Reviewerly/exHarmony huggingface dataset:
ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection

In this study, new feature descriptors are designed for medical image retrieval and change detection applications, respectively. Inspired by isomerism, the authors propose a novel feature descriptor named antithetic isomeric cluster pattern (ANTIC). The ANTIC is defined by the two properties: cluster patterns and antithetic isomerism (ANTI). The cluster pattern corresponds to successive pixel intensity differences at antithetical orientations. Furthermore, the ANTI is characterised by two aspects: first, the clusters are oppositely oriented (antithetical) to each other and second, both adhere to a defined isomeric property. The ANTIC identifies the lines and corner point information in the local neighbourhood across various directions. To attain enhanced robustness, they further proposed multiresolution ANTIC by integrating the multiresolution Gaussian filter. Moreover, to reduce the feature dimensionality, they extended their work to rotation invariant features. The proposed method outperforms other widely used feature descriptors in biomedical and retinopathy image retrieval applications. In addition, they extracted spatiotemporal features by designing intra‐ANTIC and inter‐ANTIC to detect motion changes in video sequences. They validated the effectiveness of these features by conducting experiments on CDNet 2014 dataset. The proposed descriptor achieves better performance in various challenging conditions for change detection as compared to other state‐of‐the‐art techniques.
Retest test selection for product-line regression testing of variants and versions of variants

Testing is a crucial activity of product-line engineering. Due to shared commonality, testing each variant individually results in redundant testing processes. By adopting regression testing strategies, variants are tested incrementally by focusing on the variability between variants to reduce the overall testing effort. However, product lines evolve during their life-cycle to adapt, e.g., to changing requirements. Hence, quality assurance has also to be ensured after product-line evolution by efficiently testing respective versions of variants. In this paper, we propose retest test selection for product-line regression testing of variants and versions of variants. Based on delta-oriented test modeling, we capture the commonality and variability of an evolving product line by means of differences between variants and versions of variants. We exploit those differences to apply change impact analyses, where we reason about changed dependencies to be retested when stepping from a variant or a version of a variant to its subsequent one by selecting test cases for reexecution. We prototypically implemented our approach and evaluated its effectiveness and efficiency by means of two evolving product lines showing positive results.
A steady state quantum classifier

We report that under some specific conditions a single qubit model weakly interacting with information environments can be referred to as a quantum classifier. We exploit the additivity and the divisibility properties of the completely positive (CP) quantum dynamical maps in order to obtain an open quantum classifier. The steady state response of the system with respect to the input parameters was numerically investigated and it's found that the response of the open quantum dynamics at steady state acts non-linearly with respect to the input data parameters. We also demonstrate the linear separation of the quantum data instances that reflects the success of the functionality of the proposed model both for ideal and experimental conditions. Superconducting circuits were pointed out as the physical model to implement the theoretical model with possible imperfections.
UAV Collision Avoidance Based on Varying Cells Strategy

This paper presents a trajectory planning strategy for collision avoidance in unmanned aerial vehicles. First, a varying cell strategy is proposed to integrate aerodynamic constraints into trajectory planning. Basic avoidance actions in the varying cell strategy are adapted accordingly to go through different cells, enabling more flexible avoidance maneuvers. Second, given the limited decision-making time involved, offline and online path planning methods are developed to increase the convergence rate. Finally, Monte Carlo simulations demonstrate that the proposed method satisfies aerodynamic constraints, while both the convergence and collision avoidance rates are better than that achieved by fixed cell-based methods.
Multiview 3D human pose estimation using improved least-squares and LSTM networks

In this paper we present a deep learning based method to estimate the human pose in 3D when multiple 2D views are available. Our system is composed of a cascade of specialized systems. Firstly, 2D poses are obtained using a deep neural network for the detection of skeleton keypoints in each available view. Then, the 3D coordinates of each keypoint are reconstructed with our proposed least squares optimization method, that analyzes the quality of the 2D detections to decide either to consider or reject them. Once the 3D poses are obtained for each time step, full body pose estimation is performed with a long short-term memory (LSTM) neural network, that takes advantage of the process history to refine the final pose estimation. We provide evidence of the suitability of our contributions in an extensive experimental study. Finally, we were able to prove experimentally that our method obtains competitive results when it is compared to recent representative works in the literature.
An effective vector filter for impulse noise reduction based on adaptive quaternion color distance mechanism

The measure of color distances plays an important role in color image processing. An effective color distance method is based on quaternion representation, which computes a color distance by weighted average of the distances of luminance and quaternion chromaticity. However, the mechanism of assigning fixed weights to luminance and chromaticity distances cannot always effectively measure color distances, since in a color image chromaticity can change significantly. To address this issue, this paper proposes an adaptive weighted quaternion color distance method. Based on the new color distance measure, the robust outlyingness ratio and local reachability density, which are defined in grayscale images, are extended to color images to implement a coarse-to-fine color noise detection operator. In noise filtering, a weighted vector median filter is employed to restore the pixels judged as noisy. Experimental results exhibit the validity of the proposed method by showing better performance in terms of both objective criteria and visual effect, compared to other widely-used color image filtering methods.
A spatially constrained shifted asymmetric Laplace mixture model for the grayscale image segmentation

In this paper, the grayscale image segmentation problem is investigated and a new mixture model with shifted asymmetric Laplace distribution component is proposed. Instead of the conventional Gaussian model, the shifted asymmetric Laplace distribution model is adopted to model the pixels. The spatial constraint on neighboring pixels is introduced into the proposed shifted asymmetric Laplace mixture model, which makes the model be robust to noise and outliers of the images. The unknown model parameters are estimated via the expectation-maximization (EM) algorithm, which can guarantee convergence to a local minimum. The experimental verification is performed on both synthesized images and images of real chip to prove the effectiveness of our image segmentation method.
Design recommendations for online cybersecurity courses

Nowadays, a significant amount of free online cybersecurity training courses are offered. When preparing further courses, the designer has to decide what to teach and how to do it. In this paper, we provide with a set of recommendations for both issues. Concerning topic selection, 35 free online courses are analysed using NIST's NICE reference framework. Thus, several training gaps are discovered. Concerning the way of preparing the course (or refining it after the first edition), a set of good practices is proposed based on students' performance and commitment in a cybersecurity MOOC with +2,000 initially active students. To foster further research in this area, an open-source framework is released to enable the analysis of students' performance in EdX MOOCs.
Improved generative adversarial networks with reconstruction loss

We propose a simple regularization scheme to handle the problem of mode missing and unstable training in the generative adversarial networks (GAN). The key idea is to utilize the visual features learned by the discriminator. We reconstruct the real data by feeding the generator with the real data features extracted by the discriminator. A reconstruction loss is added to the GAN's objective function to enforce the generator can reconstruct from the features of the discriminator, which helps to explicitly guide the generator towards to near the probable configurations of real data. The proposed reconstruction loss improves the performance of GAN, produces higher quality images on different dataset, and can be easily combined with other regularization loss functions such as gradient penalty to improve the performance of various GANs. We conducted experiments on the widespread adopted architecture DCGAN and the complicated ResNet architecture across different datasets, the results of which show the effectiveness and robustness of our proposed method.
Facial Action Unit Recognition and Intensity Estimation Enhanced Through Label Dependencies

The inherent dependencies among facial action units (AUs) caused by the underlying anatomic mechanism are essential for the proper recognition of AUs and the estimation of intensity levels, but they have not been exploited to their full potential. We are proposing novel methods to recognize AUs and estimate intensity via hybrid Bayesian networks (BNs). The upper two layers are latent regression BNs (LRBNs), and the lower layers are BNs. The visible nodes of the LRBN layers are the representations of ground-truth AU occurrences or AU intensities. Through the directed connections from latent layer and visible layer, an LRBN can successfully represent relationships between multiple AUs or AU intensities. The lower layers include BNs with two nodes for AU recognition, and BNs with three nodes for AU intensity estimation. The bottom layers incorporate measurements from facial images with AU dependencies for intensity estimation and AU recognition. Efficient learning algorithms of the hybrid Bayesian networks are proposed for AU recognition as well as intensity estimation. Furthermore, the proposed hybrid BN models are extended for facial expression-assisted AU recognition and intensity estimation, as AU relationships are closely related to facial expressions. We test our methods on three benchmark databases for AU recognition and two benchmark databases for intensity estimation. The results demonstrate that the proposed approaches faithfully model the complex and global inherent AU dependencies, and the expression labels available only during training can boost the estimation of AU dependencies for both AU recognition and intensity estimation.
Slimming javascript applications: An approach for removing unused functions from javascript libraries

A common practice in JavaScript development is to ship and deploy an application as a large file, called bundle, which is the result of combining the application code along with the code of all the libraries the application depends on. Despite the benefits of having a single bundle per application, this approach leads to applications being shipped with significant portions of code that are actually not used, which unnecessarily inflates the JavaScript bundles and could slow down website loading because of the extra unused code. Although some static analysis techniques exist for removing unused code, our investigations suggest that there is still room for improvements. The goal of this paper is to address the problem of reducing the size of bundle files in JavaScript applications. In this context, we define the notion of Unused Foreign Function (UFF) to denote a JavaScript function contained in dependent libraries that is not needed at runtime. Furthermore, we propose an approach based on dynamic analysis that assists developers to identify and remove UFFs from JavaScript bundles. We report on a case-study performed over 22 JavaScript applications, showing evidence that our approach can produce size reductions of 26% on average (with reductions going up to 66% in some applications). It is concluded that removing unused foreign functions from JavaScript bundles helps reduce their size, and thus, it can boost the results of existing static analysis techniques.
Improving educational web search for question-like queries through subject classification

Students use general web search engines as their primary source of research while trying to find answers to school-related questions. Although search engines are highly relevant for the general population, they may return results that are out of educational context. Another rising trend; social community question answering websites are the second choice for students who try to get answers from other peers online. We attempt discovering possible improvements in educational search by leveraging both of these information sources. For this purpose, we first implement a classifier for educational questions. This classifier is built by an ensemble method that employs several regular learning algorithms and retrieval based approaches that utilize external resources. We also build a query expander to facilitate classification. We further improve the classification using search engine results and obtain 83.5% accuracy. Although our work is entirely based on the Turkish language, the features could easily be mapped to other languages as well. In order to find out whether search engine ranking can be improved in the education domain using the classification model, we collect and label a set of query results retrieved from a general web search engine. We propose five ad-hoc methods to improve search ranking based on the idea that the query-document category relation is an indicator of relevance. We evaluate these methods for overall performance, varying query length and based on factoid and non-factoid queries. We show that some of the methods significantly improve the rankings in the education domain.
Effects of adaptive training on metacognitive knowledge monitoring ability in computer-based learning

An experimental study was conducted with 8th grade students (N = 41), ranging from 13 to 14 years old, whereby they solved first-degree equations using a step-based intelligent tutoring system. The aim of the study is to assess the training of the metacognitive knowledge monitoring ability carried out by an animated pedagogical agent integrated into the tutoring system. Students from the experimental group received metacognitive instructions from the agent, as well as some hints and feedback, whereas those from the control group received only hint-messages and feedback. Pretest and posttest sessions were carried out in order to collect the metacognitive knowledge monitoring ability indexes, as well as the students' performance grades for first degree equations solving. The results indicated that students who received metacognitive instruction presented greater improvement on metacognitive knowledge monitoring ability indexes. The study also monitored students' performance in solving equations during experimental sessions and found that those who received metacognitive instructions completed more equations correctly. This paper also discusses the implications of the results for the theory of metacognitive knowledge monitoring ability.
Nonnegative matrix factorization for clustering ensemble based on dark knowledge

Traditional cluster ensemble (CE) methods use labels produced by base learning algorithms to obtain an ensemble result. These base learning algorithms can also obtain other information, such as parameter, covariance, or probability data, which is called dark knowledge. In this paper, we propose a method for integrating dark knowledge, which is usually ignored, into the ensemble learning process. This provides more information about the base clustering. We apply nonnegative matrix factorization (NMF) to the clustering ensemble model based on dark knowledge. First, different base clustering results are obtained by using various clustering configurations, before dark knowledge of every base clustering algorithm is extracted. NMF is then applied to the dark knowledge to obtain integrated results. Experimental results show that the method outperforms other clustering ensemble techniques.
Fast and Communication-Efficient Algorithm for Distributed Support Vector Machine Training

Support Vector Machines (SVM) are widely used as supervised learning models to solve the classification problem in machine learning. Training SVMs for large datasets is an extremely challenging task due to excessive storage and computational requirements. To tackle so-called big data problems, one needs to design scalable distributed algorithms to parallelize the model training and to develop efficient implementations of these algorithms. In this paper, we propose a distributed algorithm for SVM training that is scalable and communication-efficient. The algorithm uses a compact representation of the kernel matrix, which is based on the QR decomposition of low-rank approximations, to reduce both computation and storage requirements for the training stage. This is accompanied by considerable reduction in communication required for a distributed implementation of the algorithm. Experiments on benchmark data sets with up to five million samples demonstrate negligible communication overhead and scalability on up to 64 cores. Execution times are vast improvements over other widely used packages. Furthermore, the proposed algorithm has linear time complexity with respect to the number of samples making it ideal for SVM training on decentralized environments such as smart embedded systems and edge-based internet of things, IoT.
Item orientated recommendation by multi-view intact space learning with overlapping

Item orientated recommendation has recently been proposed to address the issue of finding the most suitable users of certain item so as to maximize the revenue of its manufacturer with limited advertising budget, which cannot be settled very well by the traditional recommendation algorithms. Existing multi-view models are instructive to solve this problem but either need extra information or suffer from scalability issue due to the high complexity when dealing with large number of records. This paper presents a solution to meet the need of improving both the quality and the scalability of item orientated recommendation by designing a new multi-view model. The basic idea is to visualize the rating records of each user as feature representations from an individual view to partially characterize the property of items. From the multiple but insufficient view representations, the latent feature representation of each item in the intact space, the view generation function of each user, and the overlapping of preferences among different users can be discovered, which are used to predict the unknown ratings from users to a certain item. Extensive experiments have been conducted on three types of datasets, and the results have confirmed the effectiveness of the proposed algorithm.
The passenger flow status identification based on image and WiFi detection for urban rail transit stations

During the peak hours, the concentration of passenger flow is relatively high for some busy subway lines, if the measures can’t be taken in time, more serious accidents may happen, which will influence the social image of the subway. At present, the passenger flow of the key stations is judged mainly by the experience of the staffs, and then the corresponding measures are taken, the errors may be large, and the relevant technical research is urgently needed. First, a data collection device called “the elf of passenger flow-collecting”, which integrates high definition camera image acquisition equipment and WIFI probe technology was set up. It can be used to collect the original passenger flow data of congestion points of subway stations. Second, a convolution neural network passenger flow identification algorithm based on deep learning is designed, which is used to estimate the P0 of stations. Third, because of the error in the video image recognition algorithm, the WIFI probe data acquisition scheme is designed, and the SQL preprocessing assembly for WIFI data processing is established. The noise of WIFI probe is preprocessed, and the flow rate of P5 based on WIFI probe is obtained. The difference between P0 and P5 is defined, and the degree of the difference between P0 and P5 is calculated, so the final passenger flow P6 can be obtained. Finally, the Songjiang University Hall Station of Shanghai Metro line 9 was taken as an experimental analysis object, the high definition camera and WIFI probe are set up on the spot, the passenger flow video data and the WIFI data are collected synchronously, so the real-time passenger flow in the station's internal position is estimated, and the accuracy is corrected, meanwhile the passenger flow early warning of the station position is obtained. An emergency response plan based on passenger flow early warning level is proposed, and the flow chart of passenger flow density inside Songjiang University hall station is drawn. The construction of the equipment platform and the identification and correction methods of passenger flow are of good practical guiding significance for the Metro to run safely.
Quantitative evaluation of overlaying discrepancies in mobile augmented reality applications for AEC/FM

Augmented Reality (AR) is a trending technology that provides a live view of the real and physical environment augmented by virtual elements, enhancing the information of the scene with digital information (sound, video, graphics, text or geo-location). Its application to architecture, engineering and construction, and facility management (AEC/FM) is straightforward and can be very useful to improve the on-site work at different stages of the projects. However, one of the most important limitations of Mobile Augmented Reality (MAR) is the lack of accuracy when the screen overlays the virtual models on the real images captured by the camera. The main reasons are errors related to tracking (positioning and orientation of the mobile device) and image capture and processing (projection and distortion issues). This paper shows a new methodology to mathematically perform a quantitative evaluation, in world coordinates, of those overlaying discrepancies on the screen, obtaining the real-scale distances from any real point to the sightlines of its virtual projections for any AR application. Additionally, a new utility for filtering built-in sensor signals in mobile devices is presented: the Drift-Vibration-Threshold function (DVT), a straightforward tool to filter the drift suffered by most sensor-based tracking systems.