Reviewerly/exHarmony huggingface dataset:
Data clustering using unsupervised machine learning

Artificial intelligence (AI) and machine learning (ML) have been active in various research fields and improved results. However, most of them applied or focused on supervised learning. Most of the real-time appliances employ unsupervised machine learning since these models utilize unstructured raw data to expand system performance then service area. In addition, unsupervised models play a vital role in deep learning (DL) models for training complex structures with several parameters with effective data processing. Clustering defines as an unsupervised learning model considered as an essential problem in numerous data-driven fields. Many works currently focus on using deep neural clustering models to study a clustering-friendly representation, ensuing in momentous growth of clustering performance. Currently, collected data always has high-dimensional and multifaced representations, making data analysis challenging for researchers. Feature selection and feature representation effectively solve the high issues mentioned above by removing irrelevant, redundant data and facilitating a restored empathetic of the learning model. In this chapter, unsupervised-based clustering approaches briefly elaborate along with cluster evolution criteria in terms of distance measurements and clustering loss functions. Then, a taxonomy of neural network-based deep clustering models, feature selection, classification issues in ML is discussed. Finally, the chapter highlights some interesting challenges and future outlooks in unsupervised deep clustering.
MR in video guided liver surgery

In the context of image/video guided surgery, mixed reality (MR) is used for visualization and interaction with 3D organs models, including tumors and vascular structures. For laparoscopic liver resection, the use of MR is a very promising approach, set to revolutionize planning and execution of these complex interventions. This technology allows surgeons to collaborate on planing and executing laparoscopic liver resections, simplifying complex procedures, and enhancing patient outcomes. Furthermore, it gives more confidence to the surgeon about his performance and guides him to achieve higher accuracy in the execution of the planned resection. To achieve this, accurate organ models are created from high-quality MR/CT medical images and integrated in the MR visualization platform for planning and navigation by the surgeons. In this chapter, we will give an overview about the different steps involved in such procedures, based mainly on work we performed in Norway.
Quality evaluation of light fields

The light field (LF) as an immersive imaging technology has received significant attention from both academia and industry in the last decade. LF enables a wide variety of applications in computer vision and computer graphics domain. Due to its novel representation it has unique challenges in the imaging pipeline. These challenges render the quality assessment of the LF content a necessity. In this chapter, various aspects regarding the LF quality assessment are discussed, including LF-related distortion characteristics, subjective user studies, and objective quality metrics. The chapter provides an overview of the current state in LF quality assessment.
Assuring AI methods for economic policymaking

AI methods are becoming more common in the field of economics, but these models must be bias-free, fair, and explainable. In other words, we need AI assurance. Economic forecasting has benefited from machine learning techniques, such as neural networks, to increase model performance, but these AI techniques must be audited, accountable, and interpretable to be useful for economic policymaking. The rise of natural language processing and large language models has created new challenges for economic policymaking institutions, which need to be aware of AI assurance and how to harness them safely.
Introduction to microblogging sites

Some of the most prominent online social microblogging platforms today include Tumblr (http://www.tumblr.com), Twitter (https://twitter.com), and Sina Weibo (https://weibo.com). These microblogging services have become popular communication tools, allowing for quick transmission of information. Every day, huge numbers of data are generated on these sites as a result of commercial, scholarly, and social activity. Fraud detection, market analysis, spam posts, detection of spammers, categorization or grouping of users based on their behavior, customer retention, and extraction of vital news, as well as production control and science discovery, can all benefit from this crowdsourced data. Data from microblogs is increasingly being used to create real-time search engines and recommendation systems, as well as services that mine and summarize reactions of the general populace to events. Microblogging sites, in addition to a wide range of uses, also introduce a number of obstacles in exploiting crowdsourced data, including the requirement to filter out dangerous content uploaded by spammers and the necessity to organize the profusion of data. As indicated in the next part, the goal of this book is to offer techniques for addressing these two issues.
Subjective and objective quality assessment for omnidirectional video

Video quality assessment is generally important to assess any kind of immersive media technology, for example, to evaluate a captured content, algorithms for encoding and projection, and systems, as well as for technology optimization. This chapter provides an overview of the two types of video quality assessment, subjective testing with human viewers, and quality prediction or estimation using video quality metrics or models. First, viewing tests with humans as the gold standard for video quality are reviewed in light of their instantiation for omnidirectional video (ODV). In the second part of the chapter, the less time-consuming, better scalable second type of assessment with objective video quality metrics and models is discussed, considering the specific requirements of ODV. Often they incorporate computational models of human perception and content properties. ODV introduces the challenges of interactivity compared to standard 2D video and typically spherical projection distortions due to its omnidirectional, “point-of-view” (in terms of camera-shot type) nature. Accordingly, subjective tests for ODV include specific considerations of the omnidirectional nature of the presented content and dedicated head-rotation or even additional eyetracking data capture. In the last part of the chapter, it is shown how to improve objective video quality prediction by taking into account user behavior and projection distortions.
An introduction to AI assurance

In this chapter, we present a brief introduction about the concept, dimensions, and challenges of AI assurance. The goal is to lay the path for the concepts presented in this book. AI is often assessed by its ability to consistently deliver accurate predictions of behavior in a system. A critical, often overlooked, aspect of developing AI algorithms is that performance is a function of the task the algorithm is assigned, the domain over which the algorithm is intended to operate, and changes to these elements in time. These parameters and their constituent parts form the basis which makes assuring AI a challenge. Algorithms need to be characterized by understanding the factors that contribute to stable performance across an operational environment (e.g., no dramatic perturbation by small changes and/or no effects measurable over time). This chapter presents a high-level introduction to AI Assurance and points readers to related areas of interest in the book.
Introduction to statistical modeling in machine learning

In data mining, statistical learning is a set of approaches for modeling and analyzing big and complex information. Statisticians are exploring this topic, which is combined with advances in computer science, particularly machine learning. The purpose of this chapter is to provide a deep overview of the major machine-learning techniques and algorithms with prediction of prison overcrowding as a case study. With the advent of big data technology and high-performance computers, machine learning has opened up new prospects for data-intensive science across all disciplinary and technical domains. The purpose of this chapter is to provide a deep overview of the major machine-learning techniques and algorithms with predictions on prison overcrowding. Additionally, when we use statistics to construct a data representation, we have to analyze to infer connections between variables to find insights. Machine learning is the process of gaining a comprehensive understanding of data via mathematical and/or statistical models to make predictions. This chapter emphasizes the advantages of machine-learning algorithms from an application viewpoint to help use illustrative examples from the study on prison overcrowding and proposed Regression-Based Prison Overcrowding Prediction Model (RBPOP).
Role of smart technologies in detecting cognitive impairment and enhancing assisted living

With escalation in the aged population due to improvement in healthcare and medicine, cases of dementia and other cognitive disorders have been on the rise. External care is needed as an individual's functionality declines with the disease progression. A majority of affected older adults prefer to receive care in their own homes, although due to a shortage of care providers, many must reside in assisted living facilities. Family members typically assume the emotional and financial burdens of care in both circumstances Therefore, it is critical to implement technological solutions in living facilities; to detect cognitive problems in the nascent stages and to provide assisted care. This chapter aims to summarize the developments in an important class of solutions, smart home systems.
Digital twins and cybersecurity in healthcare systems

Digital twins in health care can be utilized to build digital representations of a person or health care devices which can employ an integration of technologies like Internet of Things (IoT), Artificial Intelligence (AI) models and cloud computing. These technologies will increase the attack surface on digital twins which may require multiple correlated vulnerabilities to cause critical security threats to digital twin systems, and impact on millions of patients. This chapter will provide the most researched techniques and methods that can be used to build not only a cyber secure framework for digital twins but to also provide a privacy and trust framework to protect both the patient and health providers in an efficient way. Various solutions will be suggested like intelligent Intrusion Detection Systems (IDS), efficient authentication methods, protected communication channel, privacy by design, enhance trust with block chain integration and better compliance with current related standards.
Extended reality

Extended reality (XR) is an umbrella term that includes augmented (AR), virtual (VR), and mixed realities (MR). Widespread applications of these technologies in improving user interaction and experience led to its popularity in the recent past. Moreover, the availability of equipment that can be commercially accessed by users for complex VR and MR application has also popularized this technology and driven research into fields that explore the use of XR in specific application domains. However, multiple views on the definitions and concepts in this regard are presented in existing literature. This chapter explores the three types of realities and their contextual meaning with respect to XR. In addition, this chapter also explores the differences between these technologies. Additionally, this chapter explores other related concepts in literature that expand the idea of XR to ∗R or All-R, to provide a comprehensive view on existing and related literature in this field. Finally, it explores the different technologies and platforms available for XR development along with an introduction to the potential use of XR in healthcare.
Aerodynamic principles

The aerodynamic principles are essential for understanding the physics involved in fixed-wing UAVs. This chapter defines some basic definitions of aerodynamics. Section 2.1 presents the importance of knowledge of the aerodynamic principles, the definition of the atmosphere, atmospheric pressure, the standard atmosphere, air temperature, air density, airplane wings, the Bernoulli theorem, and the center of pressure. In Section 2.2 the forces acting in flight are shown, and definitions of flight opposition (resistance), thrust, and lift are provided. Section 2.3 presents the concepts of the axes of an airplane, aircraft control surfaces, and the structure of an aircraft.
Algorithms for Convex Hull Finding in Undirected Graphical Models

An undirected graphical model is a joint distribution family defined on an undirected graph, and the convex hull of a node set in the graph is the minimal convex subgraph containing it. It has been shown that a graphical model is collapsible onto the minimal local sub-model induced by the convex hull which contains variables of interest under Gaussian and multinomial distributions. This motivates many scholars to design algorithms for finding the unique convex hull containing nodes of interest in a graph. In this paper, we propose two algorithms called, respectively, the node absorption algorithm (NA) and the inducing path absorption algorithm (IPA), to find the minimal convex subgraph containing variables of interest in an undirected graph. These algorithms can be used as potential tools to find the minimal sub-model including variables of interest onto which a graphical model of large-scale can be collapsible. Experiments show that the proposed IPA significantly outperforms the NA and other existing algorithms. Furthermore, we apply the IPA to a gene network so as to collapse a large network onto a smaller network including the interested variables, and thus to achieve the aim of structural dimension reduction.
Blockades of blockchain in supply chain management

Blockchain is a set of disseminated ledgers in a peer-to-peer system. This know-how has widespread submissions in both the economic and noneconomic domains. It is facilitating the growth of cryptocurrency in India and helping in the process of digitalization in numerous sectors. The process of the espousal of the emerging technology “Blockchain” in the Supply Chain Management has its pros and cons. The process requires transparency at each stage of the supply chain from procurement of raw substantial, work in progress, and product creation. The customer throughout the industry wants traceability function to operate at every node in the network. There are various hurdles in the adoption of this technology such as collaboration changes, sharing of information with supply chain partners, standardization, Quantum computing, bootstrapping, fraudulent practices, data privacy, change in behavior, data storage, and scalability, etc. The risk involved in the implementation needs to be overcome with the help of further research in the domain of technology solutions such as smart contracts, and suitability, etc. As the complexity is increasing in logistics management due to increased players in the industry so blockchain should be implemented for proper monitoring and efficient transfer of goods from one place to another. The revolution phase started by the application of blockchain technology has just started it needs to be explored more in the light of future uncertainty.
Issues and challenges of blockchain technology implementation in the meat supply chain

Consumers are now increasingly looking for transparency and full traceability to make sure that they purchase genuine meat. One of the persistent problems in the meat business is unreported and unregulated. The objective of this study is to analyze the issues and challenges of blockchain implementation in the goat meat business. The telephonic interviewing method is used to explore the awareness of Blockchain applications in the meat business. The goat (sheep) owners, meat sellers, and meat consumers are taken as a sample to analyze the issues and challenges of blockchain technology. Transparency, Decentralized trust, Cost savings, and Efficiency are benefits of blockchain technology in the meat market. Our research findings highlighted that a major obstacle to blockchain implementation is unawareness of blockchain technology. Service providers of blockchain technology should listen to the stakeholders of the meat business and develop easy-to-use blockchain technological solutions.
The application of blockchain in talent supply chain management

The talent supply chain in human resources includes various functions like talent sourcing, hiring, contracting, background verification, employee database management, skill-mapping at various stages of the supply chain linked to hiring, learning, internal mobility, and payroll processing. While Human Resource Management is at the threshold of adopting the Social, Mobile, Analytics, and Cloud-based technology, adopting blockchain technology is not far behind. Many organizations see blockchain as a breakthrough in hiring, background verification, payroll processing, for record-keeping related to employee's data and maintaining legal contracts in the gig economy. In talent sourcing, data security is a matter of concern. Data obtained from various sources like applicants or employees are subject to cyber security threats. Another concern is resume fraud, which pertains to presenting oneself favorably to recruiters through information in the resume that is fraudulent like fake qualification and course certificates, awards, client feedback, etc. Authenticating data remains a gray area, with intermediaries like background verification agencies adding to the recruitment cost. The presence of intermediaries in the supply chain like hiring agencies also adds to lack of confidentiality of personal data. In the talent supply chain, skill-mapping is important for judging quality of hires, internal performance assessment, and identifying learning and development needs. Again, for contractual employment, it is important to respect the rights of all actors involved in the contractual process so that the process is risk-free, fair, and legal. Both cybersecurity threats and fraudulent practices can be mitigated by using blockchain technology in employee data storage, validation, and management. Similarly, blockchain helps in providing a direct link to suppliers along with supplier validation and entering contracts. This does away with the presence of intermediaries in sourcing. Thus, this study discusses how blockchain technology can provide effective solutions for multiple use cases in the talent supply chain through distributed ledgers, consensus mechanisms, public and private blockchains, smart contracts, usage of tokens, and cryptocurrency for transfer of payments, etc. The study further discusses the limitations and barriers to implementing blockchain technology and the implications of adopting blockchain technology in talent supply chain management.
Background verification using blockchain: case of Ajay, a star performer

This paper analyzes the case of an employee Ajay, a star performer in his organization, whose curriculum vitae information was later revealed to be false. The revelation came after a considerable amount of time, training effort, cost had been invested by the company on Ajay and vice versa. What should the company do? While terminating his employment or ignoring the truth and carrying on seems as the two obvious alternatives, are there any other options that the company could explore? Is there a better solution to this problem? These are the questions that this article aims to answer. When the job market is becoming tough for candidates to secure a job in a company, it is not very uncommon for applicants to manipulate their curriculum vita and furnish incorrect information. Therefore, there is a need for organizations to invest vital resources on elaborate verification processes, which as mentioned above is working out to be extremely cost-intensive and time consuming. This paper focuses on a specific technology that has, of late, gained momentum in this regard. In the era of industrial revolution 5.0 the application of information technology in the HR domain has ensured an efficient and secure system for all functions including employee verification check. Ever since its emergence in 1991, blockchain technology, though originally was designed with the idea of cryptocurrencies and bitcoins in mind, is now finding use in diverse applications such as healthcare and advertising. The recruitment industry has also identified the potential of this technology and is finding ways and means of incorporating this idea in its processes. Today, the recruitment industry operates with job portals, jobseekers, firms, and other intermediaries, which gives rise to an environment where the sharing of accurate and relevant information has become the need of the hour. The blockchain technology has the necessary solutions to the problems faced by the HR domain in terms of recruitments. Specifically, this paper proposes the blockchain-based employee background verification system as a solution to the problem at hand. The case holds the definitive advantage of the blockchain technology over the existing verification system. It illustrates the challenges of an organization in not only identifying the right candidate but also checking the authenticity of the information furnished by the candidates on record.
Use cases of blockchain technology for sustainable global HR operations in industry 4.0

Blockchain is a novel IT technology that will unquestionably provide solutions to business community in Industry 4.0. Blockchain, being new in the industry require a detailed explanation for easy adoption. It is already in the phase of active research and adoption by the companies. Therefore it is important to explore the benefits of blockchain technology for the corporates. This paper is a comprehensive review of the use cases of blockchain technology for sustainable human resource management. The paper would be extremely useful for beginners in understanding the concept of blockchain technology and its wide applications in the field of HRM. One very important area of its application is human resource management, which includes talent acquisition, payroll management, employee record keeping etc. Though the benefits of blockchain technology have been widely researched in the financial and logistics sector, major challenges in personnel management can also be minimized by introducing blockchain technology. The information presented in this paper has been collected from various published sources of information. Plenty of work talking about application of blockchain technology in logistics, supply-chain has been done. This paper is an original contribution to the body of human resource management literature.