Reviewerly/exHarmony huggingface dataset:
Polar Transformation on Image Features for Orientation-Invariant Representations

The choice of image feature representation plays a crucial role in the analysis of visual information. Although vast numbers of alternative robust feature representation models have been proposed to improve the performance of different visual tasks, most existing feature representations [e.g., handcrafted features or convolutional neural networks (CNNs)] have a relatively limited capacity to capture the highly orientation-invariant (rotation/reversal) features. The net consequence is suboptimal visual performance. To address these problems, this study adopts a novel transformational approach, which investigates the potential of using polar feature representations. Our low level consists of a histogram of oriented gradient, which is then binned using annular spatial bin-type cells applied to the polar gradient. This gives gradient binning invariance for feature extraction. In this way, the descriptors have significantly enhanced orientation-invariant capabilities. The proposed feature representation, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">orientation-invariant histograms of oriented gradients</i> , is capable of accurately processing visual tasks (e.g., facial expression recognition). In the context of the CNN architecture, we propose two polar convolution operations, referred to as full polar convolution and local polar convolution, and use these to develop polar architectures for the CNN orientation-invariant representation. Experimental results show that the proposed orientation-invariant image representation, based on polar models for both handcrafted features and deep learning features, is both competitive with state-of-the-art methods and maintains compact representation on a set of challenging benchmark image datasets.
Fast Learning With Polynomial Kernels

This paper proposes a new learning system of low computational cost, called fast polynomial kernel learning (FPL), based on regularized least squares with polynomial kernel and subsampling. The almost optimal learning rate as well as the feasibility verifications including the subsampling mechanism and solvability of FPL are provided in the framework of learning theory. Our theoretical assertions are verified by numerous toy simulations and real data applications. The studies in this paper show that FPL can reduce the computational burden of kernel methods without sacrificing its generalization ability very much.
Metrological advantage at finite temperature for Gaussian phase estimation

In the context of phase estimation with Gaussian states, we introduce a quantifiable definition of metrological advantage that takes into account thermal noise in the preparation procedure. For a broad set of states---isotropic nonpure Gaussian states---we show that squeezing is not only necessary but sufficient to achieve metrological advantage. We interpret our results in the framework of resource theory and discuss possible sources of advantage other than squeezing. Our work is a step towards using phase estimation with pure and mixed states to define and quantify nonclassicality. This work is complementary with studies that defines nonclassicality using quadrature displacement estimation.
Improving the precision of frequency estimation via long-time coherences

In recent years, several estimation strategies have been formulated to determine the value of an unknown parameter in the most precise way, taking into account the presence of noise. These strategies typically rely on the use of quantum entanglement between the sensing probes and they have been shown to be optimal in the asymptotic limit in the number of probes, as long as one performs measurements on shorter and shorter time scales. Here, we present a different approach to frequency estimation, which exploits quantum coherence in the state of each sensing particle in the long time limit and is obtained by properly engineering the environment. By means of a commonly used master equation, we show that our strategy can overcome the precision achievable with entanglement-based strategies for a finite number of probes. We discuss a possible implementation of the scheme in a realistic setup that uses trapped ions as quantum sensors.
Optimizing cloud solutioning design

The economics of the cloud model has been encouraging IT enterprises to migrate from on-premise environments to public, private, or hybrid cloud solutions. To perform such a migration, a cloud offering needs to be chosen and a cloud solution needs to be built. In industrial settings, cloud designers may spend days or even weeks to come up with an acceptable cloud solution at a low cost/price. Like any manual process, it is obvious that such a cloud solution design process is error prone, time consuming, and does not guarantee an optimal output, e.g. a solution with a minimum cost/price. Different from existing works that solve the problem from the user’s angle, we solve it from the cloud provider’s prospective, who aims at offering customized cloud solutions for different user requirements at low costs. Such difference requires a unique way of problem modeling. Through analyzing real business data, we abstract the problem into a general attribute–value combinations and formulate a powerful integer programming optimization model to solve it. The general form of the optimization model allows various definitions of customer requirements as well as cloud offerings. Our novel optimization approach for cloud solution design satisfies client requirements, cloud offering constraints, and produces a solution at a minimum cost in a short time, if one exists. We evaluated our solution on realistic data against two baseline approaches. The numerical results show both the effectiveness and efficiency of our approach as well as its practical potential.
Assessment of feature fusion strategies in visual attention mechanism for saliency detection

Saliency detection is a hot topic in the field of computer vision and pattern recognition, thus plenty of saliency models have been exploited and extended to various visual correspondence applications. Currently, it's still confronted with a variety of obstacles and challenges, although it has been studied for decades. With the progress of saliency detection, different computational models and salient features have been proposed and some of them improve and compensate the deficiencies of the others. In this paper, we focus on investigating the salient feature fusion strategies in human visual attention mechanism for saliency detection (e.g., linear and non-linear), in order to efficiently incorporate various salient cues for achieving a better result. Based on the complementary principle, we firstly construct a saliency map based on the information of the image background. Then, we generate a supplemental saliency map from the compactness saliency features. Finally, we evaluate the performance of six individual fusion strategies including both linear and non-linear models in terms of three publicly available image datasets. Experimental results show that our designed non-linear fusion strategy based on least-square method outperforms the other fusion strategies in saliency detection.
Single image super resolution via neighbor reconstruction

Super Resolution (SR) is a complex, ill-posed problem where the aim is to construct the mapping between the low and high resolution manifolds of image patches. Anchored neighborhood regression for SR (namely A+ [27]) has shown promising results. In this paper we present a new regression-based SR algorithm that overcomes the limitations of A+ and benefits from an innovative and simple Neighbor Reconstruction Method (NRM). This is achieved by vector operations on an anchored point and its corresponding neighborhood. NRM reconstructs new patches which are closer to the anchor point in the manifold space. Our method is robust to NRM sparsely-sampled points: increasing PSNR by 0.5 dB compared to the next best method. We comprehensively validate our technique on standardised datasets and compare favourably with the state-of-the-art methods: we obtain PSNR improvement of up to 0.21 dB compared to previously-reported work.
Bi-direction hierarchical LSTM with spatial-temporal attention for action recognition

Human action recognition in naturalistic videos is an important task with a broad range of applications. Recently, the encoder-decoder framework based on attention mechanism has been applied to action recognition. Although such conventional methods reach state-of-the-art, they always face a bottlen eck of distinguishing similar actions. To solve this problem, we propose a novel recurrent attention convolutional neural network (RACNN), which incorporates convolutional neural networks (CNNs), long short-term memory (LSTM) and attention mechanism. Inspired by the composition of the action, the pre-action and the result of action might be important parts of an action, we introduce bi-direction LSTM with hierarchical structure. Additionally, the separated spatial-temporal attention is employed into our method. Furthermore, we find that incorporating spatio-temporal features extracted from three-dimensional CNNs (3DCNNs) and RGB features can enhance the relationship mined in each frame. Our comprehensive experimental results on two benchmark datasets, i.e., HMDB51 and UCF101, verify the effectiveness of our proposed methods and show that our proposals can significantly outperform the current state-of-the-art methods.
Hulls of cyclic codes overZ4

The hulls of linear and cyclic codes over finite fields have been of interest and extensively studied due to their wide applications. In this paper, the hulls of cyclic codes of odd length n over the ring Z 4 have been focused on. Their characterization has been established in terms of the generators viewed as ideals in the quotient ring Z 4 [ x ] ∕ 〈 x n − 1 〉 . An algorithm for computing the types of the hulls of cyclic codes of arbitrary odd length over Z 4 has been given. The 2-dimensions of the hulls of cyclic codes of length n over Z 4 and the number of cyclic codes of length n over Z 4 having hulls of a given 2-dimension are determined. The average 2-dimension E ( n ) of the hulls of cyclic codes of odd length n over Z 4 has been established. A general formula for E ( n ) has been established together with its upper and lower bounds. It turns out that E ( n ) grows the same rate as n . A brief discussion on hulls of cyclic codes over Z p 2 , where p is an odd prime, is provided as well.
Sun orientation estimation from a single image using short-cuts in DCNN

The illumination effect is essential for the realistic results in images which are created by inserting virtual objects into real scene. For outdoor scenes, automatic estimation of sun orientation condition from a single outdoor image is fundamental for inserting 3D models to a single image. Traditional methods for outdoor sun orientation estimation often use handcraft illumination features or cues. These cues heavily rely on the experiences of human and pre-processing progresses using other image understanding technologies such as shadow and sky detection, geometry recovery and intrinsic image decomposition, which limit their performances. We propose an end to end way of outdoor sun orientation estimation via a novel deep convolutional neural network (DCNN), which directly outputs the orientation of the sun from an outdoor image. Our proposed SunOriNet contains a contact layer that directly contacts the intermediate feature maps to the high-level ones and learns hierarchical features automatically from a large-scale image dataset with annotated sun orientations. The experiments reveal that our DCNN can well estimate sun orientation from a single outdoor image. The estimation accuracy of our method outperforms state-of-the-art DCNN based methods.
An Advert Creation System for Next-Gen Publicity

With the rapid proliferation of multimedia data in the internet, there has been a fast rise in the creation of videos for the viewers. This enables the viewers to skip the advertisement breaks in the videos, using ad blockers and ‘skip ad’ buttons – bringing online marketing and publicity to a stall. In this paper, we demonstrate a system that can effectively integrate a new advertisement into a video sequence. We use state-of-the-art techniques from deep learning and computational photogrammetry, for effective detection of existing adverts, and seamless integration of new adverts into video sequences. This is helpful for targeted advertisement, paving the path for next-gen publicity. Code related to this paper is available at: https://youtu.be/zaKpJZhBVL4 .
A symbolic genetic programming approach for identifying models of learning-by-doing

In this study, we apply a symbolic regression approach to generate and investigate new potential univariate learning curve functional forms to forecast human learning responses efficiently and stably. Past studies have compared learning models in the literature to one another. Yet, continued interest in model development and comparison suggests that the question remains open as to whether there are other useful and yet-undiscovered models. We address the question of whether the existing literature contains the best model choices, or if additional forms have merit. We employ a multigenic genetic programming algorithm to secondary field data from a range of manual sewing tasks. We identified an array of potentially useful empirical forms and examined whether these forms match or improve upon extant forms. Among two-parameter functional forms, the log-linear form performed well in efficiency and stability for both models of cumulative experience, and cumulative working time. A three-parameter hyperbolic model was found and top-ranked as a model of cumulative work and a model of cumulative time in the three-parameter learning curve functional forms. We also found that 4-parameter models show characteristics of over-fitting and have small marginal differences in efficiency and stability for models of cumulative working time, which suggests that a three-parameter model may be a good choice, in general.
Mutifractals based multimodal 3D image registration

Multimodal registration is a method to register the volumes of different modalities, for e.g., computed tomography (CT) and magnetic resonance (MR). Mutual information (MI) based methods are widely used for multimodal registration. The MI characterizes the statistical dependence between the voxel intensities of volumes. Robustness of the MI based registration is affected, when there is a low correspondence between the voxel intensities of volumes. This can be improved by integrating the geometric characteristics of volumes like complexity, singularity and irregularity with registration. A novel approach for 3D multimodal image registration based on the multifractal characterization of volumes is being proposed in this paper. The proposed method uses multifractal formalism to incorporate geometric characteristics into registration. Multifractal formalism involves determination of Holder exponent followed by computation of Hausdorff dimension. Holder exponents quantify the local regularity of the volumes and Hausdorff dimensions quantify the global regularity (multifractality) of the volumes. The performance of the proposed algorithm is evaluated using synthetic phantom images for different noise levels and 41 clinical 3D brain images of 7 different patients from a public domain database. The above-mentioned test platforms highlight the efficiency of the proposed method towards improving the robustness and accuracy of registration.
Are You Tampering with My Data?

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks, causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.
Linear complexity of generalized cyclotomic sequences of period $$2p^{m}$$

In this paper, we construct two generalized cyclotomic binary sequences of period $$2p^{m}$$ based on the generalized cyclotomy and compute their linear complexity, showing that they are of high linear complexity when $$m\ge 2$$ .
Dynamic Collaborative Tracking

Correlation filter has been demonstrated remarkable success for visual tracking recently. However, most existing methods often face model drift caused by several factors, such as unlimited boundary effect, heavy occlusion, fast motion, and distracter perturbation. To address the issue, this paper proposes a unified dynamic collaborative tracking framework that can perform more flexible and robust position prediction. Specifically, the framework learns the object appearance model by jointly training the objective function with three components: target regression submodule, distracter suppression submodule, and maximum margin relation submodule. The first submodule mainly takes advantage of the circulant structure of training samples to obtain the distinguishing ability between the target and its surrounding background. The second submodule optimizes the label response of the possible distracting region close to zero for reducing the peak value of the confidence map in the distracting region. Inspired by the structure output support vector machines, the third submodule is introduced to utilize the differences between target appearance representation and distracter appearance representation in the discriminative mapping space for alleviating the disturbance of the most possible hard negative samples. In addition, a CUR filter as an assistant detector is embedded to provide effective object candidates for alleviating the model drift problem. Comprehensive experimental results show that the proposed approach achieves the state-of-the-art performance in several public benchmark data sets.
$\ell_{1}$ -Norm Heteroscedastic Discriminant Analysis Under Mixture of Gaussian Distributions

Fisher's criterion is one of the most popular discriminant criteria for feature extraction. It is defined as the generalized Rayleigh quotient of the between-class scatter distance to the within-class scatter distance. Consequently, Fisher's criterion does not take advantage of the discriminant information in the class covariance differences, and hence, its discriminant ability largely depends on the class mean differences. If the class mean distances are relatively large compared with the within-class scatter distance, Fisher's criterion-based discriminant analysis methods may achieve a good discriminant performance. Otherwise, it may not deliver good results. Moreover, we observe that the between-class distance of Fisher's criterion is based on the l2 -norm, which would be disadvantageous to separate the classes with smaller class mean distances. To overcome the drawback of Fisher's criterion, in this paper, we first derive a new discriminant criterion, expressed as a mixture of absolute generalized Rayleigh quotients, based on a Bayes error upper bound estimation, where mixture of Gaussians is adopted to approximate the real distribution of data samples. Then, the criterion is further modified by replacing l2 -norm with l1 one to better describe the between-class scatter distance, such that it would be more effective to separate the different classes. Moreover, we propose a novel l1 -norm heteroscedastic discriminant analysis method based on the new discriminant analysis (L1-HDA/GM) for heteroscedastic feature extraction, in which the optimization problem of L1-HDA/GM can be efficiently solved by using the eigenvalue decomposition approach. Finally, we conduct extensive experiments on four real data sets and demonstrate that the proposed method achieves much competitive results compared with the state-of-the-art methods.
A Robust Generic Multi-Authority Attributes Management System for Cloud Storage Services

Attribute Based Encryption (ABE) schemes face number of essential challenges for achieving data privacy in Cloud Storage Services (CSS). One crucial challenge is the existence of a single authority for attributes management. The second challenge is the lack of an efficient attribute revocation mechanism that prohibits an unauthorized access instantaneously after an attribute revocation is requested. The third challenge is the avoidance of using attribute revocation scenario as a mechanism for user revocation. In this paper, a robust generic multi-authority attributes management system is presented to overcome these challenges for CSS. The proposed system can be implemented using either Ciphertext Policy ABE (CP-ABE) or Key Policy ABE (KP-ABE) technique that possesses a specific set of characteristics. In addition, the proposed system presents a novel structure for user digital identity that prohibits the collusion between system users. Moreover, the proposed system does not oblige cooperation between the participated attributes authorities to accomplish any of the proposed system tasks. Furthermore, the attributes authorities can join or leave the proposed system without mandating the proposed system re-initialization. Finally, the presented performance measures prove the proposed system validity to accomplish all the specified goals with acceptable performance.
QA 3 : A natural language approach to question answering over RDF data cubes

In this paper we present QA3, a question answering (QA) system over RDF data cubes. The system first tags chunks of text with elements of the knowledge base, and then leverages the well-defined structure of data cubes to create a SPARQL query from the tags. For each class of questions with the same structure a SPARQL template is defined, to be filled in with SPARQL fragments obtained by the interpretation of the question. The correct template is chosen by using an original set of regex-like patterns, based on both syntactical and semantic features of the tokens extracted from the question. Preliminary results obtained using a limited set of templates are encouraging and suggest a number of improvements. QA3 can currently provide a correct answer to 27 of the 50 questions of the test set of the task 3 of QALD-6 challenge, remarkably improving the state of the art in natural language question answering over data cubes.