Reviewerly/exHarmony huggingface dataset:
Error estimation of 3D reconstruction in 3D digital image correlation

Three-dimensional digital image correlation (3D-DIC) is widely used in various measurement fields. Once a DIC setup is established, different parameters, such as lens focal length, pixel size, stereo angle, and working distance, are easy to know or measure; the range of the matching error is generally 0.01–0.02 pixels. It would be very convenient if the measurement error could be estimated based on the parameters above without performing the entire DIC calculation process. In this study, a numerical simulation was conducted to study the variation of the 3D reconstruction error with the stereo angle in different measurement areas. It was found that the error of the center point was closely related to the error of the full field. Therefore, using simple parameters to describe the 3D reconstruction error of the center point is of great significance for estimating the overall error of a DIC system. The general solution of the 3D reconstruction equation was simplified on the premise that high order terms of pixel coordinates could be omitted. The derived error estimation formulas had a simple form and described the 3D reconstruction error of the center point in three directions. Through a set of static experiments, the 3D reconstruction error of a planar object under different stereo angles (from 10° to 120°) was studied. The matching error was calculated by a 2D-DIC software and inserted into the proposed formulas to obtain the estimated error. The estimated error was compared with the results of a 3D-DIC software, and the effectiveness of the proposed method was verified.
Hybrid Lagrange interpolation differential evolution algorithm for path synthesis

A hybrid Lagrange interpolation differential evolution algorithm (HLIDE) is proposed in this study for path synthesis of four-bar mechanisms. HLIDE combines two algorithms of differential evolution (DE) with Lagrange interpolation local search (LILS). LILS performs a local search in the neighborhood of the best individual to enhance the local exploitation capability of DE. In addition, an adaptive local search strategy is presented to further improve the efficiency of LILS. This technique can adaptively apply LILS based on the performance of LILS and DE in previous generations. To evaluate the efficiency and accuracy of HLIDE, five cases of path synthesis involving four-bar mechanisms were tested. Moreover, three well-known evolutionary algorithms, i.e., particle swarm optimization, teaching-learning-based optimization, and differential evolution, were implemented and compared in the five cases. Previous solutions for the same path generation problems by different evolutionary algorithms are summarized and compared in this study. Experimental results showed that HLIDE has significantly better performance in solving the synthesis problems of mechanisms when compared to other algorithms.
An Intuitionistic Fuzzy Approach to the Hungarian Algorithm

In the paper a new type of assignment problem is formulated, in which the costs of assigning tasks to candidates are intuitionistic fuzzy pairs. Additional constraints are formulated to the problem: an upper limit to the cost of assigning a particular resource to perform a particular activity and preferences defined in advance for assigning the resources by an index matrix. We propose for the first time the Hungarian algorithm for finding an optimal solution of this new type of assignment problem, based on the concept of index matrices.
Change impact analysis for maintenance and evolution of variable software systems

Understanding variability is essential to allow the configuration of software systems to diverse requirements. Variability-aware program analysis techniques have been proposed for analyzing the space of program variants. Such techniques are highly beneficial, e.g., to determine the potential impact of changes during maintenance. This article presents an interprocedural and configuration-aware change impact analysis (CIA) approach for determining the possibly impacted source code elements when changing the source code of a product family. The approach also supports engineers, who are adapting the code of specific product variants after an initial pre-configuration. The approach can be adapted to work with different variability mechanisms, it is more precise than existing CIA approaches, and it can be implemented using standard control flow and data flow analysis. We report evaluation results on the benefit and performance of the approach using industrial product lines.
Interpretable explanations of black box classifiers applied on medical images by meaningful perturbations using variational autoencoders

The growing popularity of black box machine learning methods for medical image analysis makes their interpretability to a crucial task. To make a system, e.g. a trained neural network, trustworthy for a clinician, it needs to be able to explain its decisions and predictions. In this work, we tackle the problem of generating plausible explanations for the predictions of medical image classifiers, that are trained to differentiate between different types of pathologies and healthy tissues. An intuitive solution to determine which image regions influence the trained classifier is to find out whether the classification results change when those regions are deleted. This idea can be formulated as a minimization problem and thus efficiently implemented. However, the meaning of "deletion" of image regions, in our case pathologies in medical images, is not defined. We contribute by defining the deletion of pathologies, as the replacement by their healthy looking equivalent generated using variational autoencoders. The experiments with a classification neural network on OCT (Optical Coherence Tomography) images and brain lesion MRIs show that a meaningful replacement of "deleted" image regions has significant impact on the reliability of the generated explanations. The proposed deletion method is proven to be successful since our approach delivers the best results compared to four other established methods.
Recommender Systems with Heterogeneous Side Information

In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneous and can be roughly categorized into flat and hierarchical side information. While side information has been proved to be valuable, the majority of existing systems have exploited either only flat side information or only hierarchical side information due to the challenges brought by the heterogeneity. In this paper, we investigate the problem of exploiting heterogeneous side information for recommendations. Specifically, we propose a novel framework jointly captures flat and hierarchical side information with mathematical coherence. We demonstrate the effectiveness of the proposed framework via extensive experiments on various real-world datasets. Empirical results show that our approach is able to lead a significant performance gain over the state-of-the-art methods.
A novel security scheme for behavioral authentication systems based on keystroke dynamics

This article presents a comprehensive analysis behavioral authentication systems based on keystroke dynamics using android mobile devices. Behavioral security authentication is an efficient biometric‐based security system that can be used to authenticate users. It is exploited to strengthen password authentication efficiently and inexpensively because no extra hardware is required in most of these schemes. Keystroke dynamic rhythm uses combinations of timing features and nontiming features that are extracted and processed from several devices such as classical keyboard and built‐in software keyboard in touch screen and smart phone devices. This work presents a comprehensive analysis of using biometric behavioral authentication system‐based on keystroke dynamics. Neural network classifiers are used in this work. The performance results show that keystroke dynamics provides good level in performance measures as a second authentication factor. The distinguishable role for nontiming features in addition to the timing features is demonstrated. These features have a significant role in improving the performance measures. The proposed model achieves low error rate of 0.3% for false acceptance rate, a false rejection of 1.5%, and an equal error rate of 0.9%. These are considered excellent enhancements when compared with previous reported results. Suggestions for future directions, and challenges for using behavioral‐based authentication systems are also highlighted.
Panoramic Background Image Generation for PTZ Cameras

Being able to cover a wide range of views, pan-tilt-zoom (PTZ) cameras have been widely deployed in visual surveillance systems. To achieve a global-view perception of a surveillance scene, it is necessary to generate its panoramic background image, which can be used for the subsequent applications such as road segmentation, active tracking, and so on. However, few works have been reported on this problem, partially due to the lack of benchmark dataset and the high complexity of panoramic image generation of PTZ cameras. In this paper, we build, for the first time to our best knowledge, a benchmark PTZ camera dataset with multiple views, and derive a complete set of panoramic transformation formulas for PTZ cameras. We further propose a fast multi-band blending method to address the efficiency issue in panoramic image fusion and mosaicing. Some related panoramic transformations are also developed, such as cylindrical and overlooking transformations. Our proposed approach exhibits impressive accuracy and efficiency in PTZ panorama generation as well as panoramic image mosaicing.
Enhanced skeleton and face 3D data for person re-identification from depth cameras

Person re-identification is typically performed using 2D still images or videos, where photometric appearance is the main visual cue used to discover the presence of a target subject when switching from different camera views across time. This invalidates any application where a person may change dress across subsequent acquisitions as can be the case of patients monitoring at home. Differently from RGB data, 3D information as acquired by depth cameras can open the way to person re-identification based on biometric cues such as distinguishing traits of the body or face. However, the accuracy of skeleton and face geometry extracted from depth data is not always adequate to enable person recognition, since both these features are affected by the pose of the subject and the distance from the camera. In this paper, we propose a method to derive a robust skeleton representation from a depth sequence and to complement it with a highly discriminative face feature. This is obtained by selecting skeleton and face samples based on their quality and using the temporal redundancy across the sequence to derive and refine cumulated models for both of them. Extracting skeleton and face features from such cumulated models and combining them for the recognition allow us to improve rank-1 re-identification accuracy compared to individual cues. A comparative evaluation on three benchmark datasets also shows results at the state-of-the-art.
Watch &amp; Do: A Smart IoT Interaction System with Object Detection and Gaze Estimation

The Internet of Things (IoT) attempts to help people access Internet-connected devices, applications, and services anytime and anywhere. However, how providing an efficient and intuitive method of interaction between people and IoT devices is still an open challenge. In this paper, we propose a novel interaction system called Watch & Do, where users can control an IoT device by gazing at it and doing simple gestures. The proposed system mainly consists of: 1) object detection module; 2) gaze estimation module; 3) hand gesture recognition module; and 4) IoT controller module. The target device is identified by various deep learning-based gaze estimation and object detection techniques. Afterwards, hand gesture recognition is applied to generate an IoT device control command which is transmitted to the IoT platform. The experimental results and case studies demonstrate the feasibility of the proposed system and imply the future research directions.
Graph-based context-aware collaborative filtering

Context-aware recommender systems (CARS) are specially designed to take into account the contextual conditions under which a user experiences an item, with the goal of generating improved recommendations. A known difficulty when constructing recommender systems is data sparseness, which reduces the effectiveness of collaborative filtering algorithms. While using contextual information provides fine-grained signals for the recommendation process, it makes the data even sparser and increases the computational complexity. In this paper, we present a method for making context-aware recommendations, which is less sensitive to data sparseness. The proposed method exploits the transitivity of the interactions between users and items on the user-item graph to augment the direct interactions, thus reducing the negative effect of sparse data. Based on graph transitivity we introduce a new graph-based association measure that we use as a measure of similarity between two users or two items in nearest neighbor recommendation methods. This combination of graph-based similarity measure with nearest neighbor methods allows considering more contextual conditions at a lower risk of being affected by data sparseness caused by additional contextual dimensions. We experimentally evaluated the proposed method on three contextually-tagged data sets. The results show that our method outperforms several baselines and state-of-the-art context-aware recommendation methods.
Lexicase Selection Beyond Genetic Programming

Lexicase selection is a selection method that was developed for parent selection in genetic programming. In this chapter, we present a study of lexicase selection in a non-genetic-programming context, conducted to investigate the broader applicability of the technique. Specifically, we present a framework for solving Boolean constraint satisfaction problems using a traditional genetic algorithm, with linear genomes of fixed length. We present results of experiments in this framework using three parent selection algorithms: lexicase selection, tournament selection (with several tournament sizes), and fitness-proportionate selection. The results show that when lexicase selection is used, more solutions are found, fewer generations are required to find those solutions, and more diverse populations are maintained. We discuss the implications of these results for the utility of lexicase selection more generally.
Robust Trajectory Execution for Multi-robot Teams Using Distributed Real-time Replanning

Robust trajectory execution is an extension of cooperative collision avoidance that takes pre-planned trajectories directly into account. We propose an algorithm for robust trajectory execution that compensates for a variety of dynamic changes, including newly appearing obstacles, robots breaking down, imperfect motion execution, and external disturbances. Robots do not communicate with each other and only sense other robots’ positions and the obstacles around them. At the high-level we use a hybrid planning strategy employing both discrete planning and trajectory optimization with a dynamic receding horizon approach. The discrete planner helps to avoid local minima, adjusts the planning horizon, and provides good initial guesses for the optimization stage. Trajectory optimization uses a quadratic programming formulation, where all safety-critical parts are formulated as hard constraints. At the low-level, we use buffered Voronoi cells as a multi-robot collision avoidance strategy. Compared to ORCA, our approach supports higher-order dynamic limits and avoids deadlocks better. We demonstrate our approach in simulation and on physical robots, showing that it can operate in real time.
A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

The term web-app defines the current dynamic pragmatics of the website, where the user has control. Finding faults in such dynamic content is challenging, as to whether the fault is exposed or not depends on its execution path. Moreover, the complexity and uniqueness of each web application make fault assessment an extremely laborious and expensive task. Also, artificial fault injection models are run in controlled and simulated environments, which may not be representative of the real-world fault data. Classifying faults can intelligently enhance the quality of the web-apps by the assessment of probable faults. In this paper, an empirical study is conducted to classify faults in bug reports of three open-source web-apps (qaManager, bitWeaver, and WebCalendar) and reviews of two play store web-apps (Dineout: Reserve a Table and Wynk Music). Five supervised learning algorithms (naïve Bayesian, decision tree, support vector machines, K-nearest neighbor, and multi-layer perceptron) have been first evaluated based on the conventional term frequency-inverse document frequency (tf-idf) feature extraction method, and subsequently, a feature selection method to improve classifier performance is proposed using particle swarm optimization (a nature-inspired, meta-heuristic algorithm). This paper is a preliminary exploratory study to build an automated tool, which can optimally categorize faults. The empirical analysis validates that the particle swarm optimization for feature selection in fault classification task outperforms the tf-idf filter-based classifiers with an average accuracy gain of about 11% and nearly 26% average feature reduction. The highest accuracy of 93.35% is shown by the decision tree after feature selection.
PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights

A dynamic adjustment of parameters for the particle swarm optimization (PSO) utilizing an interval type-2 fuzzy inference system is proposed in this work. A fuzzy neural network with interval type-2 fuzzy number weights using S-norm and T-norm is optimized with the proposed method. A dynamic adjustment of the PSO allows the algorithm to behave better in the search for optimal results because the dynamic adjustment provides good synchrony between the exploration and exploitation of the algorithm. Results of experiments and a comparison between traditional neural networks and the fuzzy neural networks with interval type-2 fuzzy numbers weights using T-norms and S-norms are given to prove the performance of the proposed approach. For testing the performance of the proposed approach, some cases of time series prediction are applied, including the stock exchanges of Germany, Mexican, Dow-Jones, London, Nasdaq, Shanghai, and Taiwan.
Super-Resolution of Brain MRI Images Using Overcomplete Dictionaries and Nonlocal Similarity

Recently, the magnetic resonance imaging (MRI) images have limited and unsatisfactory resolutions due to various constraints such as physical, technological, and economic considerations. Super-resolution techniques can obtain high-resolution MRI images. The traditional methods obtained the resolution enhancement of brain MRI by interpolations, affecting the accuracy of the following diagnose process. The requirement for brain image quality is fast increasing. In this paper, we propose an image super-resolution method based on overcomplete dictionaries and the inherent similarity of an image to recover the high-resolution (HR) image from a single low-resolution (LR) image. We use the linear relationship among images in the measurement domain and frequency domain to classify image blocks into smooth, texture, and edge feature blocks in the measurement domain. The dictionaries for different blocks are trained using different categories. Consequently, an LR image block of interest may be reconstructed using the most appropriate dictionary. In addition, we explore the nonlocal similarity of the image to tentatively search for similar blocks in the whole image and present a joint reconstruction method based on compressed sensing (CS) and similarity constraints. The sparsity and self-similarity of the image blocks are taken as the constraints. The proposed method is summarized in the following steps. First, a dictionary classification method based on the measurement domain is presented. The image blocks are classified into smooth, texture, and edge parts by analyzing their features in the measurement domain. Then, the corresponding dictionaries are trained using the classified image blocks. Equally important, in the reconstruction part, we use the CS reconstruction method to recover the HR brain MRI image, considering both nonlocal similarity and the sparsity of an image as the constraints. This method performs better both visually and quantitatively than some existing methods.
Where are we now? Re-visiting the Digital Earth through human-centered virtual and augmented reality geovisualization environments

The original Digital Earth concept, formulated by Al Gore (1998), is essentially a virtual reality system. In this (imagined) system, users are able to freely explore all possible recorded knowledg...
A novel hybrid share reporting strategy for blockchain miners in PPLNS pools

In blockchain pool mining, Pay-Per-Last-N-Shares (PPLNS) is one of the most commonly used reward mechanisms in practice, in which the mining pools will distribute the reward among the miners whose reported shares fall in the last N shares, according to the proportion of the number of shares of the miner in the last N shares. In the PPLNS mechanism, miners' reporting strategies may impose significant impacts to their rewards via determining the number of shares appeared in the last N shares. As such, how to strategically report their shares to the pool has become an important decision faced by miners. In this paper, we study the share reporting problem in PPLNS pools, and establish a share reporting model for a miner to optimize his/her rewards. We also propose a novel hybrid share reporting strategy for the miner based on our model, and design computational experiments to evaluate our hybrid share reporting strategy. The experimental results show that the hybrid strategy outperforms two baseline share reporting strategies commonly used in practice. This work is the first attempt to study the share reporting issue faced by miners in PPLNS pools, and it can provide important managerial insights for blockchain pool miners when making their share reporting decisions.