Reviewerly/exHarmony huggingface dataset:
Review on Text Detection Methods on Scene Images

Recently, a lot of effort has been put into developing text detection methods on natural scene images in academic research and industry. In general, text detection refers to localizing all text instances in an image which can be further processed with an Optical Character Recognition (OCR) software in order to obtain machine-readable characters. The amount of published methods is constantly growing which makes it very challenging to be up-to-date with all approaches and state-of-the-art methods. Review papers become outdated in a less than a year from being published. Deep learning, a fast-growing field by itself, has become a mainstream approach in developing text detection methods. In this paper we present the up-to-date state-of-the-art methods in this challenging field. The methods are compared by their accuracy and real-time performance. We also present the most popular evaluation datasets for scene text detection.
Using Historical Information of Patients for Prior Authorization Learning

Prior authorization (or preauthorization) is a control mechanism used by a Health Maintenance Organization (HMO) to minimize the waste of resources by analyzing each medical request. One of the strategies used to optimize this task is the use of a system that automates part of this process through a machine learning approach. This paper presents an approach that introduces the historical request from beneficiaries to the learning process in preauthorization in order to improve performance of the classification. For this, different approaches to data pre-processing and classification were tested. In the proposed methodology three types of historical representation were tested: binary, term frequency (TF) and term frequency-inverse document frequency (TF-IDF). For each of these representations, feature selection and transformation algorithms were applied: consistency subset eval, wrapper subset eval and Kernel PCA (KPCA). In the classification were tested three classic algorithms of machine learning: Random Forest (RF), Support Vector Machine (SVM) and K-neareast neighbors (KNN). In order to evaluate the results obtained, we used the Precision, Recall and Kappa index metrics for each of the problem classes. A comparison based on the hypothesis test Z is also performed to evaluate the improvement that the addition of the historical information has brought about to the learning process. The results point to a significant improvement in the performance of classifiers, where the representation in term frequency combined with KPCA brought the most positive results.
An efficient neurodynamic model to solve nonconvex nonlinear optimization problems and its applications

Abstract This paper presents a recurrent neural network for solving nonconvex nonlinear optimization problems subject to nonlinear inequality constraints. First, the p ‐power transformation is exploited for local convexification of the Lagrangian function in nonconvex nonlinear optimization problem. Next, the proposed neural network is constructed based on the Karush–Kuhn–Tucker (KKT) optimality conditions and the projection function. An important property of this neural network is that its equilibrium point corresponds to the optimal solution of the original problem. By utilizing an appropriate Lyapunov function, it is shown that the proposed neural network is stable in the sense of Lyapunov and convergent to the global optimal solution of the original problem. Also, the sensitivity of the convergence is analysed by changing the scaling factors. Compared with other existing neural networks for such problem, the proposed neural network has more advantages such as high accuracy of the obtained solutions, fast convergence, and low complexity. Finally, simulation results are provided to show the benefits of the proposed model, which compare to or outperform existing models.
TCTracVis

Visualizing traceability links between unit tests and tested classes is an important factor for effectively understanding the development and evolution of software systems. This paper presents a prototype tool that is used to visualize traceability links between unit tests and tested classes. Two types of visualization techniques are adopted in our tool: small colored squares to visualize test cases in a specific class, and tree graph to visualize traces between a specific method and its related test cases for a selected class.
GSR-MAR: Global Super-Resolution for Person Multi-Attribute Recognition

Person attribute recognition aims to predict attribute labels based on person's appearance usually captured from surveillance cameras. It is a challenging problem in computer vision due to poor imaging quality with complex background clutter and unconstrained viewing conditions from various angles and distances between person and surveillance cameras. In this paper, we address such a problem using an end-to-end network called Global Super-Resolution for Multi Attribute Recognition (GSR-MAR). GSR-MAR integrates a conversion process of low-resolution input images into high-resolution images and predicts person attributes from input images. Before performing the classification process, GSR-MAR not only converts low-resolution images to high-resolution images to recover details of image textures but also captures larger context information by using large separable convolutional layers. The experiment results on two popular benchmark datasets demonstrate the performance improvement and effectiveness of our GSR-MAR model over competing baselines.
Hash Code Based Image Authentication Using Rotation Invariant Local Phase Quantization

Image authenticationReddy, Umamaheswar remains a key issue in todays age of multimedia technology. WithArya, Utkarsh the easy availability of a multitude of hackingKarsh, Ram Kumar techniques as well as picture editing tools suchLaskar, Rabul Hussian as Photoshop, image security remains a key issue with a lot of research scope. As such, our paper presents a model to authenticate images transmitted across a non-secure channel. Cryptographic based hashing techniques have been shown to be more robust as compared to watermarking techniques. Therefore, we propose a model using Rotation invariant Local Phase Quantization (RI-LPQ) to generate a feature vector for the input image. LPQ is blur insensitive, and coupled with rotation invariance property, it provides an accurate representation of texture features of the image.
Transient Performance of the Particle Swarm Optimization Algorithm From System Dynamics Point of View

Abstract In this paper, the performance of the particle swarm optimization(PSO) algorithm is studied from the system dynamics point of view. The dynamics of the particles in PSO algorithm are considered as second-order systems. Depending on the selections of the parameters, the second-order systems have over-damped, critically damped, underdamped, or undamped responses. Different responses give the algorithm different types of performance. Therefore, in this paper, we derive the conditions for parameters in the PSO algorithm such that the particles have different responses. The exploration and exploitation of PSO are discussed numerically. Moreover, due to the fact that the discrete model of PSO is converted from a continuous model by certain sampling ratio, the sampling ratio variable is introduced to the PSO algorithm. With different sampling ratios, the stability region of the PSO algorithm is increased and the performance of the algorithm is changed. Numerical examples are provided to demonstrate the performance of the PSO algorithm with different selections of the parameters.
AH-CoLT: an AI-Human Co-Labeling Toolbox to Augment Efficient Groundtruth Generation

Two critical aspects in building successful AI-based visual recognizers are having access to enough representative labeled data and creating a robust learning paradigm. The need for large scale training set is clearly understood in supervised learning models especially with deep structure; however, even for semi-supervised or unsupervised algorithms having a large set of labeled data is crucial for quantitative and comparative model performance evaluation. Reaching to the limits of manual annotation by human experts or via crowd sourcing, for the next big breakthroughs in the machine learning domain we need much more data/label pairs. To address this need, our AI-Human Co-Labeling Toolbox (AH-CoLT) presents an efficient semi-automatic groundtruth generation framework for unlabeled images/videos. AH-CoLT enables accurate groundtruth labeling by incorporating the outcomes of state-of-the-art AI recognizers into a time-efficient human-based review and revise process. In this paper, we evaluated the performance of the proposed AH-CoLT framework on generating groundtruth labels on human pose images. The resultant pose labels using our toolbox are shown to be more accurate and achieved in a more time-efficient way, when compared to the groundtruth pose labels generated by crowd sourcing.
Augmenting Hand-Drawn Art with Global Illumination Effects through Surface Inflation

We present a method for augmenting hand-drawn characters and creatures with global illumination effects. Given a single view drawing only, we use a novel CNN to predict a high-quality normal map of the same resolution. The predicted normals are then used as guide to inflate a surface into a 3D proxy mesh visually consistent and suitable to augment the input 2D art with convincing global illumination effects while keeping the hand-drawn look and feel. Along with this paper, a new high resolution dataset of line drawings with corresponding ground-truth normal and depth maps will be shared. We validate our CNN, comparing our neural predictions qualitatively and quantitatively with the recent state-of-the art, show results for various hand-drawn images and animations, and compare with alternative modeling approaches.
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization

Online minimization of an unknown convex function over the interval [0, 1] is considered under first-order stochastic bandit feedback, which returns a random realization of the gradient of the function at each query point. Without knowing the distribution of the random gradients, a learning algorithm sequentially chooses query points with the objective of minimizing regret defined as the expected cumulative loss of the function values at the query points in excess to the minimum value of the function. An approach based on devising a biased random walk on an infinite-depth binary tree constructed through successive partitioning of the domain of the function is developed. Each move of the random walk is guided by a sequential test based on confidence bounds on the empirical mean constructed using the law of the iterated logarithm. With no tuning parameters, this learning algorithm is robust to heavy-tailed noise with infinite variance and adaptive to unknown function characteristics (specifically, convex, strongly convex, and nonsmooth). It achieves the corresponding optimal regret orders (up to a √(log T) or a log log T factor) in each class of functions and offers better or matching regret orders than the classical stochastic gradient descent approach which requires the knowledge of the function characteristics for tuning the sequence of step-sizes.
A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter

Abstract An adaptive covariance inflation scheme is proposed for the ensemble Kalman filter (EnKF) to mitigate the loss of ensemble variance. Adaptive inflation methods are mostly based on a Bayesian approach, which considers the inflation factor as a random variable with a given prior probability distribution and then combines it with the inflation likelihood through Bayes' rule to obtain its posterior distribution. In this work, we introduce a numerical implementation of this generic Bayesian approach that uses a particle filter (PF) to compute a Monte Carlo approximation of the inflation posterior distribution. To alleviate the sample attrition issue, the proposed PF employs an artificial dynamical model for the inflation factor based on the well‐known smoothing‐kernel West and Liu model. The positivity constraint on the inflation factor is further imposed through an inverse‐Gamma transition density, with parameters that suggest analytical expressions. The resulting PF–EnKF scheme is straightforward to implement, and can use different numbers of particles in its EnKF and PF components. Numerical experiments are conducted with the Lorenz‐96 model to demonstrate the effectiveness of the proposed method under various experimental scenarios.
Pre-Service Teachers' Digital Competencies to Support School Students' Digital Literacies

The chapter has two main foci. The first focus is on the types of literacy practices needed by young people to work in a contemporary digital environment. Policies that impact on the development of digital literacy development are explored. The aspects underpinning digital literacy are examined and a sociocultural approach explained. Aspects of safety and ethics are focused on. The first half concludes by discussing digital games and ways these can be used to develop digital literacies in schools. The second focus is on the digital competencies that pre-service teachers can develop to support teaching of digital literacies. Different models for developing digital competencies are outlined. The aspect of critical understanding is then examined. This is followed by exploring digital story telling. Important considerations for developing digital competencies within and beyond university training are examined. The chapter then provides some suggestions for further research in this field.
A Hierarchical Approach Based on ACO and PSO by Neighborhood Operators for TSPs Solution

It is known that some of the algorithms in optimization field have originated from inspiration from animal behaviors in nature. Natural phenomena such as searching behavior of ants for food in a collective way, movements of birds and fish groups as swarms provided the inspiration for solutions of optimization problems. Traveling Salesman Problem (TSP), a classical problem of combinatorial optimization, has implementations in planning, scheduling and various scientific and engineering fields. Ant colony optimization (ACO) and Particle swarm optimization (PSO) techniques have been commonly used for TSP solutions. The aim of this paper is to propose a new hierarchical ACO- and PSO-based method for TSP solutions. Enhancing neighboring operators were used to achieve better results by hierarchical method. The performance of the proposed system was tested in experiments for selected TSPLIB benchmarks. It was shown that usage of ACO and PSO methods in hierarchical structure with neighboring operators resulted in better results than standard algorithms of ACO and PSO and hierarchical methods in literature.
Visual Clarity Analysis and Improvement Support for Presentation Slides

Presentation slides offer effective ways to deliver information in various fields. It has become easier to create slides owing to advanced presentation software such as PowerPoint. However, novices still face difficulty in designing slides that are easily comprehensible, as few slide evaluation methods exist that can objectively judge the quality of slides. In this paper, we analyze the features extracted from slides and tackle a simple classification problem, i.e., whether the input single-page slide is easy to understand. For evaluation, we created a new dataset of 1,000 PowerPoint slides with visual clarity label by using a crowdsourcing service. Using the 30% of the slides with high/low clarity rating, we achieved an accurate classification rate of 90.3%. We further proposed a feedback system that supports the improvement of slide designs. User study demonstrates that our system, which uses feedback on visual clarity scores and areas that should be modified, effectively supports slide improvement.
Deep Distributional Sequence Embeddings Based on a Wasserstein Loss

Abstract Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.
Alignment of Local and Global Features from Multiple Layers of Convolutional Neural Network for Image Classification

Convolutional networks have been extensively applied to obtain features spaces for classification tasks. Although those achieve high accuracy in many scenarios, typically only the top layers of the network are explored. Hence, a relevant question arises from this fact: are initial layers useful in terms of discriminative ability? In this paper, we leverage the complementary description offered by such first layers. Our method consists of features extraction in multiple layers, followed by feature selection, fusion of feature maps from the different layers, and space alignment. Through an extensive experimentation with different datasets and studying different training strategies, our results show that local information, coming from the first layers, may significantly improve the classification performance when merged with a global descriptor extracted from a top layer of the network. We report different methods for reducing the dimensionality of the local descriptors, and guidelines on how to align them so that to perform fusion. Our study encourages future studies on combining feature maps from multiple layers, which may be relevant in particular for transfer learning scenarios.
Ballistic Object Trajectory and Launch Point Estimation from Radar Measurements using Long-Short Term Memory Networks

In this paper, we consider the problem of estimating the trajectory of the ballistic object, especially the ballistic missiles, and its launch point based on radar measurements using promising deep learning technique-the Long-Short Term Memory(LSTM) Network. The conventional way of estimating the trajectories are solely based on the dynamic model of a missile to the Filter(KF). the implement Kalman However, since dynamics of missile change over the flight phases(boost, ballistic, reentry phase) and the flight phase of the missile would be difficult to differentiate using only radar observations, here we suggest a model-free method that utilizes the LSTM for the estimation. By implementing the suggested method, we can accurately estimate the trajectories and launch points of ballistic missiles only using the radar measurement without any consideration of the dynamics and parameters of the missile flight system.
Copy-Move Forgery Detection using SIFT and GLCM-based Texture Analysis

Easier access to editing tools and growing risk of image manipulation has encouraged extensive research in copy-move forgery detection. Although the current methods have been able to detect this tampering to a good extent, their accuracies drop when tested on images with different sizes of tampered regions and in the presence of similar but genuine objects in the image. In this paper, these issues are addressed by including a novel GLCM-based Texture Analysis Filter that gives information about the textural similarity of the keypoint neighbourhoods by using difference of GLCM contrasts as the similarity metric. Experimental results show that the proposed technique can address a variety of different tampering scenarios and outperforms the existing state-of-the-art Copy-Move Forgery Detection(CMFD) techniques by handling multiple forgeries, returning corresponding geometrical parameters and significantly improving the false positive rates.
Designing Public Digital Cultural Service Interactive System Based on Reality–Based Interaction Principles

Internet of Things (IoT), big data, artificial intelligence (AI), and the new media environment have changed the traditional pattern of communication in public cultural service. Public digital cultural service (PDCS) system is a kind of cyber physical system that integrates physical and digital cultural resources. The public could access cultural knowledge more conveniently from the PDCS system. However, there are existing of some accessibility issues in the interactive process between the public cultural service digital terminals and users. This paper explored methods of designing PDCS interactive system using reality-based interaction (RBI) principles to enhance the user experience. RBI principles using in PDCS provides natural communication and displays ways, which are helpful in solving the problem of traditional public culture service transmission. The proposed methods supply the intelligent services according to the different information input by users such as the actions, facial expressions, voice, and physiological signals. User’s context is also considered. At the same time, public cultural service data is obtained, analyzed and visualized to provide public digital cultural knowledge to users. In this way, the general population and special groups like the elderly people could use the PDCS interactive system without any obstacles. The research is helpful to improve the accessibility and usability of PDCS system.
Constants in Titchmarsh divisor problems for elliptic curves

Inspired by the analogy between the group of units $$\mathbb {F}_p^{\times }$$ of the finite field with p elements and the group of points $$E(\mathbb {F}_p)$$ of an elliptic curve $$E/\mathbb {F}_p$$, E. Kowalski, A. Akbary & D. Ghioca, and T. Freiberg & P. Kurlberg investigated the asymptotic behaviour of elliptic curve sums analogous to the Titchmarsh divisor sum $$\sum \nolimits _{p \le x} \tau (p + a) \sim C x$$. In this paper, we present a comprehensive study of the constants C(E) emerging in the asymptotic study of these elliptic curve divisor sums in place of the constant C above. Specifically, by analyzing the division fields of an elliptic curve $$E/\mathbb {Q}$$, we prove bounds for the constants C(E) and, in the generic case of a Serre curve, we prove explicit closed formulae for C(E) amenable to concrete computations. Moreover, we compute the moments of the constants C(E) over two-parameter families of elliptic curves $$E/\mathbb {Q}$$. Our methods and results complement recent studies of average constants occurring in other conjectures about reductions of elliptic curves by addressing not only the average behaviour, but also the individual behaviour of these constants, and by providing explicit tools towards the computational verifications of the expected asymptotics.