Reviewerly/exHarmony huggingface dataset:
Impact of Fake News in VR compared to Fake News on Social Media, a pilot study

With the rise of easily shareable online information, fake news have become a pressing issue in our societies. News credibility depends of several factors (reliance on a source of information, media outlet, etc.). We investigate here the immersion factor by comparing in a pilot study (N=6) the impact of fake news in VR (with a 360 degrees video), and fake news on social media (2D videos).
Delay-Based True Random Number Generator in Sub-Nanomillimeter IoT Devices

True Random Number Generators (TRNGs) use physical phenomenon as their source of randomness. In electronics, one of the most popular structures to build a TRNG is constructed based on the circuits that form propagation delays, such as a ring oscillator, shift register, and routing paths. This type of TRNG has been well-researched within the current technology of electronics. However, in the future, where electronics will use sub-nano millimeter (nm) technology, the components become smaller and work on near-threshold voltage (NTV). This condition has an effect on the timing-critical circuit, as the distribution of the process variation becomes non-gaussian. Therefore, there is an urge to assess the behavior of the current delay-based TRNG system in sub-nm technology. In this paper, a model of TRNG implementation in sub-nm technology was created through the use of a specific Look-Up Table (LUT) in the Field-Programmable Gate Array (FPGA), known as SRL16E. The characterization of the TRNG was presented and it shows a promising result, in that the delay-based TRNG will work properly, with some constraints in sub-nm technology.
Application of Blockchain in IoT Data Trust and Information Available Technology

Due to the lack of flexible and effective processing mechanism in the process of data interaction and information transmission in Internet of Things(IoT), this has seriously hindered the process of realizing "interconnection of all things, sharing of all information " in IoT. In order to optimize the above problems, this paper introduces Blockchain technology, and focuses on the analysis of the mapping relationship of Blockchain technology for the hierarchical architecture of IoT. At the perception level of IoT, Merkle tree technology is used to realize the encryption and security of data; at the network level of IoT, the consensus mechanism for the correct use of the information is compared and analyzed. Based on the preliminary discussion of the application of the above Blockchain technology in IoT, the development trend of " IoT+Blockchain" technology in the future is conceived.
MBT: A Context Aware Mission Based Tracker

Implementing a robust tracker production is a challenging topic because of problematic cases, such as partial occlusion, fully occlusion, deformation, motion blur, fast motion, illumination variation, background clutter and scale variations. On the other hand an object to track has lots of features, such as color, speed, shape, height as well as the context. In order to produce robust object tracking system, use of these features are important. In this paper, the implementation of the Context Aware Mission Based Tracker (MBT) which is a context aware scale adaptive object tracking system based on fast Discriminative Scale Space Tracking (fDSST) is explained. MBT is integrated with color detection, image segmentation, fast move and stabilization mode to introduce context awareness. In addition to the modes, we constructed two different blocks which are velocity control block (VCB) and occlusion control block (OCB) to improve the fDSST and a Kalman filter to increase the location estimation performance. MBT is checked against other object tracing algorithms through the experiments with the Online Tracking Benchmark (OTB50) dataset. Results indicate that tracking performance is significantly improved by our contribution and became the best among the discriminative correlation filter (DCF) based trackers. Although is the ability to quickly calculate the response values of a large number of candidate samples with fast Fourier transform (FFT), making the DCF-based tracker much faster than convolutional neural networks (CNNs) based trackers. Thereby, MBT tracks the target object without CNNs applications.
Aggregation Bias: A Proposal to Raise Awareness Regarding Inclusion in Visual Analytics

Data is a powerful tool to make informed decisions. They can be used to design products, to segment the market, and to design policies. However, trusting so much in data can have its drawbacks. Sometimes a set of indicators can conceal the reality behind them, leading to biased decisions that could be very harmful to underrepresented individuals, for example. It is challenging to ensure unbiased decision-making processes because people have their own beliefs and characteristics and be unaware of them. However, visual tools can assist decision-making processes and raise awareness regarding potential data issues. This work describes a proposal to fight biases related to aggregated data by detecting issues during visual analysis and highlighting them, trying to avoid drawing inaccurate conclusions.
Fighting Detection Based on Hybrid Features

This paper presents a fighting detection algorithm based on hybrid features. An effective coding scheme called locality-constrained linear coding (LLC) is used for the features by incorporating histograms of quadruples optical flow gradient (HQOFG) into local spatio-temporal features. Our results confirm that the fighting detector only with a linear support vector machine for sequence classification performs well. Compared to the fighting detector using bag-of-words (BOW) framework and local spatio-temporal features, the detector proposed in the paper improves the recognition accuracy.
Resisting Large Data Variations via Introspective Transformation Network

Training deep networks that generalize to a wide range of variations in test data is essential to building accurate and robust image classifiers. Data variations in this paper include but not limited to unseen affine transformations and warping in the training data. One standard strategy to overcome this problem is to apply data augmentation to synthetically enlarge the training set. However, data augmentation is essentially a brute-force method which generates uniform samples from some pre-defined set of transformations. In this paper, we propose a principled approach named introspective transformation network (ITN) that significantly improves network resistance to large variations between training and testing data. This is achieved by embedding a learnable transformation module into the introspective network, which is a convolutional neural network (CNN) classifier empowered with generative capabilities. Our approach alternates between synthesizing pseudo-negative samples and transformed positive examples based on the current model, and optimizing model predictions on these synthesized samples. Experimental results verify that our approach significantly improves the ability of deep networks to resist large variations between training and testing data and achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN, CIFAR-10 and miniImageNet.
Neural Embedding &amp; Hybrid ML Models for Text Classification

Representation of knowledge remains a problem for models of machine learning (ML). The Paragraph vector is one of the current methods for embedding the text, where many parameters govern the utility of representation. In this context, we are addressing the effect, on the text classification area, of Paragraph Vector-Distributed Memory (PV-DM) as variant of doc2vec. In comparison, we apply other classification systems focused on doc2vec forms, and a collection of classifiers with current practices in this article. Then, we incorporate hybrid ML methods to improve the quality of classification. The experiments, on benchmarking dataset, prove that the results obtained are excellent, with 99% accuracy in the system based on the PV-DM with average method, and majority voting as a classifier.
Early Results of a Usability Evaluation of Two Digital Human Model -based Ergonomic Software Applying Eye-Tracking Methodology Comparison of the usability of ViveLab and Jack software

Analysis software for ergonomics are more and more wide spread among researchers. There are various software for ergonomics available on the market, and it would be important to know which one to choose in various research tasks. Although data on the capabilities of the software can be found relatively easily, a comparison regarding their ease of use and the quality of their user interface cannot be found in the literature. In this article, the usability of two software were compared. A cloud based Hungarian software, ViveLab and the well-known Jack software was chosen. In addition to the traditional software usability testing technique based on screen and event recording and user camera, eye-tracking methodology was also applied. The goal of this research is to find out which software is more usable in different situations. The results were divisive and in some cases astonishing. We are not able to give a definite answer which software is easier to use. There were several cases when ViveLab was easier and almost as much cases when Jack. There are areas for improvement in both software. Our results can help researchers to choose software for their specific tasks. Furthermore, the results can give additional information on which user interface elements and concepts are worth to improve by developers of the ergonomic software, and, in some cases, may inspire developers of other 3D-based software in general.
A Digital Ecosystem for Teaching-Learning English in Higher Education

The aim of the research is to analyse pre-service teachers' perceptions about the effectiveness of the digital ecosystem implemented in the area of English morphosyntax in b-learning modality. Based on this goal, a qualitative research approach was adopted. One hundred forty-three learners were enrolled in the subject, and a total of 43 participated in the study. The instrument used to collect data was unstructured individual interviews. The findings suggested students' positive attitudes towards the implemented digital ecosystem. It highlighted that the students were active participants and knew how to manage their learning and how to collaborate with their classmates. The flexibility and accessibility provided by the technological tools available in the model allowed students to combine their personal and professional obligations with lifelong learning. It overcame all the spatial and temporal barriers and developed a kind of learning just-in-time, just-for-students, anytime and anywhere.
An information-theoretic instance-based classifier

Classification algorithms are used in many areas to determine new class labels given a training set. Many classification algorithms, linear or not, require a training phase to determine model parameters by using an iterative optimization of the cost function for that particular model or algorithm. The training phase can adjust and fine-tune the boundary line between classes. However, the process may get stuck in a local optimum, which may or may not be close to the desired solution. Another disadvantage of training processes is that upon arrival of a new sample, a retraining of the model is necessary. This work presents a new information-theoretic approach to an instance-based supervised classification. The boundary line between classes is calculated only by the data points without any external parameters or weights, and it is given in closed-form. The separation between classes is nonlinear and smooth, which reduces memorization problems. Since the method does not require a training phase, classified samples can be incorporated in the training set directly, simplifying a streaming classification operation. The boundary line can be replaced with an approximation or regression model for parametric calculations. Features and performance of the proposed method are discussed and compared with similar algorithms.
Rolling-circles: Detect and reduce noise via erosion of finite-difference moduli for preserving signals

Noise is ubiquitous in nature; it decreases the measurement or data reliability. This reliability may be increased by using a novel method that detects and reduces noise for preserving signals, called the Rolling-circle method. Our method treats noisy signals by eroding finite-difference moduli, circle radii, with either a version for denoising signals or a version for smoothing signals. Both versions were compared to a moving average filter, a simple median filter, an adaptive median filter, a Savitzky-Golay filter, and two wavelet threshold filters. For these filters, we tested four cases: denoising a histogram, denoising a Raman spectrum, smoothing a well-log, and denoising a synthetic signal. In such cases, our method has surpassed the mentioned filters as measured through well-known metrics. A quantitative metric, called 0-metric, is also proposed in this work for assessing the amount of preserved data. The preservation of a clean signal was only achieved entirely by the proposed method, which we believe that no filter ever did thus far.
Investigating Trainees’ Nonverbal Behaviors in Virtual Patients Communication in Virtual Reality

In this paper, we want to explore which verbal and non-verbal communication behaviors health professions trainees will employ during the training of interpersonal skills through the interaction experience with virtual patients. We investigated participants' eye gaze position, nodding gesture, speeches, interaction distance, and questions participants asked in a pilot study. We compared the results of verbal behaviors and non-verbal behaviors observed in the pilot study with what we would expect to find given prior research from real-world interactions. Then we tried to explore the potential reasons if our results are different from what we expect.
A Multi-Input Neural Network with Dense Flow and Spatio-Temporal Features for Anomaly Detection

IoT today is expanding at a rapid rate. The number of connected devices is expected to hit 70 billion by 2025 and will increase further at an exponential rate. Additionally, the surge in IoT surveillance devices makes manual human monitoring impossible and presents a pressing need for automated monitoring. Thus, we present a paper that aims to detect anomalies from surveillance videos and identify the point and duration of anomaly occurrence in the video as well. We propose a novel multi-input neural network incorporating spatio-temporal features and dense flow features to train our dataset. We show the ability of our model to capture anomalous content better than baseline approaches. The low false alarm rate shows the resistance of our model to normal videos, despite containing a lot of motion. The introduction of our new standardized dataset also opens scope for further research in this field.
Analytical Review of the Current Curriculum Standards in Information Technologies

The article provides us with an analytical overview of the current state (the beginning of 2018) of the curriculum system for undergraduate and graduate programs in computing (computing is the academic name of the field of Information Technology or IT), inter alia it considers: definition of the concept of curriculum, modern architecture of the curriculum system, construction principles and content of relevant curricula of the last decade, including the following documents: CC2005, CS2013, CE2016, SE2014, GSwE2009, IS2010, MSIS2016, IT2017, CSEC2017. The article provides a comparative analysis of curricula in terms of methodological solutions in describing educational content and learning objectives, highlighting the minimum required part of the curriculum knowledge (core), composition and methods of using didactic parameters for describing pedagogical emphasis on curricula and learning outcomes. The article will be useful for methodologists-developers of curricula and educational standards, in particular for comparing the domestic educational regulatory and methodological base with international experience in standardizing curricula in the field of training IT personnel. The information presented in the article can also be useful to students and graduate students to understand the international structure of the methodological foundations of the modern IT education system.
A Customized Input Device for Simulating the Detection of Hazardous Materials

Although consumer VR controllers work well for gaming and general VR usage, they are not necessarily appropriate for specific professional needs. Instead of attempting to build generalized hand controllers with the goal of satisfying a broad range of users, we built a novel input device for the specific needs of a specific target audience—firefighters. We accomplished this by talking with over 30 firefighters. Their input was in the form of one-on-one discussions, focus groups, questionnaires, and feedback on our work in progress. Based on their input, we 1) identified a specific need— the simulation of detecting hazardous materials, 2) built a physical air monitoring device, that more closely matches firefighters actual air monitoring devices than standard VR controllers, and 3) implemented a hazardous materials scenario that consist of both physical and virtual elements.
Logistic map as a Fourier’s series expansion: numerical analysis

Abstract Logistic map is one of the simplest, and at the same time the most commonly used dynamic system, which is characterized by chaos. The article presents approximations of the logistic map through its extension into the Fourier’s series. Obtained in such way dynamical systems were analyzed, among others for the Lyapunov exponent and bifurcation diagrams. Furthermore the issue of the density of the iterated variable and some applications in chaos based cryptography were commented.
[DC] The Immersive Space to Think: Immersive Analytics for Multimedia Data

Recent improvements in technologies for augmented reality and virtual reality have enabled new ways of performing meaningful sensemaking on research-based tasks. This area of research, immersive analytics, allows for users to offload cognitive processing onto the environment and leverage distributed cognition to more easily extract concepts and themes from many disparate data sources. Immersive analytics has previously been mostly restricted to large abstract numerical or quantitative datasets. However, sensemaking based on text, image, video, or audio is also a cognitively intensive task. I propose to design, develop, and refine software that enables immersive analytics of qualitative data. Furthermore, I propose to research both novice user sensemaking and collaborative sensemaking using this software to better understand users utilize space to offload cognition.
Visual Guidance Methods in Immersive and Interactive VR Environments with Connected 360° Videos

There is emerging research in using 360-degree panoramas in virtual reality (VR) for “360 VR” with choice of navigation and interaction. Unlike standard VR with the freedom of synthetic graphics, there are challenges in designing appropriate user interfaces (UIs) for 360 VR navigation within the limitations of fixed assets. We designed a novel software system called RealNodes that presents an interactive and explorable 360 VR environment. We developed four visual guidance UIs for 360 VR navigation. A comparative study determined choice of UI had a significant effect on task completion times, showing one of the methods, Arrow, was best.
Deep Learning for Modulation Classification: Signal Features in Performance Analysis

An increasing trend towards making the modulation classification (MC) algorithms better-suited for real world implementation in Cognitive Radio (CR) equipment, can be seen as recent works adopt novel agile deep learning models and datasets which include different kinds of signal impairments. Considering the large range of studies in the field, a unifying examination of the common state-of-the-art methods will benefit the future developments by providing a starting point for comparison of their efficiency for recognition of various communication signals. The purpose of this study is to provide a comparative analysis which gives clarity on the ways in which the dataset's type and contents (statistical features of the signals as well as the presence of noise) influences the classification performance. Thus, a more systematic approach to choosing the appropriate input data and a suitable classifier model can be employed in future developments.
Interplay of eustatic, tectonic and autogenic controls on a Late Devonian carbonate platform, northern Canning Basin, Australia

Abstract Frasnian reef complexes along the northern margin of the Canning Basin in northwestern Australia evolved during rifting of the Fitzroy Trough. Geological investigations of the Frasnian Hull platform, which developed on an active tilted fault‐block, reveal significant lateral and vertical facies variations superimposed on prominent metre‐scale cyclicity. This study uses numerical analyses of facies and magnetic susceptibility data from three measured sections along the Hull platform to test whether a tectonic signal can be distinguished from eustatic and other signals. Geostatistical analysis of facies variations reveals an exponential distribution of thin (&lt;3 m) facies, characteristic of stochastic depositional processes. Thick subtidal facies predominate in the Guppy Hills (GH) and southeastern Hull Range (SHR) sections near the hangingwall margin, and thick shallow‐subtidal to intertidal facies dominate the Horse Springs drillcore (HD 14) section near the footwall margin. Power and wavelet spectral analyses indicate a strong periodic component; Average Spectral Misfit and spectral optimisation methods confirm the presence of Milankovitch eccentricity signals and suggest the presence of obliquity and precession signals. However, the results also expose strong temporal and spatial variation providing evidence for tectonic control. Spectral analyses show strongest periodicity is recorded in short intervals that are not correlated across the platform and provide evidence of variations in sedimentation rate and hiatuses. Time series for the neighbouring GH and SHR sections show no overall statistical correlation, and Markov analysis indicates weakly ordered vertical facies transitions that do not correlate across the platform. Subtidal to intertidal facies data from HD 14 core suggest that at least 35% of the section is absent, almost obscuring the Milankovitch signal. The results indicate a complex set of controls on deposition on the Hull platform with local tectonic effects having produced spatio‐temporal moderation of the underlying eustatic signals and autogenic processes adding a localised stochastic response.
Natural user Interfaces for Mixed Reality: Controlling Virtual Objects with your Real Hands

We explore a novel way of interacting in Mixed Reality using jointly hand dynamics and appearance. Our prototype is based on HTC Vive, Leap Motion sensor and ZED Mini stereo camera. Leap Motion allows to track the egocentric hands in 3D. To get the segmented hands, a color-based algorithm is applied to the egocentric images. A user interface consisting on interactive buttons is designed, one with physical props enabling haptic sensation. Tracking of physical objects is implemented using fiducial marker and Aruco library. Results suggest that users find attractive this novel interactive way, specially while using physical prop.