Reviewerly/exHarmony huggingface dataset:
A novel AR application for in-vehicle entertainment and education

Passengers of different transportation means, used for private (travel, commute) or revenue-generating (tours, sightseeing) purposes, usually have restricted view of the outside environment, limited by the vehicle's walls, roof and seats. Even short journeys are boring and tiring while sights cannot be experienced properly. Currently there are no proper ways of overcoming this problem, but merely escaping it, which are not particularly constructive and isolate from co-passengers. In this paper, a novel integrated augmented reality solution is proposed, which can be installed in vehicles of different types and simultaneously used by multiple co-passengers. Using current vehicle location, a live surroundings video, captured by a 360-degree camera, can be overlaid in real time with co-passengers video, on-spot information of touristic value, Points of Interest, tailored advertisements and other messages. Remote, cloud-based technologies can be utilised, which exploit available data sources of different types and sources, in order to offer intelligent added value services. End users can experience an improved reality world by wearing a VR headset and moving their head around. Journeys can become more interesting, educative and entertaining.
Improving the Performance of kNN in the MapReduce Framework Using Locality Sensitive Hashing

In this work the authors present a parallel k nearest neighbor (kNN) algorithm using locality sensitive hashing to preprocess the data before it is classified using kNN in Hadoop's MapReduce framework. This is compared with the sequential (conventional) implementation. Using locality sensitive hashing's similarity measure with kNN, the iterative procedure to classify a data object is performed within a hash bucket rather than the whole data set, greatly reducing the computation time needed for classification. Several experiments were run that showed that the parallel implementation performed better than the sequential implementation on very large datasets. The study also experimented with a few map and reduce side optimization features for the parallel implementation and presented some optimum map and reduce side parameters. Among the map side parameters, the block size and input split size were varied, and among the reduce side parameters, the number of planes were varied, and their effects were studied.
Randomized nonlinear software-oriented MDS diffusion layers

Abstract MDS diffusion layers are critical components in the design of symmetric ciphers. In this paper, after introducing some new algebraic structures, we provide new MDS matrices over special types of R -modules. With the help of the proposed methodology, we have more flexibility in designing software-oriented diffusion layers. Most notably, we construct randomized and/or nonlinear MDS diffusion layers, based upon the presented theoretical results, and discuss the resistance of the presented diffusion layers against various kinds of cryptanalysis, compared with classical linear diffusion layers.
Secret Key Rate Adaption for Multicarrier Continuous-Variable Quantum Key Distribution

Abstract A multicarrier continuous-variable quantum key distribution (CVQKD) protocol uses Gaussian subcarrier quantum continuous variables (CVs) for the transmission. Here, we define an iterative error-minimizing secret key adaption method for multicarrier CVQKD. The proposed method allows for the parties to reach a given target secret key rate with minimized error rate through the Gaussian sub-channels by a sub-channel adaption procedure. The adaption algorithm iteratively determines the optimal transmit conditions to achieve the target secret key rate and the minimal error rate over the sub-channels. The solution requires no complex calculations or computational tools, allowing for easy implementation for experimental scenarios.
Knowledge Graph Consolidation by Unifying Synonymous Relationships

Entity-centric information resources in the form of huge RDF knowledge graphs have become an important part of today’s information systems. But while the integration of independent sources promises rich information, their inherent heterogeneity also poses threats to the overall usefulness. To some degree challenges of heterogeneity have been addressed by creating underlying ontological structures. Yet, our analysis shows that synonymous relationships are still prevalent in current knowledge graphs. In this paper we compare state-of-the-art relational learning techniques to analyze the semantics of relationships for unifying synonymous relationships. By embedding relationships into latent feature models, we are able to identify relationships showing the same semantics in a data-driven fashion. The resulting relationship synonyms can be used for knowledge graph consolidation. We evaluate our technique on Wikidata, Freebase and DBpedia: we identify hundreds of existing relationship duplicates with very high precision, outperforming the current state-of-the-art method.
Hybrid Algorithm for Solving Traveling Salesman Problem

Abstract The basic genetic algorithm has the disadvantages of falling into local optimum and slow convergence. To solve this problem, a hybrid algorithm combining simulated annealing strategy is proposed. The cooling process in simulated annealing is used to complete the iterative process in the hybrid algorithm. The algorithm is used to solve the traveling salesman problem. The results show that the convergence speed and accuracy of the hybrid algorithm is significantly better than the basic genetic algorithm.
Vulgarity Classification in Comments Using SVM and LSTM

Multitudes of textual matter appear daily online. People, possessing the freedom of speech, very often tend to offend the sentiments of readers. Numerous accounts of online harassing, defaming, and bullying prevail in various social networking sites. Posting such content cannot be controlled but thanks to machine learning and deep learning such content can be identified and then removed. Jigsaw and Google have prepared tools to identify such kind of profanity appearing online, but they have not been successful to identify the type of toxicity a comment possesses. Kaggle hence put forth a challenge wherein besides identifying whether a comment is toxic, the comment can be classified into kinds of toxicity. In this challenge, categories like threats, insult, identity hate, and obscenity are taken into consideration. To complete this challenge, various machine learning and deep learning models are applied such as SVM and RNN-LSTM. Our main aim during this challenge is to study the results of using RNN-LSTM for toxic classification. The data is first vectorized using TF-IDF and bag of words. This paper also discusses the nature of the dataset. The results found to give a promising assurance in finding a solution to this problem.
OCR for Greek polytonic (multi accent) historical printed documents

This paper presents the development and implementation of a robust OCR tool and a related comprehensive workflow for the recognition of Greek printed polytonic scripts. This project is initiated and developed by an interdisciplinary team with expertise in the areas of document image processing, character segmentation and recognition, machine learning, corpus creation and digital humanities. Our paper aims to describe the design and development of the workflow around this project, including data gathering and structuring, OCR tool development, user interface development, experiments on the training procedure of the tool, evaluation, post-correction and quality control of the results.
Calculation of Phonetic Distances between Speech Sounds

A new formalism to numerically measure phonetic differences between speech sounds treating feature values of the compared phones as independent parameters that give rise to corresponding Euclidean distances is put forward. The articulatory and acoustic methods within this formalism were compared, where the corresponding results display good agreement. The more reliable and more universal character of the acoustic approach is provided by robust and precise acoustic parameters used therein. The theoretical model and the findings of this article comply also with the experimental phonetic results. The proposed approach contributes to formalization of the procedure of phone comparison and mapping needed for automatic text and speech processing.
Face Recognition on Mobile Devices Based on Frames Selection

The easy image capture process on a phone, the ability to move the camera, the non-intrusive characteristic that allows the authentication without interaction of the user, make the face a suitable biometric trait to be used on mobile devices. During the continuous use of the mobile, it is possible to analyze the expression, to determine the gender and ethnic, or to recognize the user. In this paper we propose a robust algorithm for face recognition on mobile devices. First, the best frames of a face sequence are selected based on the face pose, blurness, eyes and mouth expression. Then, a unique feature vector for the best selected frames is obtained using a deep learning model. Finally, a SoftMax function is used for authenticate the user. The experimental evaluation conducted on the UMD-AA dataset shows the robustness of the proposal, that outperforms state-of-the-art methods.
Pre-editing and post-editing

This chapter provides an accessible introductory view of pre-editing and post-editing as the starting-point for research or work in the language industry. It describes source text pre-editing and machine translation post-editing from an industrial as well as academic point of view. In the last ten to fifteen years, there has been a considerable growth in the number of studies and publications dealing with pre-editing, and especially post-editing, that have helped researchers and the industry to understand the impact machine translation technology has on translators’ output and their working environment. This interest is likely to continue in view of the recent developments in neural machine translation and artificial intelligence. Although the latest technology has taken a considerable leap forward, the existing body of work should not be disregarded as it has defined clear research lines and methods, as it is more necessary than ever to look at data in their appropriate context and avoid generalizing in the vast and diverse territory of human and machine translation.
Application of NeuroIS Tools to Understand Cognitive Behaviors of Student Learners in Biochemistry

Cognitive load has received increased focus as an area that can be more richly explored using neuroIS tools. This research study presents the application of electroencephalography and eye tracking technologies to examine cognitive load of student learners in biochemistry. In addition to leveraging the Pope Engagement Index and eye tracking analysis techniques, we seek better understanding of the relationship that various individual characteristics have with the level of cognitive load experienced. While this study focuses on a particular STEM student population as they manipulate various learning models, it has implications for further studies in human-computer interaction and other learning environments.
Sparse Orthogonal Variational Inference for Gaussian Processes

We introduce a new interpretation of sparse variational approximations for Gaussian processes using inducing points, which can lead to more scalable algorithms than previous methods. It is based on decomposing a Gaussian process as a sum of two independent processes: one spanned by a finite basis of inducing points and the other capturing the remaining variation. We show that this formulation recovers existing approximations and at the same time allows to obtain tighter lower bounds on the marginal likelihood and new stochastic variational inference algorithms. We demonstrate the efficiency of these algorithms in several Gaussian process models ranging from standard regression to multi-class classification using (deep) convolutional Gaussian processes and report state-of-the-art results on CIFAR-10 among purely GP-based models.
Control architecture of a remotely controlled vehicle in extreme CBRNE conditions

In this paper, we present a control architecture for a remotely controlled vehicle. It enables fully autonomous navigation of the vehicle while performing GPS-based waypoint or GPS-based patrolling tasks. The addressed tasks are fundamental for resolving chemical, biological, radiological, nuclear and explosive (CBRNE) threats. The control architecture is based on the Quantum geographic information system (QGIS) and the Robotic Operation System (ROS). QGIS is employed for the implementation of the user interface, whereas for the implementation of the vehicle navigation system, ROS is used. We also present a novel solution for the communication between QGIS and ROS. The execution of the waypoint and patrolling tasks is tested in simulation using the Gazebo simulator and experimentally on a Husky A200 mobile robot.
A Novel Quantum Inspired Sperm Whale Meta-heuristic for Image Thresholding

Thresholding is a renowned, simple and efficient technique/tool in image segmentation. A novel quantum inspired technique is designed on the basis of the lifestyle of sperm whales. The fundamentals of quantum features are combined with a meta-heuristic approach for designing the proposed technique. The efficiency of the proposed technique is judged in comparison with two other techniques for four test images with reference to optimal thresholds at diverse levels, the mean, standard deviation of all fitness values over different runs and the computational time. Furthermore, for all techniques, the quality of image thresholding is also judged by calculating the values of peak signal-to-noise ratio at various levels. Moreover, the supremacy of the proposed technique is statistically established by using Friedman test among all competitors. Finally, the efficiency of the proposed technique is visually exhibited by presenting convergence curves for all test images. Hence, the efficiency is visually and quantitatively established for the proposed technique in all respects.
Further study of 2-to-1 mappings over F<sub>2</sub> <sup>n</sup>

2-to-1 mappings over finite fields play important roles in symmetric cryptography, such as APN functions, bent functions, semi-bent functions and so on. Very recently, Mesnager and Qu [9] provided a systematic study of 2-to-1 mappings over finite fields. Particularly, they determined all 2-to-1 mappings of degree ≤ 4 over any finite fields. In addition, another research direction is to consider 2-to-1 polynomials with few terms. Some results about 2-to-1 monomials and binomials can be found in [9].Motivated by their work, in this present paper, we continue studying 2-to-1 mappings, particularly, over finite fields with characteristic 2. Firstly, we determine 2-to-1 polynomials with degree 5 over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> completely by the Hasse-Weil bound. Besides, using the multivariate method and the resultant of two polynomials, we present two classes of 2-to-1 trinomials and four classes of 2-to-1 quadrinomials over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> .
Pedestrian Detection and Vulnerability Decision in Videos

In this paper, we propose a real-time framework for vulnerable road user detection from outside looking camera mounted on the vehicle. Pedestrians and cyclist are more vulnerable towards accidents on the road with the vehicle, therefore we focus on detecting the pedestrians, tracking them and predicting their vulnerability towards possible accidents with the moving vehicle observing the scene. In our framework, as the vehicle moves, the camera captures the image and pedestrians are detected using the existing deformable parts based model (DPM) with an active part selection approach. Once a pedestrian is detected then for the next n frame we use obtained spatial information in the temporal scene to track the pedestrian that ensures new pedestrians entering the scene as the vehicle moves are also get detected, the DPM is used periodically. Our system predicts the vulnerability of the pedestrians based on their path of movement with respect to the vehicle. The driver is warned if the pedestrians vulnerability of collision is high low or medium. Tracking exploits the spatiotemporal relation between frames, thereby reducing the computational cost and improving the overall runtime performance of the system. Experimental results on publicly available pedestrian dataset show that our system is capable of robustly detection, tracking and finding the vulnerability of the pedestrians to alert the driver for taking the necessary action before any collision or accident occurs.
A Wide-Coverage Symbolic Natural Language Inference System.

We present a system for Natural Language Inference which uses a dynamic semantics converter from abstract syntax trees to Coq types. It combines the fine-grainedness of a dynamic semantics system with the powerfulness of a state-of-the-art proof assistant, like Coq. We evaluate the system on all sections of the FraCaS test suite, excluding section 6. This is the first system that does a complete run on the anaphora and ellipsis sections of the FraCaS. It has a better overall accuracy than any previous system.
On Games and Computation

We introduce and investigate a range of general notions of a game. Our principal notion is based on a set of agents modifying a relational structure in a discrete evolution sequence. We also introduce and study a variety of ways to model partial and erroneous information in the setting. We discuss the connection of the related general setting to logic and computation formalisms, with emphasis on the recently introduced Turing-complete logic based on game-theoretic semantics.
Deep Despeckling of SAR Images

This paper presents despeckling of Synthetic Aperture Radar (SAR) detected data using deep convolutional networks. A discriminative model learning using a deep convolutional neural network (DCNN) was used. A DCNN was used to learn speckle statistical properties to process SAR data. The idea is to use two identical subnetworks, very similar to Siamese deep neural networks , which consisted of 15 layers. The network used a residual learning strategy using a large SAR database. SAR images were multilooked in order to model a noiseless SAR images. Experimental results demonstrated promising results using synthetic and real images.
Performance Analysis of Face Recognition Techniques for Feature Extraction

Due to increasing applications which need security, face is the most natural way to recognize the person. This paper presents the performance analysis of different face recognition techniques. Linear Binary Pattern (LBP), Gabor Wavelet, Histogram of Oriented Gradient (HOG) techniques are used for feature extraction on two standard databases of images i.e., ORL and Yale Face database. Then Principal Component Analysis (PCA) is applied for feature selection with six different distance metric functions, Cosine, Euclidean, Correlation, Cityblock, Spearman and Minkowski for similarity matching of images.
Writing for Patients on the Participatory Web: Heuristics for Purpose-Driven Personas

Background: The participatory web complicates professional communicators' goals of providing accurate, usable, and trustworthy content, especially for health and medical topics. Professionals can better reach their audiences by understanding individuals'purposes for using e-health. Literature review: Previous literature has shown the need for audience analysis in e-health, and has called for personalized, nuanced, and contextualized methods for developing audience-centered content. Professional communicators in e-health can use personas as a strategy to help account for users' diverse, evolving, and extra-institutional purposes in accessing e-health, whether that content is professionally-generated or user-generated. Research questions: 1. What are patients' larger information-seeking contexts? 2. For what purposes do patients use e-health? 3. How can professional communicators leverage this deepened understanding of their audience's purposes to improve their content? Methods: In-depth interviews were conducted with seven community members who self-identified as e-health users. They were asked about their larger health information-seeking practices, specific instances of using e-health, and website preferences. Results: Participants use e-health among other sources including medical professionals. They use an array of e-health sites, including professional and user-generated sites, and have diverse purposes in using that array of sites. Conclusion and implications: The results suggest that professional communicators deepen their audience analysis to account for informational context, emotional context, and the diverse and shifting purposes of their users. Heuristics for professionals are provided to develop purpose-driven personas.
Update on the geophysical expression of the Abra sedimentary replacement Pb-Ag-Cu-Au deposit, Western Australia

SummaryThe Abra sedimentary replacement Pb-Ag-Cu-Au deposit is located in the Paleoproterozoic Edmund Basin, 900km NNE of Perth. Mineralisation at Abra has no surface expression and the deposit was discovered in 1981 by drill testing coincident magnetic and gravity anomaly highs. The deposit is hosted within siliciclastic and carbonate deposits of the Edmund Group and consists of a stratabound apron of lower grade Pb-Ag-Ba mineralisation in a laminated iron oxide and barite altered siltstone unit that overlies a funnel shaped feeder zone of chlorite altered, brecciated and veined carbonaceous siltstone containing high-grade Pb-Ag in the core, transitioning to Pb-Cu and Cu-Au at depth. As at December 2018, the Abra deposit remains unmined and has an estimated resource of 37.4Mt at 7.5% Pb and 18g/t Ag.Mutton and McInerney (1987) and McInerney et al. (1994) described the geophysical expression of Abra, and recent geophysical survey results are presented here. Abra is characterised by discrete anomaly responses in magnetic, gravity and TDEM survey data. A +450nT magnetic anomaly is observed in ground and airborne magnetic data, which is caused by magnetite within the lower part of the stratabound zone. Dense galena, barite and iron oxide mineralisation in the stratabound zone, and galena in the feeder zone, is surrounded by low-density sedimentary host rock, resulting in a +1mGal gravity anomaly. TDEM surveys have resolved massive sulphide mineralisation as EM conductors, and petrophysical testing on core samples show this is mostly caused by galena. Inverted AMT-MT data sections resolved the deposit halo as a conductive anomaly. ZTEM data failed to resolve a distinct anomaly response. DDIP surveying failed to resolve a chargeable anomaly coincident to known high-grade mineralisation, despite significant disseminated sulphide mineralisation occurring within the deposit. An IP chargeability anomaly observed on the southern side of the deposit is thought to be associated with an alteration zone and low-grade disseminated sulphide mineralisation in a fault zone.A 2D seismic reflection survey line resolved the deposit envelope as strong seismic reflectors surrounded by a seismically bland zone, and this is related to the significant density contrast between the high-density stratabound mineralisation in contact with low-density sedimentary host rocks, as the mineralisation and host rock have similar seismic velocities. Passive seismic HVSR surveying resolved the top of Abra as a subtle HVSR response below a flat impedance contrast horizon interpreted as weathered siltstone over diagenetic cemented siltstone.