Reviewerly/exHarmony huggingface dataset:
FRD-CNN: Object detection based on small-scale convolutional neural networks and feature reuse

Abstract Most of the recent successful object detection methods have been based on convolutional neural networks (CNNs). From previous studies, we learned that many feature reuse methods improve the network performance, but they increase the number of parameters. DenseNet uses thin layers that have fewer channels to alleviate the increase in parameters. This motivated us to find other methods for solving the increase in model size problems introduced by feature reuse methods. In this work, we employ different feature reuse methods on fire units and mobile units. We solved the problem and constructed two novel neural networks, fire-FRD-CNN and mobile-FRD-CNN. We conducted experiments with the proposed neural networks on KITTI and PASCAL VOC datasets.
Facebook AI’s WAT19 Myanmar-English Translation Task Submission

This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task (Nakazawa et al., 2019).Our baseline systems are BPE-based transformer models.We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination.We further improve results by using noisy channel re-ranking and ensembling.We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset.Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system.
A phygital approach to cultural heritage: augmented reality at Regaleira

&lt;p&gt;In the last two decades, contemporary society has been conceptualized and discussed around the assumption that digital technologies are its most defining feature. Jean Baudrillard’s take on the Fable of Borges suggests a replacement of the physical world by a virtual experiencing life, setting the tone for a pessimistic and somewhat fearful attitude towards technological development. On the verge of the dissemination of disruptive innovations that can be described under the “umbrella” of Internet of Things (IoT), we observe not a replacement, but a deep and complex intertwining of online and offline experiences, sketching the contours of what can become a phygital society. This alternative conceptualization of our contemporary society, combined with the possibilities of technological development, affords new opportunities for the expression and experiencing of culture, and also for the creative industries. In our research, we explore the impact of one augmented reality (AR) app designed for one of the landmarks of the Portuguese cultural heritage, Quinta da Regaleira. We explored this single-case study using qualitative data collection techniques, aiming to discuss the positive and negative outcomes of this innovation, both for the dynamizing of cultural heritage, and for the visitors. The article provides useful information for heritage spaces that wish to follow this path. It describes the main steps to be taken in the production of content and provides examples of the narrative model that helps people to visit the space and obtain information, knowing stories relevant to the past and present of the cultural heritage. The objective is to clarify, finally, due to the research developed in the Quinta da Regaleira case study, what kind of stories and experiences can be narrated, the strengths of this application, its limitations, and paths for future investigations.&lt;/p&gt;
Multiuser Time-Energy Entanglement Swapping Based on Dense Wavelength Division Multiplexed and Sum-Frequency Generation

Perfect entanglement swapping, which can be realized without the postselection by using the nonlinear optical technology, provides an important way toward generating the large-scale quantum network. We explore an entanglement-swapping-based dense wavelength division multiplexed network in the experiment. Four users receive single quantum states at different wavelengths, and we perform a time-energy entanglement swapping operation based on the sum-frequency generation to make users fully connected in the network. The results show that the fidelity of the entangled state is larger than 90% and is independent of the number of users. Our Letter demonstrates the feasibility of a proposed multiuser network, and hence paves a route toward a variety of quantum applications, including entanglement-swapping-based quantum direct communication.
Heroes or Hooligans? Media Portrayal of StopXam (Stop a Douchebag) Vigilantes in Russia

Several state-supported digital vigilante groups emerged in Russia at the downturn of the pro-Kremlin youth group Nashi (Ours), when its former commissars formed issuespecific movements to counter perceived legal and moral offences through exposure of targets on social media. One of such groups is StopXam (Stop a Douchebag), specializing in road-traffic and parking violations. StopXam participants confront the drivers and retaliate by placing stickers that read “I spit on everyone I drive where I want” on the targets’ windshields. The retaliation is often accompanied by verbal and physical fighting; the process is filmed, edited, and shared on YouTube, receiving millions of views. While digital media made such practices possible, traditional broadcasters maintain significance in rendering meaning to the phenomenon of vigilantism and in framing vigilantes, their targets, police, and other actors. As the existing literature on digital vigilantism is predominantly focused on digital media affordances, this article aims to address this gap through a qualitative analysis of traditional media coverage of StopXam. Media analysis highlights intriguing nuances of traditional media as a powerful actor and discourse setter in the digital age. Article in English DOI: 10.25285/2078-1938-2019-11-3-16-45
Sign Language Recognition Analysis using Multimodal Data

Voice-controlled personal and home assistants (such as the Amazon Echo and Apple Siri) are becoming increasingly popular for a variety of applications. However, the benefits of these technologies are not readily accessible to Deaf or Hard-of-Hearing (DHH) users. The objective of this study is to develop and evaluate a sign recognition system using multiple modalities that can be used by DHH signers to interact with voice-controlled devices. With the advancement of depth sensors, skeletal data is used for applications like video analysis and activity recognition. Despite having similarity with the well-studied human activity recognition, the use of 3D skeleton data in sign language recognition is rare. This is because unlike activity recognition, sign language is mostly dependent on hand shape pattern. In this work, we investigate the feasibility of using skeletal and RGB video data for sign language recognition using a combination of different deep learning architectures. We validate our results on a large-scale American Sign Language (ASL) dataset of 12 users and 13107 samples across 51 signs. It is named as GMU-ASL51. We collected the dataset over 6 months and it will be publicly released in the hope of spurring further machine learning research towards providing improved accessibility for digital assistants.
Lightweight Data-Security Ontology for IoT

Although current estimates depict steady growth in Internet of Things (IoT), many works portray an as yet immature technology in terms of security. Attacks using low performance devices, the application of new technologies and data analysis to infer private data, lack of development in some aspects of security offer a wide field for improvement. The advent of Semantic Technologies for IoT offers a new set of possibilities and challenges, like data markets, aggregators, processors and search engines, which rise the need for security. New regulations, such as GDPR , also call for novel approaches on data-security, covering personal data. In this work, we present DS4IoT, a data-security ontology for IoT, which covers the representation of data-security concepts with the novel approach of doing so from the perspective of data and introducing some new concepts such as regulations, certifications and provenance, to classical concepts such as access control methods and authentication mechanisms. In the process we followed ontological methodologies, as well as semantic web best practices, resulting in an ontology to serve as a common vocabulary for data annotation that not only distinguishes itself from previous works by its bottom-up approach, but covers new, current and interesting concepts of data-security, favouring implicit over explicit knowledge representation. Finally, this work is validated by proof of concept, by mapping the DS4IoT ontology to the NGSI-LD data model, in the frame of the IoTCrawler EU project.
Local preserving logistic I-Relief for semi-supervised feature selection

Logistic I-Relief (LIR), a supervised feature selection method, has been successfully extended for semi-supervised feature selection. However, exiting semi-supervised methods based on LIR are incapable of maintaining the local structure of data. In this paper, we propose a local preserving logistic I-Relief (LPLIR) algorithm for multi-class semi-supervised feature selection. The goal of LPLIR is to maximize the expected margin of the given labeled data and remain the local structural information of all given data, where the local structural information is represented by a local preserving regularization. Additionally, extensive experiments conducted on real-world datasets demonstrate that LPLIR is superior to the existing state-of-the-art semi-supervised feature selection methods.
Efficient image encryption using two-dimensional enhanced hyperchaotic Henon map

We develop a two-dimensional enhanced hyperchaotic Henon map (2D-EHHM) to overcome the problems of small chaotic range and poor security when the 2-D traditional Henon map is implemented in cryptosystems. The performance evaluations show that the 2D-EHHM has wider chaotic range, higher chaotic complexity, better ergodicity, and hyperchaotic behavior compared with certain existing chaotic maps. Based on the 2D-EHHM, we further design an efficient image encryption algorithm consisting of a multiple block substitution stage (MBSS) and a bidirectional-dynamic diffusion stage (BDDS). In the MBSS, a plain image is divided into several nonoverlapping multiple blocks to carry out permutation operation. In the BDDS, the scrambled image is redivided into nonoverlapping sub-blocks of the same size to be diffused dynamically in the forward and backward directions. Moreover, the SHA 512 function is employed to obtain a 512-bit plain image hash value treated as a raw key. A variable-length secret key (at least 128 bits), which is dynamically selected from the raw key and regarded as a valid key, is utilized to generate the initial values for the chaotic system. Simulation results and security analysis show that the proposed algorithm can resist various cryptanalytic attacks and can be applied to real-time data transmission.
E-mail-Based Phishing Attack Taxonomy

The amount of fraud on the Internet is increasing along with the availability and the popularity of the Internet around the world. One of the most common forms of Internet fraud is phishing. Phishing attacks seek to obtain a user’s personal or secret information. The variety of phishing attacks is very broad, and usage of novel, more sophisticated methods complicates its automated filtering. Therefore, it is important to form up-to-date and detailed phishing attack taxonomy, which could be used for both human education purposes as well as phishing attack discrete notation. In this paper, we propose an e-mail-based phishing attack taxonomy, which includes six phases of the attack. Each phase has at least one criterion for the attack categorization. Each category is described, and in some cases the categories have sub-classes to present the full variety of phishing attacks. The proposed taxonomy is compared to similar taxonomies. Our taxonomy outperforms other phishing attack taxonomies in numbers of phases, criteria and distinguished classes. Validation of the proposed taxonomy is achieved by adapting it as a phishing attack notation for an incident management system. Taxonomy usage for phishing attack notation increases the level of description of phishing attacks compared to free-form phishing attack descriptions.
Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach

This study introduced a detection method for magnesite and associated gangue minerals, including dolomite, calcite, and talc, based on mineralogical, chemical, and hyperspectral analyses using hand samples from thirteen different source locations and Specim hyperspectral short wave infrared (SWIR) hyperspectral images. Band ratio methods and logistic regression models were developed based on the spectral bands selected by the random forest algorithm. The mineralogical analysis revealed the heterogeneity of mineral composition for naturally occurring samples, showing various carbonate and silicate minerals as accessory minerals. The Mg and Ca composition of magnesite and dolomite varied significantly, inferring the mixture of minerals. The spectral characteristics of magnesite and associated gangue minerals showed major absorption features of the target minerals mixed with the absorption features of accessory carbonate minerals and talc affected by mineral composition. The spectral characteristics of magnesite and dolomite showed a systematic shift of the Mg-OH absorption features toward a shorter wavelength with an increased Mg content. The spectral bands identified by the random forest algorithm for detecting magnesite and gangue minerals were mainly associated with spectral features manifested by Mg-OH, CO3, and OH. A two-step band ratio classification method achieved an overall accuracy of 92% and 55.2%. The classification models developed by logistic regression models showed a significantly higher accuracy of 98~99.9% for training samples and 82–99.8% for validation samples. Because the samples were collected from heterogeneous sites all over the world, we believe that the results and the approach to band selection and logistic regression developed in this study can be generalized to other case studies of magnesite exploration.
CAiRE-COVID: A Question Answering and Multi-Document Summarization System for COVID-19 Research.

To address the need for refined information in COVID-19 pandemic, we propose a deep learning-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization for mining the available scientific literature. Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query. Fluent summaries are also provided to help understand the content in a more efficient way. In this paper, we describe our CAiRE-COVID system architecture and methodology for building the system. To bootstrap the further study, the code for our system is available at this https URL
The impact of knowledge diffusion on economic growth across countries

The aim of this paper is to examine theoretically and empirically the impact of improvement in the capability to access innovation and knowledge on economic growth and convergence among countries. Our results, obtained using an augmented Solow-Swan growth model and panel data methodology with a sample of 138 countries for the period 1990–2014, show not only that improvement in the capability to access innovation and knowledge (proxied by percentage of internet users) has a positive impact on said economic growth, fostering the technological catch-up process for developing countries and accelerating the pattern of economic convergence, but also that this improvement has an even greater effect on developing countries when democracy is well developed, accelerating the pattern of economic convergence even further.
Lane detection in dense fog using a polarimetric dehazing method

Lane detection is crucial for driver assistance systems. However, road scenes are severely degraded in dense fog, which leads to the loss of robustness of many lane detection methods. For this problem, an end-to-end method combining polarimetric dehazing and lane detection is proposed in this paper. From images with dense fog captured by a vehicle-mounted monochrome polarization camera, the darkest and brightest images are synthesized. Then, the airlight degree of polarization is estimated from angle of polarization, and the airlight is optimized by guided filtering to facilitate lane detection. After dehazing, the lane detection is carried out by a Canny operator and Hough transform. Having helped achieve good lane detection results in dense fog, the proposed dehazing method is also adaptive and computationally efficient. In general, this paper provides a valuable reference for driving safety in dense fog.
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks

Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI (XAI) approaches are not applicable. To a large extent, GNNs have remained black-boxes for the user so far. In this paper, we contribute by proposing a new XAI approach for GNNs. Our approach is derived from high-order Taylor expansions and is able to generate a decomposition of the GNN prediction as a collection of relevant walks on the input graph. We find that these high-order Taylor expansions can be equivalently (and more simply) computed using multiple backpropagation passes from the top layer of the GNN to the first layer. The explanation can then be further robustified and generalized by using layer-wise-relevance propagation (LRP) in place of the standard equations for gradient propagation. Our novel method which we denote as 'GNN-LRP' is tested on scale-free graphs, sentence parsing trees, molecular graphs, and pixel lattices representing images. In each case, it performs stably and accurately, and delivers interesting and novel application insights.
What Question Answering can Learn from Trivia Nerds

Question answering (QA)is not just building systems; this NLP subfield also creates and curates challenging question datasets that reveal the best systems.We argue that QA datasets-and QA leaderboards-closely resemble trivia tournaments: the questions agents-humans or machines-answer reveals a "winner".However, the research community has ignored the lessons from decades of the trivia community creating vibrant, fair, and effective QA competitions.After detailing problems with existing QA datasets, we outline several lessons that transfer to QA research: removing ambiguity, identifying better QA agents, and adjudicating disputes.
Automatic multi-needle localization in ultrasound images using large margin mask RCNN for ultrasound-guided prostate brachytherapy

Multi-needle localization in ultrasound (US) images is a crucial step of treatment planning for US-guided prostate brachytherapy. However, current computer-aided technologies are mostly focused on single-needle digitization, while manual digitization is labor intensive and time consuming. In this paper, we proposed a deep learning-based workflow for fast automatic multi-needle digitization, including needle shaft detection and needle tip detection. The major workflow is composed of two components: a large margin mask R-CNN model (LMMask R-CNN), which adopts the lager margin loss to reformulate Mask R-CNN for needle shaft localization, and a needle based density-based spatial clustering of application with noise algorithm which integrates priors to model a needle in an iteration for a needle shaft refinement and tip detections. Besides, we use the skipping connection in neural network architecture to improve the supervision in hidden layers. Our workflow was evaluated on 23 patients who underwent US-guided high-dose-rate (HDR) prostrate brachytherapy with 339 needles being tested in total. Our method detected 98% of the needles with 0.091 ± 0.043 mm shaft error and 0.330 ± 0.363 mm tip error. Compared with only using Mask R-CNN and only using LMMask R-CNN, the proposed method gains a significant improvement on both shaft error and tip error. The proposed method automatically digitizes needles per patient with in a second. It streamlines the workflow of transrectal ultrasound-guided HDR prostate brachytherapy and paves the way for the development of real-time treatment planning system that is expected to further elevate the quality and outcome of HDR prostate brachytherapy.
A Convolutional Neural Network-Based RF Fingerprinting Identification Scheme for Mobile Phones

Global system for mobile communications (GSM) is one of the most widely used communication standards in the world today, which still has a large number of users, so it is of great security significance to identify devices operating in a GSM network. This paper proposes a novel radio frequency fingerprinting (RFF) based device identifications method for mobile phones. A differential constellation trace figure (DCTF) physical layer RFF extraction and convolutional neural network (CNN) based classification scheme is designed to identify accessing mobile phones. Theoretical analysis shows that differential process of GSM signal can effectively reflect the characteristics of RFF from different phones. Compared with the existing RFF identification methods, CNN based classification can identify the DCTF of different devices with low complexity and high accuracy. Furthermore, the proposed DCTF-CNN method is robust to different device locations and GSM parameters. Experimental results show that the accuracy of the proposed DCTF-CNN method can reach 92.97% and 99.77% with SNR at 25 dB and 50 dB for 6 mobile phones.
Fast Single Image Defogging With Robust Sky Detection

Haze is a source of unreliability for computer vision applications in outdoor scenarios, and it is usually caused by atmospheric conditions.The Dark Channel Prior (DCP) has shown remarkable results in image defogging with three main limitations: 1) high time-consumption, 2) artifact generation, and 3) skyregion over-saturation.Therefore, current work has focused on improving processing time without losing restoration quality and avoiding image artifacts during image defogging.Hence in this research, a novel methodology based on depth approximations through DCP, local Shannon entropy, and Fast Guided Filter is proposed for reducing artifacts and improving image recovery on sky regions with low computation time.The proposed-method performance is assessed using more than 500 images from three datasets: Hybrid Subjective Testing Set from Realistic Single Image Dehazing (HSTS-RESIDE), the Synthetic Objective Testing Set from RESIDE (SOTS-RESIDE) and the HazeRD.Experimental results demonstrate that the proposed approach has an outstanding performance over state-of-the-art methods in reviewed literature, which is validated qualitatively and quantitatively through Peak Signal-to-Noise Ratio (PSNR), Naturalness Image Quality Evaluator (NIQE) and Structural SIMilarity (SSIM) index on retrieved images, considering different visual ranges, under distinct illumination and contrast conditions.Analyzing images with various resolutions, the method proposed in this work shows the lowest processing time under similar software and hardware conditions.