Reviewerly/exHarmony huggingface dataset:
Analyzing Pedestrian Behavior in Augmented Reality — Proof of Concept

With recent advancements in head-mounted displaying technologies, virtual reality pedestrian simulators have become a common tool for traffic safety research. In contrast to field studies, test track studies and traffic observations, simulators enable researchers to analyze pedestrian behavior in a safe and controlled environment. However, creating the necessary virtual environments is time-consuming, especially in terms of meeting today’s expectations regarding graphical level of detail and realism. Furthermore, VR experiments often lack a body representation or require additional sensors to create an avatar. Due to the laboratory setting, VR simulators might fail to convey the feeling of standing on an actual street. In addition, simulators on the one hand and real-world testing on the other hand leave a methodological gap on the reality-virtuality continuum. This paper presents a novel approach for an augmented reality pedestrian simulator. With this simulator, the participant experiences virtual vehicles, augmented on a real scenario, allowing for safe and controlled testing in a realistic setting. In a between-subject design, 13 participants experienced a gap acceptance scenario with virtual vehicles, while 30 participants experienced the same scenario with real vehicles in the same environment. These participants were instructed to initiate a street crossing if they considered that the gap between the two experimental vehicles was safe to cross the street. Results indicate similar, but also offset behavior for both conditions. Lower acceptance rates and later crossing initiation times could be observed in the augmented reality condition. Still, it was shown that augmented reality renders a promising tool for pedestrian research but also features limitations depending on the use case.
A Study on the Effects of Head Mounted Displays Movement and Image Movement on Virtual Reality Sickness

Virtual Reality sickness will make users discomfort. We tested 26 volunteers wearing Oculus Rift. Three different experiments were conducted: (1,2) the effect of VR head-mounted display device (HMD) movement (un)synchronizes with inclination of the image on VR sickness; (3) the effect of image movement direction on VR sickness. The Simulator Sickness Questionnaire was filled by the participants. The results showed that the VR sickness symptoms are relieved when VR HMD doesn’t synchronize with inclination of the image. In addition, the different image movement direction will affect the vertigo feeling. The results will be of help for VR content creation.
A two-level stacking model for detecting abnormal users in Wechat activities

Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times.
Lattice-Based Incremental Signature Scheme for the Authenticated Data Update in Fog Computing

To efficiently authenticate the updated data in fog computing, this paper proposed a lattice-based incremental digital signature scheme. The proposed scheme is provable secure in the standard model whose security is based on the hardness of the shortest integer solution (SIS) problem. There are several characters of the proposed scheme that are suitable for the application in. Such as, most of the time-consuming computing operations in the proposed scheme can be finished by the parallel computing. As a result, the computing speed of the proposed scheme can be improved efficiently. On the other hand, compared with a known lattice-based incremental digital signature, both the public key size and the signature length are shorter. Then the store resource and the bandwidth of the fog device can be saved efficiently when it is used in fog computing. Furthermore, a simulated experiment is given to check the functions of the proposed scheme by Java language on PC. The result shows that the computing cost to update a signature is much less than that to resign a message. At the same time, the parallel computing and pre-computing can really efficiently improve the computing speed of the proposed scheme. Since the SIS problem is hard even on quantum computers, the proposed scheme also gives a post-quantum secure solution to authenticate the updated data in fog computing.
Blockchain Applications and Trends That Promote Information Management

Blockchain revolutionary paradigm is the new and emerging digital innovation that organizations have no choice but to embrace and implement in order to sustain and manage service delivery to the customers. From disruptive to sustaining perspective, blockchain practices have transformed the information management environment with innovative products and services. Blockchain-based applications and innovations provide information management professionals and practitioners with robust and secure opportunities to transform corporate affairs and social responsibilities of organizations through accountability, integrity, and transparency; information governance; data and information security; as well as digital internet of things.
Look Out! A Design Framework for Safety Training Systems A Case Study on Omnidirectional Cinemagraphs

Accidents occur when a person’s attention is distracted. A key aspect of safety training is directing people’s attention to potential hazards. Unfortunately, creating such hazards also puts people at risk, especially during safety training in manufacturing and construction. Virtual reality provides a training mechanism by which hazardous training scenarios can be created without putting the trainee at risk. We present a general framework for safety training systems and also present results from a case study where we create and evaluate a novel safety training environment, namely, omnidirectional cinemagraphs.
Blockchain and IoT: A Proposed Security Framework

With the improvement of technology and the widespread use of smart devices, smart homes, smart cars, and almost smart everything, IoT has become a significant player that influences everyone's daily life. Most of these IoT devices are limited in computing capacities, which make them an easy target for hackers. This problem has been in the research scope for a while, and securing IoT devices without limiting their usage has become a challenge for the scientific community. In this paper, we will be discussing existing IoT attacks, and a state of the art of the proposed solutions will also be given. We will as well discuss how blockchain technology could provide a solution to IoT security problems.
Deep E-School-Nurse for Personalized Health-Centered E-Learning Administration

The increase in obesity in students due to lack of physical activities and having unhealthy diets is reaching dangerous levels. E-learning environments are gradually replacing traditional educational institutions. The health issues resulting from students' lack of activity will increase with popularity of e-learning courses. Therefore, considering health issues in designing the e-learning environment is critical. Machine learning algorithms are able to diagnose many health issues automatically. In this paper, a machine learning based e-school-nurse which can take the role of the traditional human school nurse in the e-learning environment is proposed. The proposed enurse can automatically diagnose health problems of students and design a personalized health centered e-learning curriculum for them. As a case study, the deep neural networks are used for automatic profiling of students with diabetes. Based on the student diabetes profile a personalized curriculum is designed for the students which includes physical activity and a healthy diet at appropriate intervals during the study. The proposed e-school-nurse can help students to have a healthier e-learning experience.
The Influence of Full-Body Representation on Translation and Curvature Gain

Redirected Walking (RDW) techniques allow users to navigate immersive virtual environments much larger than the available tracking space by natural walking. Whereas several approaches exist, numerous RDW techniques operate by applying gains of different types to the user’s viewport. These gains must remain undetected by the user in order for a RDW technique to support plausible navigation within a virtual environment. The present paper explores the relationship between detection thresholds of redirection gains and the presence of a self-avatar within the virtual environment. In four psychophysical experiments we estimated the thresholds of curvature and translation gain with and without a virtual body. The goal was to evaluate whether a full-body representation has an impact on the detection thresholds of these gains. The results indicate that although the presence of a virtual body does not significantly affect the detectability of these gains, it supports users with the illusion of easier detection. We discuss the possibility of a future combination of full-body representations and redirected walking and if these findings influence the implementation of large virtual environments with immersive virtual body representation.
Parallelization of Variable Rate Decompression through Metadata

Data movement has long been identified as the biggest challenge facing modern computer systems' designers. To tackle this challenge, many novel data compression algorithms have been developed. Often variable rate compression algorithms are favored over fixed rate. However, variable rate decompression is difficult to parallelize. Most existing algorithms adopt a single parallelization strategy suited for a particular HW platform. Such an approach fails to harness the parallelism found in diverse modern HW architectures. We propose a parallelization method for tiled variable rate compression algorithms that consists of multiple strategies that can be applied interchangeably. This allows an algorithm to apply the strategy most suitable for a specific HW platform. Our strategies are based on generating metadata during encoding, which is used to parallelize the decoding process. To demonstrate the effectiveness of our strategies, we implement them in a state-of-the-art compression algorithm called ZFP. We show that the strategies suited for multicore CPUs are different from the ones suited for GPUs. On a CPU, we achieve a near optimal decoding speedup and an overhead size which is consistently less than 0.04% of the compressed data size. On a GPU, we achieve average decoding rates of up to 100 GiB/s. Our strategies allow the user to make a trade-off between decoding throughput and metadata size overhead.
Efficient routing quantum information in one-dimensional tight-binding array

Abstract We present an efficient quantum router using a fully-engineered one-dimensional tight-binding array acting as quantum data bus. Quantum routing is achieved by weakly coupling the sender and the receivers to the data bus. We show that perfect transfer of a state between sender and a chosen receiver can be achieved via appropriately tuning the on-site energy applied on the site of the sender. A generalization of this model to the case of multiple receivers is then studied. The results show that the state is equally split between receivers who participate in communication. Moreover, that a similar idea can be used to create $W$ state between all receivers is further emphasized.
Graph clustering‐based crowd counting with very limited labelled samples

In this Letter, the authors present a novel graph clustering-based method for crowd counting only using very limited labelled samples. Based on an intuitional observation that the distribution of low-level features of a specific scene containing the same or similar number of pedestrians are close to each other in the feature space, the authors adopt a first neighbour propagation (FNP) based clustering method to divide all unlabelled data into different groups. Next, an active sampling learning strategy that measures representativeness and diversity of the training data is used to obtain a few informative samples for annotation. Finally, the counts of those labelled informative samples are effectively propagated to predict the unlabelled samples in the constructed clusters by FNP-based clustering. The compelling results on two benchmark datasets demonstrate that the proposed method is not only effective to estimate crowd counts with very few labelled samples but also applicable to annotate a large number of unknown video frames for scene-specific crowd counting models.
Affective Embodiment: The effect of avatar appearance and posture representation on emotions in VR

Avatar embodiment allows for the study of self-perception. Avatars can communicate an identity through appearance customization, and signal an emotional state through facial expression and body posture. Following these ideas, I have developed two lines of research to study the experience of avatar embodiment on mental health. In one, I explore how avatar representation intersects with identity. I examine how issues with equitable representation in avatar customization affects users. In the second, I explore the potential of embodied VR to affect emotional states. I manipulate how avatar postures are represented and examine the effects on user’s emotional states. Overall, my research asks what kinds of emotions and values are avatars capable of communicating and how that can affect an individual’s experience of a virtual world and be translated into therapeutic interventions.
Single image desmogging using Gradient channel prior and Information gain based bilateral

Smog is defined as an intense air pollution. It is a combination of haze and fog and it degrades the visibility of outdoor image at a great extent. Therefore, existing imaging systems are unable to obtain the potential information from these weather degraded images. Recently many image restoration models have been implemented to remove the effect of haze and fog from images. But, removing the smog from images is defined as an ill-posed problem. Therefore, in this paper, a novel desmogging model is proposed. Initially, gradient channel prior is used to estimate the optical information of smoggy images. Thereafter, a information gain based bilateral filter is designed to refine the transmission map. Finally, the restored image is obtained using an improved restoration model. Extensive experiments have been carried out by considering benchmark hazy images. Performance analysis reveal that the proposed technique outperforms the existing visibility restoration techniques.
Towards an Affordance of Embodied Locomotion Interfaces in VR: How to Know How to Move?

When we walk through the physical worlds, our locomotion is naturally accompanied by a compelling and embodied sensation of mobility and self-motion - something that is missing from many gaming, telepresence, and VR applications unless users physically move. Physically moving has long been known to enhance spatial orientation performance when compared to imagined locomotion [8], [16], [18] and controller-based movements in VR [8]
Low Power Deep-Learning Architecture for Mobile IoT Intelligence

We present and develop DeepIoT, a low power convolutional neural networks (CNNs) accelerator to enhance inference intelligence on low-end mobile Internet of Things (IoT) devices. The develop of DeepIoT architecture is motivated with the applications need to design an efficient CNN architecture which is efficient both in terms of power consumption and network model size. The proposal includes two techniques: a model compression based on hybrid quantization scheme and a binary activation function based on support vector machine (SVM). To validate the proposed solution, a VGG model with binary weights in fully connected (FC) layers and ternary weights for the convolution (CONV) layers and SVM activations is implemented on embedded FPGA platform. The evaluation shows the latency and power consumption performance of the proposed architecture is competitive compared with state-of-the-art reduced precision CNN models, such as VGG-16.
Image deblocking via shape-adaptive low-rank prior and sparsity-based detail enhancement

At low bit rates, visually annoying blocking artifacts are usually introduced in JPEG compressed images. In this paper, we proposed an image deblocking method combined with the shape-adaptive low-rank (SALR) prior, the quantization constraint (QC) prior and sparsity-based detail enhancement. We firstly design a deblocking model to obtain initial deblocked images under the maximum a posteriori (MAP) framework. More specifically, with the assumption of Gaussian quantization noise, the SALR prior is utilized to effectively separate signal from noise and preserve image edges. Compared with previous low rank priors, the SALR reconstructs a better result via shape adaptive blocks. The QC prior is also adopted to avoid over-smoothing and to enable a more accurate estimation. Finally, by extracting features of external images, the mapping matrix of sparse dictionary pairs is trained to enhance image details. Extensive experimental results demonstrate that the proposed deblocking method has superior performances in both subjective vision and objective quality.
Virtualized Speech Practice for the College Classroom

For at least 20 years, Virtual Reality (VR) environments have been created and deployed as a tool to help treat, assess, and manage the anxiety many people associate with public speaking. However, these studies have been conducted primarily in controlled environments in either a clinical or a research lab setting. In this paper we describe an adventure into the wild— our experience designing and installing a virtual reality audience environment into a university public speaking laboratory for use in practice of speeches that were classroom assignments for undergraduate students. In particular, we report on system design, how the system was incorporated into a standard university course, and how practice in front of a virtual audience affected student performance on a speech given in front of their class.
Manipulating Puppets in VR

Archiving Performative Objects aimed at applying and conserving puppetry as creative practice in VR. It included 3D scanning and interaction design to capture puppets and their varying control schemes from the archives of the Center for Puppetry Arts. This paper reports on their design and implementation in a VR puppetry set up. Its focuses on the evaluation study (n=18) comparing the interaction of non-experts vs expert puppeteers. The data initially show little differences but a more detailed discussion indicates differing qualitative assessments of puppetry that support its value for VR. Results suggests successful creative activation especially among experts.
Creating Collections with Embedded Documents for Document Databases Taking into Account the Queries

In this article, we describe a new formalized method for constructing the NoSQL document database of MongoDB, taking into account the structure of queries planned for execution to the database. The method is based on set theory. The initial data are the properties of objects, information about which is stored in the database, and the set of queries that are most often executed or whose execution speed should be maximum. In order to determine the need to create embedded documents, our method uses the type of relationship between tables in a relational database. Our studies have shown that this method is in addition to the method of creating collections without embedded documents. In the article, we also describe a methodology for determining in which cases which methods should be used to make working with databases more efficient. It should be noted that this approach can be used for translating data from MySQL to MongoDB and for the consolidation of these databases.
Image Captioning and Comparison of Different Encoders

Abstract Generation of a sentence given an image, called image captioning, has been one of the most intriguing topics in computer vision. It incorporates knowledge of both image processing and natural language processing. Most of the current approaches integrates the concepts of neural network. Different predefined convolutional neural network (CNN) models are used for extracting features from an image and uni-directional or bi-directional recurrent neural network (RNN) for language modelling. This paper discusses about the commonly used models that are used as image encoder, such as Inception-V3, VGG19, VGG16 and InceptionResNetV2 while using the uni-directional LSTMs for the text generation. Further, the comparative analysis of the result has been obtained using the Bilingual Evaluation Understudy (BLEU) score on the Flickr8k dataset.
Interactive Navigation System in Mixed-Reality for Neurosurgery

In many bedside procedures, surgeons must rely on their spatiotemporal reasoning to estimate the position of an internal target by manually measuring external anatomical landmarks. One example of such a procedure is ventriculostomy, where the surgeon inserts the catheter in the patient's skull to divert the cerebrospinal fluid and alleviate the intracranial pressure. However, one-quarter to one-third of the insertions miss the target which can ultimately lead to undesirable surgical outcomes. We have developed an interactive navigation system using mixed reality on a head-mounted display that overlays the target directly on the patient's anatomy and provides visual guidance for the surgeon to insert the catheter on the correct path to the target.
Conception of a Training System for Emergency Situation Managers

This paper presents a new training approach based on artificial intelligence for air traffic management. A first objective is to provide a conceptual and theoretical connotation on how to treat the Concept of accident causation by establishing a critical approach to the concept. Also, we propose a Design of a Training /Evaluating system based on voice recognition based on temporal and qualitative modelling which can be used in two ways:A training system for air emergency managers where the acquired skills and professional attitudes will be developed and consolidated under the supervision of an instructor.An evolving training system for the benefit of natural or anthropogenic crisis managers allowing the injection of new scenarios.Our contribution proposes improvements that could be implemented in the training of Emergency Situation Managers in order to be more efficient and use less time to manage emergency situation.
Augmented Reality Gamifies the Library

Two librarians at a University in New Hampshire attempted to integrate gamification and mobile technologies into the exploration of, and orientation to, the library's services and resources. From augmented reality to virtual escape rooms and finally an in-house app created by undergraduate, campus-based, game design students, the library team learned much about the triumphs and challenges that come with attempting to utilize new technologies to reach users in the 21st century. This chapter is a narrative describing years of various attempts, innovation, and iteration, which have led to the library team being on the verge of introducing an app that could revolutionize campus discovery and engagement.