Reviewerly/exHarmony huggingface dataset:
Governance of Ambient Assisted Living for Age Friendly Inclusive Cities

All over the world cities are ageing and elderly populations living in the cities is increasing in proportion. Ironically, the existing cities are more favorable for younger age groups than that for the elderly. With the recent developments in Artificially Intelligent (AI) technologies, numerous gadgets and applications have been invented that may support independent living for the elderly. It is one of the emerging sectors within the big shift to smart cities and is popularly termed as Ambient Assisted Living (AAL). Based on a thorough literature review this paper critically analyzes the current positioning of AAL in the shifting paradigm of existing societies into technology driven societies. In this paper state-of-the-art applications of AAL were analyzed from the governance perspective. Evidence from literature indicates that the development and dissemination of AAL gadgets and applications has been largely ad-hoc.
Introducing brain-heart crosstalks information in clinical decision support systems for TBI patients, through ICM+

We present here a novel measure called brain-heart crosstalks. The measure captured events of simultaneous increase of intracranial pressure (ICP) and heart rate (HR), and showed a relationship with mortality. We therefore propose in the present work an extension of the code, to allow for the implementation and insertion of the above measure into ICM+, a software for online bedside management and analysis of traumatic brain injury (TBI) patients.
A Comparative Study of 3 DOF Travel Techniques for Immersive Virtual Flythroughs: The Leap Motion and the Oculus Rift S Hand Controllers

This paper describes a comparative study of two different 3 degrees-of-freedom (DOF) flythrough techniques for immersive 3D environments. The results of this qualitative study indicate that the use of the Oculus Rift S hand controllers provides a greater usability than the use of Leap Motion free hand gestures system for a simple 3 DOF flythrough travel task inside immersive virtual environments. However, the correction of technical issues with the current Leap Motion free hand gesture recognition system could lead to different results.
Intelligent Parameter Tuning Using Segmented Adaptive Reinforcement Learning Algorithm

Now-a-days, intelligent systems play a crucial role in enhancing the capabilities of traditional processing techniques to handle huge amount of data and complex problems due to advances in technologies. Most of the processing problems can be resolved using traditional solutions based on optimization algorithms. In general, these solutions are normalized to increase their applications. Many of these solutions have control parameters to optimize the performance of the solution and also to maintain relative importance to specific application. Parameter tuning is a straightforward manual by determining the direction of tuning by trial and error method. But manual adjustment for these control parameters is tedious and consumes too much time and effort and sometimes becomes impractical if the solution has more parameters and requires precise tuning. With solution space being complex, machine learning has employed as a key component to setting up correct and precise parameters. Reinforcement Machine Learning algorithms can be used to solve this problem but existing algorithms requires huge amount of learning time and resources. This paper aims at solving this problem and proposes novel Segmented and Adaptive Reinforcement Learning (SARL) algorithm to train the system that can automatically tune the parameters accurately and precisely with very minimal learning time. Performance of proposed algorithm is validated in wavelet-based noise reduction technique by employing SARL algorithm to adjust 3 control parameters of Noise based Hybrid Threshold method. After integrating with the proposed SARL algorithm, the learning time of noise reduction technique is highly reduced without compromising the performance of the considered technique.
Effects of Augmented Reality Assisted Learning Materials on Students’ Learning Outcomes

This study explored the effects of augmented reality (AR) on students' learning outcomes. The participants of this study were 66 seventh-grade senior high school students from two classes in northern Taiwan. A quasi-experimental design with a control group (problem-based learning; PBL) and an experimental group (PBL with AR) was utilized. The results showed that the students who learned with the PBL and AR-assisted learning materials performed better than those who used the PBL approach in terms of answering questions that required their cognitive abilities of knowledge and comprehension, which are identified as lower cognitive ability.
Practical Error Modeling Toward Realistic NISQ Simulation

In quantum computing, research and development of devices, architecture, optimization techniques, algorithms, and applications are evolving with eagerness in parallel. To make these work mutually beneficial, practical and accurate quantum computer simulators as system-wide design frameworks are necessary. In this paper, we focus on measurement and initialization errors of qubits. These errors are inevitable because these procedures are interfaces between classical and quantum information processing. We model the quantum non-demolition detection technique as measurement and initialization methods and implement them on Intel-QS, a high-performance quantum simulator. Our case study with quantum Fourier transform on 8 qubits configuration demonstrates the importance of taking these errors into account. That is, post-selection, which improves the initialization fidelity, can enlarge the gap between the theoretical result and incorrect outputs by 3.35 times.
Proposed Quantum AI solution for the Travelling Tournament Problem

In this paper, we propose an idea/solution for the Travelling Tournament Problem, which is a unique combinatorial problem tackling both feasibility and optimality of the solution. Being an NP-Hard problem, generating solutions can be extremely cost-ly, with most of the solutions of the said problem having a time complexity of as high as O(n!). Quantum Computing and artificial intelligence, on the other hand, is making great strides, and we believe that the immense computational potential of quantum computing can be used to solve these scheduling problems. Artificial Intelligence and machine learning present us with excellent frame-work strategies that we have envisaged to incorporate in our implementation. In the following paper, we delineate the features of our solution and how we propose to incorporate the quantum and classical aspects of computing.
Shared Zones of Optimal Functioning Framework

This chapter bridges the literature on shared mental models with the individual zones of optimal functioning (IZOF) model to present a new framework called shared zones of optimal functioning (SZOF). The SZOF is an adaption and expansion of the probabilistic approach to estimate individual zones of optimal functioning, aimed at describing and explaining optimal and sub-optimal performance in team settings. In what follows, the theoretical tenets and the applied implications of this new conceptual framework are discussed. Specifically, it is illustrated how this framework can be used to (a) generate probabilistic curves linked to optimal and sub-optimal performance in teams; (b) capture teammates’ psychobiosocial synchrony and discriminate leaders and followers within a team; namely, the leader–follower dichotomy or shared leadership patterns; and (c) track “good” and “bad” momentum over time. The chapter concludes by discussing avenues for future research.
Practical Security for Cooperative Ad Hoc Systems

Existing consumer devices represent the most pervasive computational platform available, but their inherently decentralized nature poses significant challenges for distributed computing adoption. In particular, device owners must willingly cooperate in collective deployments even while others may intentionally work to maliciously disrupt that cooperation. Public, cooperative systems benefit from low barriers to entry improving scalability and adoption, but simultaneously increase risk exposure to adversarial threats via promiscuous participant adoption. In this work, I aim to facilitate widespread adoption of cooperative systems by discussing the unique security and operational challenges of these systems, and highlighting several novel approaches that mitigate these disadvantages.
Blockchain: The Novel Way to Secure Confidence!

This report intends to play out a precise audit to survey and establish the practicality of blockchain for implementing healthcare service records effectively. Traditional health records are both localized and expensive to operate; they can be improved upon by using blockchain based electronic health records (EHRs). EHRs are just electronic versions of a patient's whole clinical history. EHRs, when stored on blockchain, has some serious advantages when compared to their traditional centrally stored counterparts. The patient's medical records will be stored on a distributed network. An ethereum based decentralized application (DApp) can be incorporated to record and update medical information securely in real time using smart contracts. A decentralized application on a private blockchain network will ensure the integrity of data records and improve interoperability of the system by providing permanent access to essential details like patient's medical track record, prescription history, laboratory/ clinical reports etc. The application uses the efficiency and security of blockchain technology to solve the challenges faced by the healthcare domain.
Multi-semantic long-range dependencies capturing for efficient video representation learning

Capturing long-range dependencies has proven effective on video understanding tasks. However, previous works address this problem in a pixel pairs manner, which might be inaccurate since pixel pairs contain too limited semantic information. Besides, considerable computations and parameters will be introduced in those methods. Following the pattern of features aggregation in Graph Convolutional Networks (GCNs), we aggregate pixels with their neighbors into semantic units, which contain stronger semantic information than pixel pairs. We designed an efficient, parameter-free, semantic units-based dependencies capturing framework, named as Multi-semantic Long-range Dependencies Capturing (MLDC) block. We verified our methods on large-scale challenging video classification benchmark, such as Kinetics. Experiments demonstrate that our method highly outperforms pixel pairs-based methods and achieves the state-of-the-art performance, without introducing any parameters and much computations.
Learning through Creating Artefacts for Outdoor Settings with Smartzoos Application: Students Challenges and Experiences

Different outdoor locations, for instance, zoos can be turned into interesting authentic learning settings with the help of mobile applications, where learners can practice scientific thinking, explore concepts and relations through combining existing or creating entirely new physical and digital artefacts (Price, 2007). Mobile technologies bring along a set of different phenomena to education such as connectivity, context sensitivity, mobility, personalisation and collaboration (Liu, Scordino, et al., 2014) allowing new ways of socializing, knowledge acquisition (Looi, Song & Wong, 2012) as well as expanding learners' control and ownership over goals and learning experiences (Sharples, 2007). The concept learning through creating is not new and similar concepts such as learning through the authoring of user-generated content (Fitzgerald, 2012, etc.), learning through making (Hsu, Baldwin, Ching, 2017), learner as a creator and designer (Slussareff & Bohácková, 2016), learning through collaboratively constructing artifacts of various kind (ideas, practices, models, representations, etc.) (Paavola, Engeström & Hakkarainen, 2012) have emerged. The aforementioned approaches emphasise learners as active participants in knowledge building, in which learning happens through curation, modification or authoring digital or physical artifacts individually or in groups.
PROMAR: Practical Reference Object-based Multi-user Augmented Reality

Augmented reality (AR) is an emerging technology that weaves virtual objects into physical environments, and enables users to interact with them through viewing devices. This paper targets on multi-user AR applications, where virtual objects (VOs) placed by a user can be viewed by other users. We develop a practical framework that supports the basic multi-user AR functions of placing and viewing VOs, and our system can be deployed on off-the-shelf smartphones without special hardware. The main technical challenge we address is that when facing the exact same scene, the user who places the VO and the user who views the VO may have different view angles and distances to the scene. This setting is realistic and the traditional solutions yield a poor performance in terms of the accuracy. In this work, we have developed a suite of algorithms that help the viewers accurately identify the same scene and restore the VO under a moderate range of view angle difference. We have prototyped our system, and the experimental results have shown significant performance improvements. Our source codes and demos can be accessed at https://github.com/PROMAR2019.
Duped: Truth-Default Theory and the Social Science of Lying and Deception

Journal Article Duped: Truth-Default Theory and the Social Science of Lying and Deception Get access Duped: Truth-Default Theory and the Social Science of Lying and Deception Timothy R Levine Erik W Asp Erik W Asp Department of Neurology, University of Iowa, Iowa City, IA, USADepartment of Psychology, Hamline University, St. Paul, MN USAWesley & Lorene Artz Cognitive Neuroscience Research Center, Hamline University, St. Paul, MN, USA E-mail: easp02@hamline.edu https://orcid.org/0000-0002-9033-2425 Search for other works by this author on: Oxford Academic Google Scholar Journal of Communication, Volume 71, Issue 4, August 2021, Pages E32–E34, https://doi.org/10.1093/joc/jqaa026 Published: 04 August 2021 Article history Received: 14 May 2020 Accepted: 09 June 2020 Published: 04 August 2021 Corrected and typeset: 28 August 2021
Simulation of Performance of Air Conditioning System using Multi-nets Artificial Neural Networks (MN-ANN)

Usually, the air conditioning systems have to be compelled to maintain temperature among an exact fascinating vary. Specially, the temperature distribution isn't spatially uniform. Having sensors put in at mounted and restricted locations cannot react to the quickly variable space conditions thanks to transient and non-stationary human behavior. This paper has a tendency to use Multi-nets Artificial Neural Networks (MN-ANN) to work out and conclude the precise or close to cooling needed for the space, supported the amount of person's gift within the space. So, with the help of typical temperature range of persons within the space, we'll able to conclude what quantity temperature we've to present to cooling or heating the space. The operation is uncontrolled and should be manually turned on and off. Typically it will cause high usage of electricity that successively raises the electricity bill once the user forgot to change it off. So in order to avoid such problems automatic controlling of air conditioning systems can be used. In this paper, the simulation of performance of air conditioning systems using MN-ANN is performed. The simulation is done using Matlab R2018a.
The Effectiveness of Virtual Reality as an Instructional Medium for Multidimensional Concepts

This study explored the hypothesis that students using virtual reality (VR) education tools learn more effectively and efficiently when compared to conventional teaching methods. We investigated the specific situation of a course in digital logic design, and the task of simplifying Karnaugh maps. The students using VR observed the Karnaugh map as a higher-dimensional structure, while the students in the control group used a traditional pen-and-paper method. The 15 participants in the trial were administered either the VR education mechanism or the traditional education mechanism. Participants were asked to report their perceived level of comfort with various levels of solving K-maps. Preliminary results demonstrated that participants assigned to the VR test groups reported an increased level of understanding of Karnaugh maps after the trial. Further, participants using a desktop based headset were able to complete the experience quicker on average than the group using a phone based headset.
Tree Age Detection Using Pruning Technique

The study of tree-rings is a common task in dendrology. Usually the rings deliver information about the age of the tree, historic climate conditions and forest densities. Many different techniques exist to perform the tree-ring detection, but they commonly are semi-automatic. The main idea of this work is to propose an automatic process for the tree-ring detection. The proposed technique is based on a morphological operation. The presented automatic algorithm giving a good tree-ring recognition in most of the cases. In particular, it correctly detects the 80% of the tree-rings in our sample database.
An analysis of games played in world computer chess championships over the years

It is been 46 years since the first computer chess world championship was held in Stockholm in 1974, and there have now been 24 more with over 700 games played. This paper investigates a number of questions that a curious soul might ask regarding the results of these games, especially how they ended. The data provided herein attempts to answer several related questions. In addition, it presents data showing how ratings of the top engines have increased over the years.
Towards visualization of evolution of component-based software architectures in VR

To understand the development history of complex software architectures, software visualizations are very useful. They show dependencies and contexts in which design decisions were made, supporting programmers in understanding systems and helping them to recognize disadvantageous design decisions. IslandViz visualizes OSGi-based software architectures in Virtual Reality using an island metaphor; for now, the history of an architecture is not taken into account. We show how IslandViz can be extended to include changes on package and compilation unit level by changing the layout of islands. For this purpose we adapt the Enhanced Hexagon Tiling Algorithm and create growth corridors for each region of an island. As a result, island regions can change along with their respective packages without the possibility of being enclosed by other regions.
Implementation, Phases, and Project Management

Project management helps achieve the goals and objectives of program implementation. Project management strategies increase project success, decrease risk and costs, and help to achieve greater efficiencies and effectiveness in the execution of projects. A project has a defined beginning and end with defined scope, purpose, and allocated resources [1].
Work-in-Progress—Context is Key in Immersive Learning Environments

Immersive environments intended to enhance learning are becoming more popular and accessible as technology evolves. As VR establishes itself as a viable platform for learning experiences, the research thus far remains focused on the virtual experience. This paper highlights observations from two different educational VR experiences, ELLE the EndLess LEarner and The Middle Passage Experience to assert the need for structured pre-VR and post-VR tasks to be considered as part of the VR activity. Logistical context must be explained to learners before they participate to ensure they can manipulate the controls and progress through the experience. Educational context must be included before and after the game/simulation to enhance learning. Participants should be guided to activate any prior knowledge about the content of the simulation beforehand. Likewise, after they leave the virtual world, participants should be encouraged to reflect on the experience. We propose a set of questions for designers of educational VR to facilitate the intentional creation of meaningful pre- and post-VR activities during the design process.
Creative Toolkits for TIPS

We present a survey of toolkits employed in research workshop approaches within TIPS (Trust, Identity, Privacy and Security) domains. Our survey was developed within wider design research to develop digital service prototypes that support people in evaluating whether to trust that an online actor’s identity is not recently faked, and that a service they are registering personal information with is legitimate; and a subsequent project involving a tool that invites people to reflect on the cumulative risks of sharing apparently harmless personal information online. The radically multidisciplinary nature of both these TIPS projects has determined that we create a research space to promote exchange to, as design researchers, better understand the ‘opaque’ immediate and longer term implications of our proposed services and invite cross-disciplinary discussion towards interdisciplinary understandings. This paper is intended as an at-a-glance resource for researchers from a range of disciplinary backgrounds working on TIPS research to inform on various different material engagements, with research stakeholders, through creative workshop approaches. Our survey focused on the literature from Design (especially Participatory Design or PD, and Codesign), Human Computer Interaction (HCI) and cyber related security research. It comprises 30 papers or toolkit examples organised across: review papers; example toolkits; case studies reporting relevant toolkit use; applied toolkits for learning/knowledge exchange; research toolkits focused on demonstrating a methodological-conceptual approach (some problematising emergent or near-future technologies); and two papers that straddled the latter two categories, focusing on future practical application. We begin with an overview of our rationale and method before presenting each group of texts in a table alongside a summary discussion. We go on to discuss the various material components, affordances and terminology of the toolkits along with core concerns often left out of the reporting of research; before going on to recognise toolkits not so much as things that diagnose and fix things, but as a loose collection of readily available material and wider resources, used in particular participatory approaches, which together help account for techno-relational differences and contingencies in TIPS-related fields.
On Unsupervised Categorization in Deep Autoencoder Models

In this study the authors investigated categorization parameters of the latent space created by a class of deep autoencoder neural network models in unsupervised observation of the environment, without prior knowledge either in the form of ground truth data, or novelty detection framework based on pre-known categories. Representations created with deep autoencoder models with data representing samples of Internet datagrams demonstrated the emergence of a higher-level concept sensitive structure that can be measured and visualized. The parameters of distributions of higher-level concept samples in the latent space were measured and reported with strong positive correlation observed between the categorization performance of the model and classification accuracy in its latent space. The results provide empirical support for the connection between categorization ability of unsupervised models with self-encoding and classification accuracy of the corresponding representations.