Reviewerly/exHarmony huggingface dataset:
Text-Independent Speaker Verification with Dual Attention Network

This paper presents a novel design of attention model for text-independent speaker verification. The model takes a pair of input utterances and generates an utterance-level embedding to represent speaker-specific characteristics in each utterance. The input utterances are expected to have highly similar embeddings if they are from the same speaker. The proposed attention model consists of a self-attention module and a mutual attention module, which jointly contributes to the generation of the utterance-level embedding. The self-attention weights are computed from the utterance itself while the mutual-attention weights are computed with the involvement of the other utterance in the input pairs. As a result, each utterance is represented by a self-attention weighted embedding and a mutual-attention weighted embedding. The similarity between the embeddings is measured by a cosine distance score and a binary classifier output score. The whole model, named Dual Attention Network, is trained end-to-end on Voxceleb database. The evaluation results on Voxceleb 1 test set show that the Dual Attention Network significantly outperforms the baseline systems. The best result yields an equal error rate of 1:6%.
Learning Higher Representations from Pre-Trained Deep Models with Data Augmentation for the COMPARE 2020 Challenge Mask Task

Human hand-crafted features are always regarded as expensive, time-consuming, and difficult in almost all of the machinelearning-related tasks.First, those well-designed features extremely rely on human expert domain knowledge, which may restrain the collaboration work across fields.Second, the features extracted in such a brute-force scenario may not be easy to be transferred to another task, which means a series of new features should be designed.To this end, we introduce a method based on a transfer learning strategy combined with data augmentation techniques for the COMPARE 2020 Challenge Mask Sub-Challenge.Unlike the previous studies mainly based on pre-trained models by image data, we use a pre-trained model based on large scale audio data, i. e., AudioSet.In addition, the SpecAugment and mixup methods are used to improve the generalisation of the deep models.Experimental results demonstrate that the best-proposed model can significantly (p < .001,by one-tailed z-test) improve the unweighted average recall (UAR) from 71.8 % (baseline) to 76.2 % on the test set.Finally, the best result, i. e., 77.5 % of the UAR on the test set, is achieved by a late fusion of the two best proposed models and the best single model in the baseline.
RoadVR: Mitigating the Effect of Vection and Sickness by Distortion of Pathways for In-Car Virtual Reality

We explore a method to reduce motion sickness and allow people to use virtual reality while moving in vehicles. We put forth a usage scenario where the target VR content is based on constant road navigation so that the actual motion can enhance the VR experience. The method starts with a virtual scene and objects around an infinitely straight road. The motion of the vehicle is sensed by the GPS and IMU module. The sensed motion is reflected in a way that the virtual scene is navigated according to the vehicle motion, and its pathways distorted such that the virtual motion has a near-identical optical flow pattern to the actual. This would align the user’s visual and vestibular sense and reduce the effect of vection and motion sickness. We ran an pilot experiment to validate our approach, comparing the before and after sickness levels with the VR content (1) not aligned to the motion of the vehicle and (2) aligned by our method. Our preliminary results have shown the sickness was reduced significantly (but not eliminated to a negligible level yet) with our approach.
Margin-Mix: Semi-Supervised Learning for Face Expression Recognition

In this paper, as we aim to construct a semi-supervised learning algorithm, we exploit the characteristics of the Deep Convolutional Networks to provide, for an input image, both an embedding descriptor and a prediction. The unlabeled data is combined with the labeled one in order to provide synthetic data, which describes better the input space. The network is asked to provide a large margin between clusters, while new data is self-labeled by the distance to class centroids, in the embedding space. The method is tested on standard benchmarks for semi-supervised learning, where it matches state of the art performance and on the problem of face expression recognition where it increases the accuracy by a noticeable margin .
Effects of Synchronized Leg Motion in Walk-in-Place Utilizing Deep Neural Networks for Enhanced Body Ownership and Sense of Presence in VR

We investigate the effects of different ways of visualizing the virtual gait of the avatar in the context of Walk-in-Place (WIP) based navigation in a virtual environment (VE). In Study 1, participants navigated through a VE using the WIP method while inhabiting an avatar. We varied the visualization of the avatar’s leg motion while performing the WIP gesture: (1) Fixed Body: the legs stood still; (2) Pre-recorded Animation: the legs moved in a fixed predetermined pace (plausible but not in accordance to that of the user in general); (3) Synchronized Motion the legs moved according (synchronized) to those of the user. Our results indicated that the sense of presence and body ownership improved significantly when the leg motion was rendered synchronized to that of the user (Synchronized Motion). In addition, we developed a deep neural network (DNN) that predicted the users’ leg postures only with the head position tracking, eliminating the need for any external sensors. We carried out Study 2, to assess the effects of different gait visualizations, under two new factors: (1) virtual gait seen directly by the user looking down, or already visible by one’s shadow (i.e., no need to look down); and (2) playing a pre-recorded animation, or pre-recorded animation whose playback speed was adjusted to match with pace of the users’ actual leg motions as predicted by the DNN. The results of Study 2 showed that the virtual gait temporally synchronized with that of the user greatly improved the sense of body ownership, whether it was witnessed directly or indirectly with the shadow. However, the effect of virtual gait on presence was less marked when indirectly observed. We discuss our findings and the implications for representing the avatar locomotion in immersive virtual environments.
Design an Intelligent System to automatically Tutor the Method for Solving Problems

Nowadays, intelligent systems have been applied in many real-word domains. The Intelligent chatbot is an intelligent system, it can interact with the human to tutor how to work some activities. In this work, we design an architecture to build an intelligent chatbot, which can tutor to solve problems, and construct scripts for automatically tutoring. The knowledge base of the intelligent tutoring chatbot is designed by using the requirements of an Intelligent Problem Solver. It is the combination between the knowledge model of relations and operators, and the structures of hint questions and sample problems, which are practical cases. Based on the knowledge base and tutoring scripts, a tutoring engine is designed. The tutoring chatbot plays as an instructor for solving real-world problems. It simulates the working of the instructor to tutor the user for solving problems. By utilizing the knowledge base and reasoning, the architecture of the intelligent chatbot are emerging to apply in the real-world. It is used to build an intelligent chatbot to support the learning of high-school mathematics and a consultant system in public administration. The experimental results show the effectiveness of the proposed method in comparison with the existing systems.
Practical Quasi-Newton Methods for Training Deep Neural Networks

We consider the development of practical stochastic quasi-Newton, and in particular Kronecker-factored block-diagonal BFGS and L-BFGS methods, for training deep neural networks (DNNs). In DNN training, the number of variables and components of the gradient $n$ is often of the order of tens of millions and the Hessian has $n^2$ elements. Consequently, computing and storing a full $n \times n$ BFGS approximation or storing a modest number of (step, change in gradient) vector pairs for use in an L-BFGS implementation is out of the question. In our proposed methods, we approximate the Hessian by a block-diagonal matrix and use the structure of the gradient and Hessian to further approximate these blocks, each of which corresponds to a layer, as the Kronecker product of two much smaller matrices. This is analogous to the approach in KFAC, which computes a Kronecker-factored block-diagonal approximation to the Fisher matrix in a stochastic natural gradient method. Because the indefinite and highly variable nature of the Hessian in a DNN, we also propose a new damping approach to keep the upper as well as the lower bounds of the BFGS and L-BFGS approximations bounded. In tests on autoencoder feed-forward neural network models with either nine or thirteen layers applied to three datasets, our methods outperformed or performed comparably to KFAC and state-of-the-art first-order stochastic methods.
Enhancing academic library services provision in the distance learning environment with mobile technologies

This study aims to explore the potentials of using mobile technologies (m-techs) to enhance academic library services delivery in the distance learning environment in Ghana. The objectives are to assess the students' awareness and appreciation for mobile technology-based library services; and to assess the librarians' competencies in switching to delivering mobile technology-based services. Using a descriptive survey, a questionnaire was administered online to collect data from 382 students and 118 library staff within two public universities in Ghana. A total of 453 responses were used for the final analysis. A descriptive analysis method was used to analyze the data. The results indicated that the students were aware and had positive appreciation for the value of mobile technology-based library. They expressed great expectations about the availability of the library services on m-tech platforms in Ghana. However, the findings of this study also revealed that academic libraries in Ghana were crawling behind in switching toward the delivery of the library services on m-tech platforms due to the shortage of technical and skilled staff with m-tech competencies in the selected libraries. The findings have practical implications in assisting the academic libraries in developing roadmaps for using mobile technologies to enhance academic library services to support distance education, not only in Ghana, but also in other countries. The findings also demonstrate the proliferation of the mobile device usage within the university environment and indicate how librarians should build their competencies in switching to deliver services on m-tech platforms. As a study of this nature has not been conducted in Ghana, this study fills the gap in m-tech research in the context of library services and distance learning.
Introduction to Core-sets: an Updated Survey

In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions. For example, in clustering problems, the input is a set of points in some metric space, and a common goal is to compute a set of centers in some other space (points, lines) that will minimize the sum of distances to these points. In database queries, we may need to compute such a some for a specific query set of $k$ centers. However, traditional algorithms cannot handle modern systems that require parallel real-time computations of infinite distributed streams from sensors such as GPS, audio or video that arrive to a cloud, or networks of weaker devices such as smartphones or robots. Core-set is a "small data" summarization of the input "big data", where every possible query has approximately the same answer on both data sets. Generic techniques enable efficient coreset \changed{maintenance} of streaming, distributed and dynamic data. Traditional algorithms can then be applied on these coresets to maintain the approximated optimal solutions. The challenge is to design coresets with provable tradeoff between their size and approximation error. This survey summarizes such constructions in a retrospective way, that aims to unified and simplify the state-of-the-art.
Temporal Keypoint Matching and Refinement Network for Pose Estimation and Tracking

Multi-person pose estimation and tracking in realistic videos is very challenging due to factors such as occlusions, fast motion and pose variations. Top-down approaches are commonly used for this task, which involves three stages: person detection, single-person pose estimation, and pose association across time. Recently, significant progress has been made in person detection and single-person pose estimation. In this paper, we mainly focus on improving pose association and estimation in a video to build a strong pose estimator and tracker. To this end, we propose a novel temporal keypoint matching and refinement network. Specifically, we propose two network modules, temporal keypoint matching and temporal keypoint refinement, which are incorporated into a single-person pose estimatin network. The temporal keypoint matching module learns a simialrity metric for matching keypoints across frames. Pose matching is performed by aggregating keypoint similarities between poses in adjacent frames. The temporal keypoint refinement module serves to correct individual poses by utilizing their associated poses in neighboring frames as temporal context. We validate the effectiveness of our proposed network on two benchmark datasets: PoseTrack 2017 and PoseTrack 2018. Exprimental results show that our approach achieves state-of-the-art performance on both datasets.
Flows for simultaneous manifold learning and density estimation

We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold. Combining aspects of normalizing flows, GANs, autoencoders, and energy-based models, they have the potential to represent datasets with a manifold structure more faithfully and provide handles on dimensionality reduction, denoising, and out-of-distribution detection. We argue why such models should not be trained by maximum likelihood alone and present a new training algorithm that separates manifold and density updates. In a range of experiments we demonstrate how M-flows learn the data manifold and allow for better inference than standard flows in the ambient data space.
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression

We propose a data fusion method based on multi-fidelity Gaussian process regression (GPR) framework. This method combines available data of the quantity of interest (QoI) and its gradients with different fidelity levels, namely, it is a Gradient-enhanced Cokriging method (GE-Cokriging). It provides the approximations of both the QoI and its gradients simultaneously with uncertainty estimates. We compare this method with the conventional multi-fidelity Cokriging method that does not use gradients information, and the result suggests that GE-Cokriging has a better performance in predicting both QoI and its gradients. Moreover, GE-Cokriging even shows better generalization result in some cases where Cokriging performs poorly due to the singularity of the covariance matrix. We demonstrate the application of GE-Cokriging in several practical cases including reconstructing the trajectories and velocity of an underdamped oscillator with respect to time simultaneously, and investigating the sensitivity of power factor of a load bus with respect to varying power inputs of a generator bus in a large scale power system. We also show that though GE-Cokriging method requires a little bit higher computational cost than Cokriging method, the result of accuracy comparison shows that this cost is usually worth it.
Causal Shapley values: Exploiting causal knowledge to explain individual predictions of complex models

Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence. These values are designed to attribute the difference between a model's prediction and an average baseline to the different features used as input to the model. Being based on solid game-theoretic principles, Shapley values uniquely satisfy several desirable properties, which is why they are increasingly used to explain the predictions of possibly complex and highly non-linear machine learning models. Shapley values are well calibrated to a user's intuition when features are independent, but may lead to undesirable, counterintuitive explanations when the independence assumption is violated. In this paper, we propose a novel framework for computing Shapley values that generalizes recent work that aims to circumvent the independence assumption. By employing Pearl's do-calculus, we show how these 'causal' Shapley values can be derived for general causal graphs without sacrificing any of their desirable properties. Moreover, causal Shapley values enable us to separate the contribution of direct and indirect effects. We provide a practical implementation for computing causal Shapley values based on causal chain graphs when only partial information is available and illustrate their utility on a real-world example.
Rényi formulation of uncertainty relations for POVMs assigned to a quantum design

Information entropies provide powerful and flexible way to express restrictions imposed by the uncertainty principle. This approach seems to be very suitable in application to problems of quantum information theory. It is typical that questions of such a kind involve measurements having one or another specific structure. The latter often allows us to improve entropic bounds that follow from uncertainty relations of sufficiently general scope. Quantum designs have found use in many issues of quantum information theory, whence uncertainty relations for related measurements are of interest. In this paper, we obtain uncertainty relations in terms of min-entropies and Rényi entropies for POVMs assigned to a quantum design. Relations of the Landau–Pollak type are addressed as well. Using examples of quantum designs in two dimensions, the obtained lower bounds are then compared with the previous ones. An impact on entropic steering inequalities is briefly discussed.
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization

In distributed second order optimization, a standard strategy is to average many local estimates, each of which is based on a small sketch or batch of the data. However, the local estimates on each machine are typically biased, relative to the full solution on all of the data, and this can limit the effectiveness of averaging. Here, we introduce a new technique for debiasing the local estimates, which leads to both theoretical and empirical improvements in the convergence rate of distributed second order methods. Our technique has two novel components: (1) modifying standard sketching techniques to obtain what we call a surrogate sketch; and (2) carefully scaling the global regularization parameter for local computations. Our surrogate sketches are based on determinantal point processes, a family of distributions for which the bias of an estimate of the inverse Hessian can be computed exactly. Based on this computation, we show that when the objective being minimized is $l_2$-regularized with parameter $\lambda$ and individual machines are each given a sketch of size $m$, then to eliminate the bias, local estimates should be computed using a shrunk regularization parameter given by $\lambda^{\prime}=\lambda\cdot(1-\frac{d_{\lambda}}{m})$, where $d_{\lambda}$ is the $\lambda$-effective dimension of the Hessian (or, for quadratic problems, the data matrix).
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

Deep reinforcement learning can learn effective policies for a wide range of tasks, but is notoriously difficult to use due to instability and sensitivity to hyperparameters. The reasons for this remain unclear. When using standard supervised methods (e.g., for bandits), on-policy data collection provides hard negatives that correct the model in precisely those states and actions that the policy is likely to visit. We call this phenomenon feedback. We show that bootstrapping-based Q-learning algorithms do not necessarily benefit from this corrective feedback, and training on the experience collected by the algorithm is not sufficient to correct errors in the Q-function. In fact, Q-learning and related methods can exhibit pathological interactions between the distribution of experience collected by the agent and the policy induced by training on that experience, leading to potential instability, sub-optimal convergence, and poor results when learning from noisy, sparse or delayed rewards. We demonstrate the existence of this problem, both theoretically and empirically. We then show that a specific correction to the data distribution can mitigate this issue. Based on these observations, we propose a new algorithm, DisCor, which computes an approximation to this optimal distribution and uses it to re-weight the transitions used for training, resulting in substantial improvements in a range of challenging RL settings, such as multi-task learning and learning from noisy reward signals. Blog post presenting a summary of this work is available at: this https URL.
Factorizable Graph Convolutional Networks

Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like friends and colleagues, are usually connected via a bare edge that conceals such intrinsic connections. In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph. FactorGCN takes a simple graph as input, and disentangles it into several factorized graphs, each of which represents a latent and disentangled relation among nodes. The features of the nodes are then aggregated separately in each factorized latent space to produce disentangled features, which further leads to better performances for downstream tasks. We evaluate the proposed FactorGCN both qualitatively and quantitatively on the synthetic and real-world datasets, and demonstrate that it yields truly encouraging results in terms of both disentangling and feature aggregation. Code is publicly available at this https URL.
User Authentication and Authorization for Next Generation Mobile Passenger ID Devices for Land and Sea Border Control

Despite the significant economic benefits derived from the continuously increasing number of visitors entering the European Union through land-border crossing points or sea ports, novel solutions, such as next generation mobile devices for passenger identification for land and sea border control, are required to promote the comfort of passengers. However, the highly sensitive information handled by these devices makes them an attractive target for attackers. Therefore, strong user authentication and authorization mechanisms are required. Towards this direction, we provide an overview of user authentication and authorization requirements for this new type of devices based on the NIST Special Publication 500-280v2.1.