Reviewerly/exHarmony huggingface dataset:
Ideals of the Multiview Variety

The multiview variety of an arrangement of cameras is the Zariski closure of the images of world points in the cameras. The prime vanishing ideal of this complex projective variety is called the multiview ideal. We show that the bifocal and trifocal polynomials from the cameras generate the multiview ideal when the foci are distinct. In the computer vision literature, many sets of (determinantal) polynomials have been proposed to describe the multiview variety. We establish precise algebraic relationships between the multiview ideal and these various ideals. When the camera foci are noncoplanar, we prove that the ideal of bifocal polynomials saturate to give the multiview ideal. Finally, we prove that all the ideals we consider coincide when dehomogenized, to cut out the space of finite images.
Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts.We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision).In this work, we aim at building faceted concept hierarchies from scientific literature.Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-todescendent link with a specific facet "type-of".We use information extraction techniques to find synonyms, sibling concepts, and ancestordescendent relations from a data science corpus.And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships.It resolves conflicts by maintaining the acyclic structure of a hierarchy.
Unfolding measurement distributions via quantum annealing

A bstract High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations. We present an implementation of likelihood-based regularized unfolding on a quantum computer. The inverse problem is recast in terms of quadratic unconstrained binary optimization (QUBO), which has the same form of the Ising hamiltonian and hence it is solvable on a programmable quantum annealer. We tested the method using a model that captures the essence of the problem, and compared the results with a baseline method commonly used in precision measurements at the Large Hadron Collider (LHC) at CERN. The unfolded distribution is in very good agreement with the original one. We also show how the method can be extended to include the effect of nuisance parameters representing sources of systematic uncertainties affecting the measurement.
Weakly-supervised Cross-modal Hashing

Cross-modal hashing can efficiently retrieve data across different modalities and has been successfully applied in various domains. Although many supervised cross-modal hashing methods have been proposed, they generally focus on two modals only and assume that the labels of training data are sufficient and complete. This assumption is not practical in real scenarios. In this article, we propose the weakly supervised cross-modal hashing (WCHash), which takes into account the widely witnessed weakly supervised information ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incomplete</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">insufficient labels</i> ) of training data. Specifically, WCHash first optimizes a latent central modality with respect to other modalities. Next, it uses an efficient multi-label weak-label method to enrich the labels of training data and measures the semantic similarity between data points based on the enriched labels. After that, it uses this similarity to guide the correlation maximization between the respective data modals and the central modal and thus achieves the hash functions for cross-modal retrieval. Experimental results on real-world datasets demonstrate that WCHash is more efficient and effective than related state-of-the-art cross-modal hashing methods. WCHash can significantly reduce the complexity of cross-modal hashing on three or more modalities.
Decision support system design for determining brown sugar quality with weighted product method

Abstract Brown sugar is the result of processing the coconut juice with a distinctive taste, so that its use cannot be replaced by other types of sugar. The purpose of this research is to design of Decision Support System (DSS) using a Multi Attribute Decision Making (MADM) model by applying Weighted Product (WP) method to determine the quality of brown sugar. WP method was chosen because the problem in this research is a ranking problem. The WP method is able to select the best alternative from a number of alternatives and its superiority in weighting technique. This system is designed by using PHP and MySQL as the database programming language. This system can rank the brown sugar by calculating the criteria weight. The weight value is searched for each attribute, then conduct the ranking process to determine the optimal alternative, which is the best brown sugar and decent in terms of ranking. Based on the tests performed, a system capable to produce the best ranking in accordance with the calculations used, so that the system this can be speed up the selection of brown sugar.
Predicting Health Care Costs Using Evidence Regression

People’s health care cost prediction is nowadays a valuable tool to improve accountability in health care. In this work, we study if an interpretable method can reach the performance of black-box methods for the problem of predicting health care costs. We present an interpretable regression method based on the Dempster-Shafer theory, using the Evidence Regression model and a discount function based on the contribution of each dimension. Optimal parameters are learned using gradient descent. The k-nearest neighbors’ algorithm was also used to speed up computations. With the transparency of the evidence regression model, it is possible to create a set of rules based on a patient’s vicinity. When making a prediction, the model gives a set of rules for such a result. We used Japanese health records from Tsuyama Chuo Hospital to test our method, which includes medical checkups, exam results, and billing information from 2016 to 2017. We compared our model to an Artificial Neural Network and Gradient Boosting method. Our results showed that our transparent model outperforms the Artificial Neural Network and Gradient Boosting with an R 2 of 0 . 44 .
Improving the extracting precision of stripe center for structured light measurement

The accuracy of the stripe center extraction is affected by the uneven reflectance and large curvature change of the object surface in the structured light three-dimensional measurement system. Aiming to eliminate the stripe distortion and improve the accuracy of stripe center extraction, an improved stripe center extraction method is proposed for structured light measurement. By analyzing the principle of stripe distortion and the types of stripe noises, the mean filtering is used to eliminate the noises. Then, a stripe preprocessing method is presented to adjust the gray scale of stripe distortion. Finally, stripe centers are extracted through the curve fitting method by preprocessing. Furthermore, the proposed method addressed the problem of local discontinuity of the stripe center caused by the unevenness of brightness in the measurement of complex surface profiles. The proposed method has been validated and verified by experiments. A high accuracy in the extraction of strip centers could be obtained.
Equivalences among <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e5850" altimg="si266.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>-linear Hadamard codes

The Z2s-additive codes are subgroups of Z2sn, and can be seen as a generalization of linear codes over Z2 and Z4. A Z2s-linear Hadamard code is a binary Hadamard code which is the Gray map image of a Z2s-additive code. A partial classification of these codes by using the dimension of the kernel is known. In this paper, we establish that some Z2s-linear Hadamard codes of length 2t are equivalent, once t is fixed. This allows us to improve the known upper bounds for the number of such nonequivalent codes. Moreover, up to t=11, this new upper bound coincides with a known lower bound (based on the rank and dimension of the kernel). Finally, when we focus on s∈{2,3}, the full classification of the Z2s-linear Hadamard codes of length 2t is established by giving the exact number of such codes.
3-Dimensional facial expression recognition in human using multi-points warping

Expression in H-sapiens plays a remarkable role when it comes to social communication. The identification of this expression by human beings is relatively easy and accurate. However, achieving the same result in 3D by machine remains a challenge in computer vision. This is due to the current challenges facing facial data acquisition in 3D; such as lack of homology and complex mathematical analysis for facial point digitization. This study proposes facial expression recognition in human with the application of Multi-points Warping for 3D facial landmark by building a template mesh as a reference object. This template mesh is thereby applied to each of the target mesh on Stirling/ESRC and Bosphorus datasets. The semi-landmarks are allowed to slide along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal and localization error is assessed using Procrustes ANOVA. By using Principal Component Analysis (PCA) for feature selection, classification is done using Linear Discriminant Analysis (LDA).The localization error is validated on the two datasets with superior performance over the state-of-the-art methods and variation in the expression is visualized using Principal Components (PCs). The deformations show various expression regions in the faces. The results indicate that Sad expression has the lowest recognition accuracy on both datasets. The classifier achieved a recognition accuracy of 99.58 and 99.32% on Stirling/ESRC and Bosphorus, respectively.The results demonstrate that the method is robust and in agreement with the state-of-the-art results.
A Weighted PageRank-Based Algorithm for Virtual Machine Placement in Cloud Computing

Virtualization makes virtual machine placement (VMP) one of the most important technology in cloud computing. An effective VMP algorithm can significantly improve resource utilization of physical machines (PMs) in cloud datacenters. In this paper, we propose a VMP algorithm based on weighted PageRank (WPRVMP), which pursued to minimize the number of active PMs and improve the total resource utilization of all PMs in cloud datacenters. The impact of unplaced virtual machines (VMs) in the VMP process on the placement results, and the impact of VMP on the total resource utilization of all PMs are considered in this algorithm, to avoid the algorithm to fall into local optimal solution. In the process of selecting PM for each VM, the number of unplaced VMs of different types in the VMP process is considered in WPRVMP by weighted. The weighted PageRank algorithm is used to measure the possibility of a PM making full use of resources under different VMP conditions. The algorithm then ranks PMs according to the impact of the given VM is placed on each PM on the maximum resource utilization of all PMs, and places the VM based on the ranking results. Experimental results show that our algorithm is superior to other algorithms in reducing the number of active PMs and improving the resource utilization of PMs.
Causality analysis and fault ascription in component-based systems

This article introduces a general framework for fault ascription, which consists in identifying, within a multi-component system, the components whose faulty behavior has caused the failure of said system. Our framework uses configuration structures as a general semantical model to handle truly concurrent executions, partial and distributed observations in a uniform way. As a first contribution, and in contrast with most of the current literature on counterfactual analysis which relies heavily on a set of toy examples, we first define a set of expected formal properties for counterfactual builders, i.e. operators that build counterfactual executions. We then show that causality analyses that satisfy our requirements meet a set of elementary soundness and completeness properties. Finally we present a concrete causality analysis meeting all our requirements, and we show that it behaves well under refinement. We present several examples illustrating various phenomena such as causal over-determination or observational determinism, and we discuss the relationship of our approach with Halpern and Pearl's actual causality analysis.
Empirical Analysis of Object-Oriented Software Test Suite Evolution

The software system is evolving over the time, thus, the test suite must be repaired according to the changing code.Updating test cases manually is a time-consuming activity, especially for large test suites, which motivate the recent development of automatically repairing test techniques.To develop an effective automatic repair technique that reduces the effort of development and the cost of evolution, the developer should understand how the test suite evolves in practice.This investigation aims to conduct a comprehensive empirical study on eight Java systems with many versions of these systems and their test suites to find out how the test suite is evolving, and to find the relationship between the change in the program and the corresponding evolution in the test suite.This study showed that the test suite size is mostly increased, where the test suite complexity is stabilized.The increase (or decrease) in the code size will mostly increase (or decrease) the test suite size.However, the increasing or decreasing in the code complexity is offset by stabilizing the test suite complexity.Moreover, the percentage of the code coverage tends to be increased more than decreased, but in the mutation coverage, the opposite is true.
Quality attribute trade-offs in the embedded systems industry: an exploratory case study

Abstract The embedded systems domain has grown exponentially over the past years. The industry is forced by the market to rapidly improve and release new products to beat the competition. Frenetic development rhythms thus shape this domain and give rise to several new challenges for software design and development. One of them is dealing with trade-offs between run-time and design-time quality attributes. To study practices, processes and tools concerning the management of run-time and design-time quality attributes as well as the trade-offs among them from the perspective of embedded systems software engineers. An exploratory case study with two qualitative data collection steps, namely interviews and a focus group, involving six different companies from the embedded systems domain with a total of twenty participants. The interviewed subjects showed a preference for run-time over design-time qualities. Trade-offs between design-time and run-time qualities are very common, but they are often implicit, due to the lack of adequate monitoring tools and practices. Practitioners prefer to deal with trade-offs in the most lightweight way possible, by applying ad-hoc practices, thus avoiding any overhead incurred. Finally, practitioners have elaborated on how they envision the ideal tool support for dealing with trade-offs. Although it is notoriously difficult to deal with trade-offs, constantly monitoring the quality attributes of interest with automated tools is key in making explicit and prudent trade-offs and mitigating the risk of incurring technical debt.
An Accurate Calibration Means for the Phase Measuring Deflectometry System

Calibration is a critical step for the phase measuring deflectometry system. Existing calibration methods are mainly optimizing the calibration parameters with respect to the 2D re-projection error criterion. However, such a procedure cannot reduce metric errors in the practical application. Therefore, an accurate and practical calibration method is proposed. In which, conventional calibration means is first applied for the primary calibration. Then, a precise square planar mirror is used for the optimization of system calibration parameters. All the intrinsic and extrinsic parameters are considered as a global multi-objective optimization problem. Three metric error criteria are introduced to evaluate the 3D reconstruction accuracy of the reference mirror. Compared with classical calibration means, which apply the parameter optimization in 2D image space to minimize the re-projection errors, the proposed optimization approach is executed in 3D space directly. An experiment and comparison are conducted to verify that the proposed optimal calibration approach can effectively reduce the system deviation and to improve the system measurement accuracy.
Pedagogical Assessment of General Professional Competencies of Technical Engineers Training

The article is devoted to the actual problem of pedagogical assessment of general professional competencies of technical engineers' students.The analysis of the results of a study conducted at Peter the Great St. Petersburg Polytechnic University is presented, the criteria and characteristics for evaluating general professional competencies at various stages of their formation in the educational process are identified, and the problem areas for evaluating students' academic achievements are revealed.Experimental work confirmed the effectiveness of the identified criteria and characteristics of assessing the competencies at various stages of their formation in the educational process, namely, conducting verification work; preparation of monologic utterance on one of the studied subjects; carrying out written work in accordance with the lesson plan; conducting home reading; preparation of project work; implementation of work on a distance course; conducting an intermediate; conducting a final test; the introduction of bonus points for attendance.In assessing the success of a technical university student, the subjective assessment of the teacher is also taken into account.This form of assessment can serve as a practical basis for the activities of technical universities, allowing qualitative and quantitative assessment of changes in the educational development of students of technical universities.
Multioutput Convolution Spectral Mixture for Gaussian Processes

Multioutput Gaussian processes (MOGPs) are an extension of Gaussian processes (GPs) for predicting multiple output variables (also called channels/tasks) simultaneously. In this article, we use the convolution theorem to design a new kernel for MOGPs by modeling cross-channel dependencies through cross convolution of time-and phase-delayed components in the spectral domain. The resulting kernel is called multioutput convolution spectral mixture (MOCSM) kernel. The results of extensive experiments on synthetic and real-life data sets demonstrate the advantages of the proposed kernel and its state-of-the-art performance. MOCSM enjoys the desirable property to reduce to the well-known spectral mixture (SM) kernel when a single channel is considered. A comparison with the recently introduced multioutput SM kernel reveals that this is not the case for the latter kernel, which contains quadratic terms that generate undesirable scale effects when the spectral densities of different channels are either very close or very far from each other in the frequency domain.
An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform

Video surveillance systems play an important role in underground mines. Providing clear surveillance images is the fundamental basis for safe mining and disaster alarming. It is of significance to investigate image compression methods since the underground wireless channels only allow low transmission bandwidth. In this paper, we propose a new image compression method based on residual networks and discrete wavelet transform (DWT) to solve the image compression problem. The residual networks are used to compose the codec network. Further, we propose a novel loss function named discrete wavelet similarity (DW-SSIM) loss to train the network. Because the information of edges in the image is exposed through DWT coefficients, the proposed network can learn to preserve the edges better. Experiments show that the proposed method has an edge over the methods being compared in regards to the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), particularly at low compression ratios. Tests on noise-contaminated images also demonstrate the noise robustness of the proposed method. Our main contribution is that the proposed method is able to compress images at relatively low compression ratios while still preserving sharp edges, which suits the harsh wireless communication environment in underground mines.
A novel biologically-inspired target detection method based on saliency analysis for synthetic aperture radar (SAR) imagery

Saliency Object Detection (SOD) models driven by the biologically-inspired Focus of Attention (FOA) mechanism can result in highly accurate saliency maps. However, their application in high-resolution Synthetic Aperture Radar (SAR) images entails a number of intractable problems due to complex backgrounds. In this paper, we propose a novel hierarchical self-diffusion saliency (HSDS) method for detecting vehicle targets in large scale SAR images. To reduce the influence of cluttered returns on saliency analysis, we learn a weight vector from the training set to capture optimal initial saliency of the superpixels during saliency diffusion. By accounting for the multiple sizes of background objects, the saliency analysis is implemented in multi-scale space, and a saliency fusion strategy employed to integrate the multi-scale saliency maps. Simulation experiments demonstrate that our proposed method can produce a more accurate and stable detection performance, with decreased false alarms, compared to benchmark approaches.