Reviewerly/exHarmony huggingface dataset:
Optimization of Spiral Scanning Center Error Based on Differential Confocal

A spiral scanning center error optimization method based on differential confocal (ODC) is proposed, allowing for high-precision and high-efficiency non-contact measurement of microstructure surface topography. To begin, a probe centering method based on grating rotation successive approximation is designed to reduce the centering error of 3D shape measurement during spiral scanning. The surface morphology of the standard sample was then measured using an experimental platform based on the ODC principle. The center error is within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.75\ \mu\mathrm{m}$</tex> , and the maximum deviation is within 1.4067%, which is consistent with the commercial white light interferometer. This method has a lot of potential for accurate and efficient 3D surface topography measurement.
Applications of Data Science in Respective Engineering Domains

Data Science is answerable to the every field of technology domain even it is Electronics and Communication Engineering, Electrical Engineering, Mechanical Engineering or Civil Engineering. For example in Civil Engineering following applications like Advance Predictions in construction works, Project risk Analysis, availability of water resources, Traffic forecasting and many more similarly we have many applications from Engineering Domain. Data Science approaches has adopted many technical techniques within the Engineering domain ranging from data analysis to neural network to deep learning and it has very vast variety of particular engineering domain solutions. Data scientists today draw largely from extensions of the “analyst” of years past trained in traditional disciplines. As data science becomes an integral part of many industries and enriches research and development, there will be an increased demand for more holistic and more nuanced data science roles.
Built on SpringBoot's travel platform

In today's society, consumers can no longer meet the traditional travel services, for a variety of travel needs are also more needed, the use of Spring Boot + SSM technology constitutes an integrated mobile travel platform. Through Spring's mature MVC architecture model, we continue the basic configuration of the Spring Boot framework, realize the system architecture of multi-layer modules,and on the basis of integrated transportation, it can be known that the service plays its own resource capabilities.will also propose a variety of means of transport for better development, city and city transportation, city and township between the county and township links, are very necessary. Serving the residents of the urban area to facilitate their daily passage, while providing passenger flow distribution services, further taking rail transit as the main core, and integrating with different means of transportation. Realize that users can seamlessly connect from travel to return, and provide integrated travel services for residential users.
TEMPORAL-SPATIAL ZONING OF HYDROTHERMAL ALTERATION MINERALOGY AND GEOCHEMISTRY OF QUARTZ-ALUNITE-HOSTED EPITHERMAL AU-CU-AG ORE DEPOSITS AT SUMMITVILLE, COLORADO

The Summitville deposit is a well-studied example of a quartz-alunite-hosted Au-(Cu-Ag) epithermal ore deposit with 559 Koz Au historic production. Prior studies have documented the advanced argillic alteration mineralogic zonation around central quartz-rich ribs that contain gold and local enargite/luzonite or covellite. Our mineralogy studies expand upon previous work and document the zoning of advanced argillic altered zones >1 km laterally and >1.2 km vertically below the ore bodies. Kaolin-group clays are zoned laterally outward from central dickite-pyrophyllite ± nacrite proximal to the Au-Cu-Ag orebodies, to intermediate kaolinite-dickite ± nacrite, and to outer kaolinite in a pattern consistent with hydrothermal fluids cooling during outward flow laterally from the ore zone. The deep advanced argillic zones contain quartz-pyrophyllite-topaz to 1,400 m below the surface or >1.2 km below the ore bodies. Petrographic and SEM imaging and EMPA data demonstrate that earlier pyrophyllite was partly replaced by later muscovite along the {001} basal plane of the sheet silicate structure. Locally, zunyite (Al13Si5O20(F,OH)18Cl) also forms overgrowths atop and replaces earlier pyrophyllite and topaz (Al2SiO4(F,OH)2). The replacement of pyrophyllite-topaz by muscovite ± zunyite indicate the later addition of K+ and Cl- to stabilize the new minerals from a more neutral pH fluid compared to the magmatic-hydrothermal fluids that formed earlier advanced argillic alteration. Whole-rock geochemical analyses indicate the addition of many trace metals (Ba, As, Cs, Sn, Tl, and W) in addition to K around the Au-Cu-Ag ore zones. Because all these metals are chloride (Cl-) or bisulfide (HS-) complexed and can be transported in late, low salinity aqueous solutions this is the likely ore fluid, whereas the early magmatic gases are incapable of such metal transport. We confirmed the pathfinder elements (i.e., As, Ba, Cs, Sn, Tl, and W) are contained in late-formed muscovite and zunyite via LA-ICP-MS spot analyses. Therefore, there is compelling evidence that late chloride-bearing fluids also transported common pathfinder elements, and formed muscovite and zunyite in the deposit, and by inference also likely transported and deposited Au-(Cu-Ag) in ores as native gold with local enargite-luzonite and covellite.
Szociológia, kutatási adatok, mesterséges intelligencia: lehetőségek és tapasztalatok

Social science, research data and artificial intelligence: options and lessons learned Our aim was to facilitate the research of collected interview texts at the Research Documentation Centre of the Centre for Social Sciences.Currently it is difficult to find the parts related to specific topics or obtain a thematic mapping of lengthy interviews and collections.To this end, we identified and tested the most promising NLP tools supporting the Hungarian language.Furthermore, a suitable and domain-oriented taxonomy had to be created for the classification of available texts.Our work and experiences are described in this paper, including the adaptation of the European Language Social Science Thesaurus, the building of a gold standard for classification, the comparison of various automated indexing methods, and finally the implementation of a researcher tool supporting custom visualizations and faceted search.
Count vectorizer model based web application vulnerability detection using artificial intelligence approach

A web application is a dynamic, intricate, and interactive program that provides end-users with information and services such as utility payments, online communication, e-learning, socializing, shopping, online banking, and income tax filing etc. Web applications have become a major target for attackers due to their accessibility, availability, and ubiquity. Web application vulnerabilities are hazardous for some reasons. Attackers can harm an organizations image and status. The implementation flaws in web application allow the invader to infuse user-input that violates the syntax-based assembly of the query or infuse malicious code etc. Among various types of injection flaws, SQL injection (SQLI) is more prominent than (XML) both are considered as common application-layer web attack, which allows the attacker to bypass the security mechanisms therefore; these two are ranked as the most common vulnerabilities. Hence, a methodology for detecting evaluating both SQLI &amp; XML vulnerabilities in web applications are considered for research. This research work addresses the above mentioned flaws and proposed an Ensemble Method to classify the Structure Query Language injection vulnerabilities, we selected a benchmark dataset with 33,758 rows containing; various types of SQL and XML injection attacks. Raw data is preprocessed to remove artifacts, and then feature engineering is performed using Natural Language Processing techniques to clean the data and extract 6 types of features such as TF-IDF, Word-to-Vector, SkipGram, Count Vectorizer, Glove and Continuous Bag of words. Imbalance data is handled using sampling techniques, best features are selected using 4 types of validation techniques Significant Test, PCA, Variance Threshold and Sbest. Prepared data is provided to Ensemble Model having two stages; Stage-2 accepts URL from the user and detects presence of susceptibility in the sub domains and domains. Stage-1 having 9 different types of machine learning models Multinomial, Gaussian, Bernoulli Naive Bayes, Logistic Regression, Decision Tree, Random Forest, AdaBoost, SVC with, poly, rbf and linear kernel, these models are trained on additional vectors such as google news and glove to detect the new query either SQL or XML for presences or absence of vulnerability, using this proposed ensemble approach obtained the accuracy of 99%.
Introduction to Uno Platform

There are many options available when building cross-platform applications in the .NET ecosystem, especially when considering the variety of mobile devices and different form factors available today. Some of the popular options are using Uno Platform, .NET MAUI (Multi-platform App UI), Xamarin.Forms, Avalonia UI, and others. This book is all about Uno Platform development, but each framework has advantages and disadvantages over the others.
Aplikasi Pencarian Informasi Al-Quran dengan Ontologi Web Semantik

Al-Quran is the holy book of Muslims which consists of 30 chapters, 114 surahs, and 6236 verses. Each juz in the Quran can contain one or more surah and each surah consists of a different number of verses. Seeing the many contents of the knowledge of the Quran, then this can help someone to find and determine the theme of the surah to be read manually. In this study, the authors build a system that is expected to facilitate one's work in searching for the contents of the Al-Quran by using ontology as the backbone of the system, Methonology as a method for building an ontology model, and Prototyping as a system development method.
<i>QuickProbe</i>: Quick Physical Prototyping-in-Context Using Physical Scaffolds in Digital Environments

Abstract In this paper, we introduce a novel prototyping workflow, QuickProbe, that enables a user to create quick-and-dirty prototypes taking direct inspiration from existing physical objects. Our workflow is inspired by the notion of prototyping-in-context using physical scaffolds in digital environments. To achieve this we introduce a simple kinesthetic-geometric curve representation wherein we integrated the geometric representation of the curve with the virtual kinesthetic feedback. We test the efficacy of this kinesthetic-geometric curve representation through a qualitative user study conducted with ten participants. In this study, users were asked to generate wire-frame curve networks on top of the physical shapes by sampling multiple control points along the surface. We conducted two different sets of experiments in this work. In the first set of experiments, users were tasked with tracing the physical shape of the object. In the second set of experiments, the goal was to explore different artistic designs that the user could draw using the physical scaffolding of the shapes. Through our user studies, we showed the variety of designs that the users were able to create. We also evaluated the similarities and differences we observed between the two different sets of experiments. We further discuss the user feedback and the possible design scenarios where our QuickProbe workflow can be used.
An approach to determination of representative set of exemplars for basic-level category mapping in cognitive agents.

Cognitive agents should be equipped with computational methods enabling autonomous formation and management of their internal cognitive structures. Moreover, due to social nature of multiagent systems a dedicated subsystem is required to allow for establishing common structures allowing for successful interaction between agents. Current implementation tend to use shared and predefined system-wide ontologies, thus limiting the fexibility and autonomy of individual agents. In this research, we focus on an approach that can remove this dependency by allowing the agents to maintain individual internal ontologies and map them to internal ontologies exploited by other agents. In particular, we propose a general strategy in which agent engages in an interplay that enables determination of certain types of relation between concepts. Further, we focus on one of the initial steps of this strategy, namely, determination of the representative set of exemplars for basic-level categories, which is utilised during the interplay and serves as the baseline to identify the mapping.
Artiﬁcial intelligence, algorithms and freedom of expression

&lt;p&gt;Artificial &lt;span style="font-family: 'Times New Roman';"&gt;i&lt;/span&gt;ntelligence can be presented as an ally when moderating violent content or apparent news, but its use without human intervention to contextualize and adequately translate the expression leaves open the risk of prior censorship. This is currently under debate in the international arena since, as Artificial Intelligence lacks the capacity to contextualize what it moderates, it is being presented more as a tool for indiscriminate prior censorship than as a moderation aimed at protecting freedom of expression. Therefore, after analyzing international legislation, reports from international organizations and the terms and conditions of Twitter and Facebook, we suggest five proposals to improve algorithmic content moderation. First, we propose that States make their domestic legislation compatible with international standards of freedom of expression. We also urge them to develop public policies consisting of implementing legislation to protect the working conditions of human supervisors of automated content removal decisions. For their part, we believe that social networks should present clear and consistent terms and conditions, adopt internal policies of transparency and accountability about how AI operates in the dissemination and removal of online content and, finally, should conduct prior human rights impact assessments of their AI.&lt;/p&gt;
Crosslinguistic Semantic Textual Similarity of Buddhist Chinese and Classical Tibetan

In this paper we present the first-ever procedure for identifying highly similar sequences of text in Chinese and Tibetan translations of Buddhist <em>sūtra</em> literature. We initially propose this procedure as an aid to scholars engaged in the philological study of Buddhist documents. We create a cross-lingual embedding space by taking the cosine similarity of average sequence vectors in order to produce unsupervised similar cross-linguistic parallel alignments at word, sentence, and even paragraph level. Initial results show that our method lays a solid foundation for the future development of a fully-fledged Information Retrieval tool for these (and potentially other) low-resource historical languages.
Improving Abstractive Summarization for the Turkish Language

While the previous studies about Text Summarization focused more on the Extractive Text Summarization techniques, the recent ones are more about the Abstractive methodology. In this paper, the challenge of Abstractive Text Summarization for the Turkish language was tackled by conducting three different experiments. First, a new dataset for Turkish Text Summarization was created by collecting and preprocessing data from different corpora. Then, three abstractive models were trained. The first model was trained on the unsorted data. The two other models were trained by first sorting the data according to the task difficulty level and feeding it in chunks to the models. Both semantic similarity-based and lexical overlapping-based metrics were used to evaluate our models. The experiments yielded promising results for the models for which the data was sorted. Besides the improvements in the results, the training time was reduced for the model trained on the sorted data.
Cloud data systems

The panel will discuss the research opportunities for the database research community in the context of cloud native data services.
A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning

Proxy-based Deep Metric Learning (DML) learns deep representations by embedding images close to their class representatives (proxies), commonly with respect to the angle between them. However, this disregards the embedding norm, which can carry additional beneficial context such as class- or image-intrinsic uncertainty. In addition, proxy-based DML struggles to learn class-internal structures. To address both issues at once, we introduce non-isotropic probabilistic proxy-based DML. We model images as directional von Mises-Fisher (vMF) distributions on the hypersphere that can reflect image-intrinsic uncertainties. Further, we derive non-isotropic von Mises-Fisher (nivMF) distributions for class proxies to better represent complex class-specific variances. To measure the proxy-to-image distance between these models, we develop and investigate multiple distribution-to-point and distribution-to-distribution metrics. Each framework choice is motivated by a set of ablational studies, which showcase beneficial properties of our probabilistic approach to proxy-based DML, such as uncertainty-awareness, better behaved gradients during training, and overall improved generalization performance. The latter is especially reflected in the competitive performance on the standard DML benchmarks, where our approach compares favourably, suggesting that existing proxy-based DML can significantly benefit from a more probabilistic treatment. Code is available at http://github.com/ExplainableML/Probabilistic_Deep_Metric_Learning .
Pengembangan Media Pembelajaran Papan Dart pada Materi Bangun Datar

Education is basically a process to help humans develop their potential so that they are able to deal with any changes that occur. Through education can make people from a state of ignorance to become, smart, creative, responsible and productive. While children are still at play, it is necessary to provide game-based learning to reduce feelings of difficulty and fear, the researchers made learning media in the form of Dart Board. The use of Dart Board learning media Dart Board is one example of learning media for mathematics.&#x0D; This research is a developmental research which aims to produce learning media in the form of a dartboard called Dart Board on the subject of flat shapes and the validity of Dart Board media for class VII SMP. This media development process uses the 4D development model, namely: Define, Design, Develop and Disseminate. However, this research is limited to the develop stage.&#x0D; This research was conducted at SMP Shalahuddin Malang. This study resulted in an average expert validation of 3.83, this value is included in the very valid criteria. For trials of this learning media product, it has not been carried out because of the Covid-19 pandemic.
Agenda Analysis of the Covid-19 News with Latent Dirichlet Allocation

This study interprets the themes obtained as a result of the analysis of the internet news published during the Covid-19 pandemic in our country with Latent Dirichlet Allocation method. Apart from topic modeling, news documents were also subjected to category-based sentiment analysis and time-lapse graphics of published positive, negative and neutral news were shared. For this purpose, 37.724 news texts published in 5 different categories were collected. The period subject to analysis is December 2019 - February 2021. Although the effect of the virus has been alleviated at the moment, the themes that were on the agenda during the period when the effect of the virus was highest could be seen and the results were interpreted.
Recognition method of basketball players' shooting action based on graph convolution neural network

In order to improve the evaluation standard rate and accuracy of basketball players' shooting action recognition, a basketball players' shooting action recognition method based on graph convolution neural network is proposed. Using the learning method of three-dimensional graph convolution neural network, the optical flow data of collected video is analysed, the graph convolution neural network model is constructed, and the shooting action characteristics are extracted. The dynamic parameter identification of shooting action is realised by using graph neural network. The experimental test shows that the evaluation standard rate of shooting action recognition proposed in this paper is 0.8648, the accuracy of shooting action recognition is high, and the error is only 0.0066, which shows that the design method in this paper has good superior performance in improving the performance of basketball action recognition. It can provide technical support for the construction of basketball players' shooting action database.
Visualize the Results

Over the course of the last two chapters, we have interacted with our outlier detection service: we have built and extended functionality, created and updated tests, and diagnosed issues by reading a lot of JSON output. In this chapter, we will provide a more user-friendly method for integrating with the outlier detector. This will come in the form of a web application.
DevOps Enabled Agile: Combining Agile and DevOps Methodologies for Software Development

The Agile and DevOps software development methodologies have made revolutionary advancements in software engineering.These methodologies vastly improve software quality and also speed up the process of developing software products.However, several limitations have been discovered in the practical implementation of Agile and DevOps, including the lack of collaboration between the development, testing and delivery sectors of different software projects and high skill requirements.This paper presents a solution to bridge the existing gaps between Agile and DevOps methodologies by integrating DevOps principles into Agile to devise a hybrid DevOps Enabled Agile for software development.This study includes the development of a small-scale, experimental pilot project to demonstrate how software development teams can combine the advantages of Agile and DevOps methodologies to fulfill the gaps and provide further improvements to the speed and quality of software development process while maintaining feasible skill requirements.
A Failure Prediction Approach Supporting Multi Granularity Data Fusion for Large-scale Cloud Storage Systems

With the development of cloud computing and cloud storage technology, the data scale has grown rapidly. In order to store and process large-scale data, there are thousands of nodes and devices in the cloud storage center, resulting in a surge in the frequency of failures. In various types of failure events, storage device failure is the most important one. However, most cloud storage systems lack disk failure prediction mechanisms and could only replace disks after disk failures. It is particularly important to predict the potential risks in the system operation environment. In this paper, we propose a disk failure prediction approach that supports multi granularity data fusion, which solves problems of unbalanced samples, single data source, cross scenario model migration and insufficient generalization ability of prediction models in disk failure prediction. Through our proposed approach, the cloud storage system could accurately predict disk failures and actively push prediction results to users, so as to improve the pertinence and planning of the operation and maintenance work. The approach presented in this paper has been validated to be valid through a series of qualitative and quantitative experiments.