption by 10-15% under different test scenarios involving a variable number of tasks, processors, and the number of iterations (generations) for which the algorithm was run. The algorithm converges to the desired result within 10-15 iterations, as can be seen from the results published in this paper.
The image classification algorithm research using class information loss and joint structural similarity

Aiming at the supervised training of convolutional neural networks, the weighted joint structural similarity and class information supervised training method has been proposed. Firstly, for a small image, the convolutional neural networks that can extract high-level information of images is designed. Secondly, a weighted joint structural similarity and class information loss function training convolutional neural network are established. Finally, handwritten numbers and Cifar10 images are obtained by Mnist dataset. The image classification experiments can validate the effectiveness of the proposed network. The experimental results can show that the image classification error rate of improved network on Mnist handwritten digits and Cifar10 dataset is 0.23% and 10% respectively. Under the premise that there is no dataset increase on the Mnist dataset, the performance of proposed network exceeds the performance. The performance of all single networks on the dataset, on the Cifar10 dataset, the proposed network can achieve higher image classification accuracy with less computational effort. At the same time, the supervision of joint structural similarity and class information loss can speed up the training process of proposed network.
Data Analytics for Monitoring the Satisfactory Parameters of Airline Passengers using Machine Learning Algorithms in Python

An effective representation by machine learning algorithms is to obtain the results especially in Big Data, there are numerous applications can produce outcome, whereas a Random Forest Algorithm (RF) Gradient Boosting Machine (GBM), Decision tree (DT) in Python will able to give the higher accuracy in regard with classifying various parameters of Airliner Passengers satisfactory levels. The complex information of airline passengers has provided huge data for interpretation through different parameters of satisfaction that contains large information in quantity wise. An algorithm has to support in classifying these data’s with accuracies. As a result some of the methods may provide less precision and there is an opportunity of information cancellation and furthermore information missing utilizing conventional techniques. Subsequently RF and GBM used to conquer the unpredictability and exactness about the information provided. The aim of this study is to identify an Algorithm which is suitable for classifying the satisfactory level of airline passengers with data analytics using python by knowing the output. The optimization and Implementation of independent variables by training and testing for accuracy in python platform determined the variation between the each parameters and also recognized RF and GBM as a better algorithm in comparison with other classifying algorithms.
Transmogrified physics

Robert P Crease seeks your input on an entirely new realm of phenomena
Inverted Indexing for Information Retrieval from Motifs and Domains of Proteins

The recent advancement in technologies are generating huge amount of data and extracting information from it is being outpaced by data accumulation. The development of hybrid approaches by combining different algorithms for extraction of required from the stock-pile of data is a demand of the hour. One such algorithm is vector space model for inverted indexing that has been used traditionally for search engine indexing in computers. In bioinformatics also it has been used for assembly of DNA fragments generated after sequencing. But it has not been applied for retrieval of relevant protein sequence to the query, based on presence or absence of motifs and domains in it. In this paper the concept of inverted indexing has been applied on small motif/domain data of proteins contained in Motivated Proteins database at http://motif.gla.ac.uk/motif/index.html. The index has been built using 17 small hydrogen bonded motifs present in a dataset of 430 proteins. The entire dataset of 430 proteins has been divided into 19 classes. Seven classes’ example cyanovirin, antibiotic and concavalin etc. had very few instances (1 or 2), hence have been omitted from further studies. Rest 12 classes with more than 10 proteins were considered further for testing information retrieval (IR) strategy. The document vector of all the proteins belonging to one class was averaged and 12 queries with averaged vector were prepared for testing. The similarity coefficient (SC) was then compared between query and all the proteins of the dataset. This approach could successfully classify the query as belonging to the class from which it derived. To further validate the importance of document vector as novel attribute for classification, entire dataset of document vector was clustered to ten (10) clusters. Testing was then performed with similarity coefficient (SC) of the query with clusters obtained above. The allocation of cluster to the 12 query sequences followed the same pattern as done with relevant document search using inverted indexing approach. But clustering allocated the queries to only four (4) classes. Maximum number of query proteins (7 proteins or 58%) were found belonging to cluster 5.
TINDAK TUTUR NEGOSIASI DALAM BERMAIN PERAN SISWA SMK DAN PEMANFAATANNYA DALAM PEMBELAJARAN BAHASA INDONESIA DI SEKOLAH

AbstrakLanguage is one of the means of communication, through human language can interact (communicate) share experiences, learn from each other, and enhance intellectual abilities. In communication. The form of research used by researchers is qualitative, why? Because this research deals with qualitative characteristics. So the reason researchers chose qualitative forms were as follows (1) data collected in the original or natural conditions (2) Based on observations and interviews, (3) the data obtained from this study were in the form of words, pictures, and not numbers, (4) scientific setting, avoiding realities as provisions that cannot be separated from the context, Based on the results of data analysis, the final conclusions can be drawn about the speech acts proposed by Searle with the speech acts of negotiation in the role of class X AK 1 of Immanuel II Vocational High School II are (1) representative speech acts in the role of class X AK 1 student of SMK K Immanuel II act to inform something, show, explain, state, and express opinions, (2) directive speech acts in the role of students of class X AK 1 SMK K Immanuel II act for Requestives, Questions (questions), Requirements (Prohibitives) ( prohibit)`
Hidden messages

Trudie Dawson discusses the art of active listening and the skill of reading body language
Consider technology that helps promote social distancing

How can campuses reopen safely in the fall? That's a big question administrators at many schools are trying to answer. One key is to enable and encourage social distancing on campus. Officials at Apogee and Degree Analytics have partnered to create a solution that enables campus administrators to track where people are gathering. Heat map displays show how many people are in an area.
Automatic Telugu Handwritten Character Recognition with the Help of Legendre-Sobolev Divergent Machine Learning Model

Online handwriting recognition or character recognition is the process in which a handwritten message is recognized by processing the handwritten data. It is the way toward changing over manually written characters to machine design. In penmanship, the strokes are made out of two arrange follows in the middle of pen down and pen up marks. Wide scope of highlights is extricated to play out thse acknowledgment. A complete internet hand-written recognition system for Indian language such as Telugu that addresses the ambiguities in separation just as recognition of buttons the recognition relies on conceptual model of penmanship structure joined with either a prejudicial model for stroke command. Such a methodology be able to flawlessly incorporate language and content data in the reproductive model then manage comparative and non-comparable strokes utilizing the single discriminative stroke grouping model. In this examination, we are utilizing disparate Legendre Sobolev conditions with the assistance of AI model, to such an extent that accomplishes 99.65% precision and improved the condition of craftsmanship esteem.