Reviewerly/exHarmony huggingface dataset:
Distributed Ledger Technology Economy

Distributed ledger technology (DLT) and blockchain are very popular these days, especially due to the cryptocurrencies like Bitcoin, Ethereum, Dogecoin, and others. To better understand these cryptocurrencies and the related transactions, it is important to know how these technologies work and how secure they are. This paper presents the concepts, technologies, and security issues related to blockchain with respect to the economy.
Jakarta EE Foundations

Quite some history to reach the current state, right? That being said, you might be wondering at this moment “why this much history” or “why is security so fundamental to our applications.” Well, we will cover everything related to the rationale of this book in this chapter. Besides, we will have a look at the foundations of enterprise security.
AI’s Winograd Moment; or: How Should We Teach Machines Common Sense? Guidance from Cognitive Science

In this paper, I provide a number of state-of-the-art results for Winograd- related problems by applying a public technique for zero-shot scoring with language models. I explain how I discovered this advance and my recent experience in sharing these results with the scientific community. We are on the threshold of major advances and opportunities, but also dangerous trends. A strengthened discourse on the public benefit derived from the use of computers is badly needed and starting to emerge; here, I focus on that discourse in the context of apparent sudden, giant leaps in our ability to process text with computer: natural language processing (NLP). There is no shortage of lessons for science and ethics. Fortunately, despite the vulnerable status of public University research, the prognosis for informed public debate about the sciences of machine learning and artificial intelligence remains excellent.
The Role of Internet Identity of Victims on Cyberbullying: An Experimental Approach

For cyberbullying has been gradually prevalent with internet as a new platform of bullying, numerous researches have been exploring the predictors of cyber aggression from the various aspects. However, much less is known with regard to the exploration of the role of the identities of victim on cyberbullying behaviors experimentally. The aim of this study was to explore how the role of internet identity of victims on social media could affect cyberbullying behavior, which is a possible predictor having been noticed in previous researches and worthy to be examined. To this end a study with experimental methods was conducted. Participants were 195 college students in the age range of 17-21 years (M=18.78 years, SD=0.72, 62% female). An experimental approach based on vignettes methodology was utilized in the current study, in which participants were exposed to hypothetical cyber incident scenarios and data of their reactions were collected by a self-reported question. The result showed that participants who observe insulting speeches posted by celebrities expressed more aggression intention to the blogger than those who observe same speeches posted by peers. The findings indicated that the internet identity of victims has influence on cyberbullying and might provide novel insight into the predictors of cyberbullying.
Automatical IVUS Image Segmentation and Detection Using BLOB Analysis

In this research, experiments were conducted on the problem of image analysis in atherosclerotic disease. The study was conducted from intravascular ultrasound images that have been taken inside the blood vessels from patients with arterial wall hardening or stenosis. These images are used by experts or specialists to analyze the problem of the disease. There may be some interference from shadows or elements that occurred while taking the image that may cause the incomplete image. We propose an applied method by using computer techniques that help to analyze the components within the image to find the area of interest by enhancement image. Then, the pixel levels are analyzed and classified to divide the background from the image. Next, the morphological operation is used to readjust image properties, and blob analysis is used to identify the region of interesting pixel values. In the final process, Connected Component Analysis (CCA) was used to detect media-adventitia and lumen boundary area The efficacy of segment results was measured by comparing with the expert to measure performance as (media-adventitia, lumen) the Jaccard Index = (0.9570, 0.96695), Hausdorff Distance = (0.5259, 0.6304), Percentage Area Distance = (0.0382, 0.03395) IVUS dataset was used from Simone Balocco, Dept. Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain.
Development of a Medication-Taking Behavior Monitoring System Using Depth Sensor

Medication adherence is one of the important home care issues for the elderly living alone. Some devices have been developed to support medication adherence, but only for taking medicines out. Therefore, monitoring entire medication-taking behavior is strongly demanded. However, conventional behavior recognition using a camera has a problem in dealing with irregular movement and occasional interruption. In this paper, we propose a medication-taking behavior monitoring system that combines grasp identification and medication state identification to detect robustly completion and interruption of action. In grasp identification, hand gestures were classified into six medication-taking states using two machine learning schemes. In medication state identification, two methods: the state transition diagram and the probability estimation were implemented and compared. The system using probability estimation could detect completion of medication-taking with at least 85% accuracy and detect interruption of medication-taking with 90% or more accuracy.
Video Salient Object Extraction Model Guided by Spatio-Temporal Contrast

The classical video salient object extraction model has the problems of incomplete salient object extraction and background interference, which limits the application scenarios of video salient object. Video salient object extraction should not only consider the spatial saliency formed by the intrinsic features of video frames, but also consider the temporal saliency formed by motion information. In this paper, a video salient object extraction model based on spatio-temporal fusion is proposed to search salient objects under the guidance of spatio-temporal contrast. Firstly, the priori information about the rough position of prominent targets is determined by adaptive fusion of three-color space contrast and motion contrast along the non-selective path. Then, based on the obtained prior salience cues, the energy function is used to guide the fusion of the underlying significance cues to optimize the inter-frame salience accuracy. The energy function consists of the foreground extraction item of the current frame and the dynamic position optimization item of the next frame. Finally, a complete video salient object is extracted by the spatio-temporal adaptive fusion using super pixels level and pixels level smoothing optimization twice. Experiments on ViSal, Segtrack-V2 and DAVIS public video saliency datasets show that this model is superior to classical video saliency algorithms in accuracy, reliability and robustness.
Social Behavior and Reasoning Through Multi-Agent Systems

In computer science, multi-agent systems are a relatively new sub-field composed of numerous interconnected computing elements known as agents. To satisfy their design intents, they can take decisions for themselves, i.e., they are capable to take autonomous decisions. Also, these agents are capable of communicating with other agents not only by commuting data but also by engaging in similar the kind of social activities that we all engage in every day of our lives, like, association, coordination, and the like. Social behavior is behavior among two or more organisms inside the identical species and encompasses any conduct wherein one member affects the other. For example, the interaction of two or more humans or organizations governed toward a common goal that is mutually beneficial. Whereas, social reasoning involves the propensity to illustrate inferences about others’ intentions, mentality, and actions or activities, in order to synchronize one’s own behaviors. In this chapter, we have developed a region-specific vaccination method using multi-agent framework. We have taken the help of the Mamdani fuzzy inference system to build this multi-agent framework.
Classification of breast cancer images using completed local ternary pattern and support vector machine

Breast cancer is the major cause of deaths in women compared to other cancers. Though early detection of breast cancer reduces cancer deaths, it is a challenging task for physicians. Local binary pattern (LBP) and local ternary pattern (LTP) techniques are widely applied in texture classification applications. Since LBP is more sensitive to noise in texture classification, it needs to be improved for achieving better results. Though LTP is more robust to noise, there are few drawbacks. Completed LBP and completed local binary count techniques achieve good accuracy for texture classification, but they inherit few drawbacks of LBP. In this paper, completed LTP operator is applied on breast cancer images for better classification accuracy than LBP and completed LBP operators, by extracting sign and magnitude components. Experimental results based on breast cancer database show that the proposed technique achieved better classification accuracy than existing similar approaches.
Introduction

No AccessThe Handbook of Data Science and AIApr 2022IntroductionStefan Papp, Wolfgang Weidinger, Katherine Munro, Bernhard Ortner, Annalisa Cadonna, Georg Langs, Roxane Licandro, Mario Meir-Huber, Danko Nikolić, Zoltan Toth, Barbora Vesela, Rania Wazir, Günther ZaunerStefan PappSearch for more papers by this author, Wolfgang WeidingerSearch for more papers by this author, Katherine MunroSearch for more papers by this author, Bernhard OrtnerSearch for more papers by this author, Annalisa CadonnaSearch for more papers by this author, Georg LangsSearch for more papers by this author, Roxane LicandroSearch for more papers by this author, Mario Meir-HuberSearch for more papers by this author, Danko NikolićSearch for more papers by this author, Zoltan TothSearch for more papers by this author, Barbora VeselaSearch for more papers by this author, Rania WazirSearch for more papers by this author, Günther ZaunerSearch for more papers by this authorhttps://doi.org/10.3139/9781569908877.001SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2022Pages: 1-27Print ISBN: 978-1-56990-886-0eISBN: 978-1-56990-887-7 Copyright & Permissions© 2022 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Artificial Intelligence

Artificial intelligence is the working field with the aim of creating machines as intelligent as humans. Artificial intelligence (AI) has developed vastly from the past times with big organizations such as Facebook, Google, etc. Devoting an uncountable amount of money in the field of AI analysis and progress. The basic goals of the artificially intelligent device are to acknowledge a human voice, to find out itself, to set up things by itself, and to resolve issues in a precise manner. The topic of intelligence in a machine is debatable; however, it entails a much greater scope. The topics that contribute the foremost within the field of computing are technology, psychology, neuroscience, biology, arithmetic, social science, and philosophy. There are many applications of AI. AI was formed out of snooping by humans when they continued performing on machines that whether a machine may ever be as smart as individuals then, therefore, the principles of AI was conveyed out of uncertainty. The business is growing and increasing day by day; for now, the technology is getting used within the fields like voice, tongue process, machine vision technology, human voice recognition, cyber-skilled systems, robotic technology, and transcription recognition.
A new GSNTI concept under circumstances of digitalization: The roles of VINITI RAS and RNPLS&amp;T

RNPLS&amp;T and VINITI are two major providers of information to the State System of Sci-tech information (GSNTI). The authors discuss their organizations’ visions of a new GSNTI concept. They characterize key information products acquired by GSNTI, format and classification schemes, e. g. the new State Rubricator of Sci-tech Information published by RNPLS&amp;T.
Sentiment Analysis using COVID-19 Twitter Data

The COVID-19 pandemic has essentially transformed the way millions of people across the world live their life. As offices remained closed for months, employees expressed conflicting sentiments on the work from home culture. People worldwide now use social media platforms such as Twitter to talk about their daily lives. This study aims to gage the public’s sentiment on working from home/remote locations during the COVID-19 pandemic by tracking their opinions on Twitter. It is essential to study these trends at this point in the pandemic as organizations should decide whether to continue remote work indefinitely or reopen offices and workspaces, depending on productivity, and employee satisfaction. Tweets posted in the live Twitter timeline is used to generate the set of data and accessed through Tweepy API. About 2 lakh tweets relevant to the remote work during the pandemic were tokenized and then passed to Naive Bayes classifier that classifies the sentiments positive, negative, neutral to every tweet. Our findings emphasize on population sentiment which is the effects of the COVID-19 pandemic, especially resulting from the work from home policy.
About methods of checking graduation qualifying papers for plagiarism

The article deals with the issues of checking final qualification papers for the amount of borrowed text and ways to control the bypassing of this check. Some variants of widely used methods for verification bypassing in MS Word documents are considered, namely, by creating drawings, labels, graphic figures, SmartArt objects, and tables. Various options for manual and automated control of the presence of these bypassing methods in the checked documents are analyzed.
Melts and Glasses of the K2o-Geo2 System: Physicochemical Modelling with Correction Based on the Results of Raman Spectroscopy

A Gibbs free energy minimisation approach realized in “Selector-C” software is used to study germanate melts and glasses of the K 2 O–GeO 2 system. A database of the thermodynamic properties of structural units of germanate melts was compiled by according to the results of high-temperature Raman spectroscopy for the correction of physicochemical models. Three mechanisms for the introduction of potassium cations into the germanium-oxygen network are identified along with the effect of composition and temperature on these mechanisms. It is shown that the germanate anomaly maximum shifts with increasing temperature towards low-alkaline compositions due to the appearance of non-bridging oxygen atoms in the system.
Construction Method of Cluster Simulation Test System for Unmanned Surface Vehicle

Surface unmanned vehicle (USVs) can accomplish more complicated tasks through role assignment, Task Division and Cooperation, and it has become an important force in military application. In order to simulate and test the unmanned surface vehicle (USV) cluster under the laboratory condition, this paper presents a method of constructing the USV cluster simulation and test system according to the task execution flow and the capability requirement, this paper introduces the composition and basic test flow of the test system, and introduces the key technologies involved in the construction process, including: hydrodynamic model, physical domain simulation, multi-ship data association analysis.
Manifold spatial clustering via asymmetric convolutional denoising autoencoder

Deep unsupervised learning extracts meaningful features from unlabeled images and simultaneously serves downstream tasks in computer vision. The basic process of deep clustering methods can include features learning and clustering assignment. To enhance the discriminative ability of the features and further improve the clustering performances, a new deep clustering method namely ACMEC (asymmetric convolutional denoising autoencoder with manifold spatial embedding clustering) is proposed. In this method, an asymmetric convolution denoising autoencoder is employed to extract visual features from images, and a manifold learning algorithm is used to obtain more distinctive features, followed by a Gaussian Mixture Model (GMM) is for clustering learning. The stability of feature space is guaranteed using separately training mechanism. In addition, reconstruction from noisy images enhances the robustness of feature networks. Experimental results on nine benchmark datasets demonstrate that the proposed ACMEC method can provide the better performances such as 0.979 clustering accuracy on the MNIST dataset and 0.668 on the fashion-MNIST dataset. ACMEC is a comparable competitor to the N2D (not too deep clustering) algorithm that is with 0.979 and 0.672 clustering accuracies respectively. Moreover, it is 16.1% higher than DEC algorithm on the fashion-MNIST dataset.
Understanding Professional Care Providers Readiness Towards the Adoption of Care Robots for Elderly Care

Care robots as an emergent technological innovation are transforming the future of elderly care. This technology holds the promise of supporting aging in place and prolonging independent living for elders. The aim of this study is to evaluate the readiness perception of professional care providers for the use of care robots in elderly care and forecasting its evolution. Several studies have identified acceptance factors for aging in place technologies that are specific to the studied geographical areas, making it difficult to assume the generalization of the obtained results for other countries. In this study, relying on a focus group composed by relevant stakeholders, fuzzy cognitive maps will be used to evaluate their perceptions regarding the use of care robots in elderly care in the Moroccan context. The obtained results show an initial low readiness level that is however bound to improve within very few iterations as inferred by the fuzzy cognitive map model. This analysis also led to the identification of several recommendations for a successful adoption of care robots in elderly care.
L’epentesi consonantica nel dialetto teramano

"Consonant Epenthesis in the Dialect of Teramo. This contribution deals with the process of consonant epenthesis typical of the Southern dialects of Italy, focusing on Abruzzese. Based on corpus data of the dialect spoken in Teramo, it tackles issues concerning the quality of epenthetic segments and its relationship with the process of lenition of velar plosives, also typical of the dialectal area. The synchronic status of the two processes is also addressed, as well as the status of the utterance-initial position in the dialect. Finally, a discussion about the distribution of the different epenthetic segments is sketched. Keywords: epenthesis, prosthesis, lenition, Abruzzese, Southern Italian dialects "
Blockchain-based Internet of Things (IoT) for healthcare systems: COVID-19 perspective

Chapter Contents: Abstract 20.1 Introduction 20.2 IoT in healthcare system 20.3 COVID-19 outbreak 20.4 Blockchain 20.5 Blockchain-based IoT for healthcare systems 20.6 Advantages of proposed system 20.7 Conclusion and future scope References
Construction of University Network Governance System Based on Artificial Intelligence and Network Algorithm Recommendation

With the introduction of the concept of computer education, the Internet began to pay attention to education and learning, and a large number of Internet learning webpages came into being, covering all levels of education and science. Driven by this wave of education, the school actively encourages teachers to develop various online courses that are not restricted by time and space, and promote the innovation of learning models. Through these online platforms, some colleges and universities have developed online courses for college students, hoping to stimulate learners' intrinsic motivation and cultivate students' personality through online courses and other teaching methods. However, online courses have constructed a complex engineering system, and there are currently some problems, such as the unclear layout of online courses, and similar professional content design and evaluation methods. How to build a university network governance system has become an important research direction for experts and scholars. This article introduces artificial intelligence, calculates the construction data of the university network governance system based on the fitness function of artificial intelligence and network algorithms, and constructs a model diagram of the university network governance system. It is hoped that this research can play a positive role in the governance of university networks.
Practical Evasion of Red Pill in Modern Computers

The blue pill is a malicious stealthy hypervisor-based rootkit. The red pill is a software package designed to detect blue pills or hypervisors in general. Ever since the blue pill was originally proposed, there has been an ongoing arms race between developers trying to develop stealthy hypervisors and developers trying to detect such stealthy hypervisors. Hypervisors can also be used for monitoring and forensic purposes, while malicious software may include a red pill component to discover such a hypervisor in order to evade it. This chapter discusses a practical approach to counter such malicious software by evading the red pill components.
Didactic parameters in the development of the students’ thinking competence

This article describes the essential role and value of textbooks in the development of independent-thinking competence of students in the process of teaching the social sciences and humanities in a secondary schools, which are one of the major factors determining the content of education. The pedagogical and psychological principles outlined in the textbooks are highlighted as a crucial element in enhancing the quality and effectiveness of teaching the social sciences and humanities. It is also noted, that in achieving the desired result, when establishing the content of the social sciences and the humanities, the existing didactic parameters should be taken into account. Recommendations are given for forming the content of a school textbook focused on the development of students’ independent thinking.
The Social Media Problem

This chapter covers using CQRS to aid in increasing throughput to deal with massive scaleability. We discuss the advantages and disadvantages of such a system, along with analysing the data storage choices that might aid with such a situation.