Reviewerly/exHarmony huggingface dataset:
Deep unregistered multi-contrast MRI reconstruction

Multiple magnetic resonance images of different contrasts are normally acquired for clinical diagnosis. Recently, research has shown that the previously acquired multi-contrast (MC) images of the same patient can be used as anatomical prior to accelerating magnetic resonance imaging (MRI). However, current MC-MRI networks are based on the assumption that the images are perfectly registered, which is rarely the case in real-world applications. In this paper, we propose an end-to-end deep neural network to reconstruct highly accelerated images by exploiting the shareable information from potentially misaligned reference images of an arbitrary contrast. Specifically, a spatial transformation (ST) module is designed and integrated into the reconstruction network to align the pre-acquired reference images with the images to be reconstructed. The misalignment is further alleviated by maximizing the normalized cross-correlation (NCC) between the MC images. The visualization of feature maps demonstrates that the proposed method effectively reduces the misalignment between the images for shareable information extraction when applied to the publicly available brain datasets. Additionally, the experimental results on these datasets show the proposed network allows the robust exploitation of shareable information across the misaligned MC images, leading to improved reconstruction results.
Photonic reservoir computer using speckle in multimode waveguide ring resonators

Photonic reservoir computers (RC) come in single mode ring and multimode array geometries. We propose and simulate a photonic RC architecture using speckle in a multimode waveguide ring resonator that requires neither the ultra-high-speed analog-digital conversion nor the spatial light modulator used in other designs. We show that the equations for propagation around a multimode (MM) ring resonator along with an optical nonlinearity, and optical feedback can be cast exactly in the standard RC form with speckle mixing performing the pseudo-random matrix multiplications. The hyperparameters are the outcoupling efficiency, the nonlinearity saturation intensity, the input bias, and the waveguide properties. In particular, the number of waveguide modes is a measure of the number of effective neurons in the RC. Simulations show a ring using a strongly guiding 50-µm planar waveguide gives 206 effective neurons and excellent predictions of Mackey-Glass waveforms for a broad range of the hyperparameters, while a weakly guiding MM 200-µm diameter fiber gives 4,238 effective neurons and excellent predictions of chaotic solutions of the Kuramoto-Sivashinsky equation. We discuss physical realizations for implementing the system with a chip-scale device or with discrete components and a MM optical fiber.
Health care fraud classifiers in practice

Abstract Statistical and machine learning methods have become paramount in order to handle large size claims data as part of health care fraud detection frameworks. Among these, predictive methods such as regression and classification algorithms are widely used with labeled data. However, the imbalanced nature of health care claims data and skewness of fraud distributions result with challenges in practical applications. This paper presents the use of various classification algorithms and data pre‐processing methods on claim payment populations and overpayment scenarios with different characteristics. It can help the health care practitioners evaluate the advantages and disadvantages of these analytical methods, and choose the right classification method and apply them properly for their specific circumstances. We utilize publicly available U.S. Medicare Part B health care claims payment data from the hospitals with a number of fraud label scenarios to demonstrate potential fraud patterns. We discuss the computational demand and accuracy of the methods.
Prediction of Head Movement in 360-Degree Videos Using Attention Model

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.
Extremely simple classifier based on fuzzy logic and gene expression programming

In this paper, we propose a new design of a very simple data-driven binary classifier and conduct an empirical study of its performance. The data contain continuous and categorical variables. The classification system consists of highly interpretable fuzzy metarules. A new theorem is developed that guarantees that these metarules are equivalent to algebraic expressions. The algebraic expressions are obtained using the gene expression programming technique. The number of features in the modeled dataset does not affect the complexity of the metarules. The performance of the resulting metarules is comparable to that of the rules created by most of the popular machine learning methods. The newly introduced classifier (GPR) appears to be the simplest among the fuzzy rule-based classifiers. Its effectiveness was tested on 16 datasets and compared with 22 other classification algorithms. GPR turned out to be surprisingly good; i.e., it belongs to the group of the best classifiers when the quality criterion is the area under the ROC curve and the classification accuracy. The Scott-Knott analysis indicates that, in terms of performance, GPR is commensurate with the leading group of 3 algorithms, and the Wilcoxon test confirmed the statistical reliability of the obtained results. High interpretability is proved with examples of classification models.
Quantum and classical oblivious transfer: A comparative analysis

Secure multiparty computation has the potential to be a disruptive technique in the realm of data analysis and computation. It enables several parties to compute virtually any function while preserving the privacy of their inputs. However, most of its protocols’ security and efficiency relies on the security and efficiency of oblivious transfer (OT). In this work, we make a detailed comparison between the complexity of the hybrid quantum oblivious transfer (HQOT) protocol presented in [11] and the classical OT [12], which to the best of our knowledge, is the fastest OT protocol. We also propose an optimised version of HQOT and discuss several other OT protocols generated from oblivious keys.
Enlarging the Usable Hand Tracking Area by Using Multiple Leap Motion Controllers in VR

Leap Motion Controller (LMC) is a widely-used 3D user-interface device for virtual reality (VR) in hand tracking applications. However, the tracking area of a single LMC is not sufficient to cover the complete range of hand motion typically used in virtual reality applications, which can cause inconvenience and unnatural behavior of bare-hand interaction in a cooperated virtual environment. In this paper, we propose fusing the data from multiple LMCs to enlarge the tracking area. We present our shared-view calibration method based on a Least-squares Fitting algorithm. To track two hands in the enlarged tracking area, we propose a multi-targets tracking algorithm based on a Clustering-based Labeled Probability Hypothesis Density filter implemented by Gaussian mixture approach. A hand-recognition confidence is proposed to improve the tracking performance when hands are incorrectly recognized. The performance of the proposed algorithm was evaluated by three tests based on a five-LMCs system used on an Oculus Rift S. Results show that our system can track two hands stably in the range of 202.16 degrees horizontally and 164.43 degrees vertically, and the proposed algorithm shows superiority in tracking robustness under hand-recognition errors. The contribution of this paper is to provide a detailed guide for designing an enlarged hand-tracking system using sensor fusion.
General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python

Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).
Software Development Knowledge Management System Using Web Portal

Software development is a knowledge-intensive practice. Software development teams rely on human resources and systematic approaches to share knowledge on system design. This collaborative knowledge sharing and preserving mechanism is known as “knowledge management” in software industries. In the software development process, coordination of system design functionalities requires knowledge-sharing infrastructure within the team members. Semantic web service computing (SWSC) provides opportunities and value-added service capabilities that global software development team requires to exchange information. This chapter describes the features of an ontology-based web portal framework, called CKIA (Collaborative Knowledge Integration Architecture), for integrating distributed knowledge in a global software development project. The CKIA framework uses a hybrid knowledge-based system consisting of Structural Case-Based Reasoning (S-CBR), Rule-Based Reasoning(RBR), and an ontology-based concept similarity assessment mechanism. A business scenario is used to present some functionalities of the framework.
Full-Band LPCNet: A Real-Time Neural Vocoder for 48 kHz Audio With a CPU

This paper investigates a real-time neural speech synthesis system on CPUs that can synthesize high-fidelity 48 kHz speech waveforms to cover the entire frequency range audible by human beings. Although most previous studies on 48 kHz speech synthesis have used traditional source-filter vocoders or a WaveNet vocoder for waveform generation, they have some drawbacks regarding synthesis quality or inference speed. LPCNet was proposed as a real-time neural vocoder with a mobile CPU but its sampling frequency is still only 16 kHz. In this paper, we propose a Full-band LPCNet to synthesize high-fidelity 48 kHz speech waveforms with a CPU by introducing some simple but effective modifications to the conventional LPCNet. We then evaluate the synthesis quality using both normal speech and a singing voice. The results of these experiments demonstrate that the proposed Full-band LPCNet is the only neural vocoder that can synthesize high-quality 48 kHz speech waveforms while maintaining real-time capability with a CPU.
Generalized Zero-Shot Extreme Multi-label Learning

Extreme Multi-label Learning (XML) involves assigning the subset of most relevant labels to a data point from millions of label choices. A hitherto unaddressed challenge in XML is that of predicting unseen labels with no training points. These form a significant fraction of total labels and contain fresh and personalized information desired by end users. Most existing extreme classifiers are not equipped for zero-shot label prediction and hence fail to leverage unseen labels. As a remedy, this paper proposes a novel approach called ZestXML for the task of Generalized Zero-shot XML (GZXML) where relevant labels have to be chosen from all available seen and unseen labels. ZestXML learns to project a data point's features close to the features of its relevant labels through a highly sparsified linear transform. This L0-constrained linear map between the two high-dimensional feature vectors is tractably recovered through a novel optimizer based on Hard Thresholding. By effectively leveraging the sparsities in features, labels and the learnt model, ZestXML achieves higher accuracy and smaller model size than existing XML approaches while also promoting efficient training & prediction, real-time label update as well as explainable prediction.
Enhancing Emotional Experience by Building Emotional Virtual Characters in VR Volleyball Games

Abstract Virtual reality (VR) volleyball games can provide an immersive entertainment experience and facilitate users to get familiar with game rules. In real world, players often experience emotions from scores, strokes, passes, and the influence from their teammates or opponents, and so forth. However, seldom studies are carried out for this emotional experience in a virtual environment, as most VR volleyball games mainly concentrate on the game playing, such as body movements. In this article, we propose to enhance emotional experience by building emotional virtual characters in VR volleyball games. The virtual characters cannot only arouse their emotions but also express their facial expressions according to the game situation. Emotion patterns are learned from real‐world volleyball matches. The results demonstrate that our framework has great potential to enhance the user's emotional experience and engagement.
Spline-based medial axis transform representation of binary images

Medial axes are well-known descriptors used for representing, manipulating, and compressing binary images. In this paper, we present a full pipeline for computing a stable and accurate piece-wise B-spline representation of Medial Axis Transforms (MATs) of binary images. A comprehensive evaluation on a benchmark shows that our method, called Spline-based Medial Axis Transform (SMAT), achieves very high compression ratios while keeping quality high. Compared with the regular MAT representation, the SMAT yields a much higher compression ratio at the cost of a slightly lower image quality. We illustrate our approach on a multi-scale SMAT representation, generating super-resolution images, and free-form binary image deformation.
Two Exposure Fusion Using Prior-Aware Generative Adversarial Network

Producing a high dynamic range (HDR) image from two low dynamic range (LDR) images with extreme exposures is challenging due to the lack of well-exposed contents. Existing works either use pixel fusion based on weighted quantization or conduct feature fusion using deep learning techniques. In contrast to these methods, our core idea is to progressively incorporate the pixel domain knowledge of LDR images into the feature fusion process. Specifically, we propose a novel Prior-Aware Generative Adversarial Network (PA-GAN), along with a new dual-level loss for two exposure fusion. The proposed PA-GAN is composed of a content-prior-guided encoder and a detail-prior-guided decoder, respectively in charge of content fusion and detail calibration. We further train the network using a dual-level loss that combines the semantic-level loss and pixel-level loss. Extensive qualitative and quantitative evaluations on diverse image datasets demonstrate that our proposed PA-GAN has superior performance than state-of-the-art methods.
A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers

In this brief, we consider the problem of descriptors construction for the task of content-based image retrieval using deep neural networks. The idea of neural codes, based on fully connected layers' activations, is extended by incorporating the information contained in convolutional layers. It is known that the total number of neurons in the convolutional part of the network is large and the majority of them have little influence on the final classification decision. Therefore, in this brief, we propose a novel algorithm that allows us to extract the most significant neuron activations and utilize this information to construct effective descriptors. The descriptors consisting of values taken from both the fully connected and convolutional layers perfectly represent the whole image content. The images retrieved using these descriptors match semantically very well to the query image, and also, they are similar in other secondary image characteristics, such as background, textures, or color distribution. These features of the proposed descriptors are verified experimentally based on the IMAGENET1M dataset using the VGG16 neural network. For comparison, we also test the proposed approach on the ResNet50 network.
Design‐based properties of the nearest neighbor spatial interpolator and its bootstrap mean squared error estimator

Nearest neighbor spatial interpolation for mapping continuous populations and finite populations of areas or units is approached from a design-based perspective, that is, populations are fixed, and uncertainty stems from the sampling scheme adopted to select locations. We derive conditions for design-based pointwise and uniform consistency of the nearest neighbor interpolators. We prove that consistency holds under certain schemes that are widely applied in environmental and forest surveys. Furthermore, we propose a pseudopopulation bootstrap estimator of the root mean squared errors of the interpolated values. Finally, a simulation study is performed to assess the theoretical results.
Automated adaptation strategies for stream learning

Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.
Label-Free Regional Consistency for Image-to-Image Translation

Image-to-Image translation aims to translate images from one domain to another. Existing approaches mainly stylize the images globally, while the local consistency between regions has been under-explored. Some instance-aware methods capture the regional consistency but heavily depend on well-annotated labels of a large-scale dataset. Besides, we observe that content-alike regions should have similar style between the target and translated images, however, little attention has been paid to explore such intrinsic property as explicit prior knowledge to guide the image translation process. In this paper, we aim to explore the label-free regional consistency for image-to-image translation. We propose regional relation consistency not only to maintain the global structure but also to keep a close look at the regional consistency, thus achieving more rigorous preservation of image contents. Moreover, we employ the phase of images as a semantic prior to select regions with similar content. We present phase-guided amplitude consistency to perform a more efficient local stylization. Extensive experiments verify that our approach outperforms the existing methods with a clear margin.
Grammar guided embedding based Chinese long text sentiment classification

Abstract Although the state‐of‐the‐art sentiment classification approaches, such as LSTM and TextCNN, have achieved a good performance on Chinese short text sentiment analysis, the Chinese long text sentiment classification is still a challenge because of the sentiment change problem and the long text structure problem. Therefore, we propose a grammar guided embedding model (GGE) and a novel Chinese long text sentiment classification framework. First, the part‐of‐speech (POS) tags are introduced as the Chinese long text grammar guided information which can help classification approaches to model the Chinese long text structure and the important structure of sentiment change. Second, we proposed a simple GGE training method which considers the combination representation of word sequence and POS sequence. Finally, we proposed a unified framework which combines our novel GGE with TextCNN. Experiment results show that after using GGE, the model outperforms the state‐of‐the‐art approaches. At the same time, we also found that the GGE achieves the model converge faster, that is, it can achieve better results than without GGE when there is only a small amount of training data. Thus, we believe that the GGE can help machines better understand human language sentiment expression structure.
Neural Instant Search for Music and Podcast

Over recent years, podcasts have emerged as a novel medium for sharing and broadcasting information over the Internet. Audio streaming platforms originally designed for music content, such as Amazon Music, Pandora, and Spotify, have reported a rapid growth, with millions of users consuming podcasts every day. With podcasts emerging as a new medium for consuming information, the need to develop information access systems that enable efficient and effective discovery from a heterogeneous collection of music and podcasts is more important than ever. However, information access in such domains still remains understudied. In this work, we conduct a large-scale log analysis to study and compare podcast and music search behavior on Spotify, a major audio streaming platform. Our findings suggest that there exist fundamental differences in user behavior while searching for podcasts compared to music. Specifically, we identify the need to improve podcast search performance. We propose a simple yet effective transformer-based neural instant search model that retrieves items from a heterogeneous collection of music and podcast content. Our model takes advantage of multi-task learning to optimize for a ranking objective in addition to a query intent type identification objective. Our experiments on large-scale search logs show that the proposed model significantly outperforms strong baselines for both podcast and music queries.