Reviewerly/exHarmony huggingface dataset:
Elements of Our Hybrid Existence

Abstract This chapter introduces the core elements of physicality that will form the structure of the central parts of the book: the body and physiology, material artefacts and design, space and spatial arrangement, and digital artefacts and virtual physicality. Humans have always shaped their lives and the world from stone and metal tools to steam and electrical machines; digital technology is in some ways just another step but it is also different in the way that it is both embedded everywhere and yet also has an almost numinous or ghost-like nature. This chapter explores how human–computer interaction and industrial design have emerged as separate disciplines and yet are now converging in the study of these hybrid digital devices with physical form.
Physicality of Space

Abstract This chapter discusses space and time, both so familiar that we hardly notice them and far more complex than we ever imagine. Mathematicians and physicists have tried to make sense of space: the coordinates of GPS appear to label even empty space, and Euclid imagined geometric shapes composed of infinitely thin lines: powerful abstractions, but problematic philosophically and practically. Euclid's space was flat, smooth, and without fracture, but the surface of the earth and WiFi connectedness break this simplifying image, creating problems and design opportunities. Size, speed, and even acceleration are often relative, a fact of physics, but also evident in our day-to-day lives, from snowflakes to circuit boards. Even the ground beneath our feet is not so solid. In following chapters we will see how many of these physical properties of space and time impact on the design of digitally augmented space, and indeed computation itself.
Interacting with Physical Objects

Abstract This chapter begins by revisiting affordances: the way that our perceptions are tuned to be able to instantly and instinctively understand the action potential of objects around us: what we can do with them. However, in the constructed environment we have to design objects deliberately so that they create the right impressions: from doors that look as though they can be pushed or pulled to knobs that feel as if they turn in the appropriate direction. Happily, humans are natural affordance seekers, actively finding ways to engage with the world around us and learning cultural conventions and device idiosyncrasies. This seeking includes epistemic action: things we do to learn about the world, blurring the distinction between action and perception. The very softness and pliability of our bodies help us act, a fact that can be used to design robots, create better tools for people, and even help fold a bed sheet.
Digital Augmentation of Space

Abstract Humans have always used technology to control space, from simple huts to shut out the weather to the telephone that allows us to talk between continents. This chapter looks at how digital technology amplifies existing means of control but goes beyond as the computer now infuses space itself: from CCTV cameras, to Internet kettles, digital technology is embedded into the very fabric of our lives. At a personal level, mobile phones allow access to people and resources from anywhere, but they also allow the world to be marked and interrogated through social network check-ins and geographic search. Public displays create new roles, new interaction potential, and new ways to conceive privacy. Beyond the screen, mixed reality makes space itself the interface, where movement is interaction, and the chapter addresses computation that is literally all around us. However, these technologies that link space globally can also enable us to rediscover the local.
Physicality of Things

Abstract Newton's laws of motion are used to plan the course of missions to Mars and the braking efficiency of cars. However, well before we learn physics in school, we all have an understanding of the physical behaviour of objects around us. This chapter shows how we need to understand both: digital devices need to work within the physics of the world, but also be comprehensible. Simple rules of objects that are routinely broken in the digital world: a source of power, but potential confusion. We will use the magic world of fairy-tale to see how some properties, continuity of location in time and space and conservation of form, can in some circumstances be broken; but in others, conservation of number, are far more primitive and shocking when violated. Physical form is also critical for our emotional engagement with objects and may engage many senses, such as touch and sound, as well as visual form.
Social, Organizational, and Cultural

Abstract This chapter looks at humans as social beings, and as a species how our social identity and interactions have been forged in the physical world. However, increasingly our communication, community, and identity is mediated digitally. The chapter shows that in some ways digital technology is merely an extension of previous forms of remote communication. However, digitally enabled physical artefacts are also enabling new ways of meeting and even achieving intimacy at a distance. Physical objects mediate many human activities; understanding this helps us to digitally augment existing artefacts or design new ones; and by recognizing that organizations perform computational functions we can see the ways the physical objects encode memory and processing. Culture itself is often seen through the lens of the artefacts it creates, and social identity in terms of geographic location or physical similarities. Increasingly both are becoming redefined digitally.
Representation and Language

Abstract This chapter looks at the nature of information: numinous, without intrinsic physical form itself, and yet only ever possible to transmit, communicate, or record through representation in physical media. Ideas are given legal protection in patents, but paradoxically only protected when recorded in that patent. The notions in our head need to be externalized in order to communicate to others or give rise to action. However, this is not mere transmission of existing notions in our heads but is a crucial part of crystallizing those notions and indeed enabling us to reflect upon our own thinking. Language is at the heart of this transformation of learning from imitation to oral, written and now digital culture, and yet the origins of language are themselves rooted in our physical existence and our innate abilities as meaning seekers to interpret one another and the world. Internalizing these processes of representation and interpretation is perhaps what makes us human.
Theory and Philosophy of Physicality

Abstract Throughout this book many philosophical and theoretical threads have been encountered: from discussions of Heidegger and hammers to the nature of space and time, and the changing role of money. The book has shown how fundamental physics involves information, and how computation is intensely physical with attendant environmental implications. This chapter draws together a few points made throughout the book and summarizes them. The chapter begins with the way digital technology acts as a mirror to the nature of the material world, and by its similarities and differences reveals fresh insights about physicality. It then moves on to consider what it means to be human, embodied in a physical and now a digital environment. Finally it looks forward to the potential design future and the way all of society is being changed, and could be transformed, by a rich understanding of the interplay between our physical and digital existence.
What’s Happening Now

Abstract Many areas of study include aspects of physicality in digital research or design. The chapter begins with Weiser’s vision of ubiquitous computing where computation becomes ‘invisible’, seamlessly interwoven into every aspect of life. Sometimes it is in objects that we hold, grasp, or feel (studied by tangible user interfaces and haptic interfaces) and sometimes so close it feels personal: mobile phones, wearable technology, or even within our bodies. In virtual and augmented reality the digital world emulates or sometimes overlays the physical world, but it only affects it through our perceptions. However, in robotics and autonomous vehicles the digital world is not only made physical but also active with apparent agency of its own. Finally, the chapter looks at how digital design can now be given physical form through digital fabrication, making physical production as easy as writing in a word processor, which is enabling new communities where digital and physical crafts meet.
Body

Abstract Our bodies are obviously physical. In the case of assistive technology, this may be to ameliorate gaps in our abilities, in other cases it may be to work alongside or extend our bodily functions. Our physical size influences the technology we are able to use and may be used as a positive part of design, including for child safety. We also have temporal limits: nerve impulses taking as long to propagate through our body as the fastest Internet signals take to pass around the world. This chapter explores how the design of digital devices and environments need to take into account the physical constraints of the human body. It also discusses how the technology we carry with us, wear on our bodies, or have embedded within us means that we are all, to some extent, cyborgs.
Bomb Box: A Fortified Vault to Prevent Brute Force Attack

The Internet of things encompasses many devices connected to the Internet including wearable devices or edge devices forming a smart system to manage many tasks without the intervention of humans. Securing such systems, whether it is a part of IoT framework or any industrial software, is a very crucial task to be considered. Protecting them with passwords or even encrypting them still leaves some loopholes in the system. The attacker uses the concept of brute force to predict passwords and hack the system. To protect and prevent the system, many policies related to passwords are adopted. This includes continuous updating of passwords, which creates another stress to memorize them. This paper depicts a novel concept of a bombing algorithm to create a secure system and help remove unnecessary stress. The further section describes the related work done and the methodology adopted. The last section displays the results and evaluation of the proposed system.
Ensemble Deep Learning Models for Vehicle Classification in Motorized Traffic Analysis

Automation of vehicle classification is essential in the establishment of effective Intelligent Transportation Systems (ITS). Based on the MIOvision Traffic Camera Dataset (MIO-TCD), this paper categorizes the types of vehicles as car, bus, van, light truck, motorcycle and multi-axle truck. The classification of surveillance images is achieved using an ensemble of Deep Networks. Three networks are trained separately to make up the deep learning ensemble model with ConvNet, LeNet and EfficientNet achieving 89%, 68% and 87% classification accuracy, respectively. Results of experiments unveil that the ensemble of networks outperforms the individual networks. The ensemble of networks achieves 92.77%, which is high when compared to the performance based on genetic method in the recent literature.
Brand Logo Detection Using Slim YOLO-V4

Images have been a rich source of information in recent years. Images are available in vast quantities, and most solutions necessitate real-time picture processing. This necessitates the creation of images with human-like capabilities for detecting and locating items in images. Object Detection is a branch of Computer Vision that has applications in a variety of domains, including Face Detection, Video Surveillance, Autonomous Driving Cars, and Medical Image Processing. Object detection should be quick and accurate. For accurate detection, all portions of the image should be searched for objects of all types and sizes. This necessitates a large computation cost as well as a significant quantity of time. It is natural and requires little effort for people, and researchers aim to create models that behave similarly to humans.
Pose Driven Deep Appearance Feature Learning for Action Classification

In this work, we propose to learn the fusion process between the dominant skeletal features and the RGB features. This is in contrast to the previous fusion methods that simply fused these multimodal features, without learning the fusion process to exploit the semantic relationship between them. Here, we propose a gated feature fusion (GFF) of multimodal feature data which provides attention to the appearance stream of RGB data using the temporal skeletal data. Initially, the features from RGB and skeletal frames are extracted using CNN models. Subsequently, the gated fusion network fuses the features from pose and appearance domains using temporal convolutions which are further combined into a latent subspace. Finally, the latent subspace features are classified using fully connected layers with the combined loss embeddings. The proposed architecture has performed better than the state-of-the-art models on RGB-D action datasets.
Comparative Study of Enhanced Round Robin Algorithms with Drrha and Other Metaheuristic Algorithms

CPU scheduling has a substantial influence on system resource usage and overall performance. Scheduling Algorithms are a technique for reducing CPU resource deprivation while simultaneously maintaining fairness among the numerous programs that utilize the resources. Round Robin is a preemptive scheduling method that significantly improves response time by restricting each operation to a certain length of time known as the Time Quantum. Various efforts have been made to calculate a time quantum value to optimize these Round Robin algorithm parameters. However, this gain in response time comes at the expense of turnaround and waiting time. In this paper, we compare the conventional Round Robin CPU scheduling algorithm to updated Round Robin algorithms such as DRRHA, as well as our suggested approaches termed MDRRHA and NDRRHA, which seek to reduce process waiting time. The Quantum value for MDRRHA and NDRRHA is derived dynamically using the arithmetic mean and the normal distribution of execution time values of tasks, respectively. The recommended solutions decrease average turnaround time and average waiting time values by up to 13%. In this research, we compare different job scheduling approaches by simulating them in a variety of test situations.
Wearable Devices with Recurrent Neural Networks for Real-Time Fall Detection

Monitoring elderly and weak diseased people is one of the biggest issues in this modern world. Framing a technology for them is one of the wise contributions that can be done to society. More than 30% elderly people of age above 70 are falling every year due to bad health conditions. Fall identification is a significant issue in the medical care office. Elderly people are more inclined to fall than the others; accidental falls cause injuries, severe injuries and lead to death too. In our country, more than 30% are elderly people aged above 70, and they fall every year due to bad health conditions; nearly 40%–50% of elderly people fall every year most of them experiencing recurrent falls which may cause injuries, and it may lead to death too. Most of the elderly people experiencing recurrent falls which may cause injuries, to reduce the incident a system of monitoring and control is developed to detect the elderly person falls and can take immediate action, so here we considered two ways for preventing: one is smartphone-based and the other is a wearable device based recently in the fall detection wearable devices is the best choice because they are very much less in cost than the ambient-based overall features is to increase the acceptance and continue to monitor with its deep neural networks, deep learning has quickly altered the language processing domain. The LSTM is a typical recurring cell unit for deep learning models based on recurrent neural networks; here, in my paper I have proposed a new advanced version of Long Short-Term Memory (LSTM) which is Cerebral LSTM which shows better accuracy while training and testing the data and better ability to know about the time series prediction; using the RNN, the elderly person who falls is detected and with the help of the sensor the data gets collected and is allowed to training and testing validation with the MobiFall dataset; I have achieved a fall detection accuracy of about 98%.
Distributed Consensus Mechanism with Novelty Classification Using Proof of Immune Algorithm

Peer-to-peer lending is an emerging financial domain enabling people to receive instant credit facilities without much complicated procedures and intermediaries. Many financial institutions are focusing on setting up peer-to-peer lending platforms to enable hassle-free credit facilities with transparency between lenders and borrowers. The trust and transparency for hassle-free settlement and addressing novelties is a major concern in this domain which is hindering the mainstream adoption of such lending platforms. The objective of this paper is to propose a trusted and transparent distributed ledger approach using blockchain technology for setting up peer-to-peer lending platforms. The proposed approach creates cryptographically secured transactions stored over a publicly verifiable immutable ledger, which ensures credibility and auditability to investors and borrowers on every aspect of security. Proof of Immune Algorithm was proposed by leveraging the potential dendritic cell algorithm mimicking human immune system to provide consensus among peers involved in the lending process to enable trust.
Adversarial Image Colorization Method Based on Semantic Optimization and Edge Preservation

As a medium for transmitting visual information, image is a direct reflection of the objective existence of the natural world. Grayscale images lack more visual information than color images. Therefore, it is of great significance to study the colorization of grayscale images. At present, the problems of semantic ambiguity, boundary overflow and lack of color saturation exist in both traditional and deep learning methods. To solve the above problems, an adversarial image colorization method based on semantic optimization and edge preservation is proposed. By improving generative and discriminative networks and designing loss functions, deeper semantic information and sharper edges of images can be learned by our network. Our experiments are carried out on the public datasets Place365 and ImageNet. The experimental results show that the method in this paper can reduce the color anomaly caused by semantic ambiguity, suppress the color blooming in the image boundary area and improve the saturation of the image. Our work achieves competitive results on objective indicators of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and learned perceptual image patch similarity (LPIPS), with values of 30.903 dB, 0.956 and 0.147 on Place365 and 30.545 dB, 0.946 and 0.150 on ImageNet, which proves that this method can effectively colorize grayscale images.
The Fourth Industrial Revolution: A Technological Wave of Change

This chapter focuses on the technological wave of change called the fourth industrial revolution (4IR), which is also known as the information age or industry 4.0. It starts off with a brief history of the concept, describing the evolution through the ages, from the age of industrialization to the current technological age. The chapter then presents industry 4.0 through three lenses, which are i) the key enabling technologies that serve as its foundational pillars, such as the Internet and Cloud Computing; ii) technologies and concepts that emanate from 4IR, as well as their applications, which are discussed using use-cases; iii) the impacts of industry 4.0 on the wider society (both positive and negative). Finally, the chapter closes with a discussion on some open challenges that need to be considered in future research works to enhance the widespread adaptation and/or implementation of industry 4.0.
The Use of Partial Order Relations and Measure Theory in Developing Objective Measures of Explainability

In this paper we describe the use of two mathematical constructs in developing objective measures of explainability. The first one is measure theory, which has a long and interesting history, and which establishes abstract principles for comparing the size of general sets. At least some of the underpinnings of this theory can equally well be applied to evaluate the degree of explainability of given explanations. However, we suggest that it is meaningless, or at least undesired, to construct objective measures that allow the comparison of any two given explanations. Explanations might be non compatible, in the sense that integrating such explanations results in decreasing rather than increasing explainability. In other words, explainability is best considered as a partial order relation. Notwithstanding the use of partial order relations and measure theory, it is unwise to unconditionally apply these mathematical concepts to the field of explainability. It is demonstrated that the law of diminishing returns from economics offers a neat way to make these concepts applicable to the domain of explainability. The legal field is used as an illustration of the presented ideas.
Adaptive Knowledge Control in Digital Learning as a Factor in Improving the Quality of Education

The quality of knowledge is the most important task of the learning system at any level and stage of education. The COVID-19 pandemic has made its own adjustments to the process of organizing education, requiring the transition from traditional to distance learning as soon as possible. In the new conditions, the use of adaptive knowledge control has become relevant, taking into account the individual level of knowledge of the trainees. The study is devoted to the analysis of the features of adaptive testing, the conditions of application and the possibilities of web technologies for its organization. The article presents the results of a study aimed at organizing and conducting adaptive knowledge control as one of the means of implementing an individual learning trajectory. The study presents algorithms for constructing an individual trajectory of adaptive testing for each user. The analysis of web technologies and learning management systems that are currently used in terms of their capabilities for the implementation of the adaptive learning module is carried out. An adaptive testing module has been developed for implementation into the LMS Moodle learning management system, built taking into account the modular organization of the system. The construction of the module is based on a Markov random process with discrete states, continuous and discrete time, which makes it possible to implement a condition for completing testing with the function of viewing detailed statistics of its passage. The problem of developing and implementing an adaptive testing module in an online learning system is considered. The results of experimental work confirming the effectiveness of the implementation of the adaptive testing module are presented. The study of technology by future teachers and its application in practice will contribute to an increasingly widespread implementation in practical activities.