Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Twitter by Using TextBlob for Natural Language Processing

The Internet has become an innovative platform regarding online learning, exchanging content, sharing views.In this paper, we will use Twitter as our social networking platform.Sentiment analysis on Twitter is based on opinion mining on posts to obtain the user's point of view.The leading goal deals with how opinion mining techniques can be accessed to analyze some of the tweets in many reports involving various types of tweet languages on Twitter and classify its polarity.Practical implication shows that the proposed machine learning classifiers are efficient and highly accurate.
Human Attribute Recognition— A Comprehensive Survey

Human Attribute Recognition (HAR) is a highly active research field in computer vision and pattern recognition domains with various applications such as surveillance or fashion. Several approaches have been proposed to tackle the particular challenges in HAR. However, these approaches have dramatically changed over the last decade, mainly due to the improvements brought by deep learning solutions. To provide insights for future algorithm design and dataset collections, in this survey, (1) we provide an in-depth analysis of existing HAR techniques, concerning the advances proposed to address the HAR’s main challenges; (2) we provide a comprehensive discussion over the publicly available datasets for the development and evaluation of novel HAR approaches; (3) we outline the applications and typical evaluation metrics used in the HAR context.
The Role of Trusting Beliefs in Voice Assistants during Voice Shopping

Artificial intelligence-based voice assistants (VAs) such as Amazon Alexa deliver personalized product recommendations in order to match consumers' needs.The use of voice assistants for shopping purposes incorporates elements of risk affecting when and how they are considered trusted relationship partners.In this uncertain environment, it is unclear 'when' voice assistants are capable of gaining trust and 'how' the development of such a trusted relationship affects decisions.This research explores the effect of trusting beliefs towards voice assistants on decision satisfaction through the indirect effect of consideration set size (n. of options), in the context of voice shopping.Findings of an individual-session online experiment (N = 180) show a positive direct effect of trust on customer's satisfaction and a mediating role of set size, confirming consumers' bias towards default choices.This study highlights the consequences of trust in AI-enabled voice assistants for decision-making during utilitarian purchases.
Perspectives on Cyber Security for Offshore Oil and Gas Assets

In an ever-evolving technological industry, the oil and gas sector is already moving forward through the adaptation of Industry 4.0 and the adaptation of advanced cyber technologies through Oil and Gas 4.0. As IT/OT (information technology/operational technology) systems are evolving technologically, so are the cyber security threats faced by the offshore oil and gas assets. This paper aims to raise the awareness of cyber security threats and the organizational and technical measures that need to be adopted by the oil and gas industry for remote and complex assets in the upstream sector. A comprehensive literature review covering the areas of new IT/OT systems integration and cyber security risk analysis and management is presented. The results of a survey on the subject of cyber security for offshore oil and gas assets are also presented, and they provide valuable insight into the current industry culture and the perception of cyber security concepts. The importance of organizational culture, personnel training and involvement, as well as corporate engagement and support in the subject of cyber security is highlighted.
A Deep Hybrid Few Shot Divide and Glow Method for Ill-Light Image Enhancement

Images captured in low and ill-lighting conditions with non-uniform illumination distribution contain over-exposed and under-exposed regions simultaneously.The existing methods use handcrafted parameters for the ill-posed image decomposition and rely on image pairs or priors.The capacity of these models is limited to specific lighting conditions.The existing deep learning-based models are also not competitive when there is a lack of large-scale dataset, and some models even require paired training data.But in practice, it is challenging to capture low-light and normal-light images of the same scene.In this paper, we propose a few shot divide and glow (FSDG) method to enhance low ill-light images without exactly relying on the large scale and paired training data.We divide the input images into reflection R and illumination transmission T components by using MID-Net and amplify the illumination map in a Glow-Net.A contrast enhancement strategy is proposed to upgrade image division, which maintains regularization consistency for a few-shot divide and glow network (FSDG-Net).The FSDG-Net is end-to-end trained to learns from the correlation consistency of the input image decomposition itself.Experiments are organized under both very low exposure and ill-light conditions, where a new dataset is also proposed with challenging test images.Results show that our method consistently shows the superior performance when comparing to other state-of-the-art approaches.
Multiport based teleportation – transmission of a large amount of quantum information

We analyse the problem of transmitting a number of unknown quantum states or one composite system in one go. We derive a lower bound on the performance of such process, measured in the entanglement fidelity. The obtained bound is effectively computable and outperforms the explicit values of the entanglement fidelity calculated for the pre-existing variants of the port-based protocols, allowing for teleportation of a much larger amount of quantum information. The comparison with the exact formulas and similar analysis for the probabilistic scheme is also discussed. In particular, we present the closed-form expressions for the entanglement fidelity and for the probability of success in the probabilistic scheme in the qubit case in the picture of the spin angular momentum.
Rapid sorting of radio galaxy morphology using Haralick features

ABSTRACT We demonstrate the use of Haralick features for the automated classification of radio galaxies. The set of thirteen Haralick features represent an extremely compact non-parametric representation of image texture, and are calculated directly from imagery using the Grey Level Co-occurrence Matrix (GLCM). The GLCM is an encoding of the relationship between the intensity of neighbouring pixels in an image. Using 10 000 sources detected in the first data release of the LOFAR Two-metre Sky Survey (LoTSS), we demonstrate that Haralick features are highly efficient, rotationally invariant descriptors of radio galaxy morphology. After calculating Haralick features for LoTSS sources, we employ the fast density-based hierarchical clustering algorithm hdbscan to group radio sources into a sequence of morphological classes, illustrating a simple methodology to classify and label new, unseen galaxies in large samples. By adopting a ‘soft’ clustering approach, we can assign each galaxy a probability of belonging to a given cluster, allowing for more flexibility in the selection of galaxies according to combinations of morphological characteristics and for easily identifying outliers: those objects with a low probability of belonging to any cluster in the Haralick space. Although our demonstration focuses on radio galaxies, Haralick features can be calculated for any image, making this approach also relevant to large optical imaging galaxy surveys.
WARM: Wearable AR and Tablet-Based Assistant Systems for Bus Maintenance

This paper shows two developed digital systems as an example of intelligent garage and maintenance that targets the applicability of augmented reality and wearable devices technologies to the maintenance of bus fleets. Both solutions are designed to improve the maintenance process based on verification of tasks checklist. The main contribution of the paper focuses on the implementation of the prototypes in the company’s facilities in an operational environment with real users and address the difficulties inherent in the transfer of a technology to a real work environment, such as a mechanical workshop. The experiments have been conducted in real operation thanks to the involvement of the public transport operator DBUS, which operates public transport buses in the city of Donostia—San Sebastian (Spain). Two solutions have been developed and compared against the traditional process: one based on Tablet and another one based on Microsoft HoloLens. The results show objective metrics (Key Performance Indicators, KPI) as well as subjective metrics based on questionnaires comparing the two technological approaches against the traditional one based on manual work and paper.
Identity and Access Management Resilience against Intentional Risk for Blockchain-Based IOT Platforms

Some Internet of Things (IoT) platforms use blockchain to transport data. The value proposition of IoT is the connection to the Internet of a myriad of devices that provide and exchange data to improve people’s lives and add value to industries. The blockchain technology transfers data and value in an immutable and decentralised fashion. Security, composed of both non-intentional and intentional risk management, is a fundamental design requirement for both IoT and blockchain. We study how blockchain answers some of the IoT security requirements with a focus on intentional risk. The review of a sample of security incidents impacting public blockchains confirm that identity and access management (IAM) is a key security requirement to build resilience against intentional risk. This fact is also applicable to IoT solutions built on a blockchain. We compare the two IoT platforms based on public permissionless distributed ledgers with the highest market capitalisation: IOTA, run on an alternative to a blockchain, which is a directed acyclic graph (DAG); and IoTeX, its contender, built on a blockchain. Our objective is to discover how we can create IAM resilience against intentional risk in these IoT platforms. For that, we turn to complex network theory: a tool to describe and compare systems with many participants. We conclude that IoTeX and possibly IOTA transaction networks are scale-free. As both platforms are vulnerable to attacks, they require resilience against intentional risk. In the case of IoTeX, DIoTA provides a resilient IAM solution. Furthermore, we suggest that resilience against intentional risk requires an IAM concept that transcends a single blockchain. Only with the interplay of edge and global ledgers can we obtain data integrity in a multi-vendor and multi-purpose IoT network.
Designing a Cyber-security Culture Assessment Survey Targeting Critical Infrastructures During Covid-19 Crisis

The paper at hand presents the design of a survey aiming at the cyber-security culture assessment of critical infrastructures during the COVID-19 crisis, when living reality was heavily disturbed and working conditions fundamentally affected. The survey is rooted in a security culture framework layered into two levels, organizational and individual, further analyzed into 10 different security dimensions consisted of 52 domains. An in-depth questionnaire building analysis is presented focusing on the aims, goals, and expected results. It concludes with the survey implementation approach while underlining the framework’s first application and its revealing insights during a global crisis.
Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet

The identification of source cameras from videos, though it is a highly relevant forensic analysis topic, has been studied much less than its counterpart that uses images.In this work we propose a method to identify the source camera of a video based on camera specific noise patterns that we extract from video frames.For the extraction of noise pattern features, we propose an extended version of a constrained convolutional layer capable of processing color inputs.Our system is designed to classify individual video frames which are in turn combined by a majority vote to identify the source camera.We evaluated this approach on the benchmark VISION data set consisting of 1539 videos from 28 different cameras.To the best of our knowledge, this is the first work that addresses the challenge of video camera identification on a device level.The experiments show that our approach is very promising, achieving up to 93.1% accuracy while being robust to the WhatsApp and YouTube compression techniques.This work is part of the EU-funded project 4NSEEK focused on forensics against child sexual abuse.
A counter based approach for reducer placement with augmented Hadoop rack awareness

As the data-driven paradigm for intelligent systems design is gaining prominence, performance requirements have become very stringent, leading to numerous fine-tuned versions of Hadoop and its MapReduce programming model. However, very few researchers have investigated the effect of intelligent reducer placement on Hadoop's performance. This paper delves into this much ignored reducer placement phase for improving Hadoop's performance and proposes to spawn reduce phase of Hadoop tasks in an asynchronous fashion across nodes in a Hadoop cluster. The main contributions of this paper are: (i) to track when map phase of tasks are completed, (ii) to count the number of maps completed, and finally (iii) assign reducers to Hadoop nodes based on map counts such that run-time data copying is minimized. To this end, this paper presents a novel counter based reducer placement (CBRP) algorithm based on the counter values maintained by JobTracker at the rack and node levels. Experiments conducted demonstrate the merit of the proposed reducer placement with average improvements ranging between 5% and 17% experienced across different benchmarks with both late shuffle and early shuffle.
3D Point-of-Intention Determination Using a Multimodal Fusion of Hand Pointing and Eye Gaze for a 3D Display

This paper proposes a three-dimensional (3D) point-of-intention (POI) determination method using multimodal fusion between hand pointing and eye gaze for a 3D virtual display. In the method, the finger joint forms of the pointing hand sensed by a Leap Motion sensor are first detected as pointing intention candidates. Subsequently, differences with neighboring frames, which should be during hand pointing period, are checked by AND logic with the hand-pointing intention candidates. A crossing point between the eye gaze and hand pointing lines is finally decided by the closest distance concept. In order to evaluate the performance of the proposed method, experiments with ten participants, in which they looked at and pointed at nine test points for approximately five second each, were performed. The experimental results show the proposed method measures 3D POIs at 75 cm, 85 cm, and 95 cm with average distance errors of 4.67%, 5.38%, and 5.71%, respectively.
Automatic Student Engagement in Online Learning Environment Based on Neural Turing Machine

With the continuous and rapid growth of online courses, online learners’ engagement recognition has become a novel research topic in the field of computer vision and pattern recognition. While a few attempts to automatic engagement recognition has been studied in the literature, learning a robust engagement measure is still a challenging task. To address it, we propose a new automatic engagement recognition method based on Neural Turing Machine in this paper. In particular, we firstly extract student’s eye gaze features, facial action unit features, head pose features, and body pose features respectively, then combine these multi modal features into the final feature of our recognition task. Moreover, we propose the engagement recognition framework based on the idea of Neural Turing Machine to learn the weight of each short video feature. In consequence, the feature fused by different weights will be applied to identify the students’ engagement in learning online courses. Empirically, we show improved performance over state of the art methods to automatic engagement recognition on DAiSEE dataset.
Optimal Geometry Analysis for Target Localization With Bayesian Priors

Relative sensor-target geometry is well known to significantly affect the performance of target localization using a sensor network. This article analyzes the optimal sensor-target geometries for the problem of target localization with Bayesian priors. We present a unified geometry optimization framework for different types of sensor networks including bearing-only sensor network, range-only sensor network, received signal strength-only sensor network and mixed network of these sensor types. For geometry optimization of Bayesian target localization with these sensor networks, we establish the equivalence between the A-optimality criterion (i.e., minimizing the estimation mean squared error) and the D-optimality criterion (i.e., minimizing the area of the estimation uncertainty ellipse). Under these optimality criteria, the geometry optimization problem is shown to be mathematically equivalent to the problem of minimizing the modulus of a vector sum. This thus makes the optimal geometry conditions algebraically simple and easy to be computed. We conclude the article with extensive simulation studies to verify the accuracy of the analytical findings.
Ordination analysis in sedimentology, geochemistry and palaeoenvironment—Background, current trends and recommendations

Abstract Ordination is the name given to a group of methods used to analyse multiple variables without preceding hypotheses. Over the last few decades, the use of these methods in Earth science in general, and notably in analyses of sedimentary sources, has dramatically increased. However, with limited resources oriented towards Earth scientists on the topic, the application of ordination analysis is at times suboptimal and misuse by authors can occur. This text was written for researchers with little to no experience with ordination with the aim of exposing them to the utility and the pitfalls of this branch of exploratory statistics. To do so, a detailed review of three ordination methods is offered: principal component analysis, non‐metric multidimensional scaling and detrended correspondence analysis. A survey of 163 publications in Earth science is presented, in which these ordination methods were used together with a summary of how, why and on what type of data ordination was used. With common mistakes outlined and misuses in those publications identified. Notably, issues were found with reproducibility, documentation, data set dimensions and transformations. Based on this survey, a recommended workflow is offered for Earth scientists who wish to apply ordination. Additionally, this article is accompanied by highly annotated R scripts for novice users to use these methods.
Seg2pix: Few Shot Training Line Art Colorization with Segmented Image Data

There are various challenging issues in automating line art colorization. In this paper, we propose a GAN approach incorporating semantic segmentation image data. Our GAN-based method, named Seg2pix, can automatically generate high quality colorized images, aiming at computerizing one of the most tedious and repetitive jobs performed by coloring workers in the webtoon industry. The network structure of Seg2pix is mostly a modification of the architecture of Pix2pix, which is a convolution-based generative adversarial network for image-to-image translation. Through this method, we can generate high quality colorized images of a particular character with only a few training data. Seg2pix is designed to reproduce a segmented image, which becomes the suggestion data for line art colorization. The segmented image is automatically generated through a generative network with a line art image and a segmentation ground truth. In the next step, this generative network creates a colorized image from the line art and segmented image, which is generated from the former step of the generative network. To summarize, only one line art image is required for testing the generative model, and an original colorized image and segmented image are additionally required as the ground truth for training the model. These generations of the segmented image and colorized image proceed by an end-to-end method sharing the same loss functions. By using this method, we produce better qualitative results for automatic colorization of a particular character’s line art. This improvement can also be measured by quantitative results with Learned Perceptual Image Patch Similarity (LPIPS) comparison. We believe this may help artists exercise their creative expertise mainly in the area where computerization is not yet capable.
Experimental Realization of Optimal Energy Storage in Resonators Embedded in Scattering Media

Abstract The ability to enhance light–matter interactions by increasing the energy stored in optical resonators is inherently dependent on the resonators' coupling to the incident wavefront. In practice, weak coupling may result from resonators' irregular shapes and/or the scrambling of waves in the surrounding scattering environment. Here, a blind and non‐invasive wavefront shaping technique providing optimal coupling to resonators is presented. The coherent control of the incident wavefront relies on the lengthening of delay times of waves efficiently exciting the resonator. Using a modal approach, the optimality of the proposed technique is proven and its limitations are quantified. The proposed concept is demonstrated in microwave experiments by injecting in situ optimal wavefronts that maximize the energy stored in high‐permittivity dielectric scatterers and extended leaky cavities embedded in a complex environment. The introduced framework is expected to find important applications in the enhancement of light–matter interactions in photonic materials as well as to enhance energy harvesting.
New Risk Bounds for 2D Total Variation Denoising

2D Total Variation Denoising (TVD) is a widely used technique for image denoising. It is also an important nonparametric regression method for estimating functions with heterogenous smoothness. Recent results have shown the TVD estimator to be nearly minimax rate optimal for the class of functions with bounded variation. In this paper, we complement these worst case guarantees by investigating the adaptivity of the TVD estimator to functions which are piecewise constant on axis aligned rectangles. We rigorously show that, when the truth is piecewise constant, the ideally tuned TVD estimator performs better than in the worst case. We also study the issue of choosing the tuning parameter. In particular, we propose a fully data driven version of the TVD estimator which enjoys similar worst case risk guarantees as the ideally tuned TVD estimator.