Reviewerly/exHarmony huggingface dataset:
Protolocalization in the late seventeenth century: translating the English scientific article into French

The standard definition of localization restricts the phenomenon to digital documents, but adaptation of texts for different readerships began long before the digital era. This paper looks at two examples from the late seventeenth century; they are both translations from the Philosophical Transactions to the Journal des Sçavans in 1675. They display a strategy of selective translation, combined in the second case with reorganization of the text. While thematic analysis reveals only minor differences, analysis of process types shows that there is a move towards physical factors at the expense of human involvement. It can be hypothesized that the reason for this derives from the different types of readership of the two periodicals. This phenomenon can be termed “protolocalization”.
Sounding Gay, Speaking as a “Fag”: Auditory Gaydar and the Perception of Reclaimed Homophobic Language

Reclaiming implies that homophobic labels used by gay individuals are perceived as less offensive and their use as empowering speakers. We examined whether listeners in Italy and Britain perceived homophobic labels as “reclaimed” when men’s voices implied their homosexuality. Gay and straight male speakers used neutral or homophobic labels referring to themselves or another. Homophobic labels were offensive when used by straight speakers and when labelling others. Gay speakers self-labelling with homophobic labels were not perceived as more powerful.
Predicting Dialogue Breakdown in Conversational Pedagogical Agents with Multimodal LSTMs

Recent years have seen a growing interest in conversational pedagogical agents. However, creating robust dialogue managers for conversational pedagogical agents poses significant challenges. Agents’ misunderstandings and inappropriate responses may cause breakdowns in conversational flow, lead to breaches of trust in agent-student relationships, and negatively impact student learning. Dialogue breakdown detection (DBD) is the task of predicting whether an agent’s utterance will cause a breakdown in an ongoing conversation. A robust DBD framework can support enhanced user experiences by choosing more appropriate responses, while also offering a method to conduct error analyses and improve dialogue managers. This paper presents a multimodal deep learning-based DBD framework to predict breakdowns in student-agent conversations. We investigate this framework with dialogues between middle school students and a conversational pedagogical agent in a game-based learning environment. Results from a study with 92 middle school students demonstrate that multimodal long short-term memory network (LSTM)-based dialogue breakdown detectors incorporating eye gaze features achieve high predictive accuracies and recall rates, suggesting that multimodal detectors can play an important role in designing conversational pedagogical agents that effectively engage students in dialogue.
The role of the government in enhancing sustainable entrepreneurships

The goal of this paper is to point out the role of government in fostering the framework for sustainable entrepreneurship, with special emphasis on the public policy instruments supporting this scope and provide evidence that the size of government matters in creating the space for entrepreneurships' sustainable development and growth. The paper argues the main hypothesis that the size of a government is a stimulant for entrepreneurships' sustainable development and growth. The government's size is proportionate to the development and growth of entrepreneurships. This paper is built on the analysis and synthesis of scientific literature to describe the phenomenon of government role in enhancing entrepreneurships' sustainable development and growth. The research sample is composed of OECD countries in the period of 2007-2015. The empirical part based on the estimation of VAR model to see if the bigger government ensure sustainability and stimulate entrepreneurship or not.
An Optimal Singular Value Decomposition with LWC-RECTANGLE Block Cipher Based Digital Image Watermarking in Wireless Sensor Networks

Numerous watermarking applications require implanting strategies that supply power against normal watermarking attacks, similar to pressure, noise, sifting, and so on. Dense sending of wireless sensor networks in an unattended situation makes sensor hubs defenseless against potential assaults. With these requests, the confidentiality, integrity and confirmation of the imparted data turn out to be important. This chapter investigated the optimal Singular Value Decomposition (SVD) strategy which was proposed by utilizing the Opposition Grey Wolf Optimization (OGWO) system for image security in WSN. This is a protected method for watermarking through the installed parameters required for the extraction of watermark. The objective function is utilized, at the optimization procedure, through which the greatest attainable robustness and entropy can be attained without debasing the watermarking quality. When the optimal parameters such as ‘K’, ‘L’ and ‘M’ got the images installed with secret data, at one point, the Light Weight Cryptography (LWC)-RECTANGLE block cipher process was used to encrypt and decrypt the watermarked images, transmitted in WSN. This encryption procedure has two critical procedures such as key generation and round function. The adequacy of the proposed strategy was exhibited by comparing the results with traditional procedures with regards to the watermarking performance.
Provably Secure Pairing-Free Identity-Based Restrictive Partially Blind Signature Scheme

The restrictive partially blind signature (RPBS) scheme permits user to explicitly incorporate the common information consulted with signer into the signature, but the choice of message is restricted to certain rules. Many RPBS schemes have been designed by using either under certificate-based public key systems or bilinear pairing. But all these schemes need huge computation and storage of the certificates. In this paper, we propose the first Pairing-Free ID-based RPBS scheme and prove it is secure in the random oracle model. Compared with existing ID-based RPBS schemes, the scheme is simpler, more efficient and offer huge advantages in runtime.
3D Printing: A Remedy to Common Misconceptions about Earthquakes

Research Article| June 12, 2019 3D Printing: A Remedy to Common Misconceptions about Earthquakes Christodoulos Kyriakopoulos Christodoulos Kyriakopoulos Corresponding Author aDepartment of Earth and Planetary Sciences, University of California Riverside, Geology Building, Room 1345A, 900 University Avenue, Riverside, California 92521 U.S.A., christos@ucr.edu Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (4): 1689–1691. https://doi.org/10.1785/0220190121 Article history first online: 12 Jun 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Christodoulos Kyriakopoulos; 3D Printing: A Remedy to Common Misconceptions about Earthquakes. Seismological Research Letters 2019;; 90 (4): 1689–1691. doi: https://doi.org/10.1785/0220190121 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search The implementation of modern technologies in geosciences, and specifically the study of earthquakes, is vital for continued progress in this field. For example, the last 25 yr have seen new technologies such as Interferometric Synthetic Aperture Radar, the Global Positioning System, and light detection and ranging help scientists observe and place new constraints on the seismic cycle over unprecedented time and space scales. But what about our techniques for communicating these advances to the general public? Have we seen a similar improvement in education and outreach, and most importantly, in people’s understanding of earthquake hazard? Are we keeping up with... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
The Beltrami SAR Framework for Multichannel Despeckling

In this paper, a new framework for iterative speckle noise reduction in polarimetric synthetic aperture radar (SAR) data is introduced. Speckle is inherent to all coherent imaging systems and affects SAR imagery in the form of strong intensity variations in pixels with similar backscattering coefficients. This makes the interpretation of SAR data in several applications a difficult task. The proposed framework includes a preprocessing step capable of dealing with noise correlation usually found in single-look data. The general filtering approach is based on the Beltrami flow for denoising manifolds or images painted on manifolds. The principal contribution of this work is to adapt this approach to deal with covariance or coherency matrices instead of optical imagery. The evaluation presented suggests that this approach allows for good spatial and radiometric preservation compared to other state-of-the-art methods. Experiments are performed on the basis of synthetic and real-world experimental data. The validation of the proposed framework is accomplished using two refined error performance measures and the well-known effective number of looks measured. The source code of a parallel implementation of the proposed framework is released under the MPL 2.0 (https://www.mozilla.org/en-US/MPL/2.0/) alongside this paper.
Linked data and semantic web technologies to model context information for policy-making

Currently, several datasets released in a Linked Open Data format are available at a national and international level, but the lack of shared strategies on the representation and meaning of knowledge related to the publishing community makes it difficult to compare and use them. The paper proposes the use of semantic technologies and linked open data in order to ensure standardized frameworks for the representation of concepts in policy-making. The low-level data can thus be transformed into an enriched information model that allows its reuse and a logical reasoning on the knowledge representation.
Efficient PRNG Design and Implementation for Various High Throughput Cryptographic and Low Power Security Applications

Pseudo-Random Number Generators (PRNGs) are an integral part of cryptographic applications, such as key generations, digital signatures, Internet-of-Things (IoT) security, etc. These applications require low-power and high-throughput PRNGs along with statistically secure random numbers capability. In this paper, we propose two PRNGs by combining/modifying Blum-Blum-Shub (BBS), Xorshift and Permuted Congruential PRNGs. The first one is for general purpose applications and the second one is for low-power IoT applications. We implement the proposed PRNGs on FPGA ZedBoard Zynq™-7000. Our implementation results show that the proposed PRNGs generate 4.83 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> and 4.29 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> random numbers per-second, respectively. The total dynamic power consumption of the proposed PRNGs is 17mW at 48.31Mhz and 16mW at 42.90Mhz with a maximum throughput of 184.288MBps and 163.651MBps, respectively. We test the proposed PRNGs on Diehard battery suite and US National Institute of Standard and Technology (NIST) SP 800 - 22 suite for the analysis of randomness quality.
Multi-Robot Coverage Path Planning in 3-Dimensional Environments

Unmanned Vehicles are being used in several application domains, such as mapping, agriculture, and surveillance. In these application domains, the problem of finding a path that covers the entire Area of Interest (AoI) in a predefined environment is known as Coverage Path Planning (CPP). Even though many works have been focused on solving the CPP problem in 2D environments, the CPP problem in 3D environments has not attracted considerable attention. In this paper, we propose an algorithm capable of solving the CPP problem both in 2D and 3D environments. The algorithm can utilize multiple robots tailoring the coverage path for each robot based on its specifications, i.e., speed and type. We have performed an experimental evaluation of the algorithm in artificially synthetic environments and report the results.
Agile Scalability Engineering: The ScrumScale Method

Scalability is a property that must be carefully designed into a system. A case study in the largest Norwegian public portal, Altinn, illustrates how developers and scalability experts improved scalability and spent less time during scalability testing. With minor adjustments to an agile development process, stakeholders spend more up-front time together.
Computer Algorithm for Archaeological Projectile Points Automatic Classification

The manual archaeological projectile point morphological classification is an extensive and complex process since it involves a large number of categories. This article presents an algorithm that automatically makes this process, based on the projectile point digital image and using a classification scheme according to global archaeological approaches. The algorithm supports different conditions such as changes in scale and quality of the image. Moreover, it requires only a uniform background and an approximate north--south projectile point orientation. The principal computer methods that compose the algorithm are the curvature scale space map (CSS-map), the gradient contour on the projectile point, and the support vector machines (SVM) algorithm. Finally, the classifier was trained and tested on a dataset of approximately 800 projectile points images, and the results have shown a better performance than other shape descriptors such as Pyramid of Histograms of Orientation Gradients (PHOG), Histogram of Orientation Shape Context (HOOSC) (both used in a bag-of-words context), and geometric moment invariants (Hu moments).
Enhancing A Laparoscopy Training System with Augmented Reality Visualization

We report work in progress towards a system for laparoscopy training that is enhanced with an augmented reality display. The system can be used to perform peg-transfer training tasks using a Microsoft Hololens AR device, with full 6 degrees of freedom (DoF) shared presence with the pegboard workspace. This mode is in contrast to the conventional visualization, whose views are solely through a 2D or stereo laparoscope. In order to achieve this enhanced visualization, the system extracts the pose of the triangular prism being manipulated, and of the laparoscopic instruments with which the user manipulates the prism. These two objects are added to a CAD model of a (fixed) pegboard, and all the objects are visualized within the AR display. In the near future we expect to conduct user studies measuring accuracy and time-to-completion of this peg-transfer task using these new enhancements and compare it to conventional visualization techniques.
Materializing Interpretability

Interpretability has become a key objective in the research, development and implementation of machine learning algorithms. However, existing notions of interpretability may not be conducive to how meaning emerges in algorithmic systems that employ ML algorithms. In this provocation, we suggest that hermeneutic analysis can be used to probe assumptions in interpretability. First, we propose three levels of interpretability that may be analyzed: formality, achievability, and linearity. Second, we discuss how the three levels have surfaced in prior work, in which we conducted an explicitation interview with a developer to understand decision-making in an algorithmic system implementation. Third, we suggest that design practice may be needed to move beyond analytic deconstruction, and showcase two design projects that exemplify possible strategies. In concluding, we suggest how the proposed approach may be taken up in future work and point to research avenues.
Quality Based Software Architectural Decision Making

Decision making has an important role in the creation of Software Architecture. Such decisions are required throughout the software development life cycle and to attain the quality attributes of a software product. Decisions which are made during architectural design have direct impact on software quality. Many design patterns are available in software architecture and these design patterns help achieve quality attributes in a software product but decision-making for the solution of these design patterns is still an issue. In this paper, a new perspective of selection of design pattern for a given problem is presented. PRIC (Problem, Requirement Identification, Compatible design pattern) model is proposed which helps decision-making of selection of design pattern to attain the required quality attributes. Suitable design patterns are explained in this model for many quality attributes and this model is evaluated with the help of two examples of attaining quality attributes.
How Effective Can Simple Ordinal Peer Grading Be?

Ordinal peer grading has been proposed as a simple and scalable solution for computing reliable information about student performance in massive open online courses. The idea is to outsource the grading task to the students themselves as follows. After the end of an exam, each student is asked to rank—in terms of quality—a bundle of exam papers by fellow students. An aggregation rule then combines the individual rankings into a global one that contains all students. We define a broad class of simple aggregation rules, which we call type-ordering aggregation rules, and present a theoretical framework for assessing their effectiveness. When statistical information about the grading behaviour of students is available (in terms of a noise matrix that characterizes the grading behaviour of the average student from a student population), the framework can be used to compute the optimal rule from this class with respect to a series of performance objectives that compare the ranking returned by the aggregation rule to the underlying ground-truth ranking. For example, a natural rule known as Borda is proved to be optimal when students grade correctly. In addition, we present extensive simulations that validate our theory and prove it to be extremely accurate in predicting the performance of aggregation rules even when only rough information about grading behaviour (i.e., an approximation of the noise matrix) is available. Both in the application of our theoretical framework and in our simulations, we exploit data about grading behaviour of students that have been extracted from two field experiments in the University of Patras.
Ordered classification rules for inverse gaussian populations with unknown parameters

Let Π1,Π2,…,Πk be k (≥ 2) populations with Πi having an inverse Gaussian distribution with unknown mean μi and unknown scale-like parameter λi, respectively. We study the problem of classification of an observation when prior information suggests some orderings on parameters. When the means are equal but unknown, we derive plug-in Bayes classification rules based on the maximum likelihood estimator (MLE), Graybill-Deal type estimator and shrinkage estimator of the common mean. When all parameters are unknown and unequal, we also derive likelihood ratio-based classification rules. For more than two populations, we suggest ordered rules when λis follow an ordering. When the means are unequal, we also derive rules assuming ordering among either λis or μis. Extensive simulations are carried out to compare the proposed rules with respect to expected probabilities of correct classification. Applications of these classification rules are described using real data sets.
Hyperspectral face recognition with minimum noise fraction, histogram of oriented gradient features and collaborative representation-based classifier

Hyperspectral imaging provides new opportunities for improving face recognition accuracy. However, it poses such challenges as difficulty in data acquisition, low signal to noise ratio (SNR), and high dimensionality. In this paper, we propose a novel method for hyperspectral face recognition with g ood recognition rates. We first reduce noise adaptively from each spectral band and then crop each face. We perform minimum noise fraction (MNF) transform to the cropped face data cube in order to extract a number of MNF bands. We extract histogram of oriented gradients (HOG) features from each MNF band. We conducted some experiments to test this new method for hyperspectral face recognition with very promising results. For Hong Kong Polytechnic University Hyperspectral Face Database (PolyU-HSFD), we achieved an average correct recognition rate of 95.4% with standard deviation of 2.6 (95.4% ±2.6). For CMU Hyperspectral Face Database (CMU-HSFD), we achieved an average correct recognition rate of 98.1% with standard deviation of 0.8 (98.1% ±0.8). The reasons why we choose MNF for hyperspectral face recognition are because it can separate noise from fine features in the face data cube and at the same time reduce the dimensionality of the face data cube. In this way, our proposed face recognition method will be faster than those methods without dimensionality reduction.
End-To-End Performance Evaluation of Sensor Fusion and Bias Estimation for Multi-Sensor Hand-Off

A method for evaluation of the end-to-end performance of a hierarchical sensor fusion system with joint track correlation and fusion and bias estimation is presented along with simulation results that illustrate the method. In a track fusion system, the first level of the fusion system performs joint track correlation and fusion with bias estimation for tracks from a subset of the sensors and those fused tracks are handed off to a second-level for joint track correlation and fusion with bias estimation for tracks from the remaining sensors. The simulated scenario includes two sensors at Level 1 and one sensor at Level 2 for the fusion of a total of three sensors. The joint track correlation, fusion, and bias estimation is achieved with Murty's K-best Hypotheses algorithm. Joint track fusion and bias estimation is performed separately for all of the correlation hypotheses, and the hypotheses are scored and re- ranked to find the best. The established metrics of pattern accuracy, pattern consistency, pattern containment, and the probability of correct correlation are used to characterize performance versus track density. A joint track and bias containment metric over a cluster of tracks, pattern complex containment, is developed and proposed for assessing the end-to-end performance after handover to the sensors at Level 2. Monte Carlo simulations were performed to demonstrate and compare the pattern metrics and probability of correct correlation after hand-over for various aspect angles between sensors, various number of targets in the pattern, and various levels of sensor bias. The results show that the pattern complex containment metric matches the chi square threshold probabilities when track-to-track correlation is near perfect, and therefore, it should be a good indicator of successful sensor hand-over performance.