Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Scene Text Recognition

Scene text recognition has been widely researched with supervised approaches. Most existing algorithms require a large amount of labeled data and some methods even require character-level or pixel-wise supervision information. However, labeled data is expensive, unlabeled data is relatively easy to collect, especially for many languages with fewer resources. In this paper, we propose a novel semi-supervised method for scene text recognition. Specifically, we design two global metrics, i.e., edit reward and embedding reward, to evaluate the quality of generated string and adopt reinforcement learning techniques to directly optimize these rewards. The edit reward measures the distance between the ground truth label and the generated string. Besides, the image feature and string feature are embedded into a common space and the embedding reward is defined by the similarity between the input image and generated string. It is natural that the generated string should be the nearest with the image it is generated from. Therefore, the embedding reward can be obtained without any ground truth information. In this way, we can effectively exploit a large number of unlabeled images to improve the recognition performance without any additional laborious annotations. Extensive experimental evaluations on the five challenging benchmarks, the Street View Text, IIIT5K, and ICDAR datasets demonstrate the effectiveness of the proposed approach, and our method significantly reduces annotation effort while maintaining competitive recognition performance.
Empowerment or Limitation of the Teachers’ Rights and Abilities in the Prevailing Digital Environment

Digital education is actually a product of recent years, although it existed before in various forms. Obviously, modern devices and means of transmitting infor-mation are necessary for its development. Modern “virtual lectures”, e-learning courses, online testing, educational resources portals, as well as digital school registers and educational process monitoring systems have become everyday real-ity. However, the digitalization of the educational system has both positive and negative side. The purpose of the article is to consider the impact of digital educa-tional technologies on learning, from a university teacher’s view point, and to consider their positive aspects and problems. The study was conducted at the I.M. Sechenov First Moscow State Medical University (Sechenov University) and University at the Inter-Parliamentary Assembly of EurAsEC. The study in-volved 89 teachers who have teaching experience at the university (11.9% - 1-7 years, 28.1% - 8-10 years; 30.8% - 11-15 years; 29.2% - 16-25 years). The main results show that, respondents overly have a good opinion towards learning in virtual environments. The advantages of e-learning are complex for a particular measurement, but it has been found that some identifying variables as “age” shows statistically significant differences, while “gender” and “teaching experi-ence” are not important for evaluating the four provided variables. The study re-sults can be used to improve the perception of new educational technologies by teachers, to organize approaches for introducing innovative educational technolo-gies that expand the possibilities of progressive education on the part of admin-istrations of higher educational institutions and educational departments.
Processing Large Text Corpus Using N-Gram Language Modeling and Smoothing

The prediction of next word, letter or phrase for the user, while she is typing, is a really valuable tool for improving user experience. The users are communicating, writing reviews and expressing their opinion on such platforms frequently and many times while moving. It has become necessary to provide the user with an application that can reduce typing effort and spelling errors when they have limited time. The text data is getting larger in size due to the extensive use of all kinds of social media platforms and so implementation of text prediction application is difficult considering the size of text data to be processed for language modeling. This research paper’s primary objective is processing large text corpus and implementing a probabilistic model like N-grams to predict the next word when the user provides input. In this exploratory research, n-gram models are discussed and evaluated using Good Turing Estimation, perplexity measure and type-to-token ratio.
A DenseUnet generative adversarial network for near-infrared face image colorization

A DenseUnet Generative Adversarial Network (GAN) is proposed to colorize near-infrared (NIR) face images. The GAN generator incorporates the advantages of both DenseNet and Unet structures. The DenseNet extracts facial features effectively by increasing the network depth, and the Unet keeps important facial details through skip-connection. The generator further integrates an optimized facial loss function designed by considering pixel loss, color loss and feature loss. The proposed network is evaluated against state-of-the-art grayscale image colorization GANs over two separate datasets. Both qualitative and quantitative comparisons demonstrate that with improved network structures and loss constrains the DenseUnet GAN can colorize NIR face images with natural color, minimal face shape distortion, and rich facial details.
Introduction to Tensorflow Package

Developed by Google Brain team, tensor flow in an open source library package originally created for the tasks in heavy numerical computations (Learning TensorFlow [Authors: Tom Hope, Yehezkel S. Resheff & Itay Lieder]). Its main application is machine learning and deep learning where a computer learns from experience and the world is understood in the form of hierarchy of concepts, each concept defining its relation to simpler concepts (Deep Learning Pipeline: Building A Deep Learning Model With TensorFlow [Authors: Hisham El-Amir, Mahmoud Hamdy]). Drawing the hierarchy graph built on top of each other, the graph is deep with many layers and computation costly. Tensorflow provides faster computation and an excellent functionality compared to other popular deep learning frameworks/library. It also supports Central Processing Unit (CPUs), Graphics Processing Unit (GPUs) and distributed processing in a cluster.
Nudging users towards better security decisions in password creation using whitebox-based multidimensional visualisations

Nudging users to keep them secure online has become a growing research field in cybersecurity. While existing approaches are mainly blackbox based, showing aggregated visualisations as one-size-fits-all nudges, personalisation turned out promising to enhance the efficacy of nudges within the high variance of users and contexts. This article presents a disaggregated whitebox-based visualisation of critical information as a novel nudge. By segmenting users according to their decision-making and information processing styles, we investigate if the novel nudge is more effective for specific users than a common black-box nudge. Based on existing literature about critical factors in password security, we designed a dynamic radar chart and parallel coordinates as disaggregated visualisations. We evaluated the short-term effectiveness and users' perception of the nudges in a think-aloud prestudy and a representative online evaluation (N=1.012). Our findings suggest that dynamic radar charts present a moderately effective nudge towards stronger passwords regarding short-term efficacy and are appreciated particularly by players of role-playing games.
Recurrent convolutional neural network for session-based recommendation

The task of session-based recommendation is predicting the next recommendation item when available information only includes the anonymous behavior sequence. Previous methods of session-based recommendation usually integrate the general interest, dynamic interest, and current interest to promote recommendation performance. However, most existing methods ignore the non-monotone feature interactions when building user's dynamic interest and model item-item transitions through a linear way when building user's current interest, which reduces the performance of model. In this paper, we design a novel method for session-based recommendation with recurrent and convolutional neural network. Specifically, The Gated Recurrent Unit with item-level attention mechanism learns the user's general interest, while the convolutional operation with horizontal filter and vertical filter search for user's current interest and dynamic interest. Moreover, the outputs of recurrent operation and convolutional operation are concatenated to generate the recommendation. Furthermore, we evaluate the proposed model on three real-world datasets which come from e-commerce and music API, respectively. The experimental results show that our model outperforms the state-of-the-art methods on session-based recommendation.
An adaptive non-zero mean damping model for trajectory tracking of hypersonic glide vehicles

In this paper, we proposed an adaptive non-zero mean damping oscillation model, aiming to solve the trajectory tracking problem of hypersonic glide vehicles (HGVs). To this end, an adaptive non-zero mean damping oscillation model (ANMDO) is established based on the maneuver patterns of HGVs, the proposed model consists of the mean and maneuvering components of HGV's accelerations. In particular, a sine autocorrelation random process is applied to model the mean component, while a first-order Markov process is introduced to compensate its maneuvering counterpart that is taken as the perturbation. Moreover, we proceed to introduce the Kalman filter to estimate the trajectory, while the dynamic errors of the proposed model are analytically developed. Simulation results verified that the proposed model can achieve a better tracking accuracy and reasonable convergence compared with the conventional sine correlation model and the Singer model.
Are We Preparing Students to Build Security In? A Survey of European Cybersecurity in Higher Education Programs [Education]

We present a review of European master of science programs in cybersecurity and reflect on the presence (and lack) of knowledge and skills needed to build security in.
Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis

This paper investigates eye behaviour through blinks activity during stress conditions. Although eye blinking is a semi-voluntary action, it is considered to be affected by one’s emotional states such as arousal or stress. The blinking rate provides information towards this direction, however, the analysis on the entire eye aperture timeseries and the corresponding blinking patterns provide enhanced information on eye behaviour during stress conditions. Thus, two experimental protocols were established to induce affective states (neutral, relaxed and stress) systematically through a variety of external and internal stressors. The study populations included 24 and 58 participants respectively performing 12 experimental affective trials. After the preprocessing phase, the eye aperture timeseries and the corresponding features were extracted. The behaviour of inter-blink intervals (IBI) was investigated using the Markovian Analysis to quantify incidence dynamics in sequences of blinks. Moreover, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network models were employed to discriminate stressed versus neutral tasks per cognitive process using the sequence of IBI. The classification accuracy reached a percentage of 81.3% which is very promising considering the unimodal analysis and the noninvasiveness modality used.
Protection of visual privacy in videos acquired with RGB cameras for active and assisted living applications

Active and assisted living technologies are much needed, but some aspects of them cause user rejection due to concerns on privacy. This is even more concerning to users when visual information is used, processed, and transmitted. To respond to these concerns, and maximise user acceptance, visual privacy protection measures have to be put in place. In the past, human detection and object segmentation in video were constrained by technological limitations, and could only run with specific hardware and sensors. This paper introduces a proposal for an RGB-only based visual privacy preservation filter, which capitalises on 'deep learning'-based segmentation and pose detectors. A background update scheme is presented, which limits leakage of sensitive information when detection fails. Dilation of the mask can further prevent information leakage, but a trade-off is necessary to correctly update background information. This is achieved via a specific study which is also presented. A comparative study is performed to determine the best configuration for privacy preservation. Results show that union of dilated masks from different deep networks achieves the best overall result.
Spatial resolution of light field sectioning pyrometry for flame temperature measurement

The light field sectioning pyrometry (LFSP) has proven a significant advancement for in-situ measurement of flame temperature through a single light field camera. However, the spatial resolution of LFSP is limited, which severely inhibits the measurement accuracy. This paper aims to evaluate the spatial resolution of LFSP for flame temperature measurement quantitatively. A theoretical model of the spatial resolution is established based on optical parameters and point spread function of the light field camera. The spatial resolution is then numerically analyzed with different parameters of light field cameras. Based on the theoretical model, a novel cage-typed light field camera with a higher spatial resolution of LFSP is developed and experimentally evaluated. A significant improvement of spatial resolution about 17% and 50% in lateral and depth directions, respectively, is achieved. Results show that the spatial resolution is in good agreement with the theoretical model. The LFSP is then evaluated under different combustion cases and their temperatures are reconstructed.
Information Theoretic Secure Aggregation With User Dropouts

In the robust secure aggregation problem, a server wishes to learn and only learn the sum of the inputs of a number of users while some users may drop out (i.e., may not respond). The identity of the dropped users is not known a priori and the server needs to securely recover the sum of the remaining surviving users. We consider the following minimal two-round model of secure aggregation. Over the first round, any set of no fewer than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> users out of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users respond to the server and the server wants to learn the sum of the inputs of all responding users. The remaining users are viewed as dropped. Over the second round, any set of no fewer than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> users of the surviving users respond (i.e., dropouts are still possible over the second round) and from the information obtained from the surviving users over the two rounds, the server can decode the desired sum. The security constraint is that even if the server colludes with any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> users and the messages from the dropped users are received by the server (e.g., delayed packets), the server is not able to infer any additional information beyond the sum in the information theoretic sense. For this information theoretic secure aggregation problem, we characterize the optimal communication cost. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U \leq T$ </tex-math></inline-formula> , secure aggregation is not feasible, and when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U &gt; T$ </tex-math></inline-formula> , to securely compute one symbol of the sum, the minimum number of symbols sent from each user to the server is 1 over the first round, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/(U-T)$ </tex-math></inline-formula> over the second round.
BuildFast

Long build times in continuous integration (CI) can greatly increase the cost in human and computing resources, and thus become a common barrier faced by software organizations adopting CI. Build outcome prediction has been proposed as one of the remedies to reduce such cost. However, the state-of-the-art approaches have a poor prediction performance for failed builds, and are not designed for practical usage scenarios. To address the problems, we first conduct an empirical study on 2,590,917 builds to characterize build times in real-world projects, and a survey with 75 developers to understand their perceptions about build outcome prediction. Then, motivated by our study and survey results, we propose a new history-aware approach, named BuildFast, to predict CI build outcomes cost-efficiently and practically. We develop multiple failure-specific features from closely related historical builds via analyzing build logs and changed files, and propose an adaptive prediction model to switch between two models based on the build outcome of the previous build. We investigate a practical online usage scenario of BuildFast, where builds are predicted in chronological order, and measure the benefit from correct predictions and the cost from incorrect predictions. Our experiments on 20 projects have shown that BuildFast improved the state-of-the-art by 47.5% in F1-score for failed builds.
DCFGAN: Dynamic Convolutional Fusion Generative Adversarial Network for Text-to-Image Synthesis

Text-to-image synthesis is the task of synthesizing realistic and text-matching images according to given text descriptions. Most text-to-image generative networks consist of two modules: a pre-trained text-image encoder and a text-to-image generative adversarial network. In this paper, we propose a stronger text encoder which employs a text Transformer to extract semantically meaningful parts from text descriptions. With the stronger text encoder, the generator can obtain more meaningful text information to synthesize realistic and text-matching images. In addition, we propose a Dynamic Convolutional text-image Fusion Generative Adversarial Network (DCFGAN) which employs the Dynamic Convolutional Fusion Block to fuse text and image features efficiently. The Dynamic Convolutional Fusion block adjusts the parameters in the convolution layer according to different text descriptions to synthesize text-matching images. It improves the efficiency of fusing text features and image features in generator network. We evaluate the proposed DCF-GAN on two benchmark datasets, the CUB and the Oxford-102. The extensive experiments demonstrate that our proposed stronger text encoder and Dynamic Convolutional Fusion Layer can greatly promote the performance of text-to-image synthesis.
Effect of emoji prime on the understanding of emotional words – evidence from ERPs

Emojis play an increasingly important role as nonverbal symbols in online communication, and this study investigates their effect on the processing of subsequent words. We suppose that emojis, as an aid to emotional expression and semantic comprehension, prime the cognitive processing of the textual stimuli that follow. An affective priming experiment using ERPs was conducted, in which Semantic Relationship between the emoji and the word (Congruent, Incongruent, Non-related) was manipulated to assess their impact in an affective word categorization task. Behavioral results revealed no significant difference, while electrophysiological data showed otherwise. N1 component elicited by semantically congruent and incongruent emoji-word pairs showed greater amplitudes than those by non-related pairs. N400 elicited by incongruent pairs showed greater amplitudes than the N400 by congruent pairs. These data indicate that while emojis do not influence the outcomes of linguistic processing, emoji primes enhance attentional level, and semantically congruent contents demand fewer cognitive resources and facilitate better processing in comparison to incongruent ones. The findings point to emojis’ role not only as affective primes, but also as paralanguage that is semantically processed. Implications for computer-mediated communication are discussed.
Machine learning models to quantify the influence of PM10 aerosol concentration on global solar radiation prediction in South Africa

Solar prediction models are essential in developing countries such as South Africa as most meteorological stations are unable to consistently measure this quantity. Quantification of solar radiation is of great significance for the adoption and application of renewable energy systems. Machine learning models are infamous for their high prediction capacity and low input requirements. This study's foremost intention was to investigate the efficacy of using the most widespread machine learning techniques for solar radiation estimation across South Africa, by introducing PM10 air pollutant concentration to generalized, readily available meteorological datasets. While assessing the performance of various input parameter configurations, the techniques which were evaluated include; Artificial Neural Network (ANN), Support Vector Regression (SVR), General Regression Neural Network (GRNN), and Random Forest (RF), of which ANNs proved the most appropriate for predicting global solar radiation as indicated by the high correlation coefficients (R2 = 0.99852) and low prediction errors (RMSE = 0.22627). The results described in this work indicate that machine learning models performed excellently for hybrid models as opposed to empirical models established in South Africa. ANN models which include PM10 concentration data profoundly improved the performance of relative humidity models and reduced overall error measures for models of various input parameter configurations. In addition, a poor correlation between PM10 concentration and air temperature was observed. This work suggests that the use of generalized, hybrid ANN models to predict solar radiation across South Africa are more functional than empirical modeling and this is indicated by the high prediction accuracy and low computational effort. The suggested model suffices as an accurate prediction model which will allow for a holistic understanding of the solar capacity available across this country while encouraging the implementation and investigation of sustainable, renewable energy technologies.