Reviewerly/exHarmony huggingface dataset:
An unbiased ray-marching transmittance estimator

We present an in-depth analysis of the sources of variance in state-of-the-art unbiased volumetric transmittance estimators, and propose several new methods for improving their efficiency. These combine to produce a single estimator that is universally optimal relative to prior work, with up to several orders of magnitude lower variance at the same cost, and has zero variance for any ray with non-varying extinction. We first reduce the variance of truncated power-series estimators using a novel efficient application of U-statistics. We then greatly reduce the average expansion order of the power series and redistribute density evaluations to filter the optical depth estimates with an equidistant sampling comb. Combined with the use of an online control variate built from a sampled mean density estimate, the resulting estimator effectively performs ray marching most of the time while using rarely-sampled higher-order terms to correct the bias.
Mobile Learning as a Tool for Indigenous Language Revitalization and Sustainability in Canada

In this article, the authors explore how mobile learning can complement the Certificate of Indigenous Languages program at the University of Saskatchewan in Western Canada. Through the FRAME model analysis, the authors extract salient cultural, pedagogical, environmental, and technological characteristics that should be considered in the development of mobile learning tools and approaches for Cree language teachers. It is hoped that this article will stimulate a dialogue amongst designers and Indigenous groups regarding language sustainability through mobile learning. The article concludes with key findings: the need to follow protocols, to establish good relationships, and to design for areas of low/no bandwidth. Finally, the examination of current Indigenous language learning methods provides ideas for the development of much needed “apps” appropriate for Cree learners and teachers.
Stewart-Inspired Posture Control for a UAV Undertaking Platform Based on Dynamic Model Predictive Control

In order to improve the accuracy and dynamic characteristics of unmanned aerial vehicle (UAV) undertaking platform with Stewart mechanism, a posture control strategy based on dynamic model predictive control algorithm is proposed in this paper. First, the dynamics of the Stewart platform is analyzed, and the state space expression of the dynamic model of the Stewart platform is established. Then, the algorithm based on the dynamic model predictive control is proposed, and the model predictive controller is designed. Finally, a simulation model of the UAV undertaking platform is established, the parameters of the model predictive controller are tuned, and the undertaking platform pose control simulation experiment is carried out. Physical experiments are performed to compare the control effects of traditional PID control and dynamics-based model predictive control on the undertaking platform.The results show that the model predictive control method based on dynamics can effectively improve the control accuracy and dynamic characteristics of the UAV undertaking platform.
Cost-Effective 3D Stereo Visualization for Creative Learning

Using a pedagogic case study of development of three dimensional (3D) stereo visualization suite, in this chapter, the authors exemplify the emerging paradigm shift of educational technology progressively from simulation to virtual reality (VR). The authors present the transformation of higher education archetypes using VR, which is poised to create a whole new meaning for the young learners. It mainly personifies the computer-generated three-dimensional environments that give the viewer an intense feeling of being immersed in an artificial world. The authors clearly demonstrate exploiting a 3D stereo visualization system in learning environment for bringing in an experiential learning experience of real industrial scenario in the classroom itself. They present the design and development of 3D visualization tool that embodies complex datasets in virtual environment. Thus, the chapter epitomizes the significance of VR in edification that impacts creative learning without using real tools on a less sophisticated technological platform.
Research on the evaluation system of applied technology-oriented talents in application-oriented universities

Based on the demand situation for applied technology talents, this paper discusses the significance of constructing application-oriented talents' evaluation system for applied-Technical University, and the importance of training the applying technique-oriented talents to meet the social standard.Based on this, the paper focuses on discussing how to construct a set of evaluation terms which accord with the application of technical-oriented talents.We discussed the evaluation system from the aspects of comprehensiveness of evaluation subjects, mutual pushing of evaluation level, the identification of evaluation indexes and completeness of evaluation system.Based on the previous research and the combination of subjective and objective methods, this paper makes the evaluation standard of applied technology-based talents for the application of technology-based university and contributes to the evaluation of applied technical talents.
Auxiliary Impacts

Parallel developments in encryption technologies and cryptocurrencies has given rise to ransomware evolving into a prodigious cyberthreat. The impacts from this emergence are diverse, triggering a multitude of downstream effects on governments and organisations. Encryption is at the heart of cybersecurity, but its application can inhibit law enforcement processes and practices. It has also been the fundamental building block for ransomwares development. The rise of ransomware challenges the level of surety in organisational cyber risk management practices. Deciphering the outcomes of this parallel emergence is essential to further understanding the threat ransomware poses. The final section also provides an opportunity to reflect on the quandary ransomware creates between professional service providers and academia.
Introduction

Human-robot interaction technology has gradually shifted from computer-centered to human-centered, as natural human-robot interaction has become a new direction of human-robot interaction technology development.
From General Services to Pervasive and Sensitive Services

Ubiquitous computing and pervasive technology represent enabling technologies which have conditioned the evolution of the current information society. These ICT are transforming society and particularly changing the interfacing methods between services and user as well as their expectations from services. Such changes are related to arising of new kind of pervasive and sensitive service such as new healthcare and wellbeing personalized services. In this chapter, these services are defined in a comprehensive manner. This sense, sensitive services refers to new IS services that manage data that is not only based on the physical disposition of the user in regards to the environment but also related to more personal aspects. On its part, pervasive services are those developed by using pervasive technology and therefore can be defined as a delocalized and persistent ICT service. These definitions will be supported by an analysis of the evolution of services of the information society both from a socio-economic and industrial focus.
A Case for Using Blended Learning and Development Techniques to Aid the Delivery of a UK Cybersecurity Core Body of Knowledge

This article explores the UK's current approach in addressing the cybersecurity skills gap championed by the National Cyber Security Strategy. There have been progressive and elaborate steps taken in the UK toward professionalization of the cybersecurity field. However, cybersecurity knowledge has been labelled as inconsistent when a cybersecurity Chartered status is being proposed. The objective of this analysis was to apply an academic lens over the UK's voyage towards the establishment of a cybersecurity profession. It has been an ambitious but complex endeavor that at times has had alterations of course. Learning from this experience, a blended learning and development approach is now recommended underpinned by an overarching core knowledge framework. Such a framework could join up the existing silos of learning and development activities to benefit from, and build upon, a coherent core knowledge-base for the community. It is argued that this will provide a more satisfactory outcome to enhance the UK's cybersecurity capability on the road to a cybersecurity profession.
A location data protection protocol based on differential privacy

Location-based services (LBS) is geo-location-based a mobile information service based on spatial location that adopts wireless positioning, GIS, Internet, wireless communication, database and other related technologies. which brings both convenience and vulnerability. With the increasing of scale and value of data, most existing location privacy protection protocols cannot balance privacy and utility. To solve the revealing problems in LBS, in this paper, we first design an algorithm of the best-assisted user selection (BaUS) for constructing anonymity sets. Next, we contract a smart contract to evaluate reputation, to ensure the honesty of participants. And then a differential privacy protection protocol is proposed. The theoretical analysis and experiments show that the proposed protocol can resist background knowledge attacks effectively. Meanwhile, our protocol can improve data availability. Particularly, it realizes user-controllable privacy protection which enhances privacy preservation and better security.
Activity

Audit testing objectives, frequently, are determined in the preceding audit processes: planning as well as study and evaluation of controls. IT audit test materiality influences the audit testing nature, timing, and extent. In designing tests, the in-charge auditor must choose between statistical and non-statistical testing methodologies. Compliance and substantive testing may take the form of inquiry, observation, inspection, or re-performance. Sampling method selection reflects whether audit area statistical inferences are going to occur concerning the target population. Sampling risk, acceptable error rate, and the expected extent of errors in the population are sample size consideration factors. Chapter 5 conveys how to determine IT audit test objectives, test materiality, test methods, test designs, and designing audit tests.
Network Visualization

Visual inspection of networks is a powerful tool in exploratory analysis of social networks. However, visualization of graphs has inherent problems that may result in misleading visualizations. This chapter first introduces basic theory behind these inherent problems. Then it introduces features of common layout algorithms used in visualization. Through applied examples, the chapter explores the use of layout parameters to obtain visualizations appropriate for the research focus.
Blockchain

In 2008, the world was introduced to a new concept: Bitcoin. Although it took several years to become known among a broad public, it is considered to be a revolutionary idea. This section gives a broad overview of the Blockchain technology behind Bitcoin.
Object detection using part based semantic segmentation

Monocular vision systems are increasingly popular in driving assistance applications as they are easy to set up and do not require precise calibration or synchronization. The downside of monocular vision is the lack of 3D information, which makes the task of identifying individual objects that are close together in the image space difficult. The lack of 3D information must be compensated by high accuracy classification of the image data. This paper proposes a novel way of detecting objects using fully convolutional neural networks followed by lightweight geometric based post processing. The fully convolutional neural network has four semantic segmentation outputs corresponding to quarters of individual objects. Therefore, each pixel of the input image will be classified as either belonging to a top left, a top right, a bottom left, or a bottom right region of a whole object. If the object is occluded and only a few of the four regions are visible, the component pixels will still be labeled correctly. Based on the multiple outputs of the neural network, the pixels are grouped into connected regions using a clustering algorithm aware of the relations between the object’s quarters. The accuracy of individual obstacle instances is similar to the accuracy of the results obtained from instance segmentation networks, while the demand of resources and the number of trainable parameters is significantly reduced.
Application of improved RRT algorithm in the U-turn of vehicle

This article provides a solution for the path planning problem of AGV(Automated Guided Vehicle) during the U-turn. Paper not only modifies the sampling strategy of the traditional RRT (Rapid exploration random tree) algorithm to speed up the convergence of the algorithm, but also integrates TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) into the algorithm so that the route generated by the algorithm can meet the requirements of multiple indicators at the same time. In this way, the generation of invalid path is limited during the path planning, so the pruning step is omitted, and the running time of the algorithm is accelerated. The reliability of the algorithm is verified by simulation. The proposed method not only provides practical engineering significance for obstacle avoidance in an intelligent factory, but also theoretical reference value for other type of path planning.
Networks—B

Abstract This chapter provides the derivations of the results in the previous chapter. It also develops the theory of continuous-time Markov chains. Section 6.1 proves the results on the spreading of rumors. Section 6.2 presents the theory of continuous-time Markov chains that are used to model queueing networks, among many other applications. That section explains the relationships between continuous-time and related discrete-time Markov chains. Sections 6.3 and 6.4 prove the results about product-form networks by using a time-reversal argument.
Elementary Statistics

This chapter describes elementary statistical functions and techniques that apply to the analyses of experimental data. Statistics is probably second in subtlety only to thermodynamics (Chap. 10 ).
Data Pattern Recognition Based on Ultra-High Frequency Sigmoid and Trigonometric Higher Order Neural Networks

This chapter develops a new nonlinear model, ultra high frequency sigmoid and trigonometric higher order neural networks (UGT-HONN), for data pattern recognition. UGT-HONN includes ultra high frequency sigmoid and sine function higher order neural networks (UGS-HONN) and ultra high frequency sigmoid and cosine functions higher order neural networks (UGC-HONN). UGS-HONN and UGC-HONN models are used to recognition data patterns. Results show that UGS-HONN and UGC-HONN models are better than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models, since UGS-HONN and UGC-HONN models can recognize data pattern with error approaching 10-6.
Library Signage and Information Graphics

Libraries are gradually becoming spaces for discovery, innovation and community empowerment by catering to the various needs of the public. To harness the unlimited resources in the library, library digital signage is the perfect tool to help transform old buildings and spaces into a place of collaboration and innovation. Since it's a content-centric solution at its core, digital signs can go beyond looping a series of pictures. It's an intelligent tool that cans tailor-fit content relative to the audience, location, time of the day and even process. This chapter will address the different roles of signages and buttress the fact that Digital signage can play a major role in this communication process in addition to demonstrating a “green” environment for library patrons. Graphic Information can be displayed to the public in meeting rooms, at the front desk, in vestibules or cafeterias/cafes. The messaging can range from programs being held at the library to public service announcements to emergency messaging and so much more. There is no limit to the information that a library signage can convey to its users.
Janus-Faced Justice? The Role of Legal Technology in the Provision of Access to Justice

The move towards digitisation is a growing global trend and an inescapable reality. But so too is the increasing chasm between rights in law and rights enforced. The advent of legal technology presents exciting opportunities to bridge this gap, but it would be imprudent to assume that this is a symbiotic relationship, or that digitisation necessarily improves access to justice.
A Novel Deep-Learning-Enabled QoS Management Scheme for Encrypted Traffic in Software-Defined Cellular Networks

Mobile users are served with over-the-top (OTT) services through their cellular networks. To ensure the privacy of users and confidentiality of content, most OTT service providers encrypt their traffic. When a cellular network has no information about the type of service, a default bearer may be created. However, the default bearer may not guarantee bandwidth to a service. Therefore, users may experience degraded service due to packet loss, delay, and reduced data rates. This article proposes a novel quality-of-service (QoS) management scheme for encrypted traffic in software-defined cellular networks. We introduce a deep-learning-enabled intelligent gateway to predict the service types of encrypted flows by considering statistical and QoS features. A QoS control manager maps the bearers to ongoing flows satisfying their QoS requirements. As a proof of concept, we implement a testbed considering encrypted traffic from the Tor network. Results indicate that the proposed scheme improves the network throughput by 41%, decreases packet loss, delay, and QoS violations by 51%, 21%, and 52%, respectively, and reduces the length and size of the queue at the base station compared to those of the conventional scheme. Moreover, the convolutional-neural-network-based classifier achieves higher accuracy, precision, recall, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F1$</tex-math></inline-formula> -score, as well as lower loss values, compared to the multilayer perceptron classifier.
Mobile Assisted Vocabulary Acquisition and Wikis to Enhance Writing Skills

This project examined how a group of advanced-level EFL students read articles on their smartphone to acquire new vocabulary which they later inserted in their essays on wikis. The aim of the study was (a) to explore the students' perceptions regarding learning vocabulary from their smartphone followed by the use of wikis to improve their writing skills; (b) to analyse how peer editing and feedback can help students towards grammar and vocabulary accuracy with a view to enhancing their writing skills. The twenty-one participants used a Google application on their smartphone to perform reading and vocabulary exercises before writing essays on the wikis, followed by peer editing and feedback. Data were gathered from two questionnaires, interviews and 168 essays. The findings suggested that positive learning took place throughout the development of the study. The smartphone was considered a convenient tool for reading and performing vocabulary exercises. Peer editing and feedback were deemed crucial towards grammar and vocabulary accuracy to enhance writing skills.
AR in Education

This chapter provides a critical pathway for the uses of augmented reality (AR) in a variety of different situations. The chapter starts with an introduction to AR and then explores how these concepts can be perceived to support not just learning, but also industry. Through this journey, the reader will explore aspects like how AR can be used to support and develop individual behaviors, strategies, and what difficulties are fraught when thinking about implementing AR in education and aspects of industry (engineering, medical science, libraries) and what benefits they can have. Finally, the chapter critically analyses possible future directions AR and how this can improve retention within higher education.
DHD-puzzles

In this work triangular puzzles that are composed of unit triangles with labelled edges are considered. To be more precise, the labelled unit triangles that we allow are on the one hand the puzzle pieces that compute Schubert calculus and on the other hand the flipped K-theory puzzle piece. The motivation for studying such puzzles comes from the fact that they correspond to a class of oriented triangular fully packed loop configurations. The main result that is presented is an expression for the number of these puzzles with a fixed boundary in terms of Littlewood- Richardson coefficients.
Introduction

In this chapter, we provide a short but sufficient introduction to four topics that this thesis is built upon. It comprises of five sections on essential elements of convex geometry, the quantum resource theory formalism, quantum phase-space representation, and the notion of entropy in classical and quantum information. Importantly, the second section of this chapter deals purely with the mathematical concepts of algebraic and convex geometry that are necessary for a clear understanding of all other ideas provided. Respecting the fact that this is not generally easy for physicists to follow, we have done our best to bring the mathematical notions forward in an intuitive way with an eye on our language to be more sensible for physicists. Moreover, whenever possible, we have provided illustrations of the concepts.
Knowledge-Based Forensic Patterns and Engineering System

This chapter outlines a devoted framework that can store forensic digital data patterns permanently. The issues, challenges, conceptual multilayer framework for developing forensic pattern warehouse for betterment of forensic prediction and forecasting has been discussed. A sequence of phases is involved during the design processes of forensic pattern warehouse, and initially, conceptual modeling is one of the significant and the initial phase of forensic pattern warehouse design, because it constructs the solid framework for the next level of phases. In view of high semantic nature, there is a need to take a different approach for forensic pattern warehouse design to accommodate flexibility, isolation, extensibility, and robustness. In this way, the context-oriented forensic pattern warehouse design is one of the possible ways to manage patterns in a better way. The context of underlying pattern also helps to guide the queries to give a more satisfactory result.
Modelling time complexity of micro-genetic algorithms for online traffic control decisions

This paper describes an experimental approach to test the suitability of micro-genetic algorithms (m-GAs) to solve large combinatorial traffic control problems and establishes relationships between time to convergence and problem size. A discrete time dynamical traffic control problem with different sizes and levels of complexity was used as a test-bed. Results showed that m-GAs can tackle computationally demanding problems. Upon appropriately sizing the m-GA population, the m-GA converged to a near-optimal solution in a number of generations equal to the string length. Results also demonstrated that with the selection of appropriate number of generations, it is possible to get most of the worth of the theoretically optimal solution but with only a fraction of the computation cost. Results showed that as the size of the optimisation problem grew exponentially, the time requirements of m-GA grew only linearly thus making m-GAs especially suited for optimising large-scale and combinatorial problems for online optimisation.