Reviewerly/exHarmony huggingface dataset:
An attempt to introduce 4D tracing in detection of anomalous motion by clustering the temporal and spatial data and contouring 2D information belonging to the same timing frame

In this paper we suggest a new method for a fall detection, based on the spatiotemporal data clustering (4D tracking), collected during a fall incidence. The set of sequential frames has been extracted from the videos, then the key features have been extracted to classify falls and no-falls activities. Based on the study of the stability of the human body. The article proposes a new model of human posture representation of fall behaviour, taking into consideration the ‘inverted pendulum model’, and using multimedia analysis to observe and evaluate the continuity of the fall event in time series changes; the aim was to extract and construct the inverted pendulum structure of human posture in real-life scenarios. Firstly, a spatiotemporal evolution map of the change and variation of human posture is constructed through multimedia analysis Then 3D spatial information has been sliced into 2D. Contour plots are built from the spatial projection coordinates that belong to the same timing window. The timing window is determined based on the duration of the instability/transition phase. By contouring the spatial coordinates that belong to the same timing window we can determine whether the person is in a sitting or lying down position after their fall or whether the person is either moving or not during a prolonged period following the fall. Finally, computer vision when added to multimedia analytics, would further reduce the time consumption for 2D slicing of 3D information followed by the clustering of those 2D spatial projections (from different projections) with referent timing frames (coordinated extracted from time series extracted from video data).
Secure Blind Signature for Electronic Voting and Electronic Cashing Systems:

Due to its notable ability to hide signed information, the blind signature security technique has been widely adopted for a myriad of applications, including e-cashing and e-voting systems. Effective blind signature utilizes various parameters to balance the trade-offs between blindness and computation speed. This chapter discusses the background, development, and contemporary relevance of blind signature and elaborates on enhancements to digital and blind signature concepts such as unforgeability, nonrepudiation, blindness, and nontraceability. To that end, we present novel algorithms for blind signature that address system parameters, signing, and verification. We describe Schnorr e-cash system transactions such as register, withdrawal, payment, and deposit. We also present novel blind signature algorithms for e-voting systems using RSA, ElGamal, Okamoto-Schnorr, and DSA. Theoretical and debugging results illustrate that the proposed blind signature algorithms enhance the performance of such systems in terms of nontraceability, security, and nondouble spending.
Mixed network sentiment analysis combining sentiment features and multiple attention

In order to solve the problem of low attention of some emotional words in sentiment analysis tasks and difficulty in capturing long-distance dependence between sentences, this paper proposes a mixed sentiment analysis network (DB-BGA-CNN) that integrates multiple attention mechanisms of sentiment words. First, a more targeted emotional dictionary is obtained by expanding the dictionary, and an emotional word selection segmentation algorithm (DSS) is designed. Secondly, use Bert to encode the word vector of the sentiment words and phrases selected from the sentence and sentiment dictionary respectively to obtain the deep semantic features of the text and perform fusion. Then, use multiple attention mechanisms to realize the enhancement of sentiment analysis capabilities, and discuss which network effect is better; finally, the output vectors of each network are merged, and the activation-pooling layer is used to avoid the occurrence of overfitting. Compared with multiple existing models, the proposed model shows better performance, and the accuracy of the optimal model reaches 95.80%.
Introduction: Data and its governance in the financial services sector

Data fuel the development of artificial intelligence and are one of the most contested resources in the digital economy. But data have always been a fundamental source in the development of business in the financial services sector, whether in securities trading or consumer insurance. Over many years, the financial services industry has invested in data acquisition, storage, transfer and monetisation. The industry's data strategy was developed as a core part of business development and internal governance long before the modern information systems of blockchain and data analytics were introduced. Data have also long been an asset that the financial sector has paid to possess and monetise, and the huge mass of data that has been built up over the years is something that competitors within or outside the sector are keen to have access to, as we have seen in Open Banking. Financial firms are well aware of data as a competitive asset class and have created strategic defences to protect their interests. The book offers three contributions to the field of data governance in the digital economy. We use the financial services sector, one of the most advanced sectors, first to show the relationship between data and law by identifying different kinds of data ownership and the policy and legal tools used for owners' or users' protection, and secondly to identify the tools available for constructing a multi-layered public-private partnership for data governance. In addition, the book provides assistance for academics, practitioners and policy makers as they construct matrix systems for data governance in the financial services sectors with a view to creating a more standardised data governance and promoting a digital economy.
Silicon quantum annealing machines using conventional CMOS devices

For low-cost quantum computers, we theoretically propose two types of quantum annealing machines based on FinFET and floating-gate NAND flash memory.
Fractional-order RISE Control for Reducing Torques dedicated for Rehabilitation Robots

In rehabilitation, lower limb exoskeletons are the most used devices to help disabled persons, to relearn the walking process. In fact, referring to statistics offered by hospital centers, 40% of handicapped kids became normal if they are followed by physiotherapist doctors who force the lower limbs to do series of movements, manually. Moreover, the use of robotic systems facilitates considerably the results of such movements. The paper's interest concerns the control of exoskeletons using robust approaches.This paper aims to implement a fractional-order RISE control for 2 degrees of freedom exoskeleton.
approach to edge detection in medical imaging through histogram analysis and morphological gradient

Edge detection takes importance in image processing systems for computer-aided diagnosis, wheresharp changes in pixel intensity are analyzed to obtain fast and accurate information about regions ofinterest to the specialist. A method for feature enhancement and edge detection in medical imageswas developed using image processing by analyzing the pixel distribution histogram andmorphological gradient operation. Images from the MINI MIAS dataset and the COVID-CT datasetwere used. The method is based on image processing and is applied to mammography and chest CTimages, where blur filtering steps are accompanied by morphological gradient filtering, in addition toobtaining the threshold for edge detection by analyzing the point of maximum pixel concentrationaccording to the distribution histogram. The processing is presented in a graphical user interfacedeveloped in Python language. The method is validated by comparison with other edge detectiontechniques such as the Canny Algorithm, and with deep learning methods such as Holistically-NestedEdge Detection. The proposed method improves image quality in both mammograms and CT scanscompared to other techniques. It also presents the best performance considering internal and externaledge detection, as well as an average response time of 1.054 seconds and 2.63 % of Central Processing Unit requirement. The developed system is presented as a support tool for use in computer-aideddiagnosis processes due to its high efficiency in edge detection.
Boosting machine learning mechanisms in wireless mesh networks through quantum computing

The need of combining QC and ML to fulfill the requirements of future WMNs envisages the definition of proper network architectures with the introduction of new logical entities. After describing the role of different ML algorithms for WMNs and the main properties of QC, this work introduced the application of quantum ML for WMNs. Specifically, it proposed a centralized and a distributed architecture where quantum ML capabilities are placed in the cloud or at the edge, respectively. Design principles and information exchange procedures are deeply discussed for both these architectures, also highlighting their advantages, disadvantages, and possible future research directions.
Evaluation System of Push-up Action Based on Kinect

In order to realize automatic counting and motion evaluation of push-ups, Kinect sensor is used to collect obtain skeleton key points. By analyzing the rules and characteristics of push-up movement, the required key points are selected. The effective data are obtained by data processing methods such as vacancy filling, data conversion and mean filtering. By extracting the characteristics of push-ups, segmentation is carried out to realize movement counting. DTW algorithm is used to calculate the relative approximation of each push-up movement and the pre-set standard movement, so as to measure whether the movement is standard. The test shows that the system is feasible.
Technical and Legal Strategic Approaches Protecting the Privacy of Personal Data in Cloud-Based Big Data Applications

As a result of the importance given to the privacy over the years, various legal arrangements and technical solutions have been provided. Thanks to studies from both perspectives, despite the development of technology, the importance given to the concept of personal data has increased day by day and has reached a level that cannot be ignored. By the way, the characteristics of data have also changed with the digital revolution, and today, big data differs from traditional data in many ways. Thus traditional methods can no longer be effective in protecting the privacy. Also, cloud computing, which provides practical solutions for problems encountered in many subjects, is frequently preferred by solution architects in big data applications, and due to the data it contains, it exposes its users to various risks within the scope of being subject to diversified violations. This situation imposes different roles and responsibilities on various stakeholders for the protection of personal data. To fulfill all these roles and responsibilities, complementary and adequate strategic approaches should be used. The aim of this study is to analyze, discuss, and provide privacy protection approaches for cloud-based big data applications. First of all, from technical and legal perspectives, examinations in cloud computing and big data have been performed separately. Then existing methods, possible technical solutions, legal requirements, and obligations are shown and discussed. Lastly, within the scope of cloud-based big data applications, additional inferences have been made and shared.
Perceptual-Level Representation and Categorization

Chapter 13 explains the notion of a perceptual-level psychological capacity. Such capacities include perception, conation guided by perception—but not cognition or thought—, perceptual attention-initiation, perceptual memory, perceptual anticipation, perceptual learning, perceptual imagining. These capacities are to be discussed individually in subsequent chapters. Issues of modularity and impenetrability by “cognition” are broached. It is shown wherein perceptual categorization is perceptual, and generated within the perceptual system. Details of the enrichment of perceptual categorization, after the first-formed categorizational sketches, are elaborated. Input from scene-gist, perceptual-level memory, and perceptual-level attention-initiation figures in the micro-seconds following the first-formed categorizational sketches.
Must a Currency Be Centrally Regulated to Be Ethical?

Scharding (2019) argues that Bitcoin is unethical on Fichte’s (2012/1800) view because its instability makes it unable to guarantee that users can afford what they need to live. She contrasts Bitcoin with currencies controlled by central authorities that can guarantee their stability. Allison (2021) objects that not all centrally controlled currencies are stable and not all non-centrally controlled currencies are unstable. I clarify that both stability and a means of securing stability (typically, a central authority) are necessary, but not sufficient, for a currency to be ethical.
Recent Trends in Wearable Technologies, Challenges and Opportunities

Wearable technology is a kind of technology that a user wears and uses to receive particular information. This new technology gives students a unique information-sharing experience because of the way course materials such as movies and audios are displayed. Google Glass, Muse, Virtual Reality, and GoPro cameras are examples of wearable gadgets that are thought to have educational applications. Wearable computing is a technology that does not need the use of one’s hands. This feature is critical for students, as it allows them to walk about the educational area and engage with the actual world while freeing up their hands while keeping linked to the device’s functionality. Furthermore, wearable gadgets provide hands-free access to information and knowledge, ushering in a new age of mobile learning design. Smart textiles is a broad area of research and products that demonstrate the functionality and use of everyday materials. Textiles that can interact with the environment or the user are known as smart textiles. Smart textiles provide a foundation for increased social welfare and to save money on the welfare budget.
The Title of the Paper: Research on insurance marketing application based on hash link-table improved association rule algorithm

Aiming at the problem of slow processing efficiency of Apriori algorithm when the amount of data is large, the association rule algorithm is combined with hash link-table, and an improved association rule algorithm based on hash link-table is proposed to solve the disadvantage of long average time-consuming of traditional association rule algorithm in finding frequent itemsets. The improved association rule algorithm through hash link-table is applied to the insurance marketing scenario with large data set, analyzes the insurance purchase behavior of a large number of customers in the insurance database, finds out the insurance product sales association rules, and accurately recommends the products they are interested in to customers, so as to increase the success rate of marketing.
Impact of Transformational Leadership on Employee Performance with the mediation impact of employee engagement

The objective of the current study is to examine the mediating effect of Employee Engagement (EE) in the association between Transformational Leadership (TFL) and Employee Performance (EP). The study employed a cross sectional descriptive method and was based on 218 front line employees who were working at MOH Offices in the Jaffna District. Simple random sampling method was employed. Established questionnaires were used to measure the variables. Exploratory factor analysis was used to get the factor structure of each study construct. Subsequently regression analysis was performed to identify the association among the variables. The results revealed that TFL positively influences EP as well as EE. EE also positively influences EP. In addition, EE mediates the association between TFL and EP. The study highlights the importance of TFL in promoting performance among the health sector employees.
Securing Charities Using Blockchain

Abstract: While analyzing the problem we thought about the importance with respect to data security. Since everything is getting digitalized, security is the key point in this digital era. So, when it comes to data security or data integrity, there are serval ways through which we can achieve this. So, we started finding loop holes in digitize donation system. As this way we came on the conclusion that there are loop holes in the charity system. Hence, we have decided to use the most spectacular data security related technology i.e., “blockchain”. We are implemented blockchain technology in this proposed system, so that whatever transaction / donation done between donor and beneficiary will be transparent and open to world. which will ultimately reduce chances of corruptions usually happen in the donation system.
The Relationship among Grit, Physical Self-Efficacy and Continuous Participation Intention of University Students Who Participated in Physical Education

The purpose of this study was to examine the relationship among grit, physical self-efficacy and continuous participation intention of university students who took a physical education course. In analyzing data, frequency analysis, exploratory factor analysis, reliability analysis, confirmatory factor analysis and correlation analysis were used, and then structural equating modeling was conducted by using SPSS 18.0 and AMOS 18.0 to achieve the goal of this study. Main findings were as follows: grit had a positive effect on physical self-efficacy. Secondly, physical self-efficacy positively affected continuous participation intention. Thirdly, grit positively affected continuous participation intention. Fourthly, physical self-efficacy mediated the relationship between grit and continuous participation intention. Therefore, various educational program methods to develop grit and physical self-efficacy should be sought, not only to lead to a happy and healthy college life, but also to provide a path to lifelong sport.&#x0D;
Automatic Forensic Imaging of a Virtual USB Device with Emulated User Interaction

The bird’s-eye view of the digital forensic process is the acquisition and preservation of the evidence, the analysis of the acquired data, and the presentation of the findings. Forensic practitioners need a hands-on approach to investigate with high-quality standards. Among the ways to produce labs and demonstrations, there is forensic imaging of USB devices where the forensic expert has planted, for instance, suspicious files. Such a process is time-consuming. We intend to programmatically write a scenario of user actions rather than manually perform them. It would be much easier if we get rid of the USB device and use a virtual USB-like disk image file. The current study describes how we have automatically generated user digital artifacts on a USB-like disk image file. A host OS script would control a pre-configured VM and a guest agent. That agent would take care of the emulation of a user’s scenario.
Learners' Learning Using Design Thinking Learning Approach (DTLA) with Machine Learning

In the fast-changing world today, the human lives have turned out to be hastened and fine-tuned to the digital world. It is imperative for a human to adapt themselves, and the educational sectors also have to be reinvented. The challenges confronting the educational institutions today are the lack of interest and detachment of learners from the learning environment. The education system needs to endorse the essential capabilities to prepare learners for the future with a progression in novel teaching and learning methodology. A Design Thinking Learning Approach is one such a method that can be designed by the facilitator for preparing content for learners and to be employed by the learners for learning.
A Comparative Study of Stylometric Characteristics in Authorship Attribution

This paper presents a study of various stylometric characteristics and their uses along with various methods of feature selection, extraction, classification for authorship attribution (AA). We learned of the importance of data or corpus size, which plays a heuristic role in the choosing of a sufficient amount of features to appropriately recognize the true writer of an undesignated text or piece of document. We also analyzed that by the time features type also changed and new features introduced that made this task easier. Over time machine learning became more interactive and easy to use. Many S/W are available for the AA task like WEKA, JAGAAP.
A Study on the Composition of Distribution Facility Health Index based on Survival Analysis

Electric power companies conduct asset state evaluation assessments to prevent power facility failures. Asset state evaluation(called Health Index evaluation) refers to a comprehensive state evaluation of the power facility by score composed various features. Asset state evaluation is limited to power transmission facilities domestically and internationally. Therefore, in this paper, after comparing the overseas asset state evaluation methods, the survival analysis theory suitable for the domestic distribution facility data situation was used to describe the algorithm for constructing the distribution facility Health Index table.
A Short Paper on Web Mining Using Google Techniques

The quick improvement of data innovation throughout the most recent couple of years has brought about information development for a gigantic scope. Users make substance, for example, blog entries, tweets, informal community communications and photos; workers constantly make action logs; researchers make estimations information about the world we live in; and the web, a definitive storehouse of information, has gotten dangerous concerning adaptability. To optimize web activities using different techniques can help in bringing the insights out for a clear big data picture.
Three Problems

This chapter examines the ways in which abstract concepts threaten grounded cognition. The author argues that abstract concepts pose at least three distinct theoretical challenges: the problems of generalization, disembodiment, and flexibility. The problem of generalization emerges in the context of conceptual hierarchies where higher-level concepts are more abstract than lower-level concepts. It is hard to see how such abstractions can be captured solely by means of primary sensorimotor representations. The problem of disembodiment arises because some abstract concepts lack any clear connections to our immediate experiences. Such concepts are not likely to arise from merely abstracting away from experiential particulars. Finally, the problem of flexibility arises because the content of abstract concepts tends to vary more in response to context than the content of concrete concepts. Each of these imperils the hypothesis that conceptual processing depends entirely on simulations of experience. Any successful account of grounded cognition must address all of them.