Reviewerly/exHarmony huggingface dataset:
Co-simulated Size Number: An Elegant Novel Algorithm for Identification of Multivariate Geochemical Anomalies

Identification of geochemical anomalies is of particular importance for tracing the footprints of anomalies. This can be implemented by advanced techniques of exploratory data analysis, such as fractal/multi-fractal approaches based on priori or posteriori distribution of geochemical elements. The latter workflow involves analysis of 2D/3D produced maps, which can be mostly obtained by geostatistical algorithms. There are two challenging issues for such an analysis. The first one corresponds to handling the cross-correlation structures among the data, and the second one relates to the compositional nature of data. To tackle these problems, this paper investigates the application of Gaussian co-simulation for modeling the cross-correlated compositional data in order to recognize the multivariate geochemical anomalies in integration with fractal analysis. In this context, an innovative algorithm, namely co-simulated size number (CoSS-N), is introduced for this purpose. The compositional nature of data is addressed by additive log-ratio transformation of original data while the Gaussian co-simulation handles the reproduction of cross-correlation among the components. The co-simulated outputs are then taken into account for capturing different geochemical populations, showing different levels of backgrounds and anomalies. The algorithm is illustrated via a real case study located in Philippine wherever seven geochemical components are required to be considered. The accuracy of results is examined by statistical validation techniques, indicating the capability of the CoSS-N algorithm for multivariate identification of geochemical anomalies.
Cross-cultural study about cyborg market acceptance: Japan versus Spain

Cyborg technologies have left science fiction to become an emerging market. Cyborgs are defined as people who integrate technical elements in their bodies to improve their capacities over innate ones. Taking into consideration the human revolution that this technology can provoke, a cultural approach should be considered in any cyborg market strategy. Our research analyses how ethical awareness, innovativeness perceptions and perceived risk influence the decision to become a cyborg, analysing whether cultures as different as those of Japan and Spain show different results. We focus our study on young higher-education students, collecting a sample of 300 surveys in Japan and 286 in Spain. The findings are surprising. Ethics is the most influential variable on the intention to use this technology. The different cultural aspects concerned with body modification in Japan and Spain constitute a key concern when implanting cyborg technology. Nevertheless, we did not find statistically significant differences in the acceptance of cyborg technology between these two countries.
N-Version Machine Learning Models for Safety Critical Systems

Quality control of machine learning systems is a fundamental challenge in industries to provide intelligent services or products using machine learning. While recent advances in machine learning algorithms substantially improve the performance of intelligent tasks such as object recognition, their outputs are essentially stochastic and very sensitive to input data. Such an output uncertainty is a big obstacle to ensure the quality of safety critical applications like autonomous vehicle and hence architectural design to mitigate the impact of error output becomes a great importance. In this paper, we propose N-version machine learning architecture that aims to improve system reliability against probabilistic outputs of individual machine learning modules. The key idea of this architecture is exploiting two kinds of diversities; input diversity and model diversity. Our study first formally defines these diversity metrics and analytically shows the improved reliability by N-version machine learning architecture. Since we treat a machine learning module as a black-box, the proposed architecture and the reliability property are generally applicable to any machine learning algorithms and applications.
A Multi-label Active Learning Approach for Mobile App User Review Classification

User reviews of mobile applications convey useful feedback from users, e.g. feature requests, bug descriptions, etc. The increasing number of reviews that users submit daily makes it difficult for developers to manually analyze and classify them into proper review categories. Moreover, several review messages may contain more than one information. In this paper, we propose to use multi-label active learning as a convenient solution to the problem of mobile app user reviews classification. An unlabeled and structured dataset was built from the initially unstructured large set of review messages. Moreover, in order to reduce the effort needed to assign labels to each instance in the large constructed dataset, we opted for an Active Learning approach. Experimental results have shown that, by actively querying an oracle for labels during training a binary relevance-based classifier (with logistic regression as a base classifier), we obtained a classifier that outperformed well-known classifiers in terms of performance without the need to label the whole dataset.
SHeLA: Scalable Heterogeneous Layered Attestation

This article proposes a novel mechanism for swarm attestation, i.e., the remote attestation (RA) of a multitude of interconnected devices, also called a swarm of devices. Classical RA protocols work with one prover and one verifier. Swarm attestation protocols assume that the devices in the swarm act both as verifier and prover in order to attest the software integrity of all the devices to a root verifier, typically in a spanning-tree topology. We propose “scalable heterogeneous layered attestation (SHeLA),” a novel RA technique for swarms. Our approach consists of introducing an additional edge layer in between the root verifier and the swarm devices. The edge layer consists of geographically spread devices with a larger computational power and storage capacity than the swarm devices. The main challenges we address are related to the scalability of the swarm, the availability or visibility of the nodes (especially when they are mobile), the heterogeneity of the devices with respect to the wireless communication protocol and interface, and the granularity of the attestation in terms of detecting the sanity of individual swarm devices. We build a proof-of-concept network that allows us to evaluate the computational delay and the resource overhead of the edge and swarm devices, and to perform a thorough security analysis.
KANDINSKY Patterns as IQ-Test for Machine Learning

AI follows the notion of human intelligence which is unfortunately not a clearly defined term. The most common definition given by cognitive science as mental capability, includes, among others, the ability to think abstract, to reason, and to solve problems from the real world. A hot topic in current AI/machine learning research is to find out whether and to what extent algorithms are able to learn abstract thinking and reasoning similarly as humans can do – or whether the learning outcome remains on purely statistical correlation. In this paper we provide some background on testing intelligence, report some preliminary results from 271 participants of our online study on explainability, and propose to use our Kandinsky Patterns as an IQ-Test for machines. Kandinsky Patterns are mathematically describable, simple, self-contained hence controllable test data sets for the development, validation and training of explainability in AI. Kandinsky Patterns are at the same time easily distinguishable from human observers. Consequently, controlled patterns can be described by both humans and computers. The results of our study show that the majority of human explanations was made based on the properties of individual elements in an image (i.e., shape, color, size) and the appearance of individual objects (number). Comparisons of elements (e.g., more, less, bigger, smaller, etc.) were significantly less likely and the location of objects, interestingly, played almost no role in the explanation of the images. The next step is to compare these explanations with machine explanations.
Polarimetric underwater image recovery for color image with crosstalk compensation

In underwater color imaging, the scattering media could cause image blurring and color distortion, which are the two main problems of image degradation. In this paper, we propose a color image recovery method to solve these two problems by considering the crosstalk effects between the RGB channels during estimating the transmittances in RGB channels. The experimental results in turbid water with different colors (blue, green and yellow) show that our method can simultaneously enhance the image contrast and reduce the color distortion to realize the clear underwater vision, which is attributed to the proper estimation of the transmittances in RGB channels by compensating the crosstalk effects.
Autoencoding Binary Classifiers for Supervised Anomaly Detection

We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects the known anomalies included in training data, but it cannot detect the unknown anomalies. Meanwhile, the unsupervised approach can detect both known and unknown anomalies that are located away from normal data points. However, it does not detect known anomalies as accurately as the supervised approach. Furthermore, even if we have labeled normal data points and anomalies, the unsupervised approach cannot utilize these labels. The ABC is a probabilistic binary classifier that effectively exploits the label information, where normal data points are modeled using the AE as a component. By maximizing the likelihood, the AE in the proposed ABC is trained to minimize the reconstruction error for normal data points, and to maximize it for known anomalies. Since our approach becomes able to reconstruct the normal data points accurately and fails to reconstruct the known and unknown anomalies, it can accurately discriminate both known and unknown anomalies from normal data points. Experimental results show that the ABC achieves higher detection performance than existing supervised and unsupervised methods.
Automatic classification of complaint letters according to service provider categories

In the technological age, the phenomenon of complaint letters published on the Internet is increasing. Therefore, it is important to automatically classify complaint letters according to various criteria, such as company categories. In this research, we investigated the automatic text classification of complaint letters written in Hebrew that were sent to various companies from a wide variety of categories. The classification was performed according to company categories such as insurance, cellular communication, and rental cars. We conducted an extensive set of classification experiments of complaint letters to seven/six/five/four company categories. The classification experiments were performed using various sets of word unigrams, four machine learning methods, two feature filtering methods, and parameter tuning. The classification results are relatively high for all six measures: accuracy, precision, recall, F1, PRC-area, and ROC-area. The best accuracy results for seven, six, five, and four categories are 84.5%, 88.4%, 91.4%, and 93.8%, respectively. An analysis of the most frequently occurring words in the complaints about almost all categories revealed that the most significant issues were related to poor service and delayed delivery. An interesting result shows that only in the domain of hospitals was the subject of the domain itself (i.e., the patient, the medical treatment, the place of the treatment, and the medical staff) the most important issue. Another interesting finding is that the issue of “price” was of little or no importance to the complainants. These findings suggest that in their preoccupation with their bottom line of profitability, many service providers are blind to how paramount good service and timely delivery (and, in the case of hospitals, the domain itself) are to their clientele.
Structured learning for unsupervised feature selection with high-order matrix factorization

Feature selection aims at searching the most discriminative and relevant features from high-dimensional data to improve the performance of certain learning tasks. Whereas, irrelevant or redundant features may increase the over-fitting risk of consequent learning algorithms. Structured learning of feature selection is to embed intrinsic structures of data, such as geometric structures and manifold structures, resulting in the improvement of learning performance. In this paper, three types of structured regularizers are embedded into the feature selection framework and an iterative algorithm with proved convergence for feature selection problem is proposed. First, serving as crucial representation pipelines of local structures, three types of local learning regularizers, including graph Laplacian, neighborhood preservation and sparsity regularizer, are defined. Second, the local and global structures are integrated into one joint framework for the feature selection problem. Third, the framework is formulated as the canonical form of high-order matrix factorizations and then an efficient convergent iterative algorithm is proposed for the problem. Besides, the proposed framework is further extended to multi-view feature selection and fusion problems from an algorithmic view. Finally, the proposed algorithm is tested on eight publicly available datasets and compared to several state-of-the-art feature selection methods. Experimental results demonstrate the superiority of the proposed method against the compared algorithms in terms of clustering performance.
How developers use API documentation

Application Programming Interfaces (APIs) play a crucial role in modern software engineering. However, learning to use a new API often is a challenge for developers. In order to support the learning process effectively, we need to understand how developers use documentation when starting to work with a new API. We report an exploratory study that observed developers while they solved programming tasks involving a simple API. The results reveal differences regarding developer activities and documentation usage that a successful design strategy for API documentation needs to accommodate. Several guidelines to optimize API documentation are discussed.
3D Point Cloud Super-Resolution via Graph Total Variation on Surface Normals

Point cloud is a collection of 3D coordinates that are discrete geometric samples of an object's 2D surfaces. Using a low-cost 3D scanner to acquire data means that point clouds are often in lower resolution than desired for rendering on high-resolution displays. Building on recent advances in graph signal processing, we design a local algorithm for 3D point cloud super-resolution (SR). First, we initialize new points at centroids of local triangles formed using the low-resolution point cloud, and connect all points using a k-nearest-neighbor graph. Then, to establish a linear relationship between surface normals and 3D point coordinates, we perform bipartite graph approximation to divide all nodes into two disjoint sets, which are optimized alternately until convergence. For each node set, to promote piecewise smooth (PWS) 2D surfaces, we design a graph total variation (GTV) objective for nearby surface normals, under the constraint that coordinates of the original points are preserved. We pursue an augmented Lagrangian approach to tackle the optimization, and solve the unconstrained equivalent using the alternating method of multipliers (ADMM). Extensive experiments show that our proposed point cloud SR algorithm outperforms competing schemes objectively and subjectively for a large variety of point clouds.
Knowledge Transfer for Face Verification Using Heterogeneous Generalized Operational Perceptrons

Face verification is a prominent biometric technique for identity authentication that has been used extensively in several security applications. In practice, face verification is often performed along with other visual surveillance tasks in the computing device. Thus, the ability to share the computation and reuse the information already extracted for other analysis tasks can greatly help reduce the computation load on the devices. In this study, we propose to utilize the knowledge transfer approach for the face verification problem by building a heterogeneous neural network architecture of Generalized Operational Perceptrons on top of the intermediate features extracted for object recognition purpose. Experimental results show that using our proposed approach, a face verification system can be incorporated into an existing visual analysis system with less additional memory and computational cost, compared to other similar approaches.
A Study of the Perceptually Weighted Peak Signal-To-Noise Ratio (WPSNR) for Image Compression

The peak signal-to-noise ratio (PSNR) is the most used objective measure for assessing perceptual image quality when it comes to image and video compression tasks, despite the fact that it exhibits weak performance in reflecting human perception. To address this problem, many image quality assessment (IQA) methods were proposed, e. g. the structural similarity quality measure (SSIM) and its extension, the multi-scale SSIM (MS-SSIM). In this paper we revisit and evaluate a block-based perceptually weighted PSNR (WPSNR) which calculates weighting factors to capture visual sensitivity of local image regions. We further introduce a sample-based version of WPSNR which determines those sensitivity weights with higher spatial accuracy. These methods are computationally inexpensive compared to other similarity measures and are shown to outperform PSNR, SSIM and similar perceptual quality measures when it comes to approximate subjective ratings of JPEG or JPEG2000 compressed images.
200 FPS Constant-Time Bilateral Filter Using SVD and Tiling Strategy

This paper presents a constant-time bilateral filter that supports arbitrary range kernel designed via singular value decomposition (SVD). Bilateral filter (BF) suffers from high computational complexity in real-time processing due to the time-variant kernel. Although various accelerations for BF have been proposed, most of them have not achieved both arbitrary range kernel and tight computational complexity simultaneously. The proposed method supports arbitrary range kernel but requires half computational complexity of most state-of-the-art methods. Moreover, we present two implementation techniques well matched to the SVD approach: range fitting and tiling strategy. Experiments show that, in the cases of major range kernels, the proposed method not only runs faster (200 FPS) but also achieves higher accuracy than the state-of-the-art methods.
Single Image Depth Estimation Using Deep Adversarial Training

Scene understanding is an active area of research in computer vision that encompasses several different problems. The LiDARs and stereo depth sensor have their own restrictions such as light sensitiveness, power consumption and short-range [1]. In this paper, we propose a two-stream deep adversarial network for single image depth estimation in RGB images. For stream I network, we propose a novel encoder-decoder architecture using residual concepts to extract course-level depth features. Stream II network purely processes the information through the residual architecture for fine-level depth estimation. Also, we designed a feature map sharing architecture to share the learned feature maps of the decoder module of stream I. Sharing feature maps strengthen the residual learning to estimate the scene depth and increase the robustness of the proposed network. A benchmark NYU RGB-D v2 [2] database is used to evaluate the proposed network for single image depth estimation. Both qualitative and quantitative analysis has been carried out to analyze the effectiveness of the proposed network for scene depth prediction. Performance analysis shows that the proposed method outperforms other existing methods for single image depth estimation.
Evaluation of a Baseline Controller for Autonomous “Figure-8” Flights of a Morphing Geometry Quadcopter: Flight Performance

This article describes the design, fabrication, and flight test evaluation of a morphing geometry quadcopter capable of changing its intersection angle in-flight. The experiments were conducted at the Aircraft Computational and Resource Aware Fault Tolerance (AirCRAFT) Lab, Parks College of Engineering, Aviation and Technology at Saint Louis University, St. Louis, MO. The flight test matrix included flights in a “Figure-8” trajectory in two different morphing configurations (21° and 27°), as well as the nominal geometry configuration, two different flight velocities (1.5 m/s and 2.5 m/s), two different number of waypoints, and in three planes—horizontal, inclined, and double inclined. All the experiments were conducted using standard, off-the-shelf flight controller (Pixhawk) and autopilot firmware. Simulations of the morphed geometry indicate a reduction in pitch damping (42% for 21° morphing and 57.3% for 27° morphing) and roll damping (63.5% for 21° morphing and 65% for 27° morphing). Flight tests also demonstrated that the dynamic stability in roll and pitch dynamics were reduced, but the quadcopter was still stable under morphed geometry conditions. Morphed geometry also has an effect on the flight performance—with a higher number of waypoints (30) and higher velocity (2.5 m/s), the roll dynamics performed better as compared to the lower waypoints and lower velocity condition. The yaw dynamics remained consistent through all the flight conditions, and were not significantly affected by asymmetrical morphing of the quadcopter geometry. We also determined that higher waypoint and flight velocity conditions led to a small performance improvement in tracking the desired trajectory as well.
Graph-Based Transforms for Video Coding

In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest. This paper introduces a class of transforms called graph-based transforms (GBTs) for video compression, and proposes two different techniques to design GBTs. In the first technique, we formulate an optimization problem to learn graphs from data and provide solutions for optimal separable and nonseparable GBT designs, called GL-GBTs. The optimality of the proposed GL-GBTs is also theoretically analyzed based on Gaussian-Markov random field (GMRF) models for intra and inter predicted block signals. The second technique develops edge-adaptive GBTs (EA-GBTs) in order to flexibly adapt transforms to block signals with image edges (discontinuities). The advantages of EA-GBTs are both theoretically and empirically demonstrated. Our experimental results show that the proposed transforms can significantly outperform the traditional Karhunen-Loeve transform (KLT).