Reviewerly/exHarmony huggingface dataset:
Polar transformation of 2D X-ray diffraction patterns and the experimental validation of the hDIC technique

Deformation analysis in engineering materials and components is a subject of ongoing enquiry due to its importance for obtaining reliable prediction of strength and durability of structures and assemblies. Whilst optical methods deliver information about surface displacements, X-ray scattering methods have the capability to provide efficient assessment of crystal lattice distortion in the bulk of the component. The height digital image correlation (hDIC) technique is an alternative to conventional digital image correlation that uses the out-of-plane surface height variations for the identification of triaxial deformations. In this study, the hDIC technique was used for the determination of displacements in an aluminium specimen after 3-point bending process that creates a complex deformation state that includes both axial displacements and rotations. The surface of the specimen was prepared for the analysis by electric discharge machining (EDM) technique that has minimal effect on material properties and produces random height profile well-suited for the aim of this study. Surface height variations were measured using deep focus microscopy and used instead of pixel intensity for correlation in the DIC process. The distribution of total of elastic and plastic strains were also calculated by the evolutionary eigenstrain model using 2D X-ray diffraction patterns processed according to the polar transformation method. The agreement between hDIC and polar X-Ray diffraction analyses allowed reliable cross-validation between these two techniques.
Ship Detection Method Based on Gabor Filter and Fast RCNN Model in Satellite Images of Sea

In this paper, we design a new approach which combines the Gabor filter with the principle of selective search algorithm applied in Fast RCNN to extract and enhance the texture of the satellite image, to solve the problems of low detection precision, high probability of missed detection and error detection of ships in satellite images. Experiments show that the texture enhancement method based on Gabor filter fits the principle of selective search algorithm, which increases the number of positive samples in region proposal. The mAP of ship detection in satellite images reached 75.21%. And the detection accuracy of moving ships increased by 10%.
Detection and recognition of objects on aerial photographs using convolutional neural networks

Abstract The article is devoted to the use of neural network methods to solve the problem of detection and classification of small objects on aerial photographs. The architectures of YOLO 2 and YOLO 3 are discussed. The training procedure is described, the obtained results are analyzed. During the work it was shown that the considered architectures can be used to solve the problem of detecting and classifying objects in images obtained as a result of aerial photography.
A Survey of the Effects of Data Augmentation for Automatic Speech Recognition Systems

Data augmentation has been proposed as a method to increase the quantity of training data. It is a common strategy adopted to avoid over-fitting, reduce mismatch and improve robustness of the models. But, would the system performance improve if we add data of any nature? This paper presents a survey about data augmentation techniques and its effect on Automatic Speech Recognition systems, some experiments were carried out to support the hypothesis that adding noise is not allways help.
IBFET: Index‐based features extraction technique for scalable code clone detection at file level granularity

Summary Many techniques have been developed over the years to detect code clones in different software systems to maintain security measures. These techniques often require the source code to compare the subject system against a very large data set of big code. This paper presents index‐based features extraction technique (IBFET) to detect code clones at a very large‐scale level to billions of LOC at file level granularity. We performed preprocessing, indexing, and clone detection for more than 324 billion of LOC using a Hadoop distributed environment, which is quite faster and more efficient as compared to existing distributed indexing and clone detection techniques; meanwhile, it detects all three types of clones efficiently. The MapReduce rule of divide and conquer is used for a count and retrieve the similar features between different systems. We evaluated the execution time, scalability, precision, and recall of IBFET by using a well‐known clone detection data set IJaDataset and BigCloneBench; furthermore, we compared the results with other state‐of‐the‐art tools. Our approach is faster, flexible, scalable, and provides accurate results with high authenticity and can be implemented at a large‐scale level.
Prediction for the Maximum Frequency Deviation of Post-disturbance Based on the Deep Belief Network

A method based on the deep belief network (DBN) to predict the maximum frequency deviation of post-disturbance is proposed. The input characteristics of the deep belief network are constructed by 21-dimensional data, including load level, the value of power shortage after disturbance, electromagnetic power and the mechanical power of generators, etc. The output characteristics include the maximum frequency deviation after disturbance and its appearance time. Besides, a new sample organization way is proposed in this paper to improve the accuracy of predicted results by assembling samples with the same regular pattern in a set. The effectiveness and accuracy of the proposed sample organization way based on DBN are tested on the New England 39-bus system, by comparing the prediction results of the proposed method with the results of the simulation, the other sample organization way and other machine learning models.
Unconstrained Foreground Object Search

Many people search for foreground objects to use when editing images. While existing methods can retrieve candidates to aid in this, they are constrained to returning objects that belong to a pre-specified semantic class. We instead propose a novel problem of unconstrained foreground object (UFO) search and introduce a solution that supports efficient search by encoding the background image in the same latent space as the candidate foreground objects. A key contribution of our work is a cost-free, scalable approach for creating a large-scale training dataset with a variety of foreground objects of differing semantic categories per image location. Quantitative and human-perception experiments with two diverse datasets demonstrate the advantage of our UFO search solution over related baselines.
Energy Minimization Approach for Negative Obstacle Region Detection

Obstacle detection is an important area of research for automatic robot navigation and obstacle avoidance. However, the main focus has been on detecting positive obstacles such as vehicles, pedestrians and anything above the ground plane. Equally important task is detecting negative obstacles such as holes, ditches etc. that lies below the ground surface, which has not been studied extensively. We propose to detect such holes and potholes as an energy minimization problem. We use stereo information combined with saliency to initialize the energy function and use colour information to optimize the result. The optimized result is then subjected to hysteresis thresholding to arrive at the final negative obstacle region. Experimental results prove that the introduced method can successfully detect negative obstacles and run in real time (in the range 14-20 Hz) when using GPU for stereo matching. The proposed method was tested under three camera configurations with corridor, concrete-road and tar-road scenes. Proposed method perform well compared to the recent works.
Error assessment of grid‐based terrain shading algorithms for solar radiation modeling over complex terrain

Abstract In mountainous regions, solar radiation exhibits a strong spatial heterogeneity due to terrain shading effects. Terrain shading algorithms based on digital elevation models can be categorized into two types: area‐based and point‐specific. In this article, we evaluated two shading algorithms using designed mathematic surfaces. Theoretical shading effects over four Gauss synthetic surfaces were calculated and used to evaluate the terrain shading algorithms. We evaluated the area‐based terrain shading algorithm, Hillshade tool of ArcGIS, and the point‐specific shading algorithm from Solar Analyst (SA) in ArcGIS. Both algorithms showed shading overestimation, and Hillshade showed more accuracy with a mean absolute error (MAE) of 1.20%, as compared to the MAE of 1.66% of SA. The MAE of Hillshade increases exponentially as the spatial extent of the study area increases because the solar position for all locations on the surface is the same in Hillshade. Consequently, we suggest that the surface should be divided into more tiles in Hillshade when the discrepancy in the latitude of the whole surface is greater than 4°. Skyshed, which represents the horizon angle distribution in SA, is error‐prone over more complex terrain because horizon angle interpolation is problematic for such areas. We also propose a new terrain shading algorithm, with solar positions calculated using local latitude for each cell and the horizon angle calculated for every specific time interval, but without projections. The new model performs better than Hillshade and SA with an MAE of 0.55%.
Glottal Source Contribution to Higher Order Modes in the Finite Element Synthesis of Vowels

Articulatory speech synthesis has long been based on one-dimensional (1D) approaches. They assume plane wave propagation within the vocal tract and disregard higher order modes that typically appear above 5 kHz. However, such modes may be relevant in obtaining a more natural voice, especially for phonation types with significant high frequency energy (HFE) content. This work studies the contribution of the glottal source at high frequencies in the 3D numerical synthesis of vowels. The spoken vocal range is explored using an LF (Liljencrants–Fant) model enhanced with aspiration noise and controlled by the R d glottal shape parameter. The vowels [ɑ], [i], and [u] are generated with a finite element method (FEM) using realistic 3D vocal tract geometries obtained from magnetic resonance imaging (MRI), as well as simplified straight vocal tracts of a circular cross-sectional area. The symmetry of the latter prevents the onset of higher order modes. Thus, the comparison between realistic and simplified geometries enables us to analyse the influence of such modes. The simulations indicate that higher order modes may be perceptually relevant, particularly for tense phonations (lower R d values) and/or high fundamental frequency values, F 0 s. Conversely, vowels with a lax phonation and/or low F0s may result in inaudible HFE levels, especially if aspiration noise is not considered in the glottal source model.
DeepFP for Finding Nash Equilibrium in Continuous Action Spaces

Finding Nash equilibrium in continuous action spaces is a challenging problem and has applications in domains such as protecting geographic areas from potential attackers. We present DeepFP, an approximate extension of fictitious play in continuous action spaces. DeepFP represents players’ approximate best responses via generative neural networks which are highly expressive implicit density approximators. It additionally uses a game-model network which approximates the players’ expected payoffs given their actions, and trains the networks end-to-end in a model-based learning regime. Further, DeepFP allows using domain-specific oracles if available and can hence exploit techniques such as mathematical programming to compute best responses for structured games. We demonstrate stable convergence to Nash equilibrium on several classic games and also apply DeepFP to a large forest security domain with a novel defender best response oracle. We show that DeepFP learns strategies robust to adversarial exploitation and scales well with growing number of players’ resources.
Design of Rao–Blackwellized Point-Mass Smoother for Conditionally Linear and Gaussian Models

This paper deals with the state estimation of nonlinear stochastic dynamic systems. Stress is laid on the numerical solution to the functional recursive relations providing conditional probability density functions of the state. In particular, a novel Rao-Blackwellized point-mass smoother with two implementations is proposed for conditionally linear and Gaussian state-space models, where part of the state vector is estimated by a computationally expensive point-mass smoother, whereas the remaining part of the state vector by a set of computationally efficient linear smoothers. Such decomposition results in a computationally less demanding smoother than the standard point-mass smoother for the considered models. The properties of the proposed smoother are discussed and its performance is numerically illustrated.
Systematic Design Optimization of Capacitive Touch Sensor Electrode Patterns

This paper presents a systematic methodology for the pattern optimization of the sensing electrodes of a capacitive touch sensor panel (TSP). The TSPs discussed in this paper are characterized by using a combination of quasielectrostatic and time-domain SPICE type circuit simulations. A TSP with an optimized electrode pattern is fabricated and measured and its predicted touch response is confirmed. Moreover, the transmission of electromagnetic waves through this surface has been characterized, to assess the potentially useful application of integrating antennas with it, identifying transmission bands around 5GHz.
Performance Evaluation of Ensemble based Collaborative Filtering Recommender System

Recommender Systems are used for finding the needle in the haystack. Recommender systems are of one the innovations in this revolution, which addresses the problem of filtering the data/information that is more likely to. Collaborative filtering is a technique which makes automatic predictions, by collecting the preferences for the users. But collecting the preferences from the user is no easy task. The Collaborative Filtering can also be a user based k-NN collaborative filtering algorithm or an item based k-NN collaborative filtering algorithm. Each type has its merits and demerits. Aiming at this concern, an ensemble based k-NN collaborative filtering is proposed. This work fuses the merits of the user based k-NN algorithm and item-based algorithm. The experiment analysis on the MovieLens dataset gives a consistent model that is accurate and results in better personalized movie recommendations than other conventional models. The experimental results show that ensemble based k-NN algorithm clearly improves enhances the recommender system's performance, and give better recommendation quality.
Not Just a Matter of Semantics: The Relationship Between Visual and Semantic Similarity

Knowledge transfer, zero-shot learning and semantic image retrieval are methods that aim at improving accuracy by utilizing semantic information, e.g., from WordNet. It is assumed that this information can augment or replace missing visual data in the form of labeled training images because semantic similarity correlates with visual similarity. This assumption may seem trivial, but is crucial for the application of such semantic methods. Any violation can cause mispredictions. Thus, it is important to examine the visual-semantic relationship for a certain target problem. In this paper, we use five different semantic and visual similarity measures each to thoroughly analyze the relationship without relying too much on any single definition. We postulate and verify three highly consequential hypotheses on the relationship. Our results show that it indeed exists and that WordNet semantic similarity carries more information about visual similarity than just the knowledge of “different classes look different”. They suggest that classification is not the ideal application for semantic methods and that wrong semantic information is much worse than none.
A Game-Theoretic Framework for Resource Sharing in Clouds

Providing resources to different users or applications is fundamental to cloud computing. This is a challenging problem as a cloud service provider may have insufficient resources to satisfy all user requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different cloud service providers can improve resource availability and resource utilization as certain cloud service providers may have free resources available that can be “rented” by other service providers. However, different cloud service providers can have different objectives or utilities. Therefore, there is a need for a framework that can share and allocate resources in an efficient and effective way, while taking into account the objectives of various service providers that results in a multi-objective optimization problem. In this paper, we present a Cooperative Game Theory (CGT) based framework for resource sharing and allocation among different service providers with varying objectives that form a coalition. We show that the resource sharing problem can be modeled as an N-player canonical cooperative game with non-transferable utility (NTU) and prove that the game is convex for monotonic non-decreasing utilities. We propose an O(N) algorithm that provides an allocation from the core, hence guaranteeing Pareto optimality. We evaluate the performance of our proposed resource sharing framework in a number of simulation settings and show that our proposed framework improves user satisfaction and utility of service providers.
Constructive and collegial peer-review as a necessary precursor to data-driven environmental policy

As academic output increases, editors and reviewers are inundated with manuscripts, a phenomenon that has resulted in extended wait times, and, in many cases, suboptimal reviews. This is particularly an issue if data-driven environmental policy is the goal, as peer-review is an important step in this process. Numerous systemic fixes have been suggested to improve peer-review; however, we question the value of system-wide change if peer-reviewers continue to engage in behaviour that is not collegial, or in-keeping with the sentiment behind rigorous peer-review. We provide nine best practice statements that compliment proposed system-wide changes with the goal to improve the process of peer-review by helping to develop reviewer codes of conduct. These statements focus only upon reviewer conduct, not issues such as conflicts of interest or confidentiality. It is our hope that data driven improvements to peer-review models, and reviewer codes of conduct will make the peer-review process more efficient and collegial. Such improvements are necessary to promote novel and innovative academic research, as well as to enhance the diversity of voices present in the academic literature.
Towards Socially Immersive Fitness Games

Despite many benefits of playing and exercising together in terms of motivation, engagement, and social relationships, many exergames are designed to be single player, while others implement only a facade of social play (e.g., leaderboards). The challenge remains: how can exergames be designed to balance fun, exertion, and social connection? In this work, we ran an embodied sketching activity with multiplayer variations of the Sphery Racer mixed-reality fitness game, allowing us to test physical and social game mechanics. We discuss here: i) preliminary results on how these variations support a rich training and social experience; and ii) the potential of our method to surface interesting design directions. These contributions can inspire others designing in this domain, and support the development of a rich design space for co-located exergames.
A Processor-Sharing Model for the Performance of Virtualized Network Functions

The parallel execution of requests in a Cloud Computing platform, as for Virtualized Network Functions, is modeled by an M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[X]</sup> /M/1 Processor-Sharing (PS) system, where each request is seen as a batch of unit jobs. The performance of such paralleled system can then be measured by the quantiles of the batch sojourn time distribution. In this paper, we address the evaluation of this distribution for the M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[X]</sup> /M/1-PS queue with batch arrivals and geometrically distributed batch size. General results on the residual busy period (after a tagged batch arrival time) and the number of unit jobs served during this residual busy period are first derived. This enables us to provide an approximation for the distribution tail of the batch sojourn time whose accuracy is confirmed by simulation.
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images

Lymphomas are neoplasms that originate in the lymphatic system and represent one of the most common types of cancer found in the World population. The feature analysis may contribute toward results of higher relevance in the classification of the lesions. Feature extraction methods are employed to obtain data that can indicate lymphoma incidence. In this work, we investigated the multiscale and multidimensional fractal geometry with colour channels and colour models for classification of lymphoma tissue images. The fractal features were extracted from the RGB and LAB models and colour channels. The fractal features were concatenated to form the feature vector. Finally, we employed the Hermite polynomial classifier in order to evaluate the performance of the proposed approach. The colour channels obtained of histological images achieved higher accuracy values, the obtained rates were between 94% and 97%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the lesion in lymphoma images.
Adaptive Load Management of Web Applications on Software Defined Infrastructure

Auto-scalability is a common approach for management of cloud applications where resources are provisioned and de-provisioned on demand. Because of its automatic nature, auto-scaling can be exploited for various reasons that can hugely reduce the overall profit. Therefore, it becomes vital to consider the trade-off between the added revenue as a result of auto-scaling and its corresponding cost. To this end, we propose a novel autonomic solution to the management of cloud Web applications whose goal is to optimize the profit. At the core of the solution, there is an optimization module that considers revenue model as well as cost model and uses a number of run-time performance models to derive the best course of action if there is a need for adaptation. Using software defined features that include compute and network programmability, our proposed solution helps applications to optimize their resource allocation to best meet their business requirements. Our experiment results on a hybrid cloud validate the applicability of the proposed solution and demonstrate that it can increase the profit substantially higher compared to a number of baseline approaches.