Reviewerly/exHarmony huggingface dataset:
State Estimation With Trajectory Shape Constraints Using Pseudomeasurements

In this paper, a trajectory shape constraint on target motion is investigated, and the corresponding state estimation method is presented. This type of constraint occurs when only the shape of the target trajectory is known a priori, without any other necessary information to exactly describe the specific trajectory. For example, one may only know that the target moves along a straight line. This prior knowledge of trajectory can be considered as a constraint to improve tracking performance. To describe the shape constraint arising from the straight line assumption, the state vector is augmented by the states at previous time steps. This facilitates the description of the shape constraint using the components of the state vector. Pseudomeasurements are then constructed based on these relationships to incorporate the constraint into an estimation process to improve the performance. The redundancy of the complete set of pseudomeasurements for a given augmented state vector is analyzed, and the minimal set of pseudomeasurements, which describes the constraint exactly, is proposed. The time evolution equation for the augmented state and the measurement equation using the minimal pseudomeasurement set are formulated. A trajectory shape constraint Kalman filter (TSCKF) is then proposed for simultaneous filtering and smoothing. Since both the measurement vector and the state vector are high dimensional, the cubature Kalman filter is used in the proposed TSCKF to deal with the strong nonlinearity in this problem. Monte Carlo simulations illustrate the effectiveness of the proposed TSCKF and the improvement in both filtering and smoothing accuracies by incorporating the trajectory shape constraint.
Low‐light image enhancement with strong light weakening and bright halo suppressing

Low-light enhancement methods suffer from the over-enhancement problem which could induce the loss of the important texture and make images look unnatural. Moreover, some low-light images contain strong light areas that must be weakened to improve the visual effect. In this study, an enhancement method with strong light weakening and bright halo suppressing is presented. Firstly, the bright channel prior is applied to the inverted image to weaken the strong light both in and around the strong light areas. Then, a dehazing-type of algorithm with the dark channel prior is employed via superpixel segmentation to enhance the low-light image. Finally, a revised non-local denoising method is proposed to further refine the enhanced image. Experimental results showed that the proposed method achieved better visual effects compared with other state-of-the-art methods. Besides, the quantitative evaluation showed that the authors’ method outperforms the other methods both in the aspect of enhancement and denoising.
Joint interaction with context operation for collaborative filtering

In recommender systems, the classical matrix factorization model for collaborative filtering only considers joint interactions between users and items. In contrast, context-aware recommender systems (CARS) use contexts to improve recommendation performance. Some early CARS models treat user, item and context equally, unable to capture contextual impact accurately. More recent models perform context operations on users and items separately, leading to "double-counting" of contextual information. This paper proposes a new model, Joint Interaction with Context Operation (JICO), to integrate the joint interaction model with the context operation model, via two layers. The joint interaction layer models interactions between users and items via an interaction tensor. The context operation layer captures contextual information via a contextual operating tensor. We evaluate JICO on four datasets and conduct novel studies, including varying contextual influence and time split recommendation. JICO consistently outperforms competing methods, while providing many useful insights to assist further analysis.
Metadata categorization for identifying search patterns in a digital library

Purpose For digital libraries, it is useful to understand how users search in a collection. Investigating search patterns can help them to improve the user interface, collection management and search algorithms. However, search patterns may vary widely in different parts of a collection. The purpose of this paper is to demonstrate how to identify these search patterns within a well-curated historical newspaper collection using the existing metadata. Design/methodology/approach The authors analyzed search logs combined with metadata records describing the content of the collection, using this metadata to create subsets in the logs corresponding to different parts of the collection. Findings The study shows that faceted search is more prevalent than non-faceted search in terms of number of unique queries, time spent, clicks and downloads. Distinct search patterns are observed in different parts of the collection, corresponding to historical periods, geographical regions or subject matter. Originality/value First, this study provides deeper insights into search behavior at a fine granularity in a historical newspaper collection, by the inclusion of the metadata in the analysis. Second, it demonstrates how to use metadata categorization as a way to analyze distinct search patterns in a collection.
COMPUTER AIDED DIAGNOSIS SYSTEM FOR AUTOMATIC TWO STAGES CLASSIFICATION OF BREAST MASS IN DIGITAL MAMMOGRAM IMAGES

Breast cancer is the most frequent cancer type that is diagnosed in women. The exact causes of such cancer are still unknown. Early and precise detection of breast cancer using mammogram images or biopsy to provide the required medications can increase the healing percentage. There are much current research efforts to developed a computer aided diagnosis (CAD) system based on mammogram images for detecting and classification of breast masses. In this research, a CAD system is developed for automated segmentation and two-stages classification of breast masses. The first stage includes the classification of the masses into seven classes (normal, calcification, circumscribed, spiculated, ill-defined, architectural distortion, asymmetry), which is done using probabilistic neural network (PNN). The second classification stage is to define the severity of abnormality into two classes (Benign and Malignant) which were done using support vector machine (SVM). The results of applying the proposed method on two mammogram image show that the accuracy of detection and segmentation of the breast mass was 99.8% for mammographic image analysis society database (MIAS-DB) with 322 images and 97.5% for breast cancer digital repository (BCDR), BCDR-F03 and BCDR-DN01 with 936 images, while for the first classification stage has accuracy of 97.08%, sensitivity of 98.30% and specificity of 89.8%, and the second classification stage has an accuracy of 99.18%, sensitivity of 98.42% and specificity of 94.90%.
Collective compression of images using averaging and transform coding

With the advancement in photography, there is a rapid growth of images in Social Media. This increase poses a challenge for the effective storage and fast transmission of images. In order to meet this demand, for a set of images, the compression can be improved by removing redundancies present among the images in the set and the individual image. In this paper, Image Averaging and Transform Coding (IATC) is proposed for the compression of image sets. In IATC, Residual images are generated by taking the difference between the mean of all the images and original images. The wavelet coefficients of the DWT transformed mean and the residual images are Huffman encoded. The performance of proposed method is evaluated based on Correlation coefficient, Bit-Saving (%), Root Mean Square error and Compression Time for both similar and dissimilar image sets. The results show improvement in bit saving of the image sets.
<i>Ab Initio</i>Few-Mode Theory for Quantum Potential Scattering Problems

Few-mode models are a cornerstone of the theoretical work in quantum optics, with the famous single-mode Jaynes-Cummings model being only the most prominent example. In this work, we develop an ab initio few-mode theory, a framework connecting few-mode system-bath models to ab initio methods. We first present a method to derive exact few-mode Hamiltonians for noninteracting quantum potential scattering problems and demonstrate how to rigorously reconstruct the scattering matrix from such few-mode Hamiltonians. We show that, upon inclusion of a background scattering contribution, an ab initio version of the well-known input-output formalism is equivalent to standard scattering theory. On the basis of these exact results for noninteracting systems, we construct an effective few-mode expansion scheme for interacting theories, which allows us to extract the relevant degrees of freedom from a continuum in an open quantum system. As a whole, our results demonstrate that few-mode as well as input-output models can be extended to a general class of problems and open up the associated toolbox to be applied to various platforms and extreme regimes. We outline differences of the ab initio results to standard model assumptions, which may lead to qualitatively different effects in certain regimes. The formalism is exemplified in various simple physical scenarios. In the process, we provide a proof of concept of the method, demonstrate important properties of the expansion scheme, and exemplify new features in extreme regimes.7 MoreReceived 21 December 2018Revised 9 September 2019DOI:https://doi.org/10.1103/PhysRevX.10.011008Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCavity quantum electrodynamicsLight-matter interactionNonrelativistic wave equationOpen quantum systems & decoherencePhotonicsQuantum description of light-matter interactionAtomic, Molecular & OpticalGeneral PhysicsCondensed Matter, Materials & Applied Physics
Bayesian Neural Networks with Weight Sharing Using Dirichlet Processes

We extend feed-forward neural networks with a Dirichlet process prior over the weight distribution. This enforces a sharing on the network weights, which can reduce the overall number of parameters drastically. We alternately sample from the posterior of the weights and the posterior of assignments of network connections to the weights. This results in a weight sharing that is adopted to the given data. In order to make the procedure feasible, we present several techniques to reduce the computational burden. Experiments show that our approach mostly outperforms models with random weight sharing. Our model is capable of reducing the memory footprint substantially while maintaining a good performance compared to neural networks without weight sharing.
SCUT-EPT: New Dataset and Benchmark for Offline Chinese Text Recognition in Examination Paper

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.
Optimization of Tongue Gesture Processing Algorithm for Standalone Multimodal Tongue Drive System

Multimodal tongue drive system (mTDS) is an assistive technology, which uses speech, tongue, and head movements of people with tetraplegia to help them control devices such as wheelchairs, PCs, or smartphones. In order to process tongue gestures, it is required to solve the magnetic dipole equation, which is computationally expensive to execute in a wearable embedded hardware. In this paper, a support vector machine with linear kernel-based algorithm is proposed, which uses an additional magnetometer that has 96.1% accuracy with a 2.2% sensitivity and 1.8% specificity among 15 participants, is computationally efficient for mTDS, generates high-performance classification results despite small changes of magnet positions (-5 to 5 mm) and orientations (-10° to 10°) from a four-dimensional robot emulated tongue movement data compared to five other algorithms. This algorithm generates more accurate results to control devices independently despite the Midas-touch problem between speech and tongue movements.
Velocity Estimation From a Single Linear Motion Blurred Image Using Discrete Cosine Transform

There is a growing trend to use a digital camera as an instrument to measure velocity instead of a regular sensor approach. This paper introduces a new proposal for estimating kinematic quantities, namely, the angle and the relative speed, from a single motion blur image using the discrete cosine transform (DCT). Motion blur is a common phenomenon present in images due to the relative movement between the camera and the objects, during sensor exposure to light. Today, this source of kinematic data is mostly dismissed. The introduced technique focuses on cases where the camera moves at a constant linear velocity while the background remains unchanged. 2250 motion blur pictures were shot for the angle experiments and 500 for the speed estimation experiments, in a light and distance controlled environment, using a belt motor slider driven at angles between 0° and 90° and 10 preset speeds. The DCT Hough and DCT Radon results were compared to discrete Fourier transform (DFT) Hough and DFT Radon algorithms for angle estimation. The mean absolute error of the DCT Radon method for direction estimation was 4.66°. In addition, the mean relative error for speed estimation of the DCT Pseudocepstrum was 5.15%. The innovative DCT frequency analysis proposals were more accurate than all competitors evaluated for the reconstruction of the point spread function that enables calculation of relative velocity and motion direction. These results demonstrate that cameras as an instrument can be used to measure velocity even using a single linear motion blur degraded image.
Computationally resurrecting the functional Remote Associates Test using cognitive word associates and principles from a computational solver

Human creativity is usually assessed with a variety of established creativity tests. One of this is the Remote Associates Test (RAT), which aims to measure the ability of reaching remote associates with linguistic stimuli. A well known variant of the RAT exists – the compound RAT, for which normative data and solvers have been proposed in the literature. However, a different type of RAT was proposed in 1971 by Worthen and Clark – a functional form which had the potential of measuring other types of associations. However, the few test items proposed by Worthen and Clark where lost during archive transport, and cannot be accessed. In this paper, we set to reconstruct an ample set of functional items in the spirit of Worthen and Clark’s idea, using information science techniques. Cognitive word associates are used as data. The process of a former computational solver of the RAT is repurposed to create rather than solve items. The approach of constructing queries is evaluated by getting human participants to solve both functional and compound items. In the process, a previous computational approach to solving the compound RAT is also validated in the functional RAT context.
Jointly social grouping and identification in visual dynamics with causality-induced hierarchical Bayesian model

We concentrate on modeling the person-person interactions for group activity recognition. In order to solve the complexity and ambiguity problems caused by a large number of human objects, we propose a causality-induced hierarchical Bayesian model to tackle the interaction activity video, referring to the “what” interaction activities happen, “where” interaction atomic occurs in spatial, and “when” group interaction happens in temporal. In particular, Granger Causality has been characterized with multiple features to encode the interacting relationships between each individual in the group. Furthermore, to detect and identify the concurrent interactive simultaneously, we investigate the Relative Entropy as a metric to measure the reasonable motion dependency between two arbitrary individuals. Filtered by the causality dependency, causality motion features have been cast as the multiplicative probabilistic ingredients in Bayesian factors to formulate the compact learned latent interaction patterns aggregately that enable the power of discrimination. Experiments demonstrate our model outperforms state-of-the-art models.
Relevance Search over Schema-Rich Knowledge Graphs

Relevance search over a knowledge graph (KG) has gained much research attention. Given a query entity in a KG, the problem is to find its most relevant entities. However, the relevance function is hidden and dynamic. Different users for different queries may consider relevance from different angles of semantics. The ambiguity in a query is more noticeable in the presence of thousands of types of entities and relations in a schema-rich KG, which has challenged the effectiveness and scalability of existing methods. To meet the challenge, our approach called RelSUE requests a user to provide a small number of answer entities as examples, and then automatically learns the most likely relevance function from these examples. Specifically, we assume the intent of a query can be characterized by a set of meta-paths at the schema level. RelSUE searches a KG for diversified significant meta-paths that best characterize the relevance of the user-provided examples to the query entity. It reduces the large search space of a schema-rich KG using distance and degree-based heuristics, and performs reasoning to deduplicate meta-paths that represent equivalent query-specific semantics. Finally, a linear model is learned to predict meta-path based relevance. Extensive experiments demonstrate that RelSUE outperforms several state-of-the-art methods.
Box algorithm for the solution of differential equations on a quantum annealer

Differential equations are ubiquitous in models of physical phenomena. Applications like steady-state analysis of heat flow and deflection in elastic bars often admit to a second order differential equation. In this paper, we discuss the use of a quantum annealer to solve such differential equations by recasting a finite element model in the form of an Ising hamiltonian. The discrete variables involved in the Ising model introduce complications when defining differential quantities, for instance, gradients involved in scientific computations of solid and fluid mechanics. To address this issue, a graph coloring based methodology is proposed which searches iteratively for solutions in a subspace of weak solutions defined over a graph, hereafter called as the 'box algorithm.' The box algorithm is demonstrated by solving a truss mechanics problem on the D-Wave quantum computer.
Perspective-Adaptive Convolutions for Scene Parsing

Many existing scene parsing methods adopt Convolutional Neural Networks with receptive fields of fixed sizes and shapes, which frequently results in inconsistent predictions of large objects and invisibility of small objects. To tackle this issue, we propose perspective-adaptive convolutions to acquire receptive fields of flexible sizes and shapes during scene parsing. Through adding a new perspective regression layer, we can dynamically infer the position-adaptive perspective coefficient vectors utilized to reshape the convolutional patches. Consequently, the receptive fields can be adjusted automatically according to the various sizes and perspective deformations of the objects in scene images. Our proposed convolutions are differentiable to learn the convolutional parameters and perspective coefficients in an end-to-end way without any extra training supervision of object sizes. Furthermore, considering that the standard convolutions lack contextual information and spatial dependencies, we propose a context adaptive bias to capture both local and global contextual information through average pooling on the local feature patches and global feature maps, followed by flexible attentive summing to the convolutional results. The attentive weights are position-adaptive and context-aware, and can be learned through adding an additional context regression layer. Experiments on Cityscapes and ADE20K datasets well demonstrate the effectiveness of the proposed methods.
Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image

In this paper, we present Skeleton Transformer Networks (SkeletonNet), an end-to-end framework that can predict not only 3D joint positions but also 3D angular pose (bone rotations) of a human skeleton from a single color image. This in turn allows us to generate skinned mesh animations. Here, we propose a two-step regression approach. The first step regresses bone rotations in order to obtain an initial solution by considering skeleton structure. The second step performs refinement based on heatmap regressor using a 3D pose representation called cross heatmap which stacks heatmaps of xy and zy coordinates. By training the network using the proposed 3D human pose dataset that is comprised of images annotated with 3D skeletal angular poses, we showed that SkeletonNet can predict a full 3D human pose (joint positions and bone rotations) from a single image in-the-wild.
Unprovability comes to machine learning

Scenarios have been discovered in which it is impossible to prove whether or not a machine-learning algorithm could solve a particular problem. This finding might have implications for both established and future learning algorithms. Evidence of a limitation in certain machine-learning algorithms.