Reviewerly/exHarmony huggingface dataset:
Analysis and synthesis of function data of human movement

Abstract The article proposes a human motion capture method based on operational data. The thesis first uses the human body wear system to perform functional processing on the captured periodic motion data, and then extracts the data sequence for the few motions. Thereafter, the classification of the vector calculation method is carried out according to the characteristics of periodic data. Through experimental research, it is found that the functional data analysis (FDA) algorithm proposed in the thesis can accurately identify human motion behaviour, and the automatically collected data has a recognition rate that is as high as 98.9%. Therefore, we have concluded that the human body data functional analysis algorithm has higher recognition accuracy than the traditional optical capture system. Thus, it is worthy of further research and discussion.
Spatially adaptive TGV-regularized variational model for single image dehazing

Due to the intrinsic limitations of hyperparameters, most dehazing algorithms easily suffer from overenhancement or staircase artifacts. To suppress these problems, a spatially adaptive total generalized variational (TGV)-based single image dehazing algorithm is developed. The algorithm contain two modules, i.e., an adaptive coarse transmission map estimation module and a spatially adaptive TGV-based transmission refine module. The adaptive coarse transmission map estimation module is designed to prevent the dehazing result from being overenhanced with a dark channel-based adaptive control parameter. The TGV-based transmission refine module is designed to refine the coarse transmission with a texture prior-based spatially adaptive TGV-regularized variational model. Numerous experiments reveal that the proposed algorithm is comparable with or even outperforms the state-of-the-art techniques.
Understanding expressive ensemble singing through acoustics

A valuable body of research utilizes the study of acoustics to explore the combined sonic output of vocal ensembles and the processes and vocal contributions of individual voices within an ensemble. This chapter focuses on specific acoustic features that can be measured in the acoustic signal and contribute to expressive ensemble performance, including: intonation, vibrato, intensity, and timbre. The different methodological approaches to acoustics research for singing ensemble performance are discussed, alongside their context and relevance in understanding the importance of these features from a more holistic perspective. In light of the development of digital technologies, new approaches arise that address the complexities of analyzing multiple voices, spanning from synthesis-based studies to more ecological experiments. The implications for this corpus of research to influence practice and decision-making in performance are highlighted alongside the opportunities for more performers to lead research in this area, facilitated through increasingly accessible technology.
Explainability for Non-Linear Models

This chapter explores the use of LIME, SHAP, and Skope-rules explainable AI-based Python libraries to explain the decisions made by non-linear models for supervised learning tasks with structured data. In this chapter, you are going learn various ways of explaining non-linear and tree-based models and their decisions in predicting the dependent variable. In a supervised machine learning task, there is a target variable, which is also known as a dependent variable, and a set of independent variables. The objective is to predict the dependent variable as a weighted sum of input variables or independent variables, where there is high degree of feature interaction and non-linear complex relationship exists.
Adaptive Motion Compensated Spatial Temporal Filter of Colonoscopy Video

Colonoscopy is a popular procedure which is used to detect an abnormality. Early diagnosis can help to heal many patients. The purpose of this paper is removing/reducing some artifacts to improve the visual quality of colonoscopy videos to provide better information for physicians. This work complements a series of work consisting of three previously published papers. In this paper, optic flow is used for motion compensation, where a number of consecutive images are registered to integrate some information to create a new image that has/reveals more information than the original one. Colon images were classified into informative and noninformative images by using a deep neural network. Then, two different strategies were used to treat informative and noninformative images. Informative images were treated by using Lucas Kanade with an adaptive temporal mean/median filter, whereas noninformative images were treated by using Lucas Kanade with a derivative of Gaussian (LKDOG) and adaptive temporal median images. Comparison showed that this work achieved better results than those achieved by the state-of-the-art strategies for the same degraded colon images data set. The new proposed algorithm reduced the error alignment by a factor of about 0.3, with a 100% successful image alignment ratio. In conclusion, this algorithm achieved better results than the state-of-the-art approaches in case of enhancing the informative images as shown in the results section; also, it helped to reveal some information from noninformative images that have very few details/no details.
Geologic and geographic aspects of sand and gravel production in Louisiana

In Louisiana, a low-lying state on the Gulf coastal plain, one such problem is that there is limited availability of rock or stone because virtually the entire state consists of Tertiary and Quaternary sediments. Gravel production has been relatively steady in Louisiana, with annual production ranging from about 5-13 million metric t/yr since 1950. Thus, the geologic-geomorphic units in Louisiana were divided into areas of relative suitability for aggregate exploration. Regional mapping of aggregate suitability was accomplished by using the Geologic Map of Louisiana with some modifications of terminology. The sand and gravel industry has a precarious future in Louisiana because of the environmental effects associated with floodplain mining, more awareness of liability, the growing involvement of regulatory agencies, and the increasing operating costs.
Detection of Invertebrate Virus Carriers Using Deep Learning Networks to Prevent Emerging Pandemic-Prone Disease in Tropical Regions

Insects are a class of invertebrate organisms. They are a massively effective group, comprising animals such as bees, butterflies, cockroaches, flies, mosquitoes, and ants. Mosquito-borne diseases are those spread by the bite of an infected mosquito. Zika, West Nile virus, Chikungunya virus, dengue, and malaria are diseases that are spread to people by mosquitoes. The purpose of this work is to show that computer vision can classify the mosquito species that spread the dengue (emerging-pandemic prone disease) by using Convolutional Neural Network (CNN). The work is to assist the non-specialist to identify these three types of mosquito species with a simple web interface integrated with deep learning models works at the backend. Convolutional Neural Network (CNN) has been implemented to extract features from the mosquito images and identify the mosquito species, such as Aedes, Anopheles, and Culex. There are 2,111 mosquito images collected and used these mosquito images to train the CNN to perform mosquito species classification. From the experiment result, deep learning shows that it has the ability to identify the mosquito species. There are series of experiment has been conducted with data augmentation, regularization technique, stride, filters methods in convolutional layer to improve the performance and prediction of the algorithms with higher prediction results.
Sports Action Detection Based on Self-Supervised Feature Learning and Object Detection

This paper presents an action detection method for sports videos based on self-supervised feature learning and object detection. Our previous method realizes action detection based on self-supervised feature learning. However, the self-supervised feature learning works well when a single person is on a video. Thus, we introduce object detection into our method to achieve action detection for multiple persons by tracking each person. The proposed method realizes action detection without a fine-grained annotation based on self-supervised feature learning. Experimental results using real-world sports dataset show the effectiveness of the proposed method.
High-speed tracking with multi-templates correlation filters

Correlation filter (CF)-based trackers have demonstrated impressive performance on visual tracking benchmark datasets with high frame rates. A lot of research focuses on extracting strong features to learn a robust model of the object’s appearance. However, the circularly shifted samples used in traditional CF training come from one single image patch and have limited information about the target. Inspired by various data augmentation methods in deep learning, we propose a general multi-templates correlation filter-based tracker that expands the CF tracker with multihandcrafted training samples. We reformulate the original optimization problem and provide a closed-form solution to maintain the high-speed calculation. Several methods to generate the additional samples are evaluated. Comprehensive experiments illustrate that our framework obviously improves the tracking performance and is general enough to be incorporated with classical CF trackers.
Development of Educational Environment to Improve Efficiency of Online Education on Control Systems

In recent years, lectures on control systems have focused on hands-on experience using actual control equipment than before. However, the online education triggered compulsorily by the Covid-19 pandemic poses has restricted the construction of an education environment for hands-on experience. In this study, we proposed an economical and compact experimental environment for control education that enables hands-on experience even through online education. To this end, we utilized a light weight rapid control prototyping (LW-RCP), which is a lab-built RCP environment, and the environment was constructed using 3D printing. In the proposed environment, LW-RCP enabled students to focus on the learning and application of related control concepts, without the inconvenience of manual C-coding and the possibility of debugging errors. In addition, the proposed control equipment, which was manufactured using 3D printing, is an inexpensive equipment with a sufficiently small size that can be placed on a desk. Owing to the low cost and small size of the proposed environment, each student can have his/her own experimental equipment, which will enable a hands-on experience even through online education. Online education is expected to expand more and more in the future as it exhibits various advantages and potentials from traditional face-to-face classes. The proposed educational environment is expected to play a meaningful role in satisfying the demands of hands-on experience for control-related lectures.
Recommender Systems

In brick-and-mortar stores, we have salespeople guiding and recommending us the relevant products while shopping. On the other hand, on online retail platforms, there are zillions of different products available, and we have to navigate ourselves to find the right product. The situation is that users have too many options and choices available, yet don’t like to invest a lot of time going through the entire catalogue of items. Hence, the role of recommender systems (RSs) becomes critical for recommending relevant items and driving customer conversion.
Summary and conclusion

In this book we first introduced the idea behind semantic network analysis, its premises, historical development, and current place, among other computational research methods. We explained in detail the stages and tools to conduct semantic network analysis. Most of the later chapters focused on the applications of semantic network analysis in different types of texts, research settings, and disciplines. In this concluding chapter we summarize the key findings and the main features of semantic network analysis as appeared in all studies. We also provide more details on how to interpret the networks and explain the boundaries of semantic networks. Finally, we discuss the future directions in the field of semantic network analysis.
Implementation Analysis of Cryptography Toolbox in Hyperledger

Hyperledger was set up with the aim of being an open-source platform targeted at accelerating industry-wide collaboration hosted by The Linux Foundation for developing robust and dependable blockchain and distributed ledger-based technological platform that may be applied across several industry sectors to improve the efficiency, performance, and transactions of different business operations. For these purpose, various distributed ledger frameworks and libraries have been developed inside the platform. In this paper, the Ursa cryptographic library, which is one of the libraries being developed in this platform to offer its users with dependable, secure, user friendly and plug-able cryptographic applications, has been examined and the performances of both the anonymous identity creation process and the presented cryptographic algorithms are examined.
AGRICULTURE 4.0

The Fourth Industrial Revolution has stimulated the development of new technologies and techniques that are changing the global production system. The objective of this paper is to analyze the stream of innovation of Agriculture 4.0, such as its origin, features and consequences. Agriculture 4.0 is a digital model of agricultural production guided by high-performance manufacturing and is a result of a long historical process of technological development. The innovation in the agricultural sector in Brazil is essential to the maintenance of its competitiveness. However, the 4.0 concept of production is still a challenge the country needs to overcome. Agtech startups are working as a solution for technology diffusion. The Piracicaba Valley Agtech, in the state of São Paulo, is a successful case of Agriculture 4.0 concept development.
Analysis of Satisfaction in the Use of Technological Simulation Tools in the Learning of the Automatic Process Control Course

The objective of this article is to describe the satisfaction of the students in the use of technological simulation tools in the learning of the automatic process control course, generated by distance education. The Academic Satisfaction Scale (ESA) was used as a measurement instrument. As the research was carried out, the re-sults revealed that of the indicators that present the highest degree of satisfaction, 56.8% of the student’s state that they strongly agree with what was learned through the use of technological simulation tools in the subject of the course of control of automatic processes and 54.5% indicate that the technological tools used in the contents of the subject excite them. Likewise, by means of the R squared, it can be indicated that the satisfaction of the students with the developed subject, in which technological simulation tools were used (I6), influences 0.637, that is, 63.7%, on the general satisfaction of academic experience in the virtual environment (I7); This is translated with the following equation: I7 = 0.309 + 0.637 * I6.
Three robust edges stopping functions for image denoising

In this paper, we present three strong edge stopping functions for image enhancement. These edge stopping functions have the advantage of effectively removing the image noise while preserving the true edges and other important features. The obtained results show an improved quality for the restored images compared to existing restoration models.
Introduction to Cybersecurity in e-Governance Systems

The operations of e-Governance have been increased significantly because of the citizen’s demand of cost-effective and timely services. The growing span of e-Governance and its impact over the community proves that it is more than a single system. The government has made an outgrowth effort to improve their relation with citizens through e-Governance. Electronic transactions shall have the equivalent legal value such as other process of communication like written form. Information security practices such as policies, procedures should be placed and security technologies must be utilized to protect e-Governance system from attacks, threats. There are two main fundamental factors one is required level of integrity and authentication must be provided to the public key infrastructure. Second one, the security of e-Governance can be maintained by making people realize the significance of the issue. An awareness program should be conducted for the people, so that they can recognize the threats, issues of security and learn how they can resolve them. This chapter consists of the cybersecurity concept in e-Governance, e-Governance model, its issues, and the process of how to maintain security in e-Governance.
Communication Channels

A communication channel is a means by which a large amount of data is exchanged between two parties. Individuals and organizations use a variety of mediums to send and receive information on a daily basis because smart devices have become so prevalent. Smart devices can send any type of data to their intended recipients without having to visit their locations. People nowadays do not even carry cash with them, because most forms of monetary transactions can be done electronically. The communication channels that attackers utilise to promote their phishing scams are the focus of this chapter. Attackers are incredibly bright and cunning, and they use a variety of methods to gather enough information from their victims.
Proposed anti-trolling law could be used to silence critics of the Australian government

Efforts to stop anonymous trolls are unlikely to work, but could see more people sued for defamation, says Alice Klein
Security Challenges and Solutions for Healthcare in the Internet of Things

In this chapter, I discuss several challenges to healthcare information systems and some solutions for data management, access control, privacy preservation, and e-prescriptions. Most solutions advocate for a patient-centred system, a convenient, simple, privacy-preserving, secure mechanism. The availability of patient’s health information to any healthcare entity with the patient’s consent is another very basic requirement. Pseudonymisation-based solutions can provide a huge opportunity for medical research on a particular disease using anonymous health data. It can provide anonymous health data that will not have any identifiable information of the patient to researchers. Hence without compromising privacy, anonymous health data will be available for researchers. Blockchain technology-based mechanisms to deal with the healthcare industry would allow users to transfer medical data safely. This also provides users with a way to anonymously share their medical data for medical research. Blockchain technology provides a secure way to share health data and also provide improved healthcare transactions.
Freeing the free-text comment

Free-text gathering, as part of operational surveys, evaluations, or in other routine aspects of quality assurance in higher education, is a commonly used tool to collect student data. The processing of free-text data requires due ethical consideration. There are many ethical considerations when it comes to data processing, not all of which appear to be easy bedfellows. For example, principles around open data and reproducible research counsel against many frequent sampling opportunities, and instead advocate the collection of high-quality data that can be repurposed and revisited by many researchers. By contrast, data protection and privacy approaches advocate for individuals to be made aware of exactly how their data will be used and by whom, and place more caveats on the free sharing of data. Within higher education, there are also principles of feedback and ‘customer service’ which impact how free-text data is collated. When students are offered an opportunity to comment on their experiences in education, the information they provide should be heard and responded to. Text mining may offer the opportunity to resolve some of these conflicts.
Impact of Cyber Maturity Level on Health Sector

The development of technology has accelerated the digital transformation in the health sector. As a result of digitization, the increasing network connections of devices and the transport of health data between different environments can leave medical devices and data vulnerable to new cybersecurity vulnerabilities. As cyber attacks to be carried out using these vulnerabilities may cause consequences that may threaten human life, the implementation of an effective cyber security is of critical importance in the health sector, as in other sectors. The fact that the focus of the institutions / organizations in the health sector is on the treatment of patients causes the necessary investment in cyber security to not be provided. In this case, it is unclear what the information security / cyber security risks in health systems are, what can be done to reduce these risks, how health data should be protected or how it can affect the institution when exposed to a cyber attack, and how much the existing security measures will protect the institution / organization. For this reason, institutions/organizations should be aware of their cyber security levels and increase their resilience against these attacks in order to minimize the impact of cyber security attacks on their institutions. In this paper, the Cyber Maturity Level Determination Method, which is a method that institutions/organizations can apply to increase their cyber security resilience, is recommended. In accordance with this method, institutions/organizations should measure their current cyber maturity level and increase their maturity by taking effective security measures. The Cyber Maturity Level Determination Method offers a method by which each institution/organization can determine and increase their maturity with a unique configuration by considering all of them without focusing on any of the good practices.
The role of technology in crisis management and product recall in food supply chains

This chapter considers food governance and how it frames crisis management and product recalls in food supply chains. Effective food recalls following a food safety or legality related incident are supported by traceability systems ranging from paper based to those that apply the newest technology. This chapter is considers the value of Distributed Ledger Technologies (DLTs) for improvements in food supply chain governance structures that are especially tested during product recalls. The focus is on identifying advantages of Blockchain systems within public-private partnerships (PPPs) for food governance. There is great potential to reduce information asymmetry, a key barrier to supply chain development, innovation and efficiency, and effective crisis management and product recalls with the use of DLTs including Blockchain. PPPs for supply chain governance deliver value at the supply chain and wider stakeholder level including developing Blockchain consortia to improve overall efficiency and integrity in data collection, storage and sharing.
Service Skills Training in Restaurants Using Virtual Reality

Currently, training in restaurants often take the form of on-the-job training (OJT), where trainees are accompanied by trainers who guide them as they serve the customers. However, it is difficult for trainers to understand the cognitive and decision-making processes of the trainees objectively, which makes OJT guidance extremely difficult. Objective measurement indices are also needed. To mitigate these issues, we developed a VR job-training system that focuses on two elements that pose challenges for the actual situation: awareness and priority-setting.