Reviewerly/exHarmony huggingface dataset:
Deep-Learning-Based Pupil Center Detection and Tracking Technology for Visible-Light Wearable Gaze Tracking Devices

In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.
Neural Reversible Steganography with Long Short-Term Memory

Deep learning has brought about a phenomenal paradigm shift in digital steganography. However, there is as yet no consensus on the use of deep neural networks in reversible steganography, a class of steganographic methods that permits the distortion caused by message embedding to be removed. The underdevelopment of the field of reversible steganography with deep learning can be attributed to the perception that perfect reversal of steganographic distortion seems scarcely achievable, due to the lack of transparency and interpretability of neural networks. Rather than employing neural networks in the coding module of a reversible steganographic scheme, we instead apply them to an analytics module that exploits data redundancy to maximise steganographic capacity. State-of-the-art reversible steganographic schemes for digital images are based primarily on a histogram-shifting method in which the analytics module is often modelled as a pixel intensity predictor. In this paper, we propose to refine the prior estimation from a conventional linear predictor through a neural network model. The refinement can be to some extent viewed as a low-level vision task (e.g., noise reduction and super-resolution imaging). In this way, we explore a leading-edge neuroscience-inspired low-level vision model based on long short-term memory with a brief discussion of its biological plausibility. Experimental results demonstrated a significant boost contributed by the neural network model in terms of prediction accuracy and steganographic rate-distortion performance.
Advances in Machine Learning and Deep Neural Networks

We are currently experiencing the dawn of what is known as the fourth industrial revolution. At the center of this historical happening, as one of the key enabling technologies, lies a discipline that deals with data and whose goal is to extract information and related knowledge that is hidden in it, in order to make predictions and, subsequently, take decisions. Machine learning (ML) is the name that is used as an umbrella to cover a wide range of theories, methods, algorithms, and architectures that are used to this end. The articles in this special issue cover promising developments in the related areas of machine learning and deep neural networks and offers possible paths for the future.
Enhancing Photorealism Enhancement

We present an approach to enhancing the realism of synthetic images. The images are enhanced by a convolutional network that leverages intermediate representations produced by conventional rendering pipelines. The network is trained via a novel adversarial objective, which provides strong supervision at multiple perceptual levels. We analyze scene layout distributions in commonly used datasets and find that they differ in important ways. We hypothesize that this is one of the causes of strong artifacts that can be observed in the results of many prior methods. To address this we propose a new strategy for sampling image patches during training. We also introduce multiple architectural improvements in the deep network modules used for photorealism enhancement. We confirm the benefits of our contributions in controlled experiments and report substantial gains in stability and realism in comparison to recent image-to-image translation methods and a variety of other baselines.
Neural tangent kernel: convergence and generalization in neural networks (invited paper)

The Neural Tangent Kernel is a new way to understand the gradient descent in deep neural networks, connecting them with kernel methods. In this talk, I'll introduce this formalism and give a number of results on the Neural Tangent Kernel and explain how they give us insight into the dynamics of neural networks during training and into their generalization features.
Faithfully Explainable Recommendation via Neural Logic Reasoning

Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process.However, prior research rarely considers the faithfulness of the derived explanations to justify the decisionmaking process.To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning.Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the pathreasoning process for explanation generation.We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering highquality recommendations as well as ascertaining the faithfulness of the derived explanation.
Reinforcement Learning-Based Dialogue Guided Event Extraction to Exploit Argument Relations

Event extraction is a ftask for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an argument’s role often varies in different contexts. While the relationship and interactions between multiple arguments are useful for settling the argument roles, such information is largely ignored by existing approaches. This paper presents a better approach for event extraction by explicitly utilizing the relationships of event arguments. We achieve this through a carefully designed task-oriented dialogue system. To model the argument relation, we employ reinforcement learning and incremental learning to extract multiple arguments via a multi-turned, iterative process. Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually. It then uses the newly obtained information to improve the decisions of previously extracted arguments. This two-way feedback process allows us to exploit the argument relations to effectively settle argument roles, leading to better sentence understanding and event extraction. Experimental results show that our approach consistently outperforms seven state-of-the-art event extraction methods for the classification of events and argument role and argument identification.
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards a Fourier Perspective

The booming interest in adversarial attacks stems from a misalignment between human vision and a deep neural network (DNN), \ie~a human imperceptible perturbation fools the DNN. Moreover, a single perturbation, often called universal adversarial perturbation (UAP), can be generated to fool the DNN for most images. A similar misalignment phenomenon has also been observed in the deep steganography task, where a decoder network can retrieve a secret image back from a slightly perturbed cover image. We attempt explaining the success of both in a unified manner from the Fourier perspective. We perform task-specific and joint analysis and reveal that (a) frequency is a key factor that influences their performance based on the proposed entropy metric for quantifying the frequency distribution; (b) their success can be attributed to a DNN being highly sensitive to high-frequency content. We also perform feature layer analysis for providing deep insight on model generalization and robustness. Additionally, we propose two new variants of universal perturbations: (1) high-pass UAP (HP-UAP) being less visible to the human eye; (2) Universal Secret Adversarial Perturbation (USAP) that simultaneously achieves attack and hiding.
Entanglement protection of classically driven qubits in a lossy cavity

Abstract Quantum technologies able to manipulating single quantum systems, are presently developing. Among the dowries of the quantum realm, entanglement is one of the basic resources for the novel quantum revolution. Within this context, one is faced with the problem of protecting the entanglement when a system state is manipulated. In this paper, we investigate the effect of the classical driving field on the generation entanglement between two qubits interacting with a bosonic environment. We discuss the effect of the classical field on the generation of entanglement between two (different) qubits and the conditions under which it has a constructive role in protecting the initial-state entanglement from decay induced by its environment. In particular, in the case of similar qubits, we locate a stationary sub-space of the system Hilbert space, characterized by states non depending on the environment properties as well as on the classical driving-field. Thus, we are able to determine the conditions to achieve maximally entangled stationary states after a transient interaction with the environment. We show that, overall, the classical driving field has a constructive role for the entanglement protection in the strong coupling regime. Also, we illustrate that a factorable initial-state can be driven in an entangled state and, even, in an entangled steady-state after the interaction with the environment.
A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling

Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences.However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction.To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations.Specifically, we first generate a table feature for each relation.Then two kinds of global associations are mined from the generated table features.Next, the mined global associations are integrated into the table feature of each relation.This "generate-mine-integrate" process is performed multiple times so that the table feature of each relation is refined step by step.Finally, each relation's table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table.We evaluate the proposed model on three benchmark datasets.Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets.
Asynchronous Sensor Fusion of GPS, IMU and CAN-Based Odometry for Heavy-Duty Vehicles

In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear and angular speed readings from wheel tachometers on the internal Controller Area Network (CAN). These signals have different noise variance, bandwidth and sampling rate (being the latter, possibly, irregular). In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing. It is applied to achieve accuracy improvements when incorporating odometry measurements from CAN bus to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that this asynchronous multi-sensor (GPS+IMU+CAN-based odometry) fusion is advantageous in low-speed manoeuvres, improving accuracy and robustness to missing data, thanks to non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother, with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants; it can provide data to subsequent processing steps at arbitrary time instants, not necessarily coincident with the original measurement ones. Given the extra information available in the smoothing case, its estimation performance is less sensitive to the noise-variance parameter setting, compared to causal filtering. Working Matlab code is provided at the end of this work.
Health Information Systems in the COVID-19 Pandemic: A Short Survey of Experiences and Lessons Learned From the European Region

Introduction: The COVID-19 crisis provides an opportunity to reflect on what worked during the pandemic, what could have been done differently, and what innovations should become part of an enhanced health information system in the future. Methods: An online qualitative survey was designed and administered online in November 2020 to all the 37 Member States that are part of the WHO European Health Information Initiative and the WHO Central Asian Republics Information Network. Results: Nineteen countries responded to the survey (Austria, Belgium, Croatia, Czech Republic, Finland, Greece, Iceland, Ireland, Israel, Italy, Kazakhstan, Latvia, Lithuania, Romania, Russian Federation, Sweden, Turkey, United Kingdom, and Uzbekistan). The COVID-19 pandemic required health information systems (HIS) to rapidly adapt to identify, collect, store, manage, and transmit accurate and timely COVID-19 related data. HIS stakeholders have been put to the test, and valuable experience has been gained. Despite critical gaps such as under-resourced public health services, obsolete health information technologies, and lack of interoperability, most countries believed that their information systems had worked reasonably well in addressing the needs arising during the COVID-19 pandemic. Conclusion: Strong enabling environments and advanced and digitized health information systems are vital to controlling epidemics. Sustainable finance and government support are required for the continued implementation and enhancement of HIS. It is important to promote digital solutions beyond the COVID-19 pandemic. Now is the time to discuss potential solutions to obtain timely, accurate, and reliable health information and steer policy-making while protecting privacy rights and meeting the highest ethical standards.
Watch Only Once: An End-to-End Video Action Detection Framework

We propose an end-to-end pipeline, named Watch Once Only (WOO), for video action detection. Current methods either decouple video action detection task into separated stages of actor localization and action classification or train two separated models within one stage. In contrast, our approach solves the actor localization and action classification simultaneously in a unified network. The whole pipeline is significantly simplified by unifying the backbone network and eliminating many hand-crafted components. WOO takes a unified video backbone to simultaneously extract features for actor location and action classification. In addition, we introduce spatial-temporal action embeddings into our framework and design a spatial-temporal fusion module to obtain more discriminative features with richer information, which further boosts the action classification performance. Extensive experiments on AVA and JHMDB datasets show that WOO achieves state-of-the-art performance, while still reduces up to 16.7% GFLOPs compared with existing methods. We hope our work can inspire re-thinking the convention of action detection and serve as a solid baseline for end-to-end action detection. Code is available at https://github.com/ShoufaChen/WOO.
Digital media in institutional informal learning places: A systematic literature review

Digital media can have high potential for supporting learning processes. This assumption also holds true for institutional informal learning places (IILP). However, there is no systematic overview of research on the use of digital media for informal learning in such places yet. In order to fill this gap, this paper reports on the findings of a systematic literature review in the field of informal learning with digital media in IILP. A total of 26 relevant studies, conducted between 2005 and 2020, were identified via database search in Scopus and FIS, as well as through cross-referencing. Subsequently, those 26 studies are analyzed in more detail by a qualitative content analysis, which is used to investigate three research questions: 1) What are the general characteristics of digital media investigated in IILP? 2) What are the functions of digital media investigated in IILP that are relevant for learning? 3) What outcomes were measured, related to informal learning with digital media in IILP? The results show that portable and stationary digital media tools are investigated about equally often and that often, they already offer augmented reality (AR). It also shows that digital media have not yet fully exploited their potential functions, as they mainly offer the retrieval of information, and have only exploited collaboration functions only to a rather small extent. However, they can promote and support motivational and cognitive learning processes, especially with regard to knowledge acquisition and interest, as well as collaboration and social interaction.
Online experimentation and sampling in cognitive aging research.

Online data collection methods have become increasingly popular in many domains of psychology, but their use in cognitive aging studies remains relatively limited. Is it time for cognitive aging researchers to embrace these methods? Here, we weigh potential advantages and disadvantages of conducting online studies with young and older adults, relative to lab-based studies, with a particular focus on the study of human memory and aging. With online studies, it may be possible to assess whether age-related effects on cognition obtained in the laboratory generalize to other situations with different environmental or subject characteristics. However, there are many open questions about the representativeness of older adults on online data collection platforms, and issues surrounding data quality, selection effects, and other biasing characteristics, which must be carefully handled in cognitive aging studies which recruit young and older adult participants online. We consider the benefit of conducting experimentation both in the lab and online in providing converging evidence on a research question, and we offer an example of an experiment on adult age differences in associative recognition that was conducted in the laboratory and online. We also provide practical recommendations for ways to maximize the potential for online studies to contribute to our understanding of cognitive aging. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Realizing multi-qubit controlled nonadiabatic holonomic gates with connecting systems

Abstract Multi-qubit controlled gates are frequently used in quantum information processing. Although they can be built with gates from the universal set of one- and two-qubit gates, this procedure typically becomes very demanding as the number of such gates rapidly grows with the size of the computational problem. Thus, finding a way to realize them with fewer steps and therefore with reduced decoherence errors is important. Particularly, quantum information is entering noisy intermediate-scale quantum era and in this era, quantum computers do not have enough resources for full fault tolerance and therefor can only support the computation with a short duration. This makes the reduction of the steps even more important. Here, we propose the realization of multi-qubit controlled nonadiabatic holonomic gates with connecting systems. Our proposal can efficiently reduce the operation steps of the realization, thereby reducing the affection from decoherence and increasing the precision of the computation. So, it is useful to realize efficient and robust quantum information processors.
Face Image Forgery Detection by Weight Optimized Neural Network Model

This framework introduces a new automatic image forgery detection approach that involves four main stages like (i) Illumination map computation, (ii) Face detection, (iii) Feature extraction, and (iv) Classification.Initially, the processing of input image is exploited by means of illumination map estimation, which acquires two computation processes called Gray world estimates and Inverse-Intensity chromaticity.Subsequent to this, the Viola-Jones algorithm is employed for the face detection process, which is the second phase, in order to detect the face image clearly.Once after the detection process, the obtained facial image is subjected to feature extraction.For this, Grey Level Co-occurrence Matrix (GLCM) is exploited that extract the facial features from the image.After this, the classification process is carried out for the extracted facial features by employing the Neural Network (NN) classifier.On the whole, this paper mainly concerned over the optimization concept, in which the weight of the NN is optimally selected by using the renowned optimization algorithm named Whale Optimization Algorithm (WOA).To the end, the performance of the implemented model is compared over the other classical models like k-nearest neighbor (kNN), NN and Support Vector Machine (SVM) regarding certain measures like Accuracy, Sensitivity, and Specificity.