Reviewerly/exHarmony huggingface dataset:
Horizontal and Vertical Nuclear Norm-Based 2DLDA for Image Representation

2-D linear discriminant analysis (2DLDA) has been widely used in pattern recognition and image classification. 2DLDA selects discriminative features from the up and left corner of images. However, 2DLDA uses the Frobenius norm (F-norm), which is sensitive to noise or outliers in data, as a metric. In this paper, we propose a novel framework, called horizontal and vertical nuclear norm-based 2DLDA (HVNN-2DLDA) for image representation. In the proposed framework, HVNN-2DLDA methods (i.e., HNN-2DLDA and VNN-2DLDA) are proposed, and both use the nuclear norm as a criterion. The nuclear norm can provide more structure and global information for the reconstruction of noisy images. HNN-2DLDA and VNN-2DLDA represent images in the row and column directions, respectively. In addition, by combining the row and column directions, we propose a bilateral nuclear norm-based 2DLDA method called BNN-2DLDA. The advantage of BNN-2DLDA over HNN-2DLDA and VNN-2DLDA is that an image sample can be represented by both the row and the column directions instead of only the row or column direction. HVNN-2DLDA learns a set of local optimal projection vectors by maximizing the ratio of the nuclear norm of the between-class scatter matrix and the nuclear norm of the within-class scatter matrix. To verify the robustness and recognition performance in image classification of HVNN-2DLDA, six public image databases are used for experiments. The experimental results demonstrate the effectiveness and the feasibility of the proposed framework.
Trajectory representation and landmark projection for continuous-time structure from motion

This paper revisits the problem of continuous-time structure from motion, and introduces a number of extensions that improve convergence and efficiency. The formulation with a [Formula: see text]-continuous spline for the trajectory naturally incorporates inertial measurements, as derivatives of the sought trajectory. We analyze the behavior of split spline interpolation on [Formula: see text] and on [Formula: see text], and a joint spline on [Formula: see text], and show that the latter implicitly couples the direction of translation and rotation. Such an assumption can make good sense for a camera mounted on a robot arm, but not for hand-held or body-mounted cameras. Our experiments in the Spline Fusion framework show that a split spline on [Formula: see text] is preferable over an [Formula: see text] spline in all tested cases. Finally, we investigate the problem of landmark reprojection on rolling shutter cameras, and show that the tested reprojection methods give similar quality, whereas their computational load varies by a factor of two.
The hidden predictive power of cryptocurrencies and QE: Evidence from US stock market

Motivated by the increasing evidence of digital assets as hedge against traditional financial assets, this study examines the predictive power of cryptocurrencies (Bitcoin) on the US stock returns. We also hypothesize that the unconventional monetary policy namely, Quantitative Easing (QE), is an underlying factor that has sustained the evolution of cryptocurrencies. We advance the literature by accounting for the role QE in the Bitcoin predictability of stock returns. Essentially, we extend the bivariate single factor Bitcoin-based predictive model propose by Salisu et al. (2018) to a multi-factor cryptocurrency-based predictive model. Our findings are as follow: (i) when QE is measured directly, the single predictive model seems to be the preferred model; (ii) when QE is measured indirectly, via some transmission channels, the multi-factor based predictive model tend to outperform the single-factor model and (iii) relative to the historical average, the multi-factor based predictive model is the more accurate model to forecast stock returns. These results are robust to different methods of forecast performance evaluation measures and different sub-sample periods. These results have important policy implications for the investors and policymakers.
Segmentation of Sidescan Sonar Imagery Using Markov Random Fields and Extreme Learning Machine

As a widely used segmentation scheme, Markov random field (MRF) utilizes k-means clustering to calculate the initial model for sidescan sonar image segmentation. However, for the noise and intensity inhomogeneity nature of the sidescan sonar images, the segmentation results of k-means clustering have low accuracy, motivating us to use machine learning methods to initialize MRF. Meanwhile, an extreme learning machine (ELM), a supervised learning algorithm derived from the single-hidden-layer feedforward neural networks, learns faster than randomly generated hidden-layer parameters and is superior to a support vector machine (SVM). Therefore, in this paper, we proposed a novel method for sidescan sonar image segmentation based on MRF and ELM. The proposed method segments sidescan sonar images in object-highlight, object-shadow, and sea-bottom reverberation areas. Specifically, we intend to use an ELM to get an initial model for MRF. Moreover, to improve the stability of an ELM, a simple ensemble ELM (SE-ELM) based on an ensemble algorithm is utilized to obtain the prediction model. In an SE-ELM, we use an ensemble of ELMs and majority votes to determine the prediction of testing data sets. Then, the classification results of the SE-ELM are utilized to initialize MRF, termed as SE-ELM-MRF. With features consisting of pixels of small image patches, our experiments on real sonar data indicate that the SE-ELM performs better than other machine learning methods such as ELM, kernel-based extreme learning machine, SVM, and convolutional neural networks. Moreover, using SE-ELM as the initial method in the proposed SE-ELM-MRF, the segmentation results are smoother and the segmentation process converges faster than the traditional MRF.
Reference-Guided Deep Super-Resolution via Manifold Localized External Compensation

The rapid development of social network and online multimedia technology makes it possible to address traditional image and video enhancement problems, with the aid of online similar reference data. In this paper, we tackle the problem of super-resolution (SR) in this way, specifically aiming to handle the “one-to-many” problem between the image patches of low resolution (LR) and high resolution (HR). We propose a manifold localized deep external compensation (MALDEC) network to additionally utilize reference images, i. e., retrieved similar images in cloud database and reference HR frame in a video, to provide an accurate localization and mapping to the HR manifold, and compensate the lost high-frequency details. The proposed network employs a three-step recovery: 1) internal structure inference, which uses the LR image itself and the internally inferred high frequency information to preserve main structure of the HR image; 2) manifold localization, which localizes the HR manifold and constructs the correspondence between the internal inferred image and the external images; and 3) external compensation, which introduces the external references of retrieved similar patches based on manifold localization information to reconstruct the high-frequency details. The learnable components of MALDEC, internal structure inference, and external compensation, are trained jointly to make a good tradeoff between these two terms for an optimal SR result. Finally, the proposed method is examined under three tasks: cloud-based image SR, multi-pose face reconstruction, and reference frame-guided video SR. Extensive experiments demonstrate the superiority of our method than the state-of-the-art SR methods in both objective and subjective evaluations, and our method offers new state-of-the-art performance.
Service oriented architecture maturity models: A systematic literature review

In recent years, the maturity model research has gained wide acceptance in the area of information systems and many Service Oriented Architecture (SOA) maturity models have been proposed. However, there are limited empirical studies on in-depth analysis and validation of SOA Maturity Models (SOAMMs). The objective is to present a comprehensive comparison of existing SOAMMs to identify the areas of improvement and the research opportunities. A systematic literature review is conducted to explore the SOA adoption maturity studies. A total of 20 unique SOAMMs are identified and analyzed in detail. A comparison framework is defined based on SOAMM design and usage support. The results provide guidance for SOA practitioners who are involved in selection, design, and implementation of SOAMMs. Although all SOAMMs propose a measurement framework, only a few SOAMMs provide guidance for selecting and prioritizing improvement measures. The current state of research shows that a gap exists in both prescriptive and descriptive purpose of SOAMM usage and it indicates the need for further research.
Structurally Incoherent Low-Rank 2DLPP for Image Classification

Preserving projection-based methods are good for finding the manifold structure embedded in data. As they use the Euclidean distance as a metric, which is sensitive to noise and outliers in data, nuclear norm-based 2D locality preserving projection (NN-2DLPP) is thus proposed to improve the robustness of 2DLPP. However, NN-2DLPP does not consider the discriminant ability of data. In order to improve the discriminant ability of preserving projection methods, in this paper, we use preserving projection learning with structurally incoherence of data and propose structurally incoherent low-rank 2DLPP (SILR-2DLPP) for image classification. This approach provides a discriminative representation of preserving projection learning by recovering the distinct different classes of the data. SILR-2DLPP searches the optimal subspace and low-rank representation simultaneously. We further extend SILR-2DLPP to a kernel case and propose kernel SILR-2DLPP (KSILR-2DLPP) to obtain a nonlinear representation. The theoretical analysis including the convergence and computational complexity of SILR-2DLPP are presented. To verify the performance of SILR-2DLPP and KSILR-2DLPP, six well-known image databases were used in the experiments. The experimental results show that the proposed methods are superior to the previous preserving projection methods for image classification.
Probabilistic Databases with an Infinite Open-World Assumption

Probabilistic databases (PDBs) introduce uncertainty into relational databases by specifying probabilities for several possible instances. Traditionally, they are finite probability spaces over database instances. Such finite PDBs inherently make a closed-world assumption: non-occurring facts are assumed to be impossible, rather than just unlikely. As convincingly argued by Ceylan et al. (KR '16), this results in implausibilities and clashes with intuition. An open-world assumption, where facts not explicitly listed may have a small positive probability can yield more reasonable results. The corresponding open-world model of Ceylan et al., however, assumes that all entities in the PDB come from a fixed finite universe. In this work, we take one further step and propose a model of "truly" open-world PDBs with an infinite universe. This is natural when we consider entities from typical domains such as integers, real numbers, or strings. While the probability space might become infinitely large, all instances of a PDB remain finite. We provide a sound mathematical framework for infinite PDBs generalizing the existing theory of finite PDBs. Our main results are concerned with countable, tuple-independent PDBs; we present a generic construction showing that such PDBs exist in the infinite and provide a characterization of their existence. This construction can be used to give an open-world semantics to finite PDBs. The construction can also be extended to so-called block-independent-disjoint probabilistic databases. Algorithmic questions are not the focus of this paper, but we show how query evaluation algorithms can be lifted from finite PDBs to perform approximate evaluation (with an arbitrarily small additive approximation error) in countably infinite tuple-independent PDBs.
Competitive and collaborative representation for classification

Deep network recently has achieved promising performance for classification task with massive training samples. The behavior of this model, however, would be diminished obviously when the training set is small. Meanwhile, linear representation based classifiers have widely applied into many fields. These classifiers mostly attempt to take advantage of the correct class to code the test sample through appending l1-norm or nuclear norm which highly takes computation. Under these observations, we present a novel competitive and collaborative representation classification (Co-CRC) that employs the properties of training data with l2-norm regularization to create a competitive environment which enables the correct class to make more contribution to coding. Additionally, the proposed competitive weight in this paper enhances the competitive relation among all classes and is beneficial for the classifier to find the correct class. Extensive experimental results over popular benchmarks including object, scene and face images database indicate that the proposed algorithm totally based on l2-norm regularization takes less computation to obtain rather sparse coding and mostly outperforms several state-of-the-art approaches.
Shape-Optimizing and Illumination-Smoothing Image Stitching

Image stitching is usually subject to projective distortion and color difference problems. This paper presents an illumination-smoothing image stitching method based on the shape-optimizing hybrid transformation. An automatic mesh generation strategy is especially designed to reduce the calculation of the hybrid transformation within a reasonable range and guarantee the accuracy of image alignment. We consider stitching multiple images while constraining the distortion with the hybrid transformation and alleviate the color difference. In this paper, the method of color correction is adapted according to the characteristics of image stitching so as to achieve illumination-smoothing results. Moreover, the triangulation of the matching feature points is used to partition the color regions of the image and then more abundant color information is obtained for the calculation of the color transformation model. For general horizontal images, our method can achieve better results with less distortion in comparison with state-of-the-art methods. Various experimental results validate our new method.
Ontology Alignment Based on Word Embedding and Random Forest Classification

Ontology alignment is crucial for integrating heterogeneous data sources and forms an important component of the semantic web. Accordingly, several ontology alignment techniques have been proposed and used for discovering correspondences between the concepts (or entities) of different ontologies. Most alignment techniques depend on string-based similarities which are unable to handle the vocabulary mismatch problem. Also, determining which similarity measures to use and how to effectively combine them in alignment systems are challenges that have persisted in this area. In this work, we introduce a random forest classifier approach for ontology alignment which relies on word embedding for determining a variety of semantic similarity features between concepts. Specifically, we combine string-based and semantic similarity measures to form feature vectors that are used by the classifier model to determine when concepts align. By harnessing background knowledge and relying on minimal information from the ontologies, our approach can handle knowledge-light ontological resources. It also eliminates the need for learning the aggregation weights of a composition of similarity measures. Experiments using Ontology Alignment Evaluation Initiative (OAEI) dataset and real-world ontologies highlight the utility of our approach and show that it can outperform state-of-the-art alignment systems. Code related to this paper is available at: https://bitbucket.org/paravariar/rafcom .
Evaluating query and storage strategies for RDF archives

There is an emerging demand on efficiently archiving and (temporal) querying different versions of evolving semantic Web data. As novel archiving systems are starting to address this challenge, foundations/standards for benchmarking RDF archives are needed to evaluate its storage space efficiency a nd the performance of different retrieval operations. To this end, we provide theoretical foundations on the design of data and queries to evaluate emerging RDF archiving systems. Then, we instantiate these foundations along a concrete set of queries on the basis of a real-world evolving datasets. Finally, we perform an extensive empirical evaluation of current archiving techniques and querying strategies, which is meant to serve as a baseline of future developments on querying archives of evolving RDF data.
Markets for rules: the promise and peril of blockchain distributed governance

Purpose The purpose of this paper is to explore the possible contributions of blockchain technology to creating new governance structures that facilitate social cooperation. Design/methodology/approach Conceptual analysis with key ideas in new institutional economics and political theory is used in this paper. Findings Blockchain technology provides a new tool through which political entrepreneurs can credibly alienate some of their power within a system of rules that they have established. Originality/value This paper links discussion of blockchain entrepreneurship in commercial markets to research into private governance.
Accurate calibration approach for non-overlapping multi-camera system

This work describes an accurate approach to calibrate multiple cameras fixed inside a rig without overlapping views. An apparatus consisting of two fixed chessboard targets is designed for the calibration under general motion conditions. Sub-pixel corner localization and perspective-n-point determination techniques are employed to obtain individual poses from a sequence of target-corresponding images captured by each camera. Then the rig and apparatus parameters are refined by a non-linear optimization technique from individual poses based on geometric constrained adjustment. Robust localization of target points provides good initial values for the optimization, which ensures the reliability of calibration. Results of experiments show that the proposed approach has the advantages of high accuracy, low costs, and simple structure. In addition, it is suitable for onsite calibration.
Solutions for counteracting human deception in social engineering attacks

Purpose The purpose of this paper is to investigate the top three cybersecurity issues in organizations related to social engineering and aggregate solutions for counteracting human deception in social engineering attacks. Design/methodology/approach A total of 20 experts within Information System Security Association participated in a three-round Delphi study for aggregating and condensing expert opinions. Three rounds moved participants toward consensus for solutions to counteract social engineering attacks in organizations. Findings Three significant issues: compromised data; ineffective practices; and lack of ongoing education produced three target areas for implementing best practices in countering social engineering attacks. The findings offer counteractions by including education, policies, processes and continuous training in security practices. Research limitations/implications Study limitations include lack of prior data on effective social engineering defense. Research implications stem from the psychology of human deception and trust with the ability to detect deception. Practical implications Practical implications relate to human judgment in complying with effective security policies and programs and consistent education and training. Future research may include exploring financial, operational and educational costs of implementing social engineering solutions. Social implications Social implications apply across all knowledge workers who benefit from technology and are trusted to protect organizational assets and intellectual property. Originality/value This study contributes to the field of cybersecurity with a focus on trust and human deception to investigate solutions to counter social engineering attacks. This paper adds to under-represented cybersecurity research regarding effective implementation for social engineering defense.
Augmenting cascaded correlation filters with spatial–temporal saliency for visual tracking

We herein propose a novel visual tracking approach using cascaded discriminative correlation filters (DCFs). The approach consists of two stages. In the first stage, a DCF is trained with high-level convolutional features to initially estimate the location of the object. In the second stage, another DCF is trained using low-level convolutional features to refine the object location. To efficiently track the deformable or occluded objects, spatial–temporal saliency is introduced to enhance the second stage DCF. The proposed approach is tested on the VOT2015 and OTB-13 benchmark datasets. The experimental results show that our tracker achieves state-of-the-art performance and performs extremely well in tracking nonrigid, fast moving, or occluded objects.
A solar irradiation GIS as decision support tool for the Province of Salta, Argentina

The province of Salta is characterized by its solar energy high potential. The use of solar resource would improve living conditions in the area, diversify the energy matrix, promote more sustainable production systems and reduce greenhouse gases emissions. However, there are only a few studies that describe in high spatial resolution the variability of the solar resource in Argentina. Multidimensional tools, that consider the environment and the socio-economic situation, have to be considered for adequate support decision-making, such as solar collector location assessment and photovoltaic potential. In this sense, a deep evaluation of the solar resource is needed first, as solar irradiation is an essential input variable for the design and evaluation of solar application systems. In this paper, we detail the methodology used to elaborate a GIS tool to support decisions related to renewable energy policies and solar technology design. A comparison between global solar irradiation measurements in situ, empirical models, and data provided by Land Surface Analysis Satellite Applications Facility (LSA-SAF), is performed in daily, monthly and annual basis for a seven-year period. This analysis validates the use of this satellite data for the determination of solar irradiation in the region.