Reviewerly/exHarmony huggingface dataset:
Efficient Calculation of Minimum Distance Between Capsules and Its Use in Robotics

The problem of minimum distance calculation between line-segments/capsules, in 3D space, is an important subject in many engineering applications, spanning CAD design, computer graphics, simulation, and robotics.In the latter, the human-robot minimum distance is the main input for collision avoidance/detection algorithms to measure collision imminence.Capsules can be used to represent humans and objects, including robots, in a given dynamic environment.In this scenario, it is important to calculate the minimum distance between capsules efficiently, especially for scenes (situations) that include a high number of capsules.This paper investigates the utilization of QR factorization for performing efficient minimum distance calculation between capsules.The problem is reformulated as a bounded variable optimization in which an affine transformation, deduced from QR factorization, is applied on the region of feasible solutions.A geometrical approach is proposed to calculate the solution, which is achieved by computing the point closest to the origin from the transferred region of feasible solutions.This paper is concluded with numerical tests, showing that the proposed method compares favorably with the most efficient method reported in the literature.
Underwater Imaging Based on LF and Polarization

The underwater image restoration based on polarization information has achieved good results in improving the image quality in scattering media. However, the previous methods are difficult to obtain the true distribution of degree of polarization in the scene. In this paper, we combine synthetic aperture imaging with polarimetric imaging, and propose a method for retrieving radiation of object based on the degree of polarization and intensity of backscattering at the multi-view image. In addition, compared with the previous methods, the proposed method can achieve simultaneous acquisition of four-dimensional light field information and polarization information, effectively increasing the information dimension obtained by single imaging. In order to verify the effectiveness and superiority of the proposed method, we have established a relevant experimental platform and compared with the experimental results of the previous methods, and obtained the expected experimental results.
Experimental demonstration of quantum pigeonhole paradox

We experimentally demonstrate that when three single photons transmit through two polarization channels, in a well-defined pre- and postselected ensemble, there are no two photons in the same polarization channel by weak-strength measurement, a counterintuitive quantum counting effect called the quantum pigeonhole paradox. We further show that this effect breaks down in second-order measurement. These results indicate the existence of the quantum pigeonhole paradox and its operating regime.
Superpixel based continuous conditional random field neural network for semantic segmentation

Recently, unifying conditional random fields (CRFs) into convolutional neural network (CNN) and enlarging spatial feature maps are two principal strategies for improving semantic segmentation performance. It is observed that building CRF defined on full resolution features into CNN can generate segmentation masks of better quality, however there exist several challenges related to training efficiency and computation costs. To design a more powerful segmentation model by using CRF and full resolution features, this paper proposes a novel fully supervised scheme for semantic segmentation based on superpixels and continuous CRF (C-CRF). Our new architecture includes three subnetworks: a unary network, a pairwise network and a superpixel based continuous CRF network (SCCN). The unary network upsamples low resolution features via de-convolutional structures, while pairwise network learns pixel-level pairwise similarities. The outputs of the two networks are fed to the SCCN that consists of two differentiable superpixel pooling layers and a differentiable C-CRF layer, leading to inference between superpixels in an image. The whole framework is trained in an end-to-end way by the jointly pixel-level and superpixel-level supervised learning strategy. Besides, C-CRF inference is fused with pixel-level prediction during the test procedure to decrease the effect of superpixel quality. In the experiments, we evaluate the power of the three subnetworks and the learning strategy comprehensively. Results on three benchmark datasets demonstrate that compared with baseline networks and other CRF based semantic segmentation methods, performance gain can be achieved by using the proposed SCCN.
Optimized K-Means Clustering Model based on Gap Statistic

Big data has become famous to process, store and manage massive volumes of data.Clustering is an essential phase in big data analysis for many real-life application areas uses clustering methodology for result analysis.The data clustered sets have become a challenging issue in the field of big data analytics.Among all clustering algorithm, the K-means algorithm is the most widely used unsupervised clustering approach as seen from past.The K-means algorithm is the best adapted for deciding similarities between objects based on distance measures with small datasets.Existing clustering algorithms require scalable solutions to manage large datasets.However, for a particular domain-specific problem the initial selection of K is still a significant concern.In this paper, an optimized clustering approach presented which is calculated the optimal number of clusters (k) for specific domain problems.The proposed approach is an optimal solution based on the cluster performance measure analysis based on gab statistic.By observation, the experimental results prove that the proposed model can efficiently enhance the speed of the clustering process and accuracy by reducing the computational complexity of the standard k-means algorithm which achieves 76.3%.
Forensic investigation of cross platform massively multiplayer online games: Minecraft as a case study

Minecraft, a Massively Multiplayer Online Game (MMOG), has reportedly millions of players from different age groups worldwide. With Minecraft being so popular, particularly with younger audiences, it is no surprise that the interactive nature of Minecraft has facilitated the commission of criminal activities such as denial of service attacks against gamers, cyberbullying, swatting, sexual communication, and online child grooming. In this research, there is a simulated scenario of a typical Minecraft setting, using a Linux Ubuntu 16.04.3 machine (acting as the MMOG server) and Windows client devices running Minecraft. Server and client devices are then examined to reveal the type and extent of evidential artefacts that can be extracted.
A Fast Scene Text Detector Using Knowledge Distillation

Incidental scene text detection is a challenging problem because of arbitrary orientation, low resolution, perspective distortion, and variant aspect ratios of text in natural images. In this paper, we present an end-to-end trainable deep model, which can effectively and efficiently locate multi-oriented scene text. Our detector includes a student network and a teacher network, and they inherit complex VGGNet and lightweight PVANet architecture, respectively. While deploying for text detection, the teacher network is used to guide the training process of a student via knowledge distilling so as to maintain the tradeoff between accuracy and efficiency. We have evaluated the proposed detector on three popular benchmarks, and it achieves F-measures of 83.7%, 57.27%, and 90% on ICDAR2015 Incidental Scene Text, COCO-Text, and ICDAR2013, respectively, which outperforms the most state-of-the-art methods.
Optimal surface segmentation with convex priors in irregularly sampled space

Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications. However, nodes in the graph based optimal surface segmentation method typically encode uniformly distributed orthogonal voxels of the volume. Thus the segmentation cannot attain an accuracy greater than a single unit voxel, i.e. the distance between two adjoining nodes in graph space. Segmentation accuracy higher than a unit voxel is achievable by exploiting partial volume information in the voxels which shall result in non-equidistant spacing between adjoining graph nodes. This paper reports a generalized graph based multiple surface segmentation method with convex priors which can optimally segment the target surfaces in an irregularly sampled space. The proposed method allows non-equidistant spacing between the adjoining graph nodes to achieve subvoxel segmentation accuracy by utilizing the partial volume information in the voxels. The partial volume information in the voxels is exploited by computing a displacement field from the original volume data to identify the subvoxel-accurate centers within each voxel resulting in non-equidistant spacing between the adjoining graph nodes. The smoothness of each surface modeled as a convex constraint governs the connectivity and regularity of the surface. We employ an edge-based graph representation to incorporate the necessary constraints and the globally optimal solution is obtained by computing a minimum s-t cut. The proposed method was validated on 10 intravascular multi-frame ultrasound image datasets for subvoxel segmentation accuracy. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional segmentations.
Image watermarking based on integer wavelet transform-singular value decomposition with variance pixels

With the era of rapid technology in multimedia, the copyright protection is very important to preserve an ownership of multimedia data. This paper proposes an image watermarking scheme based on Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD). The binary watermark is scrambled by Arnold transform before embedding watermark. Embedding locations are determined by using variance pixels. Selected blocks with the lowest variance pixels are transformed by IWT, thus the LL sub-band of 8×8 IWT is computed by using SVD. The orthogonal U matrix component of U3,1 and U4,1 are modified using certain rules by considering the watermark bits and an optimal threshold. This research reveals an optimal threshold value based on the trade-off between robustness and imperceptibility of watermarked image. In order to measure the watermarking performance, the proposed scheme is tested under various attacks. The experimental results indicate that our scheme achieves higher robustness than other scheme under different types of attack.
Carrier-phase estimation for simultaneous quantum key distribution and classical communication using a real local oscillator

The simultaneous quantum and classical communication (SQCC) protocol is an appealing protocol since it allows continuous-variable quantum key distribution (CV-QKD) and classical communication to be implemented simultaneously by using the same communication infrastructure and on a single wavelength. However, the main challenge of the SQCC protocol is its low tolerance of phase noise, limiting the transmission distance under the real local oscillator (LO) design. To eliminate the phase drift without the aid of the reference signal, the classical carrier phase estimation (CPE) algorithm in the field of coherent optical communication is employed for phase recovery and data extraction. Besides, the power of the residual phase noise in classical communication and the power of the excess phase noise in CV-QKD are derived, including the effects of the employed laser linewidth, the transversal filter length, and the classical signal power. On this basis, the performances of the SQCC protocol under the trusted and the untrusted phase noise model are discussed, respectively. Our work confirms the feasibility of the CPE algorithm in the SQCC protocol and demonstrates the conditions required for this protocol to operate on a coherent optical communication system.
Parameter Self-Adaptation in an Ant Colony Algorithm for Continuous Optimization

ACO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> is a well-established ant colony optimization algorithm for continuous-domain optimization. We present an approach for the dynamic adaptation of the ACOR algorithm's controlling parameters, focusing on the search width parameter, based on using several pre-specified parameter configurations, which we call personalities. Before an ant starts to generate a candidate solution, it stochastically adopts a personality based on the relative past success of different personalities. The success of a personality is measured, in turn, by the survival rate of the previous solutions generated by the ants adopting that personality. The premise of our approach is that some personalities will be more appropriate than others for different phases of the search. In addition, our adaptive approach can accommodate solution recombination, the use of which within ACO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> was recently explored in the previous work. It allows the frequency of applying recombination and the type of recombination operator to be dynamically adapted, by having one or more recombination personalities among the competing personalities. We evaluate these proposals experimentally on two applications: 1) training feedforward neural networks for classification using 65 benchmark datasets from the University of California Irvine repository and 2) optimizing several popular synthetic benchmark continuous-domain functions. Our experimental results indicate that our proposals perform better than the standard ACO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> on both applications, to a statistically significant extent.
MOLI: Smart Conversation Agent for Mobile Customer Service

Human agents in technical customer support provide users with instructional answers to solve a task that would otherwise require a lot of time, money, energy, physical costs. Developing a dialogue system in this domain is challenging due to the broad variety of user questions. Moreover, user questions are noisy (for example, spelling mistakes), redundant and have various natural language expressions. In this work, we introduce a conversational system, MOLI (the name of our dialogue system), to solve customer questions by providing instructional answers from a knowledge base. Our approach combines models for question type and intent category classification with slot filling and a back-end knowledge base for filtering and ranking answers, and uses a dialog framework to actively query the user for missing information. For answer-ranking we find that sequential matching networks and neural multi-perspective sentence similarity networks clearly outperform baseline models, achieving a 43% error reduction. The end-to-end P@1(Precision at top 1) of MOLI was 0.69 and the customers’ satisfaction was 0.73.
Evaluation of Clustering Methods in Compression of Topological Models and Visual Place Recognition Using Global Appearance Descriptors

This paper presents an extended study about the compression of topological models of indoor environments. The performance of two clustering methods is tested in order to know their utility both to build a model of the environment and to solve the localization task. Omnidirectional images are used to create the compact model, as well as to estimate the robot position within the environment. These images are characterized through global appearance descriptors, since they constitute a straightforward mechanism to build a compact model and estimate the robot position. To evaluate the goodness of the proposed clustering algorithms, several datasets are considered. They are composed of either panoramic or omnidirectional images captured in several environments, under real operating conditions. The results confirm that compression of visual information contributes to a more efficient localization process through saving computation time and keeping a relatively good accuracy.
AlphaGo, Locked Strategies, and Eco-Cognitive Openness

Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. I maintain that these differences lead to important consequences when we analyze computational AI programs, such as AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by locked abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning, because they are limited in what I call eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead.
In Praise of Artifice Reloaded: Caution With Natural Image Databases in Modeling Vision

Subjective image quality databases are a major source of raw data on how the visual system works in naturalistic environments. These databases describe the sensitivity of many observers to a wide range of distortions of different nature and intensity seen on top of a variety of natural images. Data of this kind seems to open a number of possibilities for the vision scientist to check the models in realistic scenarios. However, while these natural databases are great benchmarks for models developed in some other way (e.g., by using the well-controlled artificial stimuli of traditional psychophysics), they should be carefully used when trying to fit vision models. Given the high dimensionality of the image space, it is very likely that some basic phenomena are under-represented in the database. Therefore, a model fitted on these large-scale natural databases will not reproduce these under-represented basic phenomena that could otherwise be easily illustrated with well selected artificial stimuli. In this work we study a specific example of the above statement. A standard cortical model using wavelets and divisive normalization tuned to reproduce subjective opinion on a large image quality dataset fails to reproduce basic cross-masking. Here we outline a solution for this problem by using artificial stimuli and by proposing a modification that makes the model easier to tune. Then, we show that the modified model is still competitive in the large-scale database. Our simulations with these artificial stimuli show that when using steerable wavelets, the conventional unit norm Gaussian kernels in divisive normalization should be multiplied by high-pass filters to reproduce basic trends in masking. Basic visual phenomena may be misrepresented in large natural image datasets but this can be solved with model-interpretable stimuli. This is an additional argument in praise of artifice in line with Rust and Movshon (2005).
Geostatistical Estimation of Multi-Domain Deposits with Transitional Boundaries: A Sensitivity Study for the Sechahun Iron Mine

In mineral resource estimation, identification of the geological domains to be used for modeling, and the type of boundaries dividing them, is a major concern. Generally, the variables within a domain are estimated with an assumption of the hard boundaries (sharp contact). However, in many cases, the geologic structures that generate a deposit are transitional (overlapping of several geologic domains). Consequently, boundary identification of the geological domains is essential for an accurate estimate of resources. This paper considers a real application to examine whether the addition of geologic information benefits grade estimation in the presence of transitional boundaries. Results proved that the accuracy of the grade estimation can be improved by adding geological information and there is a significant sensitivity in grade estimation results in the existence of transitional boundaries.
Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking

This paper presents a novel multi-objective optimization based sensor selection method for multi-target tracking in sensor networks. The multi-target states are modelled as multi-Bernoulli random finite sets and the multi-Bernoulli filter is used to propagate the multi-target posterior density. The proposed method is designed to select the sensor that provides the most reliable cardinality estimate, since more accurate cardinality estimate indicates more accurate target states. In the multi-Bernoulli filter, the updated multi-target density is a multi-Bernoulli random finite set formed by a union of legacy tracks and measurement-updated tracks. The legacy track and the measurement-updated track have different theoretical and physical meanings, and hence these two kinds of tracks are considered separately in the sensor management problem. Specifically, two objectives are considered: (1) maximizing the mean cardinality of the measurement-updated tracks, (2) minimizing the cardinality variance of the legacy tracks. Considering the conflicting objectives simultaneously is a multi-objective optimization problem. Tradeoff solutions between two conflicting objectives will be derived. Theoretical analysis and examples show that the proposed approach is effective and direct. The performance of the proposed method is demonstrated using two scenarios with different levels of observability of targets in the passive sensor network.