Reviewerly/exHarmony huggingface dataset:
Application of Non-Extensive Statistical Physics on the particle size distribution in natural carbonate fault rocks

This contribution focuses on the results of combined generalized statistical mechanics and microstructural analyses of carbonate fault rock samples, which derive from active extensional fault zones cropping out in peninsular Italy. Specifically, this work is aimed at deciphering the role played by both pre-existing structural elements and structural diagenesis on the dimension and distribution of the largest survivor grains. The studied extensional fault zones were exhumed from shallow crustal depths during the Plio-Quaternary downfaulting of the Apennines. They crosscut Mesozoic platform limestones and dolostones. Despite the different host rocks, all carbonate fault rocks exhibit very similar cataclastic textures but dissimilar pore characteristics. In this work, we apply for the first time the principles of the Non-Extensive Statistical Physics to the study of fault rocks. By considering the Tsallis entropy, which is expressed by the Q-value representing the hierarchical organization of the system, we consider the crossover point value, V0, computed for both 1 cm2 and 9 cm2 representative fault rock images at the transition from Q-exponential to exponential behaviors. As a result, we document that the limestone grain-supported rocks, which mainly localize in the outer fault cores, are always characterized by smaller V0 values with respect to the dolostone ones. These data are interpreted as due to both burial and thrusting-related pressure solution seams, which pervasively formed in the limestone host rocks. Conversely, the amount of V0 variations computed from outer to inner fault cores are associated to the effects of widespread calcite cement precipitation, which mainly occurred in the limestone matrix-supported fault rocks. By considering the weighted, average areas of survivor grains with dimension larger than V0 a as a proxy for sorting, we document an increase from outer to inner fault cores in all the study fault zones. At a close view, all dolomite-rich fault rock samples show very similar median values, whereas the matrix-supported fault rock samples of the inner limestone fault cores are characterized by smaller median values relative to the grain-supported of the limestone outer fault cores. This discrepancy is interpreted as also due to widespread cementation processes, which formed aggregate grains in the fine-grained fault rocks encompassing the main slip surfaces of the limestone fault cores.
Density matrix simulation of quantum error correction codes for near-term quantum devices

Abstract Fault-tolerant quantum computing requires many qubits with long lifetimes and accurate quantum gate operations. However, external noise limits the computing time and hampers accurate quantum gate operations. Quantum error correction (QEC) codes may extend such limits, but imperfect gate operations during QEC cause errors, which could cancel out QEC. We used density matrix simulations to examine the performance of QEC codes with five qubits. In current quantum devices, less than ten qubits are needed to conduct sufficient gate operations within their lifetime so that it is feasible to implement QEC codes. We analyzed the maximum tolerable error rate and error correction effect of individual QEC codes according to the qubit arrangement and gate accuracy. Assuming a 0.1% gate error probability, a logical <?CDATA $| 1\rangle $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">∣</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">〉</mml:mo> </mml:math> state encoded by a five-qubit QEC code is expected to have a 0.25 higher fidelity than its physical counterpart.
Analysis of VR Sickness and Gait Parameters During Non-Isometric Virtual Walking with Large Translational Gain

The combination of room-scale virtual reality and non-isometric virtual walking techniques is promising-the former provides a comfortable and natural VR experience, while the latter relaxes the constraint of the physical space surrounding the user. In the last few decades, many non-isometric virtual walking techniques have been proposed to enable unconstrained walking without disrupting the sense of presence in the VR environment. Nevertheless, many works reported the occurrence of VR sickness near the detection threshold or after prolonged use. There exists a knowledge gap on the level of VR sickness and gait performance for amplified non-isometric virtual walking at well beyond the detection threshold. This paper presents an experiment with 17 participants that investigated VR sickness and gait parameters during non-isometric virtual walking at large and detectable translational gain levels. The result showed that the translational gain level had a significant effect on the reported sickness score, gait parameters, and center of mass displacements. Surprisingly, participants who did not experience motion sickness symptoms at the end of the experiment adapted to the non-isometric virtual walking well and even showed improved performance at a large gain level of 10x.
The State of the Art in Blockchain Research (2013–2018): Scientometrics of the Related Papers in Web of Science and Scopus

Blockchain is relatively a new technology that takes its roots from a distributed data structure shared on a decentralized network. Although the first attempts of the blockchain technology started in developing cryptocurrencies, this technology has provided opportunities for other fields related to the management of security issues and shared resources. Thus, studies on various aspects of the blockchain technology have formed a growing trend in the related literature. The objective of this chapter is to present the current trends, statistics, and relationships from the growing body of literature on blockchain technology. This research is supported by scientometrics, which is the methodology used to analyze data extracted from online scientific databases to obtain the major trends, research agendas, demographics, particular metrics, networks, and understand leading topics. This research is based on papers published in the Web of Science (WoS) and Scopus databases between 2013 and 2018. The intent of this research is to shed light on the holistic view of blockchain literature and to support the researchers and practitioners in this field.
Using Non-Binary LDPC and MDPC Codes in the McEliece Cryptosystem

In this paper we study the use of non-binary Low-Density Parity-Check and Moderate-Density Parity-Check codes for the McEliece cryptosystem. We generalize existing constructions by using codes and errors that are both nonbinary. We devise a decoding technique which is inspired by the binary Bit Flipping decoder and whose complexity grows linearly with the code length. We show that the non-binary schemes are potentially able to reach the same security levels of the binary ones, for equal or smaller key sizes, but with reduced decryption failure rate, the latter being a relevant parameter in view of countering statistical attacks.
A survey paper on keystroke dynamics authentication for current applications

Online learning is a common tool among university students that depends solely on the Internet medium. However, the lack of monitoring during examination sessions has resulted in the rising cases of online cheating among the students. As such, better mechanisms to replace the use of password authentication are needed to enhance the level of security for the online learning assessment. Keystroke dynamics is one of the most popular methods used, because it does not require any extra devices other than the keyboard. In addition, it can be used as a tool to secure online learning system, although the username and password are known to the user. This paper aimed to comprehensively investigate authentication systems, specifically the keystroke dynamic authentication system for current applications. Besides, the applications and the keystroke benchmarking dataset were also reviewed to have a better insight about their security and usability and to improve the level of security in the current online learning environment.
Current Status and Future of Regional Seismic Network Monitoring in the Central and Eastern United States

Abstract The central and eastern United States (CEUS) is an area of generally low-to-moderate seismic hazard with a number of large cities with high seismic risk, a history of occasional damaging earthquakes, and seismic activity induced by wastewater disposal. Seismic monitoring in the CEUS, which began at the beginning 1900s, has undergone many changes through time. Over the past two decades, broadband digital seismic stations connected by internet communications have become widespread. Modern data processing systems to automatically locate earthquakes and assign event magnitudes in near-real time have become the norm, and, since the inception of the Advanced National Seismic System in 2000, more than 10,000 earthquakes have been located and cataloged. Continuously recorded digital seismic data at 100 samples per second are allowing new avenues of research into earthquake source parameters, ground-motion excitation, and seismic wave propagation. Unfortunately, over the past two decades the number of regional seismic network (RSN) centers has diminished due to consolidations and terminations, as funding has tightened. Nevertheless, the public in different parts of the CEUS still looks to local experts for information when earthquakes take place or when they have questions about earthquakes and seismic hazard. The current RSNs must evolve to encompass the need for local seismic information centers and to serve the needs of present and future research into the causes and effects of CEUS earthquakes.
Scheduling Fair Resource Allocation Policies for Cloud Computing through Flow Control

In this short paper, we discuss the problem of resource allocation for cloud computing. The cloud provides a variety of resources for users based on their requirements. Thus, one of the main issues in cloud computing is to design an efficient resource allocation scheme. Each job generated by a user in the cloud has some resource requirements. In this work, we propose a resource allocation method which aims at maximizing the resource utilization and distributing the system’s resources in a fast and fair way, by controlling the flow according to the resources available and by analyzing the dominant demands of each job. Moreover, by parallelizing the computations required, the runtime of the proposed strategy increases linearly as the number of jobs N increases. Here, we present some initial experimental results for small sets of users, that have shown that our strategy allocates the available resources among user jobs in a fair manner, while increasing the overall utilization of each resource.
Circle vector function applied to ray tracing in MCM for calculating effective emissivities of blackbody cavities

The Monte Carlo method (MCM) is widely applied to calculating the effective emissivities of blackbody cavities, one of its works is ray tracing. Considering that most blackbody cavities are composed of revolution walls, we introduce circle vector function into ray tracing in MCM, due to its simplicity and lucidity in dealing with revolution surface. Based on the circle vector function, cavity walls are described in mathematical models, local coordinate systems are built and reflection direction vectors are analyzed. Four typical cavities, including six different cavity walls, are studied as examples. The average normal effective emissivities of the blackbody cavity with an inclined bottom are given as an overall application and the calculated results are in very well agreement with those in references.
The Limitations of Reality

Existing conceptualisations of mixed reality technologies embody a fundamental assumption, that they are a tool to simulate consistent 'realities'. We present three examples of how this assumption constrains designers:
Path-Flyer: A Benchmark of Quadrotor Path Following Algorithms

This paper presents Path-Flyer, a simulation benchmark for testing path following algorithms on a quadrotor vehicle. A complete and realistic mathematical model, based on the real Asctec Hummingbird quadrotor, is described. Wind disturbance and noise on the measured states are also included in the benchmark and can be customized. Four path following algorithms are implemented: Non-Linear Guidance Law (NLGL), Carrot-Chasing and an adaptive version of both algorithms. This benchmark has been prepared to add new algorithms with ease. Experimental tests are compared with the simulation results of Path-Flyer to prove its validity. Path-Flyer is available online.
Trusted Lightweight Communication for IoT Systems Using Hardware Security

This paper explores cutting-edge techniques for protecting cryptographic keys in Internet of Things (IoT) systems based on web protocols. In this context, we evaluated the use of security hardware with application-layer encryption on top of the Constrained Application Protocol (CoAP) for communication between constrained devices and cloud middleware. More precisely, we propose to protect keys against tampering on devices with the help of a secure element and to use memory isolation techniques, such as those provided by Intel CPUs using Software Guard Extension (SGX), on middleware. If properly implemented, this enables privacy-preserving services where even the service provider is unable to decipher exchanged data. Finally, we validated this solution on constrained nodes by measuring performance and energy requirements on an ultra-low-power microcontroller connected to a commercial secure element.
Unsupervised Broadcast News Video Shot Segmentation and Classification

Shot segmentation and categorization is one of the most important and primary steps in building efficient and well sorted video storage and retrieval system. News video story segmentation and commercial detection surely deserve special attention for creation of a well-categorized system as different types of news are being broadcast over a variety of news channels in television. However, in order to garner more viewers, different news channels use different spatial and temporal structures for broadcasting news. Keeping this approach in mind, we present an unsupervised method for shot detection, and subsequently, commercial and news shots are classified. The proposed method does not depend on different temporal and spatial structures adopted by different news channels, and our method needs significantly less number of manual parameters.
Implementation of an Emergency Indicating Line Follower and Obstacle Avoiding Robot

One of the most available robots in applications are the line followers. These robots normally follow either white or a black line. This paper introduces an Autonomous Emergency Indicating Line Follower and Obstacle Avoiding Robot. The robot moves on a specific path determined by the user and detects the obstacle that comes in its way. The vehicle robot stops and diverts its route and returns back to original path, once the obstacle has been overcome. Whenever the robot gets lost from the track, the robot automatically calls to the saved number using a GSM module, to indicate emergency. The robot is achieved through the implementation of many sensors interacting with the controllers. An experimental study of time taken by the robot for different length obstacle as well as for different speed of the robot is also performed.
Salient object detection via double random walks with dual restarts

In this paper, we propose a novel saliency model based on double random walks with dual restarts. Two agents (also known as walkers) respectively representing the foreground and background properties simultaneously walk on a graph to explore saliency distribution. First, we propose the propagation distance measure and use it to calculate the initial distributions of the two agents instead of using geodesic distance. Second, the two agents traverse the graph starting from their own initial distribution, and then interact with each other to correct their travel routes by the restart mechanism, which enforces the agents to return to some specific nodes with a certain probability after every movement. We define the dual restarts to take into account interaction between and weighting of two agents. Extensive evaluations demonstrate that the proposed algorithm performs favorably against other state-of-the-art methods on four benchmark datasets.
Dense Model for Automatic Image Description Generation with Game Theoretic Optimization

Due to the rapid growth of deep learning technologies, automatic image description generation is an interesting problem in computer vision and natural language generation. It helps to improve access to photo collections on social media and gives guidance for visually impaired people. Currently, deep neural networks play a vital role in computer vision and natural language processing tasks. The main objective of the work is to generate the grammatically correct description of the image using the semantics of the trained captions. An encoder-decoder framework using the deep neural system is used to implement an image description generation task. The encoder is an image parsing module, and the decoder is a surface realization module. The framework uses Densely connected convolutional neural networks (Densenet) for image encoding and Bidirectional Long Short Term Memory (BLSTM) for language modeling, and the outputs are given to bidirectional LSTM in the caption generator, which is trained to optimize the log-likelihood of the target description of the image. Most of the existing image captioning works use RNN and LSTM for language modeling. RNNs are computationally expensive with limited memory. LSTM checks the inputs in one direction. BLSTM is used in practice, which avoids the problem of RNN and LSTM. In this work, the selection of the best combination of words in caption generation is made using beam search and game theoretic search. The results show the game theoretic search outperforms beam search. The model was evaluated with the standard benchmark dataset Flickr8k. The Bilingual Evaluation Understudy (BLEU) score is taken as the evaluation measure of the system. A new evaluation measure called GCorrectwas used to check the grammatical correctness of the description. The performance of the proposed model achieves greater improvements over previous methods on the Flickr8k dataset. The proposed model produces grammatically correct sentences for images with a GCorrect of 0.040625 and a BLEU score of 69.96%
SlimML: Removing Non-critical Input Data in Large-scale Iterative Machine Learning

The core of many large-scale machine learning (ML) applications, such as neural networks (NN), support vector machine (SVM), and convolutional neural network (CNN), is the training algorithm that iteratively updates model parameters by processing massive datasets. From a plethora of studies aiming at accelerating ML, being data parallelization and parameter server, the prevalent assumption is that all data points are equivalently relevant to model parameter updating. In this article, we challenge this assumption by proposing a criterion to measure a data point's effect on model parameter updating, and experimentally demonstrate that the majority of data points are non-critical in the training process. We develop a slim learning framework, termed SlimML, which trains the ML models only on the critical data and thus significantly improves training performance. To such an end, SlimML efficiently leverages a small number of aggregated data points per iteration to approximate the criticalness of original input data instances. The proposed approach can be used by changing a few lines of code in a standard stochastic gradient descent (SGD) procedure, and we demonstrate experimentally, on NN regression, SVM classification, and CNN training, that for large datasets, it accelerates model training process by an average of 3.61 times while only incurring accuracy losses of 0.37 percent.
Verifying the Integrity of Hyperlinked Information Using Linked Data and Smart Contracts

We present an approach to verify off-chained information using Linked Data, Smart Contracts, and RDF graph hashes stored on a Distributed Ledger. We use the notion of a Linked Pedigree, i.e. a decentralised dataset for storing hyperlinked information, as modelling foundation. We evaluate our approach by comparing different ways to build the Smart Contract. We develop a cost model and show, based on our implementation, that for managing multiple Linked Pedigree instances, a single larger Smart Contract is superior to multiple smaller Smart Contracts for supply chains shorter than 50 participants.
A Virtual Reality Simulator for Training Gaze Control of Wheeled Tele-Robots

People who cannot use their hands may use eye-gaze to interact with robots. Emerging virtual reality head-mounted displays (HMD) have built-in eye-tracking sensors. Previous studies suggest that users need substantial practice for gaze steering of wheeled robots with an HMD. In this paper, we propose to apply a VR-based simulator for training of gaze-controlled robot steering. The simulator and preliminary test results are presented.
A Bayesian semiparametric Archimedean copula

An Archimedean copula is characterised by its generator. This is a real function whose inverse behaves as a survival function. We propose a semiparametric generator based on a quadratic spline. This is achieved by modelling the first derivative of a hazard rate function, in a survival analysis context, as a piecewise constant function. Convexity of our semiparametric generator is obtained by imposing some simple constraints. The induced semiparametric Archimedean copula produces Kendall’s tau association measure that covers the whole range (−1,1). Inference on the model is done under a Bayesian approach and for some prior specifications we are able to perform an independence test. Properties of the model are illustrated with a simulation study as well as with a real dataset.
Libra Critique Towards Global Decentralized Financial System

The advent of the Bitcoin blockchain as a peer-to-peer, open, and decentralized financial system has empowered millions of people globally to make transactions without any trusted third parties. This realization leads to the birth of more than 2300 cryptocurrencies in the last decade. Despite this evolvement, the Bitcoin and other cryptocurrencies are facing many challenges right now. Facebook's Libra is a new kind of cryptocurrency built on the foundation of blockchain technology conceived by Libra Association - twenty-eight high-profile governing members that will work together to create a more inclusive financial system using consortium blockchain technology. The goal of the Libra is to serve as a solid foundation for a highly scalable, frictionless, and traceable global financial systems with extremely high transactions rate. However, the proposed Libra cryptocurrency has yet to face many issues. In this paper, we critically analyzed the status of the Libra, its underlying concept, and the challenges faced by it once launched as a global decentralized financial system. Currently, the proposed Libra is very ambiguous with uncertain goals and intentions. In particular, we explored the placement technology of Libra, which is not very much well define in comparison with Bitcoin and other public blockchains which are fully decentralized, transparent, borderless and fully open for transparency. Also, we explore the security and privacy features of Libra concerning around three-billion user base of Facebook social media ecosystem (i.e., Messenger, WhatsApp, Instagram, and Facebook).