Reviewerly/exHarmony huggingface dataset:
Construction of probabilistic event trees for eruption forecasting at Sinabung volcano, Indonesia 2013–14

Eruptions of Sinabung volcano, Indonesia have been ongoing since 2013. Since that time, the character of eruptions has changed, from phreatic to phreatomagmatic to magmatic explosive eruptions, and from production of a lava dome that collapsed to a subsequent thick lava flow that slowly ceased to be active, and later, to a new lava dome. As the eruption progressed, event trees were constructed to forecast eruptive behavior six times, with forecast windows that ranged from 2 weeks to 1 year: November 7–10, December 12–14, and December 27, 2013; and January 9–10, May 13, and October 7, 2014. These event trees were successful in helping to frame the forecast scenarios, to collate current monitoring information, and to document outstanding questions and unknowns. The highest probability forecasts closely matched outcomes of eruption size (including extrusion of the first dome), production of pyroclastic density currents, and pyroclastic density current runout distances. Events assigned low probabilities also occurred, including total collapse of the lava dome in January 2014 and production of a small blast pyroclastic density current in February 2014.
ZH: A Complete Graphical Calculus for Quantum Computations Involving Classical Non-linearity

We present a new graphical calculus that is sound and complete for a universal family of quantum circuits, which can be seen as the natural string-diagrammatic extension of the approximately (realvalued) universal family of Hadamard+CCZ circuits.The diagrammatic language is generated by two kinds of nodes: the so-called 'spider' associated with the computational basis, as well as a new arity-N generalisation of the Hadamard gate, which satisfies a variation of the spider fusion law.Unlike previous graphical calculi, this admits compact encodings of non-linear classical functions.For example, the AND gate can be depicted as a diagram of just 2 generators, compared to ∼ 25 in the ZX-calculus.Consequently, N-controlled gates, hypergraph states, Hadamard+Toffoli circuits, and diagonal circuits at arbitrary levels of the Clifford hierarchy also enjoy encodings with low constant overhead.This suggests that this calculus will be significantly more convenient for reasoning about the interplay between classical non-linear behaviour (e.g. in an oracle) and purely quantum operations.After presenting the calculus, we will prove it is sound and complete for universal quantum computation by demonstrating the reduction of any diagram to an easily describable normal form.
Fusing Object Semantics and Deep Appearance Features for Scene Recognition

Scene images generally show the characteristics of large intra-class variety and high inter-class similarity because of complicated appearances, subtle differences, and ambiguous categorization. Hence, it is difficult to achieve satisfactory accuracy by using a single representation. For solving this issue, we present a comprehensive representation for scene recognition by fusing deep features extracted from three discriminative views, including the information of object semantics, global appearance, and contextual appearance. These views show diversity and complementarity of features. The object semantics representation of the scene image, denoted by spatial-layout-maintained object semantics features, is extracted from the output of a deep-learning-based multi-classes detector by using spatial fisher vectors, which can simultaneously encode the category and layout information of objects. A multi-direction long short-term memory-based model is built to represent contextual information of the scene image, and the activation of the fully connected layer of a convolutional neural network is used to represent the global appearance of scene image. These three kinds of deep features are then fused to draw a final conclusion for scene recognition. Extensive experiments are conducted to evaluate the proposed comprehensive representation on three benchmarks scene image database. The results show that the three deep features complement to each other strongly and are effective in improving recognition performance after fusion. The proposed method can achieve scene recognition accuracy of 89.51% on the MIT67 database, 78.93% on the SUN397 database, and 57.27% on the Places365 databases, respectively, which are better percentages than the accuracies obtained by the latest reported deep-learning-based scene recognition methods.
Comparing the Performance Potentials of Interval and General Type-2 Rule-Based Fuzzy Systems in Terms of <i>Sculpting the State Space</i>

This paper provides application-independent perspectives on why improved performance usually occurs as one goes from an interval type-2 (IT2) fuzzy system to a general type-2 (GT2) fuzzy system. This is achieved by using the horizontal-slice representation of a GT2 fuzzy set and GT2 fuzzy system and by examining first- and second-order rule partitions as well as novelty partitions for the horizontal slices. It demonstrates that, for triangle and trapezoid secondary membership functions, the numbers of first- and second-order rule partitions are exactly the same for IT2 and GT2 fuzzy systems, but that a maximum amount of change always occurs in every second-order rule partition of a GT2 fuzzy system. This does not always occur in such partitions of an IT2 fuzzy system. Furthermore, when type reduction (TR) is used in a GT2 fuzzy system, the total number of novelty partitions is directly proportional to the number of horizontal slices; consequently, there are many more such partitions in a GT2 fuzzy system that uses TR than occur in an IT2 fuzzy system that also uses TR. It is the author's conjecture that it is the maximum changes that occur in every second-order rule partition, as well as the greater number of novelty partitions when TR is used, that provide a GT2 fuzzy system with the potential to outperform an IT2 fuzzy system.
Cross-view image synthesis using geometry-guided conditional GANs

We address the problem of generating images across two drastically different views, namely ground (street) and aerial (overhead) views. Image synthesis by itself is a very challenging computer vision task and is even more so when generation is conditioned on an image in another view. Due the difference in viewpoints, there is small overlapping field of view and little common content between these two views. Here, we try to preserve the pixel information between the views so that the generated image is a realistic representation of cross view input image. For this, we propose to use homography as a guide to map the images between the views based on the common field of view to preserve the details in the input image. We then use generative adversarial networks to inpaint the missing regions in the transformed image and add realism to it. Our exhaustive evaluation and model comparison demonstrate that utilizing geometry constraints adds fine details to the generated images and can be a better approach for cross view image synthesis than purely pixel based synthesis methods.
Geogenic versus anthropogenic behaviour and geochemical footprint of Al, Na, K and P in the Campania region (Southern Italy) soils through compositional data analysis and enrichment factor

Geochemical studies that focus on environmental applications tend to approach the chemical elements as individual entities and may therefore offer only partial and sometimes biased interpretations of their distributions and behaviour. A potential alternative approach is to consider a compositional data analysis, where every element is part of a whole. In this study, an integrated methodology, which included compositional data analysis, multifractal data transformations and interpolation, as well as enrichment factor analysis, was applied to a geochemical dataset for the Campania region, in the south of Italy, focusing in particular on the behaviour, footprints and sources of a smaller pool of elements: Al, Na, K and P. The initial dataset included 3669 topsoil samples, collected at an average sampling density of 1 site per 2.3 km2, and analyzed (after an aqua regia extraction) by a combination of ICP-AES and ICP-MS for 53 elements. Frequency based methods (Clr biplot, Enrichment Factor computation) and frequency spatial-method (fractal and multifractal plots) allowed identifying the relationships between the elements and their possible source patterns in Campania soils in relation to a natural occurring concentrations in geogenic material (rocks, soils and sediments) or human input. Results showed how the interpretation of concentration and behaviour of Al, Na, K and P was enhanced thanks to the application of data log-ratio transformation in univariate and multivariate analysis compared to the use of raw or log-normal data. Multivariate analyses with compositional biplot allowed the identification of four element associations and their potential association with the underling geology and/or human activities. When focusing on the smaller pool of elements (Al, P, K and Na), these relationships with the unique geology of the region, were largely confirmed by multifractal interpolated maps. However, when the local background was used for the calculation of the enrichment factor, the resulting interpolated maps allowed to identify smaller areas where the greater concentrations of P could not be possibly associated to a mineralisation (e.g., ultrapotassic rocks) but were more likely to be associated to anthropogenic input such as agriculture activities with potentially extensive use of phosphate fertilizers. The integrated approach of this study allowed a more robust qualitative and quantitative evaluation of elemental concentration, providing in particular new and vital information on the distribution and patterns of P in soils of the Campania region, but also a viable, more robust, methodological approach to regional environmental geochemistry studies.
DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization

Traditional Network Functions Virtualization (NFV) implementations are somehow too heavy and do not have enough functionality to conduct complex tasks. In this work, we propose a lightweight NFV framework named DeepNFV, which is based on the Docker container running on the network edge, and integrates state-of-the-art deep learning models with NFV containers to address some complicated problems, such as traffic classification, link analysis, and so on. We compare the DeepNFV framework with several existing works, and detail its structures and functions. The most significant advantage of DeepNFV is its lightweight design, resulting from the virtualization and low-cost nature of the container technology. Also, we design this framework to be compatible with edge devices, in order to decrease the computational overhead of the central servers. Another merit is its strong analysis ability brought by deep learning models, which make it suitable for many more scenarios than traditional NFV approaches. In addition, we also describe some typical application scenarios, regarding how the NFV container works and how to utilize its learning ability. Simulations demonstrate its high efficiency, as well as the outstanding recognition performance in a typical use case.
Approximate logic neuron model trained by states of matter search algorithm

An approximate logic neuron model (ALNM) is a single neural model with a dynamic dendritic structure. During the training process, the model is capable of reducing useless synapses and unnecessary branches of dendrites by neural pruning function. It provides a simplified dendritic morphology for each particular problem. Then, the simplified model of ALNM can be substituted with a logic circuit, which is easy to implement on hardware. However, the computational capacity of this model has been greatly restricted by its learning algorithm, the back-propagation (BP) algorithm, because it is sensitive to initial values and easy to be trapped into local minima. To address this critical issue, we have investigated the capabilities of heuristic optimization methods that are acknowledged as global searching algorithms. Through comparison experiments, a states of matter search (SMS) algorithm has been verified to be the most suitable training method for ALNM. To evaluate the performance of SMS, six benchmark datasets are utilized in the experiments. The corresponding results are compared with the BP algorithm, other optimization methods, and several widely used classifiers. In addition, the classification performances of logic circuits trained by SMS are also presented in this study.
A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications

Anomalous runtime behavior detection is one of the most important tasks for performance diagnosis in High Performance Computing (HPC). Most of the existing methods find anomalous executions based on the properties of individual functions, such as execution time. However, it is insufficient to identify abnormal behavior without taking into account the context of the executions, such as the invocations of children functions and the communications with other HPC nodes. We improve upon the existing anomaly detection approaches by utilizing the call stack structures of the executions, which record rich temporal and contextual information. With our call stack tree (CSTree) representation of the executions, we formulate the anomaly detection problem as finding anomalous tree structures in a call stack forest. The CSTrees are converted to vector representations using our proposed stack2vec embedding. Structural and temporal visualizations of CSTrees are provided to support users in the identification and verification of the anomalies during an active anomaly detection process. Three case studies of real-world HPC applications demonstrate the capabilities of our approach.
Forecasting of solar power ramp events: A post-processing approach

The growing integration level of wind and solar energy resources introduces new regulating and operating challenges in the electric grid. Ramp-rate limits of conventional power plants in the generation mix impose an operating constraint on renewable energy sources to the point that, at high integration levels, the ramp-rates of wind and solar resources must be managed by situational awareness tools that are based on forecasts, especially the ramp events forecasts. To leverage such tools, a post-processing adjusting approach is developed in this paper for improving the capability of hour-ahead combined forecasts of solar power to capture ramp events. The performance evaluation is conducted with several evaluation metrics that consider the accuracy of forecasts in terms of ramp events. The results of a case study demonstrate the efficacy of the adjusting approach. Probabilistic forecasts are also generated to quantify the uncertainty associated with the solar power ramp event forecasts and the uncertainty analysis is carried out.
A novel framework for improving multi-population algorithms for dynamic optimization problems: A scheduling approach

This paper presents a novel framework for improving the performance of multi-population algorithms in solving dynamic optimization problems (DOPs). The fundamental idea of the proposed framework is to incorporate the concept of scheduling into multi-population methods with the aim to allocate more function evaluations to the best performing sub-populations. Two methods are developed based on the proposed framework, each of which uses a different approach for scheduling the sub-populations. The first method combines the quality of sub-populations and the degree of diversity among them into a single feedback parameter for detecting the best performing sub-population. The second method uses the learning automata as the central unit for performing the scheduling operation. In order to validate the applicability of the proposed methods, they are incorporated into three well-known algorithms for DOPs. The experimental results show the efficiency of the scheduling approach for improving the multi-population methods on the moving peaks benchmark (MPB) and generalized dynamic benchmark generator.
The development and evaluation of Robot Light Skin: A novel robot signalling system to improve communication in industrial human–robot collaboration

In a human–robot collaborative production system, the robot could make request for interaction or notify the human operator if an uncertainty arises. Conventional industrial tower lights were designed for generic machine signalling purposes which may not be the ultimate solution for robot signalling in a collaborative setting. In this type of system, human operators could be monitoring multiple robots while carrying out a manual task so it is important to minimise the diversion of their attention. This paper presents a novel robot signalling solution, the Robot Light Skin (RLS),which is an integrated signalling system that could be used on most articulated robots. Our experiment was conducted to validate this concept in terms of its effect on improving operator's reaction time, hit-rate, awareness and task performance. The results showed that participants reacted faster to the RLS as well as achieved higher hit-rate. An eye tracker was used in the experiment which shows a reduction in diversion away from the manual task when using the RLS. Future study should explore the effect of the RLS concept on large-scale systems and multi-robot systems.
Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data

The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data.
A secure image encryption method using scan pattern and random key stream derived from laser chaos

Internet allows people in any corner of the world to share instantly all types of information. Secure sharing of sensitive multimedia data necessitates confidentiality service, which is offered by encipherment mechanism. In this paper, a fast and secure image encryption method using scan patterns and true random key streams is proposed. The scan pattern is used to scramble the original image by means of pixel permutation. The random key stream is generated employing a photonics based approach and then used to perform a bitwise XOR operation to get the encrypted image. The proposed method takes very less time for encryption and has substantial resistance with statistical, differential and entropy attacks.
Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC'2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.
Bound constraints handling in Differential Evolution: An experimental study

Bound constraints are lower and upper limits for coordinate values of feasible solutions. This paper is devoted to the issue of handling bound constraints in Differential Evolution (DE). We overview the majority of popular Bound Constraint Handling Methods (BCHMs), which can be classified as penalty function methods, repair methods and specialized mutation methods. We discuss and empirically verify how BCHMs influence the pattern of individuals generated by DE. We take 7 different DE algorithms and 17 different BCHMs, and perform experimental analysis of their combinations to characterize their efficiency in exploitation and exploration. As a basis for the experiments, we take a 10-dimensional quadratic function with various locations of the minimum and CEC′2017 benchmark set, which defines 30 optimization problems in 10, 30, 50 and 100 dimensions. We observe that DE algorithms differ significantly in the degree to which their efficiency depends on the choice of particular BCHMs. We identify which BCHMs usually lead to efficient optimization for the majority of DE algorithms. Similarly, we identify BCHMs which should definitely be avoided.
3D strain evolution in concrete using in situ X-ray computed tomography testing and digital volumetric speckle photography

The fracture process for concrete materials under compression is a complex three-dimensional phenomenon that occurs as a result of material heterogeneity and complicated mixed-mode cracking mechanisms. To understand the failure mechanism of concrete, it is necessary to have a technique that can probe the interior of the material as it is being loaded. In this paper, we use an industrial computed tomography (CT) system to obtain the volumetric image of a concrete circular cylinder under compression by taking advantage of the ability of X-rays to penetrate opaque materials. In addition, we employ a newly developed 3D strain analysis technique called digital volumetric speckle photography (DVSP) to calculate the displacement and strain distribution in two longitudinal meridian sections and a midpoint transverse section of the specimen. We are able to reveal the regions of strain localization and their evolutions, which eventually lead to the failure of the specimen.