Reviewerly/exHarmony huggingface dataset:
Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India

The present study exploits high-resolution hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor from the Hutti-Maski gold deposit area, India, to map hydrothermal alteration minerals. The study area is a volcanic-dominated late Archean greenstone belt that hosts major gold mineralization in the Eastern Dharwar Craton of southern India. The study encompasses pre-processing, spectral and spatial image reduction using Minimum Noise Fraction (MNF) and Fast Pixel Purity Index (FPPI), followed by endmember extraction using n-dimensional visualizer and the United States Geological Survey (USGS) mineral spectral library. Image derived endmembers such as goethite, chlorite, chlorite at the mine site (chlorite mixed with mined materials), kaolinite, and muscovite were subsequently used in spectral mapping methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and its hybrid, i.e. SIDSAMtan. Spectral similarity matrix of the target and non-target-based method has been proposed to find the possible optimum threshold needed to obtain mineral map using spectral mapping methods. Relative Spectral Discrimination Power (RSDPW) and Confusion Matrix (CM) have been used to evaluate the performance of SAM, SID, and SIDSAMtan. The RSDPW and CM illustrate that the SIDSAMtan benefits from the unique characteristics of SAM and SID to achieve better discrimination capability. The Overall Accuracy (OA) and kappa coefficient (ҡ) of SAM, SID, and SIDSAMtan were computed using 900 random validation points and obtained 90% (OA) and 0.88 (ҡ), 91.4% and 0.90, and 94.4% and 0.93, respectively. Obtained mineral map demonstrates that the northern portion of the area mainly consists of muscovite whereas the southern part is marked by chlorite, goethite, muscovite and kaolinite, indicating the propylitic alteration. Most of these minerals are associated with altered metavolcanic rocks and migmatite.
On parsimonious models for modeling matrix data

Finite mixture modeling is a popular technique for capturing heterogeneity in data. Although the vast majority of the theory developed in this area up to date deals with vector-valued data, some recent advancements have been made to expand the concept to matrix-valued data, for example, by means of matrix Gaussian mixture models. Unfortunately, matrix mixtures tend to suffer from the overparameterization issue due to a high number of parameters involved in the model. As a result, this may lead to problems such as overfitting and mixture order underestimation. One possible approach of addressing the overparameterization issue that has proven to be effective in the vector-valued framework is to consider various parsimonious models. One of the most popular classes of parsimonious models is based on the spectral decomposition of covariance matrices. An attempt to generalize this class and make it applicable in the matrix setting is made. Estimation procedures are thoroughly discussed for all models considered. The application of the proposed methodology is studied on synthetic and real-life data sets.
Cross-Project Just-in-Time Bug Prediction for Mobile Apps: An Empirical Assessment

Bug Prediction is an activity aimed at identifying defect-prone source code entities that allows developers to focus testing efforts on specific areas of software systems. Recently, the research community proposed Just-in-Time (JIT) Bug Prediction with the goal of detecting bugs at commit-level. While this topic has been extensively investigated in the context of traditional systems, to the best of our knowledge, only a few preliminary studies assessed the performance of the technique in a mobile environment, by applying the metrics proposed by Kamei et al. in a within-project scenario. The results of these studies highlighted that there is still room for improvement. In this paper, we faced this problem to understand (i) which Kamei et al.'s metrics are useful in the mobile context, (ii) if different classifiers impact the performance of cross-project JIT bug prediction models and (iii) whether the application of ensemble techniques improves the capabilities of the models. To carry out the experiment, we first applied a feature selection technique, i.e., InfoGain, to filter relevant features and avoid models multicollinearity. Then, we assessed and compared the performance of four different well-known classifiers and four ensemble techniques. Our empirical study involved 14 apps and 42, 543 commits extracted from the COMMIT GURU platform. The results show that Naive Bayes achieves the best performance with respect to the other classifiers and in some cases outperforms some well-known ensemble techniques.
Security Awareness: The First Step in Information Security Compliance Behavior

In this study, we use the attentional phase of social learning theory to link workplace security-related experiences and observations to employees’ security awareness. The responses of 398 organizational employees serve to test our research model using structural equational modeling with AMOS 22.0. The results show security awareness arises from both explicit and subjective security experiences in the workplace. Our respondents indicate knowledge of a physical system has little, if any, effect on security awareness. However, security education, policy, visibility and managerial security participation are important for producing security awareness. Furthermore, managerial participation strengthens the links between organizational security efforts and security awareness. We discuss the implications of our study for future security compliance research and practice.
Classification of Isolated Volcano-Seismic Events Based on Inductive Transfer Learning

Domain-specific problems where data collection is an expensive task are often represented by scarce or incomplete data. From a machine learning perspective, this type of problems has been addressed using models trained in different specific domains as the starting point for the final objective-model. The transfer of knowledge between domains, known as transfer learning (TL), helps to speed up training and improve the performance of the models in problems with limited amounts of data. In this letter, we introduce a TL approach to classify isolated volcano-seismic signals at “Volcán de Fuego”, Colima (Mexico). Using the well-known convolutional architecture (LeNet) as a feature extractor and a representative data set containing regional earthquakes, volcano-tectonic earthquakes, long-period events, volcanic tremors, explosions, and collapses, our proposal compares the generalization capabilities of the models when we only fine-tune the upper layers and fine-tune overall of them. Compared with the other state-of-the-art techniques, classification systems based on TL approaches provide good generalization capabilities (attaining nearly 94% of events correctly classified) and decreasing computational time resources.
Learning data streams online — An evolving fuzzy system approach with self-learning/adaptive thresholds

Recognizing the weakness of the existing evolving fuzzy systems (EFSs) where the selection and determination of thresholds for the structure and parameter learning are completely relied on the trial and error strategy, this paper proposes a novel and symmetrical approach with self-learning/adaptive thresholds (EFS-SLAT) for EFSs to overcome such a fundamental weakness. Departing from the common but implicit assumption in the existing EFS approaches that the thresholds are fixed parameters throughout the learning process, EFS-SLAT treats the thresholds as dynamical parameters which are varying with the evolution of systems being learned. By utilizing the online training errors as an indicator to reflect the underfitting and overfitting state of an EFS model, the proposed EFS-SLAT selects and adjusts the values of threshold parameters automatically and dynamically based on the evolving speed and nonlinear degree of the EFS. By testing EFS-SLAT on several well-known benchmark examples, and comparing it with many state-of-the-art approaches, EFS-SLAT is verified to be capable of giving preferable results.
Manifold Siamese Network: A Novel Visual Tracking ConvNet for Autonomous Vehicles

Visual tracking is a vital component of autonomous driving perception system. Siamese networks have achieved great success in both accuracy and speed for visual tracking tasks. These Siamese trackers share a similar framework in which each tracker consists of two network branches for exploring semantic information. However, the performance of Siamese trackers is limited by an insufficient semantic template and an unsatisfactory updating strategy. To tackle these problems, we propose a manifold Siamese network for visual tracking that can simultaneously utilize semantic and geometric information. A manifold sample pool is constructed to exploit the manifold structure of image object sequences. This sample pool is dynamically learned via a fast Gaussian mixture model (GMM). After obtaining a manifold sample template, we design a deep architecture based on a correlation filter (CF) network and append a novel manifold feature branch. The network remains fully convolutional and can train a template to discriminate exemplar image and arbitrarily size search image. Then, a triplet occlusion score function cooperates with an effective update method that is established to prevent model drift. Extensive experiments show that the proposed tracking algorithm performs favorably compared with the state-of-the-art methods on three standard benchmark datasets at a high framerate, which is very suitable for autonomous driving.
Effects of Tracking Area Shape and Size on Artificial Potential Field Redirected Walking

Immersive Virtual Environment systems that utilize Head Mounted Displays and a large tracking area have the advantage of being able to use natural walking as a locomotion interface. In such systems, difficulties arise when the virtual world is larger than the tracking area and users approach area boundaries. Redirected walking (RDW) is a technique that distorts the correspondence between physical and virtual world motion to steer users away from boundaries and obstacles, including other co-immersed users. Recently, a RDW algorithm was proposed based on the use of artificial potential fields (APF), in which walls and obstacles repel the user. APF-RDW effectively supports multiple simultaneous users and, unlike other RDW algorithms, can easily account for tracking area dimensions and room shape when generating steering instructions. This work investigates the performance of a refined APF-RDW algorithm in different sized tracking areas and in irregularly shaped rooms, as compared to a Steer-to-Center (STC) algorithm and an un-steered control condition. Data was generated in simulation using logged paths of prior live users, and is presented for both single-user and multi-user scenarios. Results show the ability of APF-RDW to steer effectively in irregular concave shaped tracking areas such as L-shaped rooms or crosses, along with scalable multi-user support, and better performance than STC algorithms in almost all conditions.
BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving

In autonomous driving community, numerous benchmarks have been established to assist the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation. However, the more meaningful dynamic evolution of the surrounding objects of ego-vehicle is rarely exploited, and lacks a large-scale dataset platform. To address this, we introduce BLVD, a large-scale 5D semantics benchmark which does not concentrate on the static detection or semantic/instance segmentation tasks tackled adequately before. Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction. This benchmark will boost the deeper understanding of traffic scenes than ever before. We totally yield 249, 129 3D annotations, 4, 902 independent individuals for tracking with the length of overall 214, 922 points, 6, 004 valid fragments for 5D interactive event recognition, and 4, 900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime). The benchmark can be downloaded from our project site https://github.com/VCCIV/BLVD/.
Mask-Aware Networks for Crowd Counting

Crowd counting problem aims to count the number of objects within an image or a frame in the videos and is usually solved by estimating the density map generated from the object location annotations. The values in the density map, by nature, take two possible states: zero indicating no object around, a non-zero value indicating the existence of objects and the value denoting the local object density. In contrast to traditional methods which do not differentiate the density prediction of these two states, we propose to use a dedicated network branch to predict the object/non-object mask and then combine its prediction with the input image to produce the density map. Our rationale is that the mask prediction could be better modeled as a binary segmentation problem and the difficulty of estimating the density could be reduced if the mask is known. A key to the proposed scheme is the strategy of incorporating the mask prediction into the density map estimator. To this end, we study five possible solutions, and via analysis and experimental validation we identify the most effective one. Through extensive experiments on three public datasets, we demonstrate the superior performance of the proposed approach over the baselines and show that our network could achieve the state-of-the-art performance.
The Effect of Narrow Field of View and Information Density on Visual Search Performance in Augmented Reality

Many optical-see-through displays have a relatively narrow field of view. However, a limited field of view can constrain how information can be presented and searched through. To understand these constraints, we present a series of experiments that address the interrelationships between field of view, information density, and search performance. We do so by simulating various fields of view using two approaches: limiting the field of view presented on a Microsoft HoloLens optical-see-through head-worn display and dynamically changing the portion of a large tiled-display wall on which information is presented, for head-tracked users in both cases. Our results indicate a significant effect of information density and field of view on search performance, with potential search performance benefits of using a larger FOV between ca. 7-28%. Furthermore, while grids guided visual search, they did not significantly affect performance.
Fast and Scalable Approaches to Accelerate the Fuzzy <i>k</i>-Nearest Neighbors Classifier for Big Data

One of the best-known and most effective methods in supervised classification is the k-nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where fuzzy approaches prove to be among the most successful, highlighting the classical fuzzy k-nearest neighbors (FkNN). However, these traditional algorithms fail to tackle the large amounts of data that are available today. There are multiple alternatives to enable kNN classification in big datasets, spotlighting the approximate version of kNN called hybrid spill tree. Nevertheless, the existing proposals of FkNN for big data problems are not fully scalable, because a high computational load is required to obtain the same behavior as the original FkNN algorithm. This article proposes global approximate hybrid spill tree FkNN and local hybrid spill tree FkNN, two approximate approaches that speed up runtime without losing quality in the classification process. The experimentation compares various FkNN approaches for big data with datasets of up to 11 million instances. The results show an improvement in runtime and accuracy over literature algorithms.
DGO: Dice Game Optimizer

In recent years, optimization algorithms have been used in many applications. Most of these algorithms are inspired by physical processes or living beings' behaviors. This article suggests a new optimization method called “Dice Gaming Optimizer“ (DGO), which simulates dice gaming laws. This algorithm is inspired by an old game and the searchers are a set of players. Each player moves in the playground based on at least one and maximum six different players called guide’s players. The number of guide’s players for each player is determined by the number of dice. DGO is tested on 23 standard benchmark test functions and also compared with eight other algorithms such as: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search (CS), Ant-Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm and Emperor Penguin Optimizer (EPO). Moreover, a real-life engineering design problem is solved by DGO. The results indicate that DGO have better performance as compared to the other well-known optimization algorithms.
Arab Sign language Recognition with Convolutional Neural Networks

The implementation of an automatic recognition system for Arab sign language (ArSL) has a major social and humanitarian impact. With the growth of the deaf-dump community, such a system will help in integrating those people and enjoy a normal life. Like other languages, Arab sign language has many details and diverse characteristics that need a powerful tool to treat it. In this work, we propose a new system based on the convolutional neural networks, fed with a real dataset, this system will recognize automatically numbers and letters of Arab sign language. To validate our system, we have done a comparative study that shows the effectiveness and robustness of our proposed method compared to traditional approaches based on k-nearest neighbors (KNN) and support vector machines (SVM).
Deep Validation: Toward Detecting Real-World Corner Cases for Deep Neural Networks

The exceptional performance of Deep neural networks (DNNs) encourages their deployment in safety-and dependability-critical systems. However, DNNs often demonstrate erroneous behaviors in real-world corner cases. Existing countermeasures center on improving the testing and bug-fixing practice. Unfortunately, building a bug-free DNN-based system is almost impossible currently due to its black-box nature, so anomaly detection is imperative in practice. Motivated by the idea of data validation in a traditional program, we propose and implement Deep Validation, a novel framework for detecting real-world error-inducing corner cases in a DNN-based system during runtime. We model the specifications of DNNs by resorting to their training data and cast checking input validity of DNNs as the problem of discrepancy estimation. Deep Validation achieves excellent detection results against various corner case scenarios across three popular datasets. Consequently, Deep Validation greatly complements existing efforts and is a crucial step toward building safe and dependable DNN-based systems.
Design by Immersion: A Transdisciplinary Approach to Problem-Driven Visualizations

While previous work exists on how to conduct and disseminate insights from problem-driven visualization projects and design studies, the literature does not address how to accomplish these goals in transdisciplinary teams in ways that advance all disciplines involved. In this paper we introduce and define a new methodological paradigm we call design by immersion, which provides an alternative perspective on problem-driven visualization work. Design by immersion embeds transdisciplinary experiences at the center of the visualization process by having visualization researchers participate in the work of the target domain (or domain experts participate in visualization research). Based on our own combined experiences of working on cross-disciplinary, problem-driven visualization projects, we present six case studies that expose the opportunities that design by immersion enables, including (1) exploring new domain-inspired visualization design spaces, (2) enriching domain understanding through personal experiences, and (3) building strong transdisciplinary relationships. Furthermore, we illustrate how the process of design by immersion opens up a diverse set of design activities that can be combined in different ways depending on the type of collaboration, project, and goals. Finally, we discuss the challenges and potential pitfalls of design by immersion.
Infrastructure-supported Perception and Track-level Fusion using Edge Computing

Data from infrastructure sensors can significantly improve the field of view for intelligent vehicles (IV), both in terms of range and completeness. In the MEC-View project, we investigate how automated driving (AD) can benefit from incorporating such data in the perception processing chain. On the infrastructure side, a central computational node, called MEC-Server, is connected to a base station and receives objects from multiple roadside sensors. Those are used to create a fused environmental model, which is distributed to vehicles close by via a managed cellular network. To use tracks received from the MEC-Server in IV perception, we propose a hybrid vehicular perception system that is able to fuse both local onboard sensor data as well as estimations by the MEC-Server. For this, we discuss multiple approaches to track-level fusion and data association, including their application in our perception system. Using careful interface design, we are able to avoid many non-linearities and are able to minimize the amount of approximations involved. For evaluation, we present a experimental setup for track-level fusion schemes that is based on virtually augmented real-world measurements and facilitates targeted adaptation of influencing variables while ensuring real-world applicability. A comparison of different fusion schemes provides insights into their relative performance and shows directions for real-world applicability.