Reviewerly/exHarmony huggingface dataset:
Frequency component vectorisation for image dehazing

Image captured in bad weather conditions confines scene prominence, appears grey and diminishes image contrast. This usually happens due to atmospheric dispersing phenomenon that affects the quality of outdoor computer vision frameworks. This deprivation relies on the gap between the object point and the camera and mostly differs for every pixel present in an image. Transmission coefficients, which state the aforementioned dependence are used to manage the haze level in each pixel. Our algorithm is subject to the presumption that the haze-free image forms clusters given in RGB space. The pixels over the image plane are often found at different locations and their distance from camera also differs. These fluctuating distances give rise to different transmission coefficients. Consequently, these colour clusters form the certain lines of colours in RGB space known as haze lines. The first step is to assure accurate estimation of the atmospheric light, for this an additional wavelet channel is recommended, based on frequency subdivision. The next step is to separate the average gradients present in the foggy regions of an image according to the frequency criteria. Lastly, the haze-free image information is retrieved by utilising the atmospheric scattering model on low and high frequencies according to the edges of unpredicted change in the field depth. Using the non-local and frequency information retrieval, proposed algorithm recovers the haze-free image, efficiently.
Fabricable Unobtrusive 3D‐QR‐Codes with Directional Light

Abstract QR code is a 2D matrix barcode widely used for product tracking, identification, document management and general marketing. Recently, there have been various attempts to utilize QR codes in 3D manufacturing by carving QR codes on the surface of the printed 3D shape. Nevertheless, significant shape editing and modulation may be required to allow readability of the embedded 3D‐QR‐codes with good decoding accuracy. In this paper, we introduce a novel QR code 3D fabrication framework aimed at unobtrusive embedding of 3D‐QR‐codes in the shape hence introducing minimal shape modulation. Essentially, our method computes bi‐directional carvings in the 3D shape surface to obtain the black‐and‐white QR pattern. By using a directional light source, the black‐and‐white QR pattern emerges as lighted and shadow casted blocks on the shape respectively. To account for minimal modulation and elusiveness, we optimize the QR code carving w.r.t. shape geometry, visual disparity and light source position. Our technique employs a simulation of lighting phenomena through carved modules on the shape to ensure adequate contrast of the printed 3D‐QR‐code.
A Methodology for Taxi Demand Prediction Using Stream Learning

Intelligent transport support systems have had a major impact on people's urban mobility. In large urban centers, transportation services still need ways to optimize vehicle supply in certain areas, according to the demand in each of them. Optimized distribution of on-demand taxi services can be part of an intelligent urban mobility plan, causing direct impacts on urban traffic, improving transport accessibility, improving safety at taxi standpoints by reduced waiting times, reduce transportation fare etc. Many vehicle-mounted sensors currently generate real-time information that is not used for processing and generating information with value. This paper proposes a Taxi Demand Forecasting methodology using stream machine learning algorithms that tackle concept drift detection on taxi data stream. A real data source made available on the New York open platform feeds a stream learning model, constructed using the Massive Online Analysis (MOA) tool - a framework for data stream mining. The stream model shows promising results in forecasting taxi demand, reaching 78% accuracy. Despite using data from a specific city, the methodology and results of this work can contribute to a more proactive demand management in other cities.
Artificial Intelligence – Challenges and Chances for Europe

As one of the building blocks of the fourth industrial revolution, artificial intelligence has attracted much public attention and sparked protracted discussions about its impact on future technological, economic and social developments. This contribution conveys insights into artificial intelligence’s basic methods and tools, its main achievements, its economic environment and the surrounding ethical and social issues. Based on the announced and taken measures of the EU organs in the area of artificial intelligence, the contribution analyses the position of Europe in the global context.
Reimagining collaboration through the lens of the posthuman: Uncovering embodied learning in noise music

While education research has largely avoided posthumanist scholarship, this analytic lens challenges the ways in which researchers have conceptualized educational technologies, i.e. collaboration and embodied learning, as primarily humanist endeavors that overtly center on human subjects within educational processes. By exploring sites of research that overtly enact posthumanist conceptions of learning, education researchers can address this oversight. In this article, I investigate posthumanist collaboration within the noise music genre, positioning noise music as a posthuman musical tradition and, in turn, a posthuman educational context. In doing so, I reframe noise (in the broad sense of the term) as a tool for engaging the posthuman through multiple educational praxes both in and outside of this specific genre. To construct this argument, I place extant literature on posthumanism and noise in conversation with descriptions of performances from the 2017 Experimental Education Series, a quarterly workshop and concert series that features a broad spectrum of noise musicians, and interviews with teaching artists from this series. Through this constellation of texts, I advocate for noise music to not only serve as a site for future research but as a potential model of posthuman education more broadly.
LsRec: Large-scale social recommendation with online update

With the ever-increasing scale and complexity of social network and online business, Recommender Systems (RS) have played crucial roles in information processing and filtering in various online applications, although suffering from such as data sparsity and low accuracy problems. Meanwhile, recent researches try to enhance the performance of RS through such social network and clustering algorithms, however, they may fail to achieve further improvement in large-scale online recommendation due to the serious information overload. In this article, a novel social recommendation approach with online update referred to as LsRec is proposed, which generally contains offline computation and online incremental update. More precisely, LsRec not only takes account of user’s social relationship, but also clusters items according to the similarity degree, furthermore, LsRec performs recommendation in each generated cluster respectively. In practice, LsRec could be capable of exploiting user-level social influence, and capturing the intricate relationship between items. In addition, theoretical proof could provide convergence guarantee for the model. Specifically, with the appealing merit of flexible online update scenario, LsRec could yield high performance in large-scale online recommendation with low computational complexity. Extensive experimental analysis over four real world datasets demonstrate the effectiveness and efficiency of LsRec, which indicates that LsRec could significantly outperform state-of-the-art recommender approaches, especially in large-scale online recommendation.
Natural resource governance: does social media matter?

In this paper, we study the relationship between communication and "transparency of information" and governance by exploring the link between social media and natural resource governance. Using a cross-country analysis, we document a robust and statistically significant positive relationship between Facebook penetration (a proxy for social media) and natural resource governance. It follows that countries with higher social media levels enjoy natural resource governance of better quality than countries with low levels of social media. The positive effect of social media is robust to controlling for other determinants of institutional quality, additional controls, and outliers, inter alia.
Beyond the Emoticon: Are There Unintentional Cues of Emotion in Email?

Email and text-based communication have become ubiquitous. Although recent findings indicate emotional equivalence between face-to-face and email communication, there is limited evidence of nonverbal behaviors in text-based communication, especially the kinds of unintentional displays central to emotion perception in face-to-face interactions. We investigate whether unintentional emotion cues occur in text-based communication by proposing that communication mistakes (e.g., typos) influence emotion perception. Across six studies, we show that communication errors amplify perceptions of email sender’s emotions—both negative (Studies 1A–2, 4, 5) and positive (Study 3). Furthermore, by contrasting perceptions of message senders who make mistakes in emotional versus unemotional contexts (Study 5), we show that people partially excuse message sender communication errors in emotional (versus unemotional) contexts, attributing such mistakes to the sender’s emotional state rather than solely their intelligence level. These studies suggest that nonverbal behavior in text-based and face-to-face communication may be more comparable than previously thought.
Visual Coverage Path Planning for Urban Environments

View planning for visual coverage is a fundamental robotics problem. Coverage for small objects (e.g. for inspection) or small scale indoor scenes have been studied extensively. However, view planning to cover a large scale urban environment remains challenging. Algorithms that can scale up to the size of such environments while providing performance guarantees are missing. In this letter, we model urban environments as a set of surfaces with k distinct surface normals whose viewing cones must be visited by a robot. We model the resulting coverage problem as a novel variant of Traveling Salesman Problem with Neighborhoods (TSPN). The neighborhoods are defined as cones, which constrain the path coverage quality. We present a polynomial time algorithm which admits an approximation factor of O(k/tan(α) max [L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> , W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> , H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> ]), where α is the maximum viewing angle, and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> , H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> , W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> are respectively the length, width, and height of a minimum enclosing box of a city scene B. In addition to the analytical upper bounds, we show in simulations that our method outperforms three baseline methods in both trajectory length and run-time. We also demonstrate our method and evaluate the coverage quality of a city containing more than 70 buildings in a photo-realistic rendering software.
Toward a Data Lifecycle Model for NSF Large Facilities

National Science Foundation large facilities conduct large-scale physical and natural science research. They include telescopes that survey the entire sky, gravitational wave detectors that look deep into our universe’s past, sensor-driven field sites that collect a range of biological and environmental data, and more. The Cyberinfrastructure Center for Excellence (CICoE) pilot project aims to develop a model for a center that facilitates community building, fosters knowledge sharing, and applies best practices in consulting with large facilities with regard to their cyberinfrastructure. To accomplish this goal, the pilot began an in-depth study of how large facilities manage their data during the course of their research. Large facilities are diverse and highly complex, from the types of data they capture, to the types of equipment they use, to the types of data processing and analysis they conduct, to their policies on data sharing and use. Because of this complexity, the pilot needed to find a single lens through which it could frame its growing understanding of large facilities and identify areas where it could best serve large facilities. As a result of the pilot’s research into large facilities, common themes have emerged which have enabled the creation of a data lifecycle model that successfully captures the data management practices of large facilities. This model has enabled the pilot to organize its thinking about large facilities, and frame its support and consultation efforts around the cyberinfrastructure used during lifecycle stages. This paper describes the model and discusses how it was applied to disaster recovery planning for a representative large facility—IceCube.
Self-Supervised Feature Extraction for 3D Axon Segmentation

Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels. Labeling is a labor-intensive process and is not scalable to whole-brain analysis, which is needed for improved understanding of brain function. We propose a self-supervised auxiliary task that utilizes the tube-like structure of axons to build a feature extractor from unlabeled data. The proposed auxiliary task constrains a 3D convolutional neural network (CNN) to predict the order of permuted slices in an input 3D volume. By solving this task, the 3D CNN is able to learn features without ground-truth labels that are useful for downstream segmentation with the 3D U-Net model. To the best of our knowledge, our model is the first to perform automated segmentation of axons imaged at subcellular resolution with the SHIELD technique. We demonstrate improved segmentation performance over the 3D U-Net model on both the SHIELD PVGPe dataset and the BigNeuron Project, single neuron Janelia dataset.
Generic Parity-Based Concurrent Error Detection for Lightweight ARX Ciphers

Cryptographic functions for constrained processing environments can be devised using lightweight cryptography.For use in safety relevant automotive applications where transient faults can occur at runtime the calculation of a cipher text requires verification.We propose an algorithm to generate a group parity based concurrent error detection for generic ciphers based on addition, rotation and XOR (ARX).The generated function is capable of detecting odd hamming weight faults according to the single event error model used in automotive applications.The the generated fault detection scheme is smaller in circuit size than presently existing concurrent error detection schemes and can be executed parallel to the cryptographic function.We provide a proof for the correctness of the generated prediction function and estimate the circuit complexity in terms of size and depth.We evaluate our solution in terms of gate count and throughput on IC synthesis level.
Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review

Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the “human firewall,” which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs’ security practices in various scenarios including big data. The intention is to determine the gap between staffs’ security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs’ security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies.
An assistive environment for EAL academic writing using formulaic sequences classification

Writing an English research article for novice English as an additional language (EAL) writers is a challenging task that requires experience and training at both the sentence and meaning levels. One strategy that EAL writers employ when writing a research article is the use of formulaic sequences (FSs). However, available FS corpora are general purpose and are very limited in size. Previous studies have reported the effectiveness of FS usage in writing using a small set of FSs. The present work proposes an assistive environment for academic writing improvement through the use of domain-specific FSs. FSs are extracted from published articles and are classified under rhetoric categories using a machine learning technique. The user can then search for and add new FSs of his/her choice from any research article using proposed prototypes. The effectiveness of the proposed approach was evaluated in a real environment. The results show a positive impact of the proposal in terms of academic writing improvement. Novice writers who worked with the proposed prototype reported a significantly higher degree of perceived usefulness than those who worked with the traditional phrasebank approach.
Zone-based keyword spotting in Bangla and Devanagari documents

In this paper, we present a word spotting system in text lines for offline Indic scripts such as Bangla (Bengali) and Devanagari. Recently, it was shown that the zone-wise recognition method improves word recognition performance than the conventional full word recognition system in Indic scripts, like Bangla, Devanagari, Gurumukhi (Roy et al. in Pattern Recogn 60: 1057-1075, 26; Bhunia et al. in Pattern Recogn 79: 12–31, 6). Inspired from this idea we consider the zone segmentation approach and use middle zone information to improve the traditional word spotting performance. To avoid the problem of zone segmentation using heuristic approach, we propose here a new HMM based approach to segment the upper and lower zone components from the text line images. The candidate keywords are searched from a line without segmenting characters or words. Also, we propose a feature combining foreground and background information of text line images for keyword-spotting by character filler models. A significant improvement in performance is noted by using both foreground and background information instead of the individual one. Pyramid Histogram of Oriented Gradient (PHOG) feature has been used in our word spotting framework. From the experiment, it has been noted that the proposed zone-segmentation based system outperforms traditional approaches of word spotting.
Subjectivity Detection for Sentiment Analysis on Twitter Data

With the quick increment in the quantity of web clients, the Internet has an enormous measure of data produced by the clients. Many people share their views regarding a topic on social media platforms such as Facebook and Twitter and give their feedback or review about a product on e-commerce web sites such as Amazon and Flipkart which leads to a huge amount of data. The identification of subjective statements from the data is known as subjectivity detection. To automate the analysis of such data, sentiment analysis is used. The aim is to find the opinionative data and classify it according to its polarity, i.e. positive, negative or neutral feedback, known as sentiment classification and then analysing it which is known as sentiment analysis. However, before performing sentiment examination, the information is exposed to different pre-processing procedures which finally give the desired optimized output. This allows us to get to know about the public’s mood or opinion about a particular topic. This summarization helps the concerned organization or public to improve their product or service based on the feedback received.
Solving quantum trajectories for systems with linear Heisenberg-picture dynamics and Gaussian measurement noise

We study solutions to the quantum trajectory evolution of $N$-mode open quantum systems possessing a time-independent Hamiltonian, linear Heisenberg-picture dynamics, and Gaussian measurement noise. In terms of the mode annihilation and creation operators, a system will have linear Heisenberg-picture dynamics under two conditions. First, the Hamiltonian must be quadratic. Second, the Lindblad operators describing the coupling to the environment (including those corresponding to the measurement) must be linear. In cases where we can solve the $2N$-degree polynomials that arise in our calculations, we provide an analytical solution for initial states that are arbitrary (i.e. they are not required to have Gaussian Wigner functions). The solution takes the form of an evolution operator, with the measurement-result dependence captured in $2N$ stochastic integrals over these classical random signals. The solutions also allow the POVM, which generates the probabilities of obtaining measurement outcomes, to be determined. To illustrate our results, we solve some single-mode example systems, with the POVMs being of practical relevance to the inference of an initial state, via quantum state tomography. Our key tool is the representation of mixed states of quantum mechanical oscillators as state vectors rather than state matrices (albeit in a larger Hilbert space). Together with methods from Lie algebra, this allows a more straightforward manipulation of the exponential operators comprising the system evolution than is possible in the original Hilbert space.
Optimized quantum filtering of matter waves with respect to incidence direction and impinging energy

Selecting the quantum signals with respect to features like direction of propagation or impinging energy makes a ubiquitous operation that is employed in a variety of quantum engineering applications. The conditions for optimally selective angular and energy filtering via homogeneous planar inclusions are determined and the derived designs exhibit substantial robustness with respect to fabrication defects. The reported findings may open new uncharted directions towards the efficient design of integrated devices exploiting quantum filtering from sharp sensors and tuners to multiport networks and quantum circuits.