Reviewerly/exHarmony huggingface dataset:
Industrial Detection Efficiency Based on Automatic Industrial CT Detection System

Abstract With the rapid development of industrial technology, nondestructive testing technology has been widely used in industry. Automatic industrial CT testing is an effective method for nondestructive testing of internal defects of large workpieces. In the 1980s, China began to study industrial CT technology. However, due to the limited conditions and demand, the automatic industrial CT has not been put into use. In this regard, the purpose of this paper is to explore the efficiency of industrial detection under the automatic industrial CT detection system. In the actual X-ray CT inspection, for the small workpiece with complex internal structure, the best detection method is to detect along the section of the internal structure of the workpiece, so that the collected data can make CT imaging clearer. At the same time, with the acceleration of nondestructive testing automation process, the traditional rotary table X-ray CT detection system has been unable to meet the needs of actual production testing. According to the existing problems, a new automatic CT detection system is proposed in this paper. The system is a CT system based on 6-DOF Industrial robot. It has 6 spatial degrees of freedom, which can flexibly realize the clamping and spatial positioning of parts. It is an effective solution to replace the rotary table for radiographic inspection. And only according to the reasonable design of the clamping device according to the testing parts, it can be directly applied to industrial production, which speeds up the development process of nondestructive testing automation technology. Finally, this paper compares the CT detection system with the traditional industrial detection system, and studies the industrial detection efficiency of the two systems. The experimental results show that the efficiency of CT detection system proposed in this paper is 33% higher than that of traditional industrial detection system. At the same time, the proposed CT detection system provides a feasible solution for the automation of industrial X-ray CT detection.
The Optimal State Estimation and IMU Fault Diagnosis of Aircraft Based on Optimal Two-Stage CKF

As a typical nonlinear system, the nonlinear state estimation problem of aircraft needs to be solved by nonlinear Kalman filter with high numerical accuracy. The standard Cubature Kalman Filter based on the spherical - radial cubature rule, which could be applied to solve high-dimensional nonlinear filter problems with minimal computational effort. However, when the nonlinear systematic dimension increases, the accuracy of CKF will decline. And CKF will lose the optimality of state estimation with the occurrence of fault. Two-Stage Kalman Filter can solve this problem, but it only applies to linear systems. In order to achieve high-precision estimation of aircraft state and fault diagnosis of the inertial measurement unit (IMU), an optimal two-stage volume Kalman filter (OTSCKF) is proposed in this paper. We take the IMU fault as random deviation and establish the aircraft and filter model. By comparing the simulation results of CKF and OTSCKF on aircraft state estimation and IMU fault diagnosis under the condition of unsteady wind field, we found that OTSCKF can obtain the optimal estimation of system state and deviation under the condition of random deviation of nonlinear system. Compared with CKF, the accuracy of OTSCKF is greatly improved. Moreover, it has the capability of fault diagnosis and robustness to IMU fault.
Trust Relationship Establishment Among Multiple Cloud Service Provider

Trust relationships among multiple Cloud Service Providers is a concept in which multiple cloud service providers from multiple distributed Identity Provider can access resources of each other, only if they are trusted with their Identity Provider. In this chapter a scheme has been proposed to enhance the security of data in a multi-cloud environment by improving trust relationships among multiple clouds. The scheme is also designed to overcome interoperability problem between different clouds. In the proposed scheme concept of proxy is used. Client organization tries to communicate with multiple cloud service providers through proxy. Client organization send resource request to cloud service providers. On receiving the resource request the cloud service provider collect the authentication confirmation from proxy. Then it sends the reply and data to requested client organization. Numerical analysis and comparative study of the proposed scheme with some of the existing scheme has been carried out.
An Image Enhancement Method for Few-shot Classification

In order to predict the unknown image categories, few-shot image classification has recently become a very hot field. However, many methods need a large number of samples to support in order to achieve enough functions. This makes the whole network de amplification to meet a large number of effective feature extraction, and reduces the efficiency of few-shot classification to a certain extent. To solve these problems, we propose a dilate convolutional network with data enhancement. This network can not only meet the necessary features of image classification without increasing the number of samples, but also has a structure that utilizes a large number of effective features without sacrificing efficiency. The cutout structure can enhance the data by adding a fixed area 0 mask matrix in the process of image input. The structure of FAU uses dilate convolution and uses the characteristics of a sequence to improve the efficiency of the network.
Efficient Large-Scale Stance Detection in Tweets

Stance detection is an important research direction which attempts to automatically determine the attitude (positive, negative, or neutral) of the author of text (such as tweets), towards a target. Nowadays, a number of frameworks have been proposed using deep learning techniques that show promising results in application domains such as automatic speech recognition and computer vision, as well as natural language processing (NLP). This article shows a novel deep learning-based fast stance detection framework in bipolar affinities on Twitter. It is noted that millions of tweets regarding Clinton and Trump were produced per day on Twitter during the 2016 United States presidential election campaign, and thus it is used as a test use case because of its significant and unique counter-factual properties. In addition, stance detection can be utilized to imply the political tendency of the general public. Experimental results show that the proposed framework achieves high accuracy results when compared to several existing stance detection methods.
ELITE: Defending Federated Learning against Byzantine Attacks based on Information Entropy

Federated learning is a distributed machine learning paradigm, where physically distributed computing nodes collaboratively train a global model. In federated learning, workers usually do not share training data with others, and thus some workers are malicious workers, who can change parameters (e.g., weights/gradients) in their models to degrade the global model’s training accuracy. This is generally called Byzantine attack. Existing solutions are either limited resistance to Byzantine attacks or not applicable to federated learning. In this paper, we propose ELITE, a robust parameter aggregation algorithm to defend federated learning from Byzantine attacks. Inspired by the observation that the parameters of malicious workers usually distract from the parameters of benign workers, we introduce entropy to efficiently detect malicious workers. We evaluate the performance of ELITE on image classification model training under three typical attacks, and experimental results show that ELITE can resist various Byzantine attacks and outperforms existing algorithms by improving the model accuracy at most up to 80%.
Theoretical and methodological basis of the students professional self-determination formation within the educational cluster "school - university - enterprise" conditions

The relevance of this research is determined by the need of fundamental theoretical positions development that form the students’ professional self-determination quality within the conditions of various educational clusters, such as associations, that effectively train independent and socially active wide profle specialists for different spheres of social live. The article presents theoretical and methodological foundations of students professional self-determination formation within the educational cluster named «school-university-enterprise», including approaches, conditions, means, and methods required for development of the necessary professional self-determination components. Basic and substantial characteristics of the professional self-determination development process have been grounded in accordance with theoretical and practical views of modern science. The article draws attention to the stages of professional self-determination development in accordance with the periods required for students to develop its components. There have been extracted and described four criteria groups of research process that allow to evaluate the completeness of the students’ professional self-determination basic components formation.
A Swear Word Detection System in SKPD Website Content of Bali Province Government

The website is one of the information service systems needed by the community to carry out various public service activities. It's just that it is necessary to do a word-checking process on the website to avoid swear words, especially on government service websites. Checking swear words on the website takes a long time, done by reading every website content that the admin will add. So we need a system that can check swear words on the website. The purpose of the research is to simplify detecting swear words on the SKPD website. In this study, a system was designed and built that can detect swear words when the admin adds and changes website content and validates criticism and suggestions that visitors have added by implementing text mining. This study uses the Nazief and Adriani algorithms for the stemming process and the Term Frequency algorithm for the weighting process. The test method used is accuracy measure. From the test results, the accuracy value is 88%, precision of 78.57%; recall of 100%; and f measure by 88%. The results obtained have not been maximized because the system has not been able to read the sentence structure, so that a word that should not be used as a swear word in a document/text becomes detected by the system as a swear word, for example, the word dog detected in the text of the news about rabid dogs.
Plausible Deniability

From the perspective of responsible data release, simulation is a useful tool for estimating risk from adversaries with an unknown amount of identified auxiliary information. We present a simple approach to simulation of attack on sampled datasets, along with an implementation, and demonstrate how a data steward might make use of it to evaluate the privacy risk of release for data gathered about students in the University of California system.
Introduction

Sentiment analysis analyses people’s viewpoints, feelings, assessments, behaviour and psychology towards living and abstract entities. It highlights viewpoints which present positively or negatively biased sentiments.
Physics News

Physik in unserer ZeitVolume 52, Issue 2 p. 63-63 Treffpunkt Forschung Physics News First published: 01 March 2021 https://doi.org/10.1002/piuz.202170209Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume52, Issue2March 2021Pages 63-63 RelatedInformation
Making Artificial Intelligence Work for Investigative Journalism

Many have envisioned the use of AI methods to find hidden patterns of public interest in large volumes of data, greatly reducing the cost of investigative journalism. But so far only a few investigative stories have utilized AI methods, in relatively narrow ways. This paper surveys what has been accomplished in investigative reporting using AI techniques, why it has been difficult to apply more advanced methods, and what sorts of investigative journalism problems might be solved by AI in the near term. Journalism problems are often unique to a particular story, which means that training data is not readily available and the cost of complex models cannot be amortized over multiple projects. Much of the data relevant to a story is not publicly accessible but in the hands of governments and private entities, often requiring collection, negotiation, or purchase. Journalistic inference requires very high accuracy, or extensive manual checking, to avoid the risk of libel. The factors that make some set of facts “newsworthy” are deeply sociopolitical and therefore difficult to encode computationally. The biggest near-term potential for AI in investigative journalism lies in data preparation tasks, such as data extraction from diverse documents and probabilistic cross-database record linkage.
Research and Construction of University Data Governance Platform Based on Smart Campus Environment

Campus data is a subset of education big data. It is a variety of data generated by teachers and students in the life, teaching, scientific research, management and service process, as well as various school affairs management status data. It has the characteristics of a wide variety of data. Contains great information value, and giving full play to its role is an indispensable part of achieving the school's strategic goals. Taking the opportunity of building a smart campus, using advanced technologies such as cloud computing, big data, Internet of Things, and artificial intelligence, through a big data management platform, the full collection of existing business data inside and outside the school is provided, and a normal data governance model is provided to eliminate data islands. Realize normal data sharing services. The article conducts research on the data governance platform, and builds a data governance platform system with functions such as a normalized data quality monitoring system, information resource catalog system and full-link data monitoring to realize university data integration, business collaboration, service upgrades and assistance Decision-making provides a solid foundation for the application and expansion of smart campuses in universities and provides a reference for smart campus builders in universities.
Background Review for Neural Trust and Multi-Agent System

This chapter provides a systematic background study in the neural trust and multi-agent system. Theoretic models are discussed in details. The concepts are explained. The existing systems are analyzed. The limitations and strength of previous research are discussed. About 59 references are cited to support the study for the investigation. The study did address the research importance and significance and finally, proposed the future directions for the research undertaken.
Detox: NLP Based Classification And Euphemistic Text Substitution For Toxic Comments

The challenges of eliminating inadvertent hate online have grown more and more observable. With rapid increase in access to technology worldwide, the number of people present on the internet has increased exponentially. The rise in hateful and toxic content on the internet is many fold. Present solutions are largely based on censorship and removal of such content and can be the cause of mental disturbance to the concerned auditor. Biases of manual moderation can thwart the efforts taken to prevent the presence of hate speech on online platforms. However, posts that convey important meanings also fall prey to such systems, which should be avoided. Such systems fail to educate their authors what should have been done differently. Additionally, a system that combines detection and substitution algorithms is largely absent. A euphemistic substitution approach could prove to be more effective. In this project we have developed a classifier using Natural Language Processing and Machine Learning to detect toxic texts and provide euphemisms to erudite the user of words that can replace the toxicity present in the original texts. The classifier informs the online platform about the toxicity of the texts so they restrict such content to available on its platform. The euphemisms are aimed to make the user aware of the toxicity in the text and suggest replacements, which, if used, can make the text inoffensive. We aim to achieve self realisation on the user's part so we can target the issue at its source.
Bayesian Estimation with Artificial Neural Network

A nonlinear filter based on an artificial neural network (ANN) is proposed to accurately estimate the state of a nonlinear dynamic system. The ANN is trained to learn the nonlinear mapping between the inputs and outputs of training data. The proposed filter is computationally efficient for online applications because estimation error can be directly estimated once the ANN is trained offline. The unscented transformation (UT) is employed in this filter to approximate the first two moments of the estimate. Under the scenarios considered in this paper, it is shown through numerical simulation that the proposed filter leads to better performance than the extended Kalman filter (EKF), unscented Kalman filter (UKF), and a state-of-the-art nonlinear filter.
Cloud Based ERP Adoption in Educational Institutions

ERP systems enable organisations to blend resource and data management onto a single platform. Introduction of cloud platforms into the mix has enabled the ERP system to add numerous services that can be scaled and highly available. The previous way of releasing versions using major releases has been turned into a continuous stream of releasing new updates with minor patches. Even though the flow of the business has remained the same, the use cases differ greatly. Not every organization can fully utilise the same ERP platform. Tailoring the ERP platform to each business process is possible due to the rapid development environment enabled by the cloud. This paper discusses the theory of managing changes applicable to modules of a particular ERP platform, Institute-level management processes, lifecycles maintenance. This strategy seems appropriate to help the ERP application’s long-term sustainability enforced by constant up dates.
Systematic Mapping of the Harmonization of SCRUM and ISO 9001

Small and medium-sized companies have limitations related to the amount of human talent and capital they have, which causes their processes to be disorganized and affects the quality of the products or services they offer. To address this problem, several solutions that allow companies to improve their processes and the way their projects are managed have appeared, some of the most common in the software industry are Scrum and the ISO 9001 standard or its Latin American variants (NTC 6001, NTG 66006 and NTE INEN 2537). In this sense, the objective of this article is to present the results obtained after carrying out a systematic mapping of the literature to identify proposals and related studies. Although it was possible to identify some related studies, the topic has great relevance for the software industry given the benefits that would be enabled in an integrated manner by implementing a quality management system based on the specifications of the ISO standard together with the agile approach Scrum. In conclusion, it was possible to observe that, although there are differences between the ISO and Scrum standards, they are not incompatible, on the contrary, they turn out to be complementary.
Trace AI Simulation of Feedforward Neural Network Visualization Optimized by Genetic Algorithm Based on Unity3D

The application of self-driving cars and automatic navigation technology requires a mature AI operation that can continuously deep learning. Training AI using traditional Neural Networks requires a large number of samples and a standard example for negative feedback adjustment. For this kind of tracking AI, a standard example cannot be obtained to make it negative feedback. This article uses Genetic Algorithm to screen ability. The weights of the Feedforward Neural Network are screened from generation to generation to obtain the solution weights. Use Genetic algorithms to replace feedback regulation in traditional neural networks, and it doesn’t require a large number of samples to form a data set. This article uses Unity3D software as a platform to establish a visual tracing of unmanned vehicles based on deep learning AI. The experimental results show that the visual tracing of unmanned vehicles can avoid obstacles and find target points autonomously, with an average fitness value of more than 3. Simulation results show that this algorithm is optimized and efficient for tracking AI. Which can be used in unmanned vehicle navigation, cargo robot handling and logistics, and has a wide range of application values.
Identification of Expectancy, Proximity, and Compatibility of the Bengali Language

Semantic errors are portrayed as in border on upon of the law of sentences. In Bengali language, we will identify errors depend on three sorts of sentence rules. They are Expectancy (n/a), Proximity (n/a), Compatibility (n/a). We are assessing every single Bengali sentence as indicated by these three sorts of techniques. Cause the accompanying way will function as significance by importance word order and will discover which sentence is sans mistake. Presently, we will place sentences into certain classes with the goal that sentences will be gathered as right or off-base. We picked a Bengali sentence comprising of completed fundamental structure of the structure Subject, Object, Verb. These sentences are broken into subject, object and verb term parts with the end goal of arrangement. From thing and action term order we classify the subject and action term into a progressively broad class and henceforth we build up the relationship. In light of the order of action terms what is more, things, we built up a relationship for subject and action term as well as action term and object of the sentence. In our tenets and terms we are demonstrating that our framework works for expectancy is 70 percent, Proximity is 80 percent, compatibility is 80 percent. Some are blunders however in most extreme sentences it can discover the mistakes.
Developing a Glossary for Software Projects

The success of a software project depends intrinsically on effective communication among stakeholders. The purpose of a glossary is to ensure that the knowledge of the domain underlying a software project be communicated properly to all the stakeholders of that project. This chapter provides the context, the rationale, and the means for developing a glossary for software projects. In doing so, it proposes a process for developing a glossary. This process is independent of any particular application domain, software development methodology, and information technology. The approaches for representing and presenting a glossary, for the consumption of humans as well as that of machines, are discussed.