Reviewerly/exHarmony huggingface dataset:
HISTORICAL PHOTOS AND VISUALIZATIONS: POTENTIAL FOR RESEARCH

Abstract. The interdisciplinary research group on four-dimensional research and communication of urban history (Urban History 4D) aims to investigate and develop methods and technologies to access extensive repositories of historical media and their contextual information in a spatial model, with an additional temporal component. This will make content accessible to different target groups, researchers and the public, via a 4D Browser and an Augmented Reality app for mobile devices. One goal is to improve the accessibility of media repositories and develop suitable solutions for data preparation and information research, making extensive use of visualizations. An interdisciplinary approach is taken to ensure that visualizations for research are comprehensible and meet scientific standards. The investigation of spatial visualizations of image repositories and their visual representation in a coherent context includes frequencies, directions, and perspectives within the image material, which can be grasped through quantitative methods. This paper introduces two main investigations into (1) quantitative data visualization with photography and (2) the plausibility and perception of 3D reconstructions.
Design of Mean Shift Tracking Algorithm Based on Target Position Prediction

For the traditional Mean Shift algorithm in target tracking, the Mean Shift tracking algorithm can not adapt to the scaling, fast motion and occlusion of the target. This paper presents an improved Mean Shift tracking algorithm based on the predicted target position. The algorithm uses inter-frame clustering motion estimation and scale estimation to predict the initial search position of Mean Shift algorithm in each iteration. The algorithm can track fast moving targets while ensuring that the search window can adapt to changes in target scale. When the target is occluded, the target prediction value can be used to track and locate the occluded target. By analyzing the simulation results in complex environments, the improved Mean Shift tracking algorithm can track moving targets in real time quickly and accurately.
A novel approach for emulating quantum computers on classical platforms

Quantum EngineeringVolume 1, Issue 2 e18 RESEARCH HIGHLIGHT A novel approach for emulating quantum computers on classical platforms Tao Xin, Corresponding Author Tao Xin xint@sustech.edu.cn orcid.org/0000-0002-8961-1945 Southern University of Science and Technology, Institute for Quantum Science and Engineering, Shenzhen, China Tao Xin, Southern University of Science and Technology, Institute for Quantum Science and Engineering, Shenzhen 518055, China. Email: xint@sustech.edu.cnSearch for more papers by this author Tao Xin, Corresponding Author Tao Xin xint@sustech.edu.cn orcid.org/0000-0002-8961-1945 Southern University of Science and Technology, Institute for Quantum Science and Engineering, Shenzhen, China Tao Xin, Southern University of Science and Technology, Institute for Quantum Science and Engineering, Shenzhen 518055, China. Email: xint@sustech.edu.cnSearch for more papers by this author First published: 06 June 2019 https://doi.org/10.1002/que2.18Citations: 7Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume1, Issue2June 2019e18 RelatedInformation
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes

We propose a novel method for computing exact pointwise robustness of deep neural networks for all convex lp norms. Our algorithm, GeoCert, finds the largest lp ball centered at an input point x0, within which the output class of a given neural network with ReLU nonlinearities remains unchanged. We relate the problem of computing pointwise robustness of these networks to that of computing the maximum norm ball with a fixed center that can be contained in a non-convex polytope. This is a challenging problem in general, however we show that there exists an efficient algorithm to compute this for polyhedral complices. Further we show that piecewise linear neural networks partition the input space into a polyhedral complex. Our algorithm has the ability to almost immediately output a nontrivial lower bound to the pointwise robustness which is iteratively improved until it ultimately becomes tight. We empirically show that our approach generates a distance lower bounds that are tighter compared to prior work, under moderate time constraints.
Cybersecurity Education: From Beginners to Advanced Players in Cybersecurity Competitions

Cybersecurity competitions have been shown to be an effective approach for promoting student engagement through active learning in cybersecurity. Players can gain hands-on experience in puzzle-based or capture-the-flag type tasks that promote learning. However, novice players with limited prior knowledge in cybersecurity usually found difficult to have a clue to solve a problem and get frustrated at the early stage. To enhance student engagement, it is important to study the experiences of novices to better understand their learning needs. To achieve this goal, we conducted a 4-month longitudinal case study which involves 11 undergraduate students participating in a college-level cybersecurity competition, National Cyber League (NCL) competition. The competition includes two individual games and one team game. Questionnaires and in-person interviews were conducted before and after each game to collect the players' feedback on their experience, learning challenges and needs, and information about their motivation, interests and confidence level. The collected data demonstrate that the primary concern going into these competitions stemmed from a lack of knowledge regarding cybersecurity concepts and tools. Players' interests and confidence can be increased by going through systematic training.
EFFECT OF QUIZIZZ TOWARDS THE ELEVENTH-GRADE ENGLISH STUDENTS’ READING COMPREHENSION IN MOBILE LEARNING CONTEXT

The research’s aim was to investigate the effect of Quizizz towards the eleventh grade students’ English study. A quasi-experimental utilizing a post-test only control group was employed for data gathering. Research’s population included eleventh-grade students in SMA Negeri 4 Singaraja. The sample was 37 students of XI IPS 1 as the experimental group who were treated through Quizizz and 36 students of XI IPS 2 as the control group who were treated through conventional teaching strategy. The method of data collection employed multiple choice test. The result showed scores of experimental group (M=83.08, p value=0.018) and control group (M=80.77, p value= 0.018). Mean score of experimental group was higher than the control group (83.08&gt;80.77) and the p value was 0.018 or less than 0.05. The small effect was supported by result specification of reading comprehension text dominated by literal recognition aspects. In conclusion, Quizizz affected students reading comprehension.
Acoustic-to-Phrase Models for Speech Recognition

Directly emitting words and sub-words from speech spectrogram has been shown to produce good results using end-to-end (E2E) trained models. Connectionist Temporal Classification (CTC) and Sequence-to-Sequence attention (Seq2Seq) models have both shown better success when directly targeting words or sub-words. In this work, we ask the question: Can an E2E model go beyond words and transcribe directly to phrases (i.e., a group of words)? Directly modeling frequent phrases might be better than modeling its constituent words. Also, emitting multiple words together might speed up inference in models like Seq2Seq where decoding is inherently sequential. To answer this, we undertake a study on a 3400-hour Microsoft Cortana voice assistant task. We present a side-by-side comparison for CTC and Seq2Seq models that have been trained to target a variety of tokens including letters, sub-words, words and phrases. We show that an E2E model can indeed transcribe directly to phrases. We see that while CTC has difficulty in accurately modeling phrases, a more powerful model like Seq2Seq can effortlessly target phrases that are up to 4 words long, with only a reasonable degradation in the final word error rate.
A simplified methodology to estimate solar irradiance and atmospheric turbidity from ambient temperature and relative humidity

Knowledge of atmospheric turbidity and solar energy resources is essential for optimum planning and operation of solar-powered processes, cleaning of solar devices, energy saving and global warming monitoring. To determine the atmospheric turbidity and solar irradiance, scientists commonly refer to three methods, which are based on ground and/or space instruments. The drawback of these methods is that they do not provide any information about the turbidity of the atmosphere and the intensity of solar irradiance when the instruments are not available. The present study provides an alternative method to quantify the atmospheric turbidity and solar irradiance from measured data of ambient temperature and relative humidity. To overcome the complexity associated with the existing techniques, simple expressions have been proposed to estimate Precipitable Water (PW), Linke's turbidity factor (TL) and aerosols transparency coefficient (k). High quality radiometric databases, recorded at PSDA station, located in the Atacama Desert, Chile, are used to validate the new method. The proposed PW's expression has shown a Mean Bias Error (MBE) of 4.19%, better than the one used in REST2v5 solar radiation code, which has a MBE of 8.06%. Besides, the new method introduced herein, estimates TL with a MBE of 1.31%, which corresponds to a Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 7.64% and 10.22% respectively. The method has successively predicted the Direct Normal Irradiance (DNI) with a high accuracy. Indeed, the average deviation (MBE) from the measured DNI is less than 1%, which corresponds to a MAE and RMSE of 3.29% and 5.21% respectively. The careful analysis of the results reveals strong influence of the atmospheric turbidity's uncertainty on the prediction uncertainty of DNI.
Application of fuzzy clustering for text data dimensionality reduction

Large textual corpora are often represented by the document-term frequency matrix whose elements are the frequency of terms; however, this matrix has two problems: sparsity and high dimensionality. Four dimension reduction strategies are used to address these problems. Of the four strategies, unsupervised-feature transformation (UFT) is a popular and efficient strategy to map the terms to a new basis in the document-term frequency matrix. Although several UFT-based methods have been developed, fuzzy clustering has not been considered for dimensionality reduction. This research explores fuzzy clustering as a new UFT-based approach to create a lower-dimensional representation of documents. Performance of fuzzy clustering with and without using global term weighting methods is shown to exceed principal component analysis and singular value decomposition. This study also explores the effect of applying different fuzzifier values on fuzzy clustering for dimensionality reduction purpose.
Energy-Saver Mobile Manipulator Based on Numerical Methods

The work presents the kinematic and dynamic control of a mobile robotic manipulator system based on numerical methods. The proposal also presents the curvature analysis of a path not parameterized in time, for the optimization of energy consumption. The energy optimization considers two aspects: the velocity of execution in curves and the amount of movements generated by the robotic system. When a curve occurs on the predefined path, the execution velocity is analyzed throughout the system in a unified method to prevent skid effects from affecting the mobile manipulator, while the number of movements is limited by the redundancy presented by the robotic system to optimize energy use. The experimental results are shown to validate the mechanical and electronic construction of the system, the proposed controllers, and the saving of energy consumption.
Grey Wolf Optimized Task Scheduling Algorithm in Cloud Computing

Cost has become the crucial feature for explaining any type of technology efficiently. It is an important aspect for designing any technology. Cloud computing has become the main stage factor of technology by its behavior. Due to its incredible applications and services, figure of choices are growing with time. Therefore, task scheduling algorithms are needed for better execution of any activity. Scheduling is done by appointment of the activities to the machine to run in a better manner. Nowadays users become greedier by changing their demands frequently with enhancement of the technologies provided through the cloud service provider by fruity organization. There are lots of scheduling algorithms based on some priority/nonpriority mechanism to perform a task. To apply filtering in the selection for best scheduling algorithm, a new technique is proposed based on the nature of grey wolf. Comparative study is also considered in this paper with this new optimizing technique. Evaluation is done based on cost factor. Quality of goodness is calculated by the experiments through CloudSim simulator.
Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit

Challenges in accurate tumor detection paves the way to haste the improvement of solid kidney tumor semantic segmentation methodologies. Accurate segmentation of kidney tumor in computer tomography (CT) images is a challenging task due to the non-uniform motion, similar appearance and various shape. Inspired by this fact, in this manuscript, we present a novel kidney tumor segmentation method using deep learning network termed as hyper vision Net model. All the existing U-net models are using a modified version of U-net to segment the kidney tumor region. In the proposed architecture, we introduced supervision layers in the decoder part, and it refines even minimal regions in the output. A dataset consists of real arterial phase abdominal CT scans of 300 patients, including 45964 images has been provided from KiTs19 for training and validation of the proposed model. Compared with the state-of-the-art segmentation methods, the results demonstrate the superiority of our approach on training dice value score of 0.9552 and 0.9633 in tumor region and kidney region, respectively.
Assessing Variability in Reading Performance with the New Greek Standardized Reading Speed Texts (IReST)

SIGNIFICANCE This article evaluates the standardized Greek version of the International Reading Speed Texts (IReST) set, which enriches interlanguage comparisons and international clinical studies of reading performance. Moreover, it investigates how specific textual and subject-related characteristics modulate the variability of reading speed across texts and readers. PURPOSE The purpose of this study was to develop a standardized Greek version of the IReST set and investigate how specific textual and subject-related factors modulate the variability of reading speed across texts and readers. METHODS The English IReST texts were translated to Greek and matched for length, content, and linguistic difficulty. The Greek IReSTs were presented at a distance of 40 cm and size of 1 M to assess reading speeds of 25 normally sighted native speakers (age range, 18 to 35 years). The participants read the texts aloud while reading time was measured by stopwatch. Reading performance included measurement of reading speed in three units of analysis. Reading efficiency was assessed using a word-level oral reading task. Statistical analysis included evaluation of subject- and text-related variability, as well as correlations between reading speed and specific textual and subject-related factors. RESULTS The average reading speed between texts was 208 ± 24 words/min, 450 ± 24 syllables/min, and 1049 ± 105 characters/min. Differences between readers accounted for the 76.6%, whereas differences across texts accounted for the 23.4% of the total variability of reading speed. Word length (in syllables per word) and median word frequency showed a statistically significant contribution to the variability of reading speed ( r = 0.95 and 0.70, respectively). Reading speed was also statistically correlated with word reading efficiency ( r = 0.68). CONCLUSIONS The addition of the Greek version in the IReST language pack is expected to be a valuable tool for clinical practice and research, enriching interlanguage comparisons and international studies of reading performance.
Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms

We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures. We highlight advantages and limitations of each method and discuss their implementation for parallel computing architectures. We quantify the relative prediction accuracy of these algorithms for the longterm forecasting of chaotic systems using as benchmarks the Lorenz-96 and the Kuramoto-Sivashinsky (KS) equations. We find that, when the full state dynamics are available for training, RC outperforms BPTT approaches in terms of predictive performance and in capturing of the long-term statistics, while at the same time requiring much less training time. However, in the case of reduced order data, large scale RC models can be unstable and more likely than the BPTT algorithms to diverge. In contrast, RNNs trained via BPTT show superior forecasting abilities and capture well the dynamics of reduced order systems. Furthermore, the present study quantifies for the first time the Lyapunov Spectrum of the KS equation with BPTT, achieving similar accuracy as RC. This study establishes that RNNs are a potent computational framework for the learning and forecasting of complex spatiotemporal systems.
Eye Movement Recordings in Natural Settings

In this chapter we consider why it is important to study eye movements in natural, real world settings and the practical considerations that need to be taken into account when collecting eye movement data in these situations. Conducting a study in a real world environment poses very different challenges than those present in laboratory-based settings. Variables are hard to control and this restricts the kinds of paradigms that can be employed. Thus, careful consideration of the research question and whether it is appropriate for real world study is a necessary first step. Mobile eye trackers are often the most obvious choice for studying eye movements in real world settings. There exists a range of mobile eye tracking systems available to buy, and researchers also build their own mobile eye trackers to solve particular issues. Selecting the right system is an important consideration for mobile eye tracking and in this chapter we will highlight some of the key choices that the researcher should consider when buying or building a mobile eye tracker. Care must be taken to ensure that the data collected are sufficiently reliable to address the research question. While the principles of eye tracking - how we detect features of the eye and use these to estimate where someone is looking - are very similar for all eye trackers, the challenges faced when collecting data in natural settings are far greater than those that face eye trackers in a controlled laboratory setting. We will consider the key threats that real world settings pose for collecting reliable eye tracking data in the hope that we not only raise awareness of these issues but also offer some suggestions for how these can be minimised or at least identified in the data. For those considering whether to run an eye tracking study in the real world, we hope that this chapter will help you as you consider your research questions and choices of mobile eye tracker. For those already committed to studying eye movement behaviour in a real world setting, we hope that our discussion of the problems and possible solutions to the challenges faced in such settings will help minimise data loss from the common challenges that we face when conducting mobile eye tracking studies.
A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification

Video forgery has been increasing over the years due to the wide accessibility of sophisticated video editing software. A highly accurate and automated video forgery detection system will therefore be vitally important in ensuring the authenticity of forensic video evidences. This article proposes a novel Triangular Polarity Feature Classification (TPFC) video forgery detection framework for video frame insertion and deletion forgeries. The TPFC framework has high precision and recall rates with a simple and threshold-less algorithm designed for real-world applications. System robustness evaluations based on cross validation and different database recording conditions were also performed and validated. Evaluation on the performance of the TPFC framework demonstrated the efficacy of the proposed framework by achieving a recall rate of up to 98.26% and precision rate of up to 95.76%, as well as high localization accuracy on detected forged videos. The TPFC framework is further demonstrated to be capable of outperforming other modern video forgery detection techniques available today.
Protecting Personal Data Using Smart Contracts

Decentralized Online Social Networks (DOSNs) have been proposed as an alternative solution to the current centralized Online Social Networks (OSNs). Online Social Networks are based on centralized architecture (e.g., Facebook, Twitter, or Google+), while DOSNs do not have a service provider that acts as central authority and users have more control over their information. Several DOSNs have been proposed during the last years. However, the decentralization of the OSN requires efficient solutions for protecting the privacy of users, and to evaluate the trust between users. Blockchain represents a disruptive technology which has been applied to several fields, among these also to Social Networks. In this paper, we propose a manageable, user-driven and auditable access control framework for DOSNs using blockchain technology. In the proposed approach, the blockchain is used as a support for the definition of privacy policies. The resource owner uses the public key of the subject to define flexible role-based access control policies, while the private key associated with the subjects Ethereum account is used to decrypt the private data once access permission is validated on the blockchain. We evaluate our solution by exploiting the Rinkeby Ethereum testnet to deploy the smart contract, and to evaluate its performance. Experimental results show the feasibility of the proposed scheme in achieving auditable and user-driven access control via smart contract deployed on the Blockchain.
IMSurReal: IMS at the Surface Realization Shared Task 2019

We introduce the IMS contribution to the Surface Realization Shared Task 2019.Our submission achieves the state-of-the-art performance without using any external resources.The system takes a pipeline approach consisting of five steps: linearization, completion, inflection, contraction, and detokenization.We compare the performance of our linearization algorithm with two external baselines and report results for each step in the pipeline.Furthermore, we perform detailed error analysis revealing correlation between word order freedom and difficulty of the linearization task.