Reviewerly/exHarmony huggingface dataset:
Speech Technology for Healthcare: Opportunities, Challenges, and State of the Art

Speech technology is not appropriately explored even though modern advances in speech technology-especially those driven by deep learning (DL) technology-offer unprecedented opportunities for transforming the healthcare industry. In this paper, we have focused on the enormous potential of speech technology for revolutionising the healthcare domain. More specifically, we review the state-of-the-art approaches in automatic speech recognition (ASR), speech synthesis or text to speech (TTS), and health detection and monitoring using speech signals. We also present a comprehensive overview of various challenges hindering the growth of speech-based services in healthcare. To make speech-based healthcare solutions more prevalent, we discuss open issues and suggest some possible research directions aimed at fully leveraging the advantages of other technologies for making speech-based healthcare solutions more effective.
Attention-based bidirectional GRU networks for efficient HTTPS traffic classification

Distributed and pervasive web services have become a major platform for sharing information. However, the hypertext transfer protocol secure (HTTPS), which is a crucial web encryption technology for protecting the information security of users, creates a supervisory burden for network management (e.g., quality-of-service guarantees and traffic engineering). Identifying various types of encrypted traffic is crucial for cyber security and network management. In this paper, we propose a novel deep learning model called BGRUA to identify the web services running on HTTPS connections accurately. BGRUA utilizes a bidirectional gated recurrent unit (GRU) and attention mechanism to improve the accuracy of HTTPS traffic classification. The bidirectional GRU is used to extract the forward and backward features of the byte sequences in a session. The attention mechanism is adopted to assign weights to features according to their contributions to classification. Additionally, we investigate the effects of different hyperparameters on the performance of BGRUA and present a set of optimal values that can serve as a basis for future relevant studies. Comparisons to existing methods based on three typical datasets demonstrate that BGRUA outperforms state-of-the-art encrypted traffic classification approaches in terms of accuracy, precision, recall, and F1-score.
End-to-End Single Image Fog Removal Using Enhanced Cycle Consistent Adversarial Networks

Single image defogging is a classical and challenging problem in computer vision. Existing methods towards this problem mainly include handcrafted priors based methods that rely on the use of the atmospheric degradation model and learning based approaches that require paired fog-fogfree training example images. In practice, however, prior-based methods are prone to failure due to their own limitations and paired training data are extremely difficult to acquire. Inspired by the principle of CycleGAN network, we have developed an end-to-end learning system that uses unpaired fog and fogfree training images, adversarial discriminators and cycle consistency losses to automatically construct a fog removal system. Similar to CycleGAN, our system has two transformation paths; one maps fog images to a fogfree image domain and the other maps fogfree images to a fog image domain. Instead of one stage mapping, our system uses a two stage mapping strategy in each transformation path to enhance the effectiveness of fog removal. Furthermore, we make explicit use of prior knowledge in the networks by embedding the atmospheric degradation principle and a sky prior for mapping fogfree images to the fog images domain. In addition, we also contribute the first real world nature fog-fogfree image dataset for defogging research. Our multiple real fog images dataset (MRFID) contains images of 200 natural outdoor scenes. For each scene, there are one clear image and corresponding four foggy images of different fog densities manually selected from a sequence of images taken by a fixed camera over the course of one year. Qualitative and quantitative comparison against several state-of-the-art methods on both synthetic and real world images demonstrate that our approach is effective and performs favorably for recovering a clear image from a foggy image.
Computing Primitive of Fully VCSEL-Based All-Optical Spiking Neural Network for Supervised Learning and Pattern Classification

We propose computing primitive for an all-optical spiking neural network (SNN) based on vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically plausible mechanisms. The spike-timing-dependent plasticity (STDP) model was established based on the dynamics of the vertical-cavity semiconductor optical amplifier (VCSOA) subject to dual-optical pulse injection. The neuron-synapse self-consistent unified model of the all-optical SNN was developed, which enables reproducing the essential neuron-like dynamics and STDP function. Optical character numbers are trained and tested by the proposed fully VCSEL-based all-optical SNN. Simulation results show that the proposed all-optical SNN is capable of recognizing ten numbers by a supervised learning algorithm, in which the input and output patterns as well as the teacher signals of the all-optical SNN are represented by spatiotemporal fashions. Moreover, the lateral inhibition is not required in our proposed architecture, which is friendly to the hardware implementation. The system-level unified model enables architecture-algorithm codesigns and optimization of all-optical SNN. To the best of our knowledge, the computing primitive of an all-optical SNN based on VCSELs for supervised learning has not yet been reported, which paves the way toward fully VCSEL-based large-scale photonic neuromorphic systems with low power consumption.
A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems

In the rapidly growing era of Internet-of-Things (IoT), healthcare systems have enabled a sea of connections of physical sensors. Data analysis methods (e.g., k-means) are often used to process data collected from wireless sensor networks to provide treatment advices for physicians and patients. However, many methods pose a threat of privacy leakage during the process of data handling. To address privacy issues, we propose a mutual privacy-preserving k-means strategy (M-PPKS) based on homomorphic encryption that neither discloses the participant’s privacy nor leaks the cluster center’s private data. The proposed M-PPKS divides each iteration of a k-means algorithm into two stages: finding the nearest cluster center for each participant, followed by computing a new center for each cluster. In both phases, the cluster center is confidential to participants, and the private information of each participant is not accessible by an analyst. Besides, M-PPKS introduces a third-party cloud platform to reduce the communication complexity of homomorphic encryption. Extensive privacy analysis and performance evaluation results manifest that the proposed M-PPKS strategy can achieve high performance. In addition, it can obtain approximate clustering results efficiently while preserving mutual private data.
Travelers’ intention to adopt virtual reality: A consumer value perspective

Through the lens of the value-based adoption model (VAM), this study examines consumers' perceived value and the intention to adopt virtual reality (VR) for evaluating various tourist destinations. The hypothesized model was tested through structural equation modeling using the responses of 208 Indian tourists. This work indicates the importance of perceived immersion and perceived physical risk as the two most vital indicators of benefits and sacrifice respectively. Moreover, perceived value was found to be one of the most important predictors in the adoption of VR. This study also identified that sensation-seeking behavior has a significant favorable influence on the behavioral intention to adopt VR for evaluating various tourist destinations. The study's findings will help departments of tourism and tourism companies attract more tourists through the power of VR.
IoT-based enterprise resource planning: Challenges, open issues, applications, architecture, and future research directions

In today's highly competitive markets, organizations can create a competitive advantage through the successful implementation of Enterprise Resource Planning (ERP) systems. ERP works with different technologies, including the Internet of Things (IoT). IoT uses a unique Internet protocol to identify, control, and transfer data to individuals as well as databases. The data is collected through IoT, stored on the cloud, and extracted and managed in through ERP. In this study, we review the challenges, open issues, applications, and architecture of the IoT-based ERP. For this purpose, we review and analyze the latest IoT-related articles to present the unique features of the IoT and discuss its impact on ERP. The results show sensors and devices connected to the Internet can manage the stored data processed in the cloud through ERP without human intervention. We also discuss the challenges and opportunities in the relationship between ERP and the IoT risen by the introduction of the cloud.
Video super-resolution based on deep learning: a comprehensive survey

Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution ones. Recently, the VSR methods based on deep neural networks have made great progress. However, there is rarely systematical review on these methods. In this survey, we comprehensively investigate 37 state-of-the-art VSR methods based on deep learning. It is well known that the leverage of information contained in video frames is important for video super-resolution. Thus we propose a taxonomy and classify the methods into seven sub-categories according to the ways of utilizing inter-frame information. Moreover, descriptions on the architecture design and implementation details are also included. Finally, we summarize and compare the performance of the representative VSR methods on some benchmark datasets. We also discuss the applications, and some challenges, which need to be further addressed by researchers in the community of VSR. To the best of our knowledge, this work is the first systematic review on VSR tasks, and it is expected to make a contribution to the development of recent studies in this area and potentially deepen our understanding of the VSR techniques based on deep learning.
N-Sanitization: A semantic privacy-preserving framework for unstructured medical datasets

The introduction and rapid growth of the Internet of Medical Things (IoMT), a subset of the Internet of Things (IoT) in the medical and healthcare systems, has brought numerous changes and challenges to current medical and healthcare systems. Healthcare organizations share data about patients with research organizations for various medical discoveries. Releasing such information is a tedious task since it puts the privacy of patients at risk with the understanding that textual health documents about an individual contains specific sensitive terms that need to be sanitized before such document can be released. Recent approaches improved the utility of protected output by substituting sensitive terms with appropriate “generalizations” that are retrieved from several medical and general-purpose knowledge bases (KBs). However, these approaches perform unnecessary sanitization by anonymizing the negated assertions, e.g., AIDS-negative. This paper proposes a semantic privacy framework that effectively sanitizes the sensitive and semantically related terms in healthcare documents. The proposed model effectively identifies the negated assertions (e.g., AIDS-negative) before the sanitization process in IoMT which further improves the utility of sanitized documents. Moreover, besides considering the sensitive medical findings, we also incorporated state-of-the-art metrics, i.e., Protected Health Information (PHI), as defined in the privacy rules such as Health Insurance Portability and Accountability Act (HIPAA), Informatics for Integrating Biology & the Bedside (i2b2), and Materialize Interactive Medical Image Control System (MIMICS). The proposed approach is evaluated on real clinical data provided by i2b2. On average the detection (for both PHI’s and medical findings) accuracy is improved with Precision, Recall and F-measure score at 21%, 51%, and 54% respectively. The overall improved data utility of our proposed model is 8% as compared to C-sanitized and 25% when comparing it with a simple reduction approach. Experimental results show that our approach effectively manages the privacy and utility trade-off as compared to its counterparts.
Threats of Adversarial Attacks in DNN-Based Modulation Recognition

With the emergence of the information age, mobile data has become more random, heterogeneous and massive. Thanks to its many advantages, deep learning is increasingly applied in communication fields such as modulation recognition. However, recent studies show that the deep neural networks (DNN) is vulnerable to adversarial examples, where subtle perturbations deliberately designed by an attacker can fool a classifier model into making mistakes. From the perspective of an attacker, this study adds elaborate adversarial examples to the modulation signal, and explores the threats and impacts of adversarial attacks on the DNN-based modulation recognition in different environments. The results show that, regardless of a white-box or a black-box model, the adversarial attack can reduce the accuracy of the target model. Among them, the performance of the iterative attack is superior to the one-step attack in most scenarios. In order to ensure the invisibility of the attack (the waveform being consistent before and after the perturbations), an appropriate perturbation level is found without losing the attack effect. Finally, it is attested that the signal confidence level is inversely proportional to the attack success rate, and several groups of signals with high robustness are obtained.
Fine-Tuning U-Net for Ultrasound Image Segmentation: Different Layers, Different Outcomes

One way of resolving the problem of scarce and expensive data in deep learning for medical applications is using transfer learning and fine-tuning a network which has been trained on a large data set. The common practice in transfer learning is to keep the shallow layers unchanged and to modify deeper layers according to the new data set. This approach may not work when using a U-Net and when moving from a different domain to ultrasound (US) images due to their drastically different appearance. In this study, we investigated the effect of fine-tuning different sets of layers of a pretrained U-Net for US image segmentation. Two different schemes were analyzed, based on two different definitions of shallow and deep layers. We studied simulated US images, as well as two human US data sets. We also included a chest X-ray data set. The results showed that choosing which layers to fine-tune is a critical task. In particular, they demonstrated that fine-tuning the last layers of the network, which is the common practice for classification networks, is often the worst strategy. It may therefore be more appropriate to fine-tune the shallow layers rather than deep layers in US image segmentation when using a U-Net. Shallow layers learn lower level features which are critical in automatic segmentation of medical images. Even when a large US data set is available, we observed that fine-tuning shallow layers is a faster approach compared to fine-tuning the whole network.
Are Modern Head-Mounted Displays Sexist? A Systematic Review on Gender Differences in HMD-Mediated Virtual Reality

Modern head-mounted displays (HMDs) are a promising technology. Thanks to their affordable cost and versatility, HMDs are gaining attention from different sectors. However, the experience reported by the users of these technologies is sometimes negative. A number of people, when using an HMD, complain of various types of physical discomfort as well as symptoms like headache, disorientation, and nausea. These symptoms, developed during or after exposure to virtual environments, are commonly referred to with the term simulator sickness. Some scientific studies have shown that women are commonly more sensitive to simulator sickness. However, a gender imbalance in the susceptibility to simulator sickness has not been widely studied in the context of modern HMDs, and the studies that have been done have reported heterogeneous findings. The present systematic review aims to gather the pieces of evidence that support and oppose a gender difference in the susceptibility of simulator sickness in the framework of modern HMDs. We also aim to individuate other gender differences in the experience of the use of these technologies to establish whether there is sufficient evidence to support a gender discrepancy in the user experience.
Don’t Forget The Past: Recurrent Depth Estimation from Monocular Video

Autonomous cars need continuously updated depth information. Thus far, depth is mostly estimated independently for a single frame at a time, even if the method starts from video input. Our method produces a time series of depth maps, which makes it an ideal candidate for online learning approaches. In particular, we put three different types of depth estimation (supervised depth prediction, self-supervised depth prediction, and self-supervised depth completion) into a common framework. We integrate the corresponding networks with a ConvLSTM such that the spatiotemporal structures of depth across frames can be exploited to yield a more accurate depth estimation. Our method is flexible. It can be applied to monocular videos only or be combined with different types of sparse depth patterns. We carefully study the architecture of the recurrent network and its training strategy. We are first to successfully exploit recurrent networks for real-time self-supervised monocular depth estimation and completion. Extensive experiments show that our recurrent method outperforms its image-based counterpart consistently and significantly in both self-supervised scenarios. It also outperforms previous depth estimation methods of the three popular groups. Please refer to our webpage <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for details.
MGAT: Multi-view Graph Attention Networks

Multi-view graph embedding is aimed at learning low-dimensional representations of nodes that capture various relationships in a multi-view network, where each view represents a type of relationship among nodes. Multitudes of existing graph embedding approaches concentrate on single-view networks, that can only characterize one simple type of proximity relationships among objects. However, most of the real-world complex systems possess multiple types of relationships among entities. In this paper, a novel approach of graph embedding for multi-view networks is proposed, named Multi-view Graph Attention Networks (MGAT). We explore an attention-based architecture for learning node representations from each single view, the network parameters of which are constrained by a novel regularization term. In order to collaboratively integrate multiple types of relationships in different views, a view-focused attention method is explored to aggregate the view-wise node representations. We evaluate the proposed algorithm on several real-world datasets, and it demonstrates that the proposed approach outperforms existing state-of-the-art baselines.
Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning

The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery; therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this article, we present novel weakly supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset, which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery. In particular, we introduce a clustering-based approach, called cluster centers (CC), and a neural architecture based on the Siamese Autoencoders (SAE), which are tailored for weakly supervised settings with very few labeled data samples. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset from a facility monitoring system, by using a multitude of performance measures. Also, the influence of hyperparameters related to feature extraction and network architecture is investigated. We find that the proposed SAE-based solutions outperform state-of-the-art anomaly detection approaches very robustly for many different hyperparameter settings on all performance measures.
Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games

Many problems in robotics involve multiple decision making agents. To operate efficiently in such settings, a robot must reason about the impact of its decisions on the behavior of other agents. Differential games offer an expressive theoretical framework for formulating these types of multi-agent problems. Unfortunately, most numerical solution techniques scale poorly with state dimension and are rarely used in real-time applications. For this reason, it is common to predict the future decisions of other agents and solve the resulting decoupled, i.e., single-agent, optimal control problem. This decoupling neglects the underlying interactive nature of the problem; however, efficient solution techniques do exist for broad classes of optimal control problems. We take inspiration from one such technique, the iterative linear-quadratic regulator (ILQR), which solves repeated approximations with linear dynamics and quadratic costs. Similarly, our proposed algorithm solves repeated linear-quadratic games. We experimentally benchmark our algorithm in several examples with a variety of initial conditions and show that the resulting strategies exhibit complex interactive behavior. Our results indicate that our algorithm converges reliably and runs in real-time. In a three-player, 14-state simulated intersection problem, our algorithm initially converges in <; 0.25 s. Receding horizon invocations converge in <; 50 ms in a hardware collision-avoidance test.
An image encryption approach on the basis of a time delay chaotic system

In the paper, the problem on image encryption on the basis of a chaotic system with time delay is addressed. Firstly a kind of time delay chaotic system is proposed, which has the different dynamical behavior over time delay. Then based on the time delay of the new system, a novel approach is designed to achieve the image encryption. Final experiments illuminate that the given image encryption method have good effectiveness with higher security.