Reviewerly/exHarmony huggingface dataset:
Machine learning for ARWs

Navigation in underground mine environments is a challenging area for the aerial robotic workers. Mines usually have complex geometries, including multiple crossings with different tunnels. Moreover, improving the safety of mines requires drones to be able to detect human workers. Thus, in this Chapter, we introduce frameworks for junction and human detection in the underground mine environments.
Cloud-based innovations for syndromic surveillance

This case is set across two Kumbh Melas, the religious mass gatherings that occur every few years at one of four cities in India, and are attended by tens of millions of pilgrims. The 2013 gathering in Allahabad, attended by over 80 million people, has been the world's largest mass gathering to date. During that event, a team of medical students and public health practitioners embarked on an audacious plan to pilot a digital syndromic surveillance system where no surveillance system existed. Tablet computers and mobile hot spots had only just arrived on the market and not everyone was familiar with these technologies. Having met with success in 2013, the team returned to India for the 2015 Mela in Nashik, seeking to convince the government that they should entirely replace paper records at over 50 clinics. This case describes how the affordances of digital technologies were leveraged to advance public health practices during the two mass gathering events in India. The case also illustrates how prioritizing human centered design practices facilitated successful adaptation and implementation of a novel digital approach to public health surveillance.
Gray wolf optimization algorithm

Gray wolf is one of the new meta-innovative algorithms for solving optimization problems, inspired by wolves’ group life and how they hunt in nature. In implementing the GWO algorithm, four types of gray wolves, such as alpha, beta, delta, and omega, have been used to model the leadership hierarchy of gray wolves. In other words, gray wolf optimization algorithm modeling includes three main steps: search for prey, prey encirclement, and prey attack. The gray wolf optimization algorithm will be introduced and modeled in this chapter. Also, by introducing some of the most important studies conducted using this algorithm in the field of water and environment, a practical example for optimizing the dam outflow has been presented.
Bayesian theory: Methods and applications

Bayesian law expresses the relationship between dependent variables. The Bayesian relation uses a numerical estimate of the probabilistic knowledge of the hypothesis before the observations occur, and provides a numerical estimate of the probabilistic knowledge of the hypothesis after the observations. This law for classifying phenomena is based on the probability of occurrence or nonoccurrence of a phenomenon and is important and widely used in probability theory. If we can choose such a separation for a given sample space that knowing which of the separated events occurred would reduce an important part of the uncertainty. This is useful because it can be used to calculate the probability of an event being conditional on the occurrence or nonoccurrence of another event. In many cases, it is difficult to calculate the probability of an incident directly. Using this theorem and conditioning one event on another, the probability can be calculated. Bayesian theory has three methods: Bayes Optimal Classifier, Naive Bayes classifier, and Bayesian network. In hydrological issues, the Bayesian network has been used more. These networks are graphical networks that represent a set of possible variables and their conditional dependencies by a directional noncyclic graph (DAG). Bayesian network nodes represent variables that can be visible values, hidden variables, or unknown parameters. The edges of this network indicate dependencies. Each node has a probability function that includes the initial probability (for parentless nodes) or conditional probabilities related to the combination of different states of the parent nodes.
Parallel chaos search-based incremental extreme learning machine

Supersaturation of total dissolved gas (TDG) in water has become a serious problem to which a great deal of attention has been devoted during the last few decades. High level of TDG can cause gas bubble trauma (GBT) and it may be caused by releasing water through the spillways of dams. In situ monitoring of TDG can help in understanding the variation of its concentration over space and time. Several approaches based on numerical models, fluid mechanic, and hydrodynamic have been proposed and successfully applied for simulating and predicting TDG concentration. However, modeling TDG using machine learning paradigm is not well documented and fewer works can be found in the literature. In the present study, we introduce an innovative approach for predicting TDG at Snake River, USA, using only the river water temperature as input variable. We introduce an improved version of the original extreme learning machine (ELM) called parallel chaos search-based incremental extreme learning machine (PC-ELM), and then we compered the results obtained using the proposed PC-ELM with those obtained using the standard ELM. The two approaches were compared using large data set collected at four USGS stations. The accuracy of the models was evaluated using relative mean absolute error (RMAE), relative root mean square error (RRMSE), Legates and McCabe index (LM), and the coefficient of determination (R2). The PC-ELM and the ELM were developed using two activation functions namely Sigmoidal function and radial basis function. According to the obtained results we demonstrated that the PC-ELM enhanced the prediction accuracy of the ELM by increasing the R2 and LM and decreeing the RMAE and RRMSE indices.
Usability of Security Mechanisms of E-Health Applications

Extant literature supports that e-health applications are developed with a proper set of security mechanisms in place. However, the majority of the security mechanisms were not considered from the users' point of view. As a result, the security of health information is becoming an important and growing concern. The objective of this study is to evaluate the usability of security mechanisms of e-Health applications functional at health facilities operating under the Addis Ababa Health Bureau, and identify the strengths and weaknesses of the usability of the security features of the e-Health applications. This study uses a qualitative research methodology. The findings revealed that out of the thirteen criteria, learnability, aesthetics and minimalist design, and user language complied; on the contrary, revocability and user suitability were not in compliance with security features according to all the experts' review of all e-Health applications. Finally, recommendations were given for practice, and suggestions were forwarded for future research.
A Study of Steganography Approach for Securing Data in a Confidential Communication Using Encryption

A steganography approach is a beneficial approach for hiding and encrypting messages in text, audio, video, and image formats. After the original text file is first encrypted, this encrypted file is embedded with an image file, and that image will be transferred to the recipient. The motive of this method is easy communication and transferring of files to end users safely. The intruder is the only one who can view the unhidden message which is in text, audio, video, or image format. Thus, the message is encrypted successfully. Although there are many advantages for the steganography, which also has some limitations, these issues are mainly aimed in this paper. In this chapter, the challenges of steganography are rectified with the help of securing techniques. Finally, in the results section, findings in image size and the complexity of image quality at the sender side and receiver side in one-bit, two-bit, three-bit, and four-bit steganography images are stated. Thus, steganography methods are taking progressive steps to transform this capability into higher contributions.
A Basic Process of Python Use for IOTAP, Data Science, and Rapid Machine Learning Model Development

Python is a more powerful high-level language created by Guido van Rossum (first appeared in 20 February 1991; 31 years ago), and its language constructs and object-oriented approach aim to help programmers write clear, logical code for small- and large-scale projects. Its design philosophy emphasizes code readability with the use of significant indentation. Python is an object oriented, scripted, and interpreted language for both learning and real-world programming. First in this paper, Python is introduced as a language, and a summary given about data science, machine learning, and IoT. Then, details are provided about the packages that are popular in the data science and machine learning communities, such as NumPy, SciPy, TensorFlow, Keras, and Matplotlib. After that, the authors will show the working criteria of Python for building IOT applications. The authors will use different code examples throughout. The learning experience, and executed examples, contained in this paper interactively used Jupyter notebook (notebook: 6.4.11) with Python (v3.10.4:9d38120e33, Mar 23 2022) on macOS Catalina-Version 10.15.7.
Relevance of Cybersecurity in the Business Models

As cyberattacks have become common due to ubiquitous hyperconnectivity of devices, modern business is highly susceptible to data breaches. This chapter aims to investigate the relevance of cyber security in business models. It explains the business impact of cyber security and the various types of cyber threats that has devastating effects on businesses. It further elaborates the various layers of business models. The major security provisioning scenarios that can be effectively applied to them are deduced and the chapter henceforth advances into the explanation of various technological concepts of cyber security. By integrating the security provisioning scenarios with the help of various cyber security technologies and the different layers of business model, an integrated framework of business model options including the most opportunistic model is developed. This research finding provides pragmatic solutions for building lucrative business model options in cyber security with optimum performance.
Privacy and Security Under Blockchain Technology

Blockchain technology enables online sharing of digital information. A blockchain acts as public ledger that everyone has access to but is not controlled by a central authority. It is a technology that empowers people and organizations to collaborate and transact in an online environment that is trusted and transparent. The blockchain technology provides many benefits, including increased transparency and traceability of transactions. However, the security and privacy issues of blockchain can outweigh its benefits. This literature-review based study explores the privacy and security challenges of blockchain technology and their solutions.
The Risks Associated With ITIL Information Security Management in Micro Companies

Information security has become a necessity for all organizations. ITIL, designed for large organizations, has also been gradually adopted by smaller companies and has incorporated practices related to information security management (ISM). This study aims to understand the main risks associated with ISM, considering the context of micro companies. For this purpose, a qualitative model was built based on four case studies of micro companies in the information technology industry. The results show that companies are concerned about information security, given the growth of external threats. However, these companies have a lack of commitment, of resources, and of knowledge that hinder the implementation of an ISM policy. Therefore, it is evident that the challenge of ISM is demanding and should be addressed, considering that the security of an organization should be analyzed in a holistic context, where all perspectives should be considered to reflect the multidisciplinary nature of security.
Evaluation of Usability of E-Commerce Websites in Pakistan Using System Usability Scale and Heuristics Methods

Online shopping platforms “Draz.pk,” “Homesopping.pk,” and “Telemart.pk,” were the most often used online stores in Pakistan. E-commerce platforms have not achieved their complete objectives, because they are faced with many usability issues. It is recommended that an evaluation of the usability of e-commerce platforms should be explored through different usability scales. This research study evaluates the usability of e-commerce platforms in Pakistan. In this research study, three e-commerce platforms were selected for usability evaluation. For this purpose total of 15 users were selected and performed the System Usability Scale method of assessing the usability. The result of the System Usability Scale method indicated usability of the “Homesopping.pk” and “Telemart.pk” was less than below 70%, which indicated that these websites need to improve the user interface. Five expert evaluators were also selected for Heuristic evaluation to perform on “Homesopping.pk” to find out the usability issues.
Aesthetic Assessment of Packaging Design Based on Con-Transformer

Different from the traditional natural images' aesthetic assessment task, the aesthetic assessment of packaging design should not only pay attention to artistic beauty, but also pay attention to functional beauty, that is, the attraction of the packaging design to consumers. In this paper, the authors propose a con-transformer packaging design aesthetic assessment method, which takes advantage of convolutional operations and self-attention mechanisms for enhanced representation learning, resulting in an effective aesthetic assessment of the packaging design images. Specifically, con-transformer integrates convolution network branch and transformer network branch to extract local representation features and global representation features of the packaging design images respectively. Finally, the fused representation features are used for aesthetic assessment. Experimental results show that the proposed method can not only effectively assess the aesthetic of packaging design images, but also be applied to the aesthetic assessment of natural images.
Argumentative explanations for pattern-based text classifiers

Recent works in Explainable AI mostly address the transparency issue of black-box models or create explanations for any kind of models (i.e., they are model-agnostic), while leaving explanations of interpretable models largely underexplored. In this paper, we fill this gap by focusing on explanations for a specific interpretable model, namely pattern-based logistic regression (PLR) for binary text classification. We do so because, albeit interpretable, PLR is challenging when it comes to explanations. In particular, we found that a standard way to extract explanations from this model does not consider relations among the features, making the explanations hardly plausible to humans. Hence, we propose AXPLR, a novel explanation method using (forms of) computational argumentation to generate explanations (for outputs computed by PLR) which unearth model agreements and disagreements among the features. Specifically, we use computational argumentation as follows: we see features (patterns) in PLR as arguments in a form of quantified bipolar argumentation frameworks (QBAFs) and extract attacks and supports between arguments based on specificity of the arguments; we understand logistic regression as a gradual semantics for these QBAFs, used to determine the arguments’ dialectic strength; and we study standard properties of gradual semantics for QBAFs in the context of our argumentative re-interpretation of PLR, sanctioning its suitability for explanatory purposes. We then show how to extract intuitive explanations (for outputs computed by PLR) from the constructed QBAFs. Finally, we conduct an empirical evaluation and two experiments in the context of human-AI collaboration to demonstrate the advantages of our resulting AXPLR method.
Video-Based Metric Learning Framework for Basketball Skill Assessment

Video-based human action recognition has become one of the research hotspots in the field of computer vision in recent years and has been widely used in the fields of intelligent human-computer interaction and virtual reality. However, most of the current existing methods and public datasets are constructed for human daily activities, and the assessment of basketball skills is still a challenging problem. In order to solve the above issues, in this paper, the authors propose a coarse-to-fine video-based metric learning framework for basketball skills assessment. Specifically, they first use a variety of models to jointly represent the action video, and then the optimal distance metric between videos is learned based on the representation. Finally, based on the distance metric, a query video is coarsely classified to obtain the corresponding label of video action, and then the video is finely classified to judge whether the action is standardized. The experiments on a collected dataset show that the proposed framework can better identify and assess the non-standard actions of basketball.
One, Five, and Ten-Shot-Based Meta-Learning for Computationally Efficient Head Pose Estimation

Many real-world applications rely on head pose estimation. The performance of head pose estimation has significantly improved with techniques like convolutional neural networks (CNN). However, CNN requires a large amount of data for training. This article presents a new framework for head pose estimation using computationally efficient first-order model-agnostic meta-learning (FO-MAML)-based method and compares the performance with existing MAML-based approaches. Experiments using one-shot, five-shot, and ten-shot settings are done using MAML and FO-MAML. A mean average error (MAEavg) of 7.72, 6.30, and 5.32 has been achieved in predicting head pose using MAML for one-, five-, and ten-shot settings, respectively. Similarly, MAEavg of 8.33, 6.84, and 6.23 has been achieved in predicting head pose using FO-MAML for one-, five-, and ten-shot settings, respectively. The computational complexity of an outer-loop update in MAML is found to be O(n2) whereas for FO-MAML it is O(n).
Security and privacy in cloud 3D printing

In this chapter, we discuss security issues related to cloud 3D printing, including data security of cloud 3D printing platforms, access control for cloud 3D printers, security of cloud 3D printing based on blockchain, and the intellectual property protection problem in cloud 3D printing. For data security, we discuss main data security problems and solutions. Access control methods of cloud 3D printing equipment are also discussed. Then, we discuss the blockchain-based service security technologies and the intellectual property protection problem in the cloud 3D printing environment.
Trade–Finance Nexus: The Centrality of the Quality of Institutions in Sub-Saharan African Leading Economies

The study examines the interaction effect of trade and institutional quality on financial sector development in 20 leading economies in sub-Saharan Africa selected based on 2018 GDP per capita ranking (top 20 richest economies by GDP per capita released by the IMF) over the period 2005–2020. Using system-generalised method of moments estimation, the results indicate that the effect of the interaction term of trade and regulatory quality on financial development is positive and significant. Further findings show unidirectional causality running from the interaction term to financial development, implying that the likelihood of trade enhancing financial development depends on the soundness of the regulatory framework. It is confirmed that the magnitude and direction of the effect of trade on financial development are sensitive to the quality of institutions. Therefore, the poor quality of regulations on business activities and financial services could undermine the salutary impact of trade on financial development. It is suggested that creating a conducive regulatory environment to improve the level of financial development is crucial for mitigating the potential impact of the weak institutional quality risks. This remains a significant prerequisite for having a competitive business environment, thereby stimulating the role of trade in the process of financial development.
Identification of the area sampled by traps: A modelling study with tsetse

Sampling with traps provides the most common means of investigating the abundance, composition and condition of tsetse populations. It is thus important to know the size of the area from which the samples originate, but that topic is poorly understood.The topic was clarified with the aid of a simple deterministic model of the mobility, births and deaths of tsetse. The model assessed how the sampled area changed according to variations in the numbers, arrangement and catching efficiency of traps deployed for different periods in a large block of homogeneous habitat subject to different levels of fly mortality. The greatest impacts on the size of the sampled area are produced by the flies' mean daily step length and the duration of trapping. There is little effect of trap type. The daily death rate of adult flies is unimportant unless tsetse control measures increase the mortality several times above the low natural rates.Formulae for predicting the probability that any given captured fly originated from various areas around the trap are produced. Using a mean daily step length (d) of 395m, typical of a savannah species of tsetse, any fly caught by a single trap in a 5-day trapping period could be regarded, with roughly 95% confidence, as originating from within a distance of 1.3km of the trap that is from an area of 5.3km2.
Bar Image Identification Based on Laplacian Eigenmap and Fuzzy SVM

Bar code image recognition is a key technology in modern logistics management. An efficient identification system is built based on manifold learning and fuzzy SVM. By integrating the fractal image segmentation technology, it realizes high automatic classification and identification of bar code image. At first, the authors conduct the preprocessing of the collected code image, including three steps, tilt correction, image binarization based on globally dynamic threshold, and fractal segmentation technology. Then, a graph-based fuzzy support vector machine is proposed to realize the high accuracy classification and identification. Experimental results indicate that the accuracy of the proposed method is higher than other compared methods in both pure and noisy samples, reaching 96.6% and 94.5%. And no huge decrease exists when some noise is added to the pure dataset, and the percentage is only 2.1%, which is much lower than the drop of other methods. It shows that the proposed method can significantly promote the identification accuracy, the generalization, and robustness to noise.
Metamorphic Rocks

Metamorphic rocks are the principal material of the planet earth.