Reviewerly/exHarmony huggingface dataset:
Geometric correction and stabilization of images collected by UASs in river monitoring

While the field conditions and the choice of an image velocimetry technique usually have the largest impact on the accuracy of unmanned aerial systems (UAS) velocimetry, significant errors can be associated with the UAS hardware. Three such sources of errors are usually considered and accounted for: (1) errors related to the imperfections of the camera optics and sensor, (2) errors introduced by the UAS motion relative to the water, and (3) projection errors related to the positioning of the camera relative to the region-of-interest. The first source produces geometric distortions whose intensities depend on the type of the camera and quality of its manufacturing. The second source of errors is related to the UAS platform itself—even in favorable weather conditions the platform and thus the camera as well usually exhibit unwanted motion as a result of its (electro)mechanics, which reduces the overall velocimetry accuracy. Once the camera distortions have been minimized and the images are stabilized, the mapping of the information from images to the real-world coordinates can begin. A specific procedure—orthorectification—aims to ensure that the image coordinate system is aligned with the water surface. This technique emulates nadir positioning of the camera (even in cases when camera is not in perfect nadir) and thus stabilizes the ratio between image and real-world distances. The accuracy of velocity estimation greatly benefits from the reduction of the geometric distortion, orthorectification, and the reduction of residual motion of the camera, which are commonly performed in the preprocessing stage of UAS velocimetry. This chapter explains various sources of errors in the acquired optical data along with the effects they can have on the final velocimetry results, and the corresponding methods for their reduction/removal. Finally, a list of tips and good practices is provided which can facilitate the reduction of UAS velocimetry errors.
Ethical Issues, Fairness, Accountability, and Transparency in AI/ML

The ethical issues of how the computer evolved to the artificial intelligence and machine learning era are explored in this chapter. To develop these intelligent systems, what are the basic principles, policies, and rules? How are these systems helpful to humankind as well as to society? How are businesses and other relevant organizations adapting AI and ML? AI and ML are booming technology. They have major applications in healthcare, computer vision, traffic networks, manufacturing, business trade markets, and so on.
Safety and Security in AI Systems

Innovation and adoption of smart things are very fast in today's era. Everyone wants a smart device to solve any problem because it is effortless, affordable, and less time-consuming. Using artificial intelligence (AI), we have solved many more problems with great success. Some areas where AI is already used are healthcare, smart city, smart grid, agriculture, astronomy, gaming, finance, tourism, social media, robotics, automotive industries, entertainment, e-commerce, education, etc. As the number of users of AI and the number of smart devices increases, the risk of the data and information we are sharing with these devices are also becoming high. For the safety and privacy of users as well as the company, AI systems, their data, and their communications must be protected. Consequently, this chapter aims to provide an overview of how safety and security can be achieved in AI systems. Learners will get knowledge about AI systems and some security mechanisms that can be helpful to their further studies.
The Mapping of Inclusion

Inclusion and inclusivity are concepts that underlie enormous degrees of change between different locations but also at different times. Knowing about these different traditions and potential pitfalls constitutes—at least in the educational science—a learning aim in and of itself. In order to facilitate learning in that regard, the authors designed and realized a project adhering to the name of the Mapping of Inclusion, in which students and faculty work collaboratively to collect, synthesize, and constantly improve knowledge about inclusion/inclusivity at different places and times. This chapter has three objectives: First, it outlines the problem statement. Secondly, it seeks to clarify its philosophical position regarding digitality and higher education while also tying these together with the specific realization (and embedding) of the Mapping of Inclusion. Third, the chapter will identify, illustrate, and explain power laws of digital teaching.
Blockchain in Supply Chain Management Prevailing in Smart Cities

Supply chain enactment engages in a diversified shareholder environment and need provenance, transparency, and trust. The establishment of a decentralised infrastructure comprising several stakeholders is made possible by blockchain technology. The blockchain technology is suited for multi-stakeholder supply chain applications because of these processes. This chapter covers the elements that make up the supply chain, outlines its needs, and describes how blockchain technology might help. This chapter focuses on the use of blockchain technology to develop data and trust management. To assist the stakeholders in making an educated choice, the significance of scalability and interoperability in a blockchain-dependent supply chain is stressed. This chapter ends by downplaying the design difficulties and untapped potential of the blockchain-dependent supply chain industry.
Agricultural Social Network

India is one of the world's largest food producers, creating the sustainability of its agricultural system of global significance. More than 67% of the Indian population still depend on agriculture or its allied sectors for their livelihood. There lies a requirement for the discussion on balancing or enriching the agriculture production in order to bring in food security for the growing population. Agriculture in recent years is facing great downfall due to uncertain monsoons, decline of soil fertility, population pressure, lack of support services, and so on. To overcome these, there is a need for communication such as dissemination of right information on right time to the farmers, which would help them towards advancing their knowledge on agriculture. The growth of active internet users over the past few years shall make this task easier. The chapter shall make an in-depth analysis on the need for the development of an agricultural social network and shall also present a prototype of the proposed system.
Digital Tools for Meaningful Learning of Preservice Teachers

The purpose of this chapter is to explore how digital tools can help preservice teachers engage in meaningful learning. The literature on theoretical stances, course design, and assessment are examined to provide a more comprehensive picture of meaningful learning. The characteristics of a meaningful learning framework are presented to provide a more nuanced understanding of how digital tools may aid the process, with a focus on meaningful learning perspectives in the context of technology-based tasks. The framework defines meaningful learning as the active, constructive, collaborative, authentic, and goal-directed process of producing meaning. The pedagogical affordances of digital tools are then explored, as well as the technical skills required by teacher educators to support preservice teachers in having meaningful educational technology experiences. The chapter concludes with a discussion of how teacher educators can use digital tools to promote meaningful learning experiences for preservice teachers.
Statistical hypothesis testing as a novel perspective of pooling for image quality assessment

Image quality assessment is usually achieved by pooling local quality scores. However, commonly used pooling strategies, based on simple sample statistics, are not always sensitive to distortions. In this short communication, we propose a novel perspective of pooling: reliable pooling through statistical hypothesis testing, which enables effective detection of subtle changes of population parameters when the underlying distribution of local quality scores is affected by distortions. To illustrate the significance of this novel perspective, we design a new pooling strategy utilising simple one-sided one-sample t-test. The experiments on benchmark databases show the reliability of hypothesis testing-based pooling, compared with state-of-the-art pooling strategies.
Implementation Aspects of Supersingular Isogeny-Based Cryptographic Hash Function

Supersingular isogeny-based cryptosystems are considered an attractive candidate for the post-quantum cryptographic world due to their smaller key sizes. Based on the traversal in a supersingular isogeny graph (expander graph), Charles, Goren, and Lauter proposed a cryptographic hash function also known as CGL hash. In this paper, we present our study on the implementation-related aspects of the compact variation of the standard CGL hash function using different forms of elliptic curves (Weierstrass, Montgomery, and Legendre). Moreover, we show that some redundant computations in the original propositions of the CGL hash function can be avoided by utilizing the unique characteristics of the different forms of the elliptic curve. We also compared the running time and the total number of collisions through the experiments with the implemented algorithms.
Survey of window types for aggregation in stream processing systems

Abstract In this paper, we present the first comprehensive survey of window types for stream processing systems which have been presented in research and commercial systems. We cover publications from the most relevant conferences, journals, and system whitepapers on stream processing, windowing, and window aggregation which have been published over the last 20 years. For each window type, we provide detailed specifications, formal notations, synonyms, and use-case examples. We classify each window type according to categories that have been proposed in literature and describe the out-of-order processing. In addition, we examine academic, commercial, and open-source systems with respect to the window types that they support. Our survey offers a comprehensive overview that may serve as a guideline for the development of stream processing systems, window aggregation techniques, and frameworks that support a variety of window types.
A novel Pseudo-label based domain adaptation method on tabular data

Tabular data is a widely used data form in many fields such as product marketing. In some cases, the domain shift between source and target domain of tabular data may occur with the changing of collection conditions such as time. The extant methods on tabular data mainly consist of neural-network-based methods and tree-based methods. They both meet challenges induced by domain shift on tabular data. First, neural-network-based methods are lack of effective mechanism to extract the features of tabular data and the performance may not be higher than tree-based models. Second, tree-based methods are lack of effective feature representations to model the associations between source domain and target domain. To improve the performance of tree-based methods for domain shift, a novel pseudo-label based domain adaptation method is proposed for the tree-based method called Xgboost. The proposed method consists of pseudo-label generation and selection strategies. The pseudo-label generation strategy can control the effects of pseudo-labels on Xgboost in a more flexible way by setting proper values of pseudo-labels. The pseudo-label selection strategy can select the pseudo-labels with high confidences under a consistency condition based on the outputs of Xgboost. The quality of pseudo-labels for the data in target domain is improved and so does the performance of Xgboost trained by the data in both source domain and target domain. In the experiment, several UCI datasets and 5G terminal datasets are used to show that the proposed methods can effectively improve the performance of Xgboost.
Succinct quantum classification algorithm based on quantum circuit model

In classical machine learning, classification algorithms are a fundamental class of learning algorithms. Evaluation of the Euclidean distance is an alternative procedure for classification algorithms. In this paper, we devise a succinct quantum classification algorithm based on the quantum circuit model, in which we could calculate the Euclidean distance between two complex vectors to classify vectors of any dimension into different clusters. More specifically, (i) we do not restrict the vectors to being real or non-negative, but instead complex; (ii) we clarify the process of preparing the entangled states needed in the algorithm; (iii) the specific unitary transformations are not required to apply; (iv) we just need to execute simple measurements, instead of performing the special projection measurements; (v) our algorithm also achieves exponential speedups over classical algorithms. Finally, we present numerical experiments of our algorithm on a quantum software (MindQuantum) and a 6-qubit superconducting quantum computer (OriginQ Wuyuan).
Role of Quantum Computing in a Data Analytics Environment

There is a strong consensus that quantum computers are likely to solve previously intractable problems, especially in data science and AI. Nowadays, processing power of machine learning algorithms is limited by that of conventional computers. Quantum computing (QC) is one of the digital trends with the quickest rate of growth, and it seems to provide an answer to large data problems. It is a combination of the fields of computer science, information theory, and quantum physics. Quantum computers use the ability of subatomic particles to exist in more than one state at once to solve complex problems. In spite of its recent origin, quantum computing is already being used in the field of data analytics. QC can process huge datasets at much quicker rates and can provide data to AI models. By comparing schemas to swiftly assess and comprehend the link between two counterparts, QC can also aid in the integration of data. The ability to perform more sophisticated analysis and build machine learning models is a benefit of employing quantum computers.
Integrating Ed Tech and Cybersecurity Into the Curriculum

The chapter aims to discuss how people interested in incorporating different aspects of cyber security into the curriculum in higher education, corporate learning, and non-traditional learning environments can do so. Due to the COVID-19 outbreak, more businesses are switching to online platforms, which has resulted in a dramatic spike in the need for cybersecurity controls. The objective is to share how the immense impact of educational technology (ED Tech) in the age of COVID-19 lockdown is digitally focused on educational content and the basic need to be flexible and adaptable for students. Overall, this chapter proposes that the need for cyber security is becoming a more crucial facet of life. So, the report by intelligence system can examine threats and the platforms/applications for processing data can be examined. Cyber ED Tech protects against a variety of attacks, including cloud security breaches, denial of service (DoS), malware, zoombombing, password policy violations, and so on.
Comparing approximate and probabilistic differential privacy parameters

This paper compares two notions of differential privacy: approximate differential privacy (ADP) and probabilistic differential privacy (PrDP). It is well-known that the PrDP implies the ADP; and it was established in [7] that the ADP implies the PrDP, after a penalty on the parameters ε and δ that are used in the definitions of both properties. We show that the condition found in [7] is tight: if it fails, we construct a randomized algorithm that has ADP but not PrDP.
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds

Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. These applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that offer instances of different VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends significantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral differences with NSGA-III. Then, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering different application sizes. To do that, we use the well-known CloudSim simulator and consider different VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and significant savings in terms of computing time (10%–17%), monetary cost (10%–40%), and spot instance interruptions (33%–100%).
Study on the eye movement characteristics of the badminton practitioners of different levels regarding visual attention

Badminton is a highly sophisticated, fierce, and competitive tactical game. It requires the same action of hitting a ball with different landing points. Therefore, the complexity of badminton practitioner's sports decision-making is relatively high. Accordingly, it is extremely important to study the difference between the eye movement characteristics of different levels of badminton athletes and the difference between the eye movement characteristics of different sports levels of amateur athletes. Overall, 15 students from the badminton professional training team of the Physical Education College of the Jiangxi Science and Technology Normal University and 15 students from the common public sports and badminton course were included as experimental participants in the present study. The laboratory experimental test on the virtual sports situation in badminton was conducted using an eye tracker. The eye movement index of both the badminton professionals and the experimental participants was recorded for statistical analysis, and the following results were obtained: (1) In the cognitive decision-masking task, the reaction time of the professional badminton practitioners was faster than that of the amateur practitioners. Similarly, in the intuitive decision-masking task, the reaction time and accuracy of the former were better than those of the latter. (2) The professional badminton practitioners' group was able to process and integrate the searched information in the process of sports attention selection information; although the amateur group was able to search and filter information, they were passive and could not actively process and assimilate the searched information. (3) The professional badminton practitioners could allocate their attention reasonably and process information in the process of attention transfer, while their amateur counterparts were affected easily by external interference factors. The level of motor intelligence of badminton practitioners in the professional group was higher than that of the amateur practitioners. Thus, these two groups of different levels showed attention transfer. (4) The mental skills of the professional group were higher than those of the amateur group.
A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems

This article deals with an artificial intelligence (AI) framework to support Internet-of-everything (IoE) applications over sixth-generation wireless (6G) networks. An integrated IoE-Edge Intelligence ecosystem is designed to effectively face the problems of Virtual Machines (VMs) placement based on their popularity, computation offloading optimization, and system reliability improvement predicting compute nodes faults. The main objective of the article is to increase performance in terms of minimization of worst end-to-end (e2e) delay, percentage of requests in outage, and the enhancement of reliability. The article focuses on the following main issues: (i) proposal of a channel-aware federated learning (FL) approach to forecast the popularity of the VMs required by IoE devices; (ii) use of an AI-based channel conditions forecasting module at the benefits of the FL process; (iii) development of a suitable VMs placement on the basis of their popularity and of an efficient tasks allocation technique based on a modified version of the auction theory (AT) and a proper matching game; (iv) enhancement of the system reliability by an echo-state-network (ESN), located on each computation node and running in the background to predict failures and anticipate tasks migration. Numerical results validate the effectiveness of the proposed strategy for IoE applications over 6G networks.
General inertial proximal stochastic variance reduction gradient for nonconvex nonsmooth optimization

Abstract In this paper, motivated by the competitive performance of the proximal stochastic variance reduction gradient (Prox-SVRG) method, a novel general inertial Prox-SVRG (GIProx-SVRG) algorithm is proposed for solving a class of nonconvex finite sum problems. More precisely, Nesterov’s momentum trick-based extrapolation accelerated step is incorporated into the framework of Prox-SVRG method. The GIProx-SVRG algorithm possesses more general accelerated expression and thus can potentially achieve accelerated convergence speed. Moreover, based on the supermartingale convergence theory and the error bound condition, we establish a linear convergence rate for the iterate sequence generated by the GIProx-SVRG algorithm. We observe that there is no theory in which the general extrapolation technique is incorporated into the Prox-SVRG method, whereas we establish such a theory in this paper. Experimental results demonstrate the superiority of our method over state-of-the-art methods.