Reviewerly/exHarmony huggingface dataset:
Virtual Reality Aided Therapy towards Health 4.0: A Two-Decade Bibliometric Analysis

Health 4.0 aligns with Industry 4.0 and encourages the application of the latest technologies to healthcare. Virtual reality (VR) is a potentially significant component of the Health 4.0 vision. Though VR in health care is a popular topic, there is little knowledge of VR-aided therapy from a macro perspective. Therefore, this paper was aimed to explore the research of VR in aiding therapy, thus providing a potential guideline for futures application of therapeutic VR in healthcare towards Health 4.0. A mixed research method was adopted for this research, which comprised the use of a bibliometric analysis (a quantitative method) to conduct a macro overview of VR-aided therapy, the identification of significant research structures and topics, and a qualitative review of the literature to reveal deeper insights. Four major research areas of VR-aided therapy were identified and investigated, i.e., post-traumatic stress disorder (PTSD), anxiety and fear related disorder (A&F), diseases of the nervous system (DNS), and pain management, including related medical conditions, therapies, methods, and outcomes. This study is the first to use VOSviewer, a commonly used software tool for constructing and visualizing bibliometric networks and developed by Center for Science and Technology Studies, Leiden University, the Netherlands, to conduct bibliometric analyses on VR-aided therapy from the perspective of Web of Science core collection (WoSc), which objectively and visually shows research structures and topics, therefore offering instructive insights for health care stakeholders (particularly researchers and service providers) such as including integrating more innovative therapies, emphasizing psychological benefits, using game elements, and introducing design research. The results of this paper facilitate with achieving the vision of Health 4.0 and illustrating a two-decade (2000 to year 2020) map of pre-life of the Health Metaverse.
Characterizing Social Relations Via NLP-Based Sentiment Analysis

We investigate and evaluate methods for the characterization of social relations from textual communication context, using e-mail as an example. Social relations are intrinsically characterized by the Cartesian product of weights on various axes (we employ valuation and intensity as examples). The prediction of these characteristics is performed by application of unsupervised learning algorithms on meta-data, communication statistics, and the results of deep linguistic analysis of the message body. Classification of sentiment polarity is chosen as the means of linguistic analysis. We find that prediction accuracy can be improved by introducing limited amounts of additional information.
Multivariate Gaussian and Student-t process regression for multi-output prediction

Abstract Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to reformulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, reformulation is not always workable and is difficult to apply to other distributions because not all matrix-variate distributions can be transformed to respective multivariate distributions, such as the case for matrix-variate Student- t distribution. In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student- t process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods. Both MV-GPR and MV-TPR have closed-form expressions for the marginal likelihoods and predictive distributions under this unified framework and thus can adopt the same optimization approaches as used in the conventional GPR. The usefulness of the proposed methods is illustrated through several simulated and real-data examples. In particular, we verify empirically that MV-TPR has superiority for the datasets considered, including air quality prediction and bike rent prediction. At last, the proposed methods are shown to produce profitable investment strategies in the stock markets.
Designing efficient accelerator of depthwise separable convolutional neural network on FPGA

In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art results for many computer vision tasks. However, the traditional CNNs are computational-intensive and memory-intensive, hence they are unsuitable for the application in mobile edge computing scenarios with limited computing resources and low power consumption. The depthwise separable CNNs can significantly reduce the number of model parameters and improve the calculation speed, so it is naturally suitable for mobile edge computing applications. In this paper, we propose a Field Programmable Gate Array (FPGA)-based depthwise separable CNN accelerator with all the layers working concurrently in a pipelined fashion to improve the system throughput and performance. To implement the accelerator, we present a custom computing engine architecture to handle the dataflow between adjacent layers by using double-buffering-based memory channels. Besides, in fully connected layers, data titling technique is adopted to divide matrix multiplication from large dimension into small matrix. Finally, our proposed accelerator for depthwise separable CNN has been implemented and evaluated on Intel Arria 10 FPGA. The results of experiment indicate that the proposed depthwise separable CNN accelerator has a performance of 98.9 GOP/s and achieve up to 17.6× speed up and 29.4× low power than CPU and GPU implementations respectively.
Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.
A Study on Improvement of Blockchain Application to Overcome Vulnerability of IoT Multiplatform Security

IoT devices are widely used in the smart home, automobile, and aerospace areas. Note, however, that recent information on thefts and hacking have given rise to many problems. The aim of this study is to overcome the security weaknesses of existing Internet of Things (IoT) devices using Blockchain technology, which is a recent issue. This technology is used in Machine-to-Machine (M2M) access payment—KYD (Know Your Device)—based on the reliability of existing IoT devices. Thus, this paper proposes a BoT (Blockchain of Things) ecosystem to overcome problems related to the hacking risk of IoT devices to be introduced, such as logistics management and history management. There are also many security vulnerabilities in the sensor multi-platform from the IoT point of view. In this paper, we propose a model that solves the security vulnerability in the sensor multi-platform by using blockchain technology on an empirical model. The color spectrum chain mentioned in this paper suggests a blockchain technique completed by using the multiple-agreement algorithm to enhance Thin-Plate Spline (TPS) performance and measure various security strengths. In conclusion, we propose a radix of the blockchain’s core algorithm to overcome the weaknesses of sensor devices such as automobile, airplane, and close-circuit television (CCTV) using blockchain technology. Because all IoT devices use wireless technology, they have a fundamental weakness over wired networks. Sensors are exposed to hacking and sensor multi-platforms are vulnerable to security by multiple channels. In addition, since IoT devices have a lot of security weaknesses we intend to show the authentication strength of security through the color spectrum chain and apply it to sensor and multi-platform using Blockchain in the future.
SAC-Net: Spatial Attenuation Context for Salient Object Detection

This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects.Our key idea is to adaptively propagate and aggregate the image context features with variable attenuation over the entire feature maps.To achieve this, we design the spatial attenuation context (SAC) module to recurrently translate and aggregate the context features independently with different attenuation factors and then to attentively learn the weights to adaptively integrate the aggregated context features.By further embedding the module to process individual layers in a deep network, namely SAC-Net, we can train the network end-to-end and optimize the context features for detecting salient objects.Compared with 29 state-of-the-art methods, experimental results show that our method performs favorably over all the others on six common benchmark data, both quantitatively and visually.
Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things

Video surveillance has become ubiquitous due to the increasing security requirements in every sphere of life. The next generation video surveillance system (VSS) possesses great challenges in various applications, such as intelligent urban surveillance systems and smart cities. In these applications, we need to deal with the fast-growing number of surveillance nodes which introduce several constraints, e.g., high latency, high bandwidth, high energy consumption, and CPU and memory usage. To address these issues, the Internet of Video Things (IoVT), which is considered to be a part of the Internet of Things (IoT), can be a solution. The IoVT is composed of visual sensors (i.e., cameras) connected to the Internet. Unlike conventional systems, the VSS under an IoVT framework provides multiple layers (i.e., edge, fog, and cloud) of communication and decision making by capturing and analyzing rich contextual and behavioral information. Since an appropriate application layer protocol (ALP) can help in alleviating the challenges of future VSSs, the selection of ALPs is important for IoVT-based systems. Therefore, this paper presents a generic architecture of an IoVT-based VSS and a comparative analysis of several ALPs, such as MQTT, AMQP, HTTP, XMPP, CoAP, and DDS, with real-time experimentation. This analysis will assist the users to choose the appropriate ALPs in various surveillance applications and determine their suitability at different nodes of the IoVT framework.
Exposure Based Multi-Histogram Equalization Contrast Enhancement for Non-Uniform Illumination Images

Non-uniform illuminated images pose challenges in contrast enhancement due to the existence of different exposure region caused by uneven illumination.Although Histogram Equalization (HE) is a well-known method for contrast improvement, however, the existing HE-based enhancement methods for non-illumination often generated the unnatural images, introduced unwanted artifacts, and washed out effect because they do not utilize the information from the different exposure regions in performing equalization.Therefore, this study proposes a modified HE-based contrast enhancement technique for non-uniform illuminated images namely Exposure Region-Based Multi-Histogram Equalization (ERMHE).The ERMHE uses exposure region-based histogram segmentation thresholds to segment the original histogram into sub-histograms.With the thresholded sub-histograms, the ERMHE then uses an entropy-controlled gray level allocation scheme to allocate new output gray level range and to obtain new thresholds that will be used to repartition the histogram prior to HE process.A total of 154 non-uniform illuminated sample images are used to evaluate the application of the proposed ERMHE.By comparing ERMHE to four existing HE-based contrast enhancement namely, Global HE, Mean Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE), qualitatively, the ERMHE produces enhanced images with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation.Quantitatively, the ERMHE achieves the highest peak signal-to-noise-ratio (PSNR), lowest Absolute Mean Brightness Error (AMBE), and second best in Discrete Entropy (DE) scores.From the analyses, the ERMHE has shown its capability in enhancing different exposure regions exist in non-uniform illuminated images.
Evaluation of the Total, Direct, and Diffuse Solar Radiations From the ERA5 Reanalysis Data in China

Surface solar radiation is essential for regional climate and environmental research. The widely used European Centre for Medium-Range Weather Forecasts ERA5 reanalysis products provide long-term spatial estimates of the total and direct solar radiation on an hourly scale; however, its accuracy has not been systematically evaluated for China. In this letter, in situ measurements from 98 sites are used to analyze the deviations in the hourly radiation from ERA5 and to compare the daily variations. The results reveal that estimates from the reanalysis correlate well with ground observations and completely reflect the regional characteristics and daily variations at individual sites. Overall, ERA5 overestimates the hourly total solar radiation and direct radiation by approximately 30.87 and 73.95 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively, whereas it underestimates the diffuse radiation by approximately 43.08 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Spatiotemporal differences are found by comparing the errors and time series of different sites. The fact that the accuracy in areas with rainy and cloudy weather is obviously lower indicates that the ERA5 analysis poorly performs when retrieving cloud properties. These conclusions can contribute to guiding the rational use of ERA5 radiation products.
BERT-based Lexical Substitution

Previous studies on lexical substitution tend to obtain substitute candidates by finding the target word's synonyms from lexical resources (e.g., WordNet) and then rank the candidates based on its contexts.These approaches have two limitations: (1) They are likely to overlook good substitute candidates that are not the synonyms of the target words in the lexical resources;(2) They fail to take into account the substitution's influence on the global context of the sentence.To address these issues, we propose an end-toend BERT-based lexical substitution approach which can propose and validate substitute candidates without using any annotated data or manually curated resources.Our approach first applies dropout to the target word's embedding for partially masking the word, allowing BERT to take balanced consideration of the target word's semantics and contexts for proposing substitute candidates, and then validates the candidates based on their substitution's influence on the global contextualized representation of the sentence.Experiments show our approach performs well in both proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks.
CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks

Deep learning techniques have been successfully applied to bioimaging problems; however, these methods are highly data demanding. An approach to deal with the lack of data and avoid overfitting is the application of data augmentation, a technique that generates new training samples from the original dataset by applying different kinds of transformations. Several tools exist to apply data augmentation in the context of image classification, but it does not exist a similar tool for the problems of localization, detection, semantic segmentation or instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images (such as stacks or videos).In this paper, we present a generic strategy that can be applied to automatically augment a dataset of images, or multi-dimensional images, devoted to classification, localization, detection, semantic segmentation or instance segmentation. The augmentation method presented in this paper has been implemented in the open-source package CLoDSA. To prove the benefits of using CLoDSA, we have employed this library to improve the accuracy of models for Malaria parasite classification, stomata detection, and automatic segmentation of neural structures.CLoDSA is the first, at least up to the best of our knowledge, image augmentation library for object classification, localization, detection, semantic segmentation, and instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images.
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance

Deployment of deep neural networks (DNNs) in safety-critical systems requires provable guarantees for their correct behaviours. We compute the maximal radius of a safe norm ball around a given input, within which there are no adversarial examples for a trained DNN. We define global robustness as an expectation of the maximal safe radius over a test dataset, and develop an algorithm to approximate the global robustness measure by iteratively computing its lower and upper bounds. Our algorithm is the first efficient method for the Hamming (L0) distance, and we hypothesise that this norm is a good proxy for a certain class of physical attacks. The algorithm is anytime, i.e., it returns intermediate bounds and robustness estimates that are gradually, but strictly, improved as the computation proceeds; tensor-based, i.e., the computation is conducted over a set of inputs simultaneously to enable efficient GPU computation; and has provable guarantees, i.e., both the bounds and the robustness estimates can converge to their optimal values. Finally, we demonstrate the utility of our approach by applying the algorithm to a set of challenging problems.
Policy search in continuous action domains: An overview

Continuous action policy search is currently the focus of intensive research, driven both by the recent success of deep reinforcement learning algorithms and the emergence of competitors based on evolutionary algorithms. In this paper, we present a broad survey of policy search methods, providing a unified perspective on very different approaches, including also Bayesian Optimization and directed exploration methods. The main message of this overview is in the relationship between the families of methods, but we also outline some factors underlying sample efficiency properties of the various approaches.
Tight, robust, and feasible quantum speed limits for open dynamics

Starting from a geometric perspective, we derive a quantum speed limit for arbitrary open quantum evolution, which could be Markovian or non-Markovian, providing a fundamental bound on the time taken for the most general quantum dynamics. Our methods rely on measuring angles and distances between (mixed) states represented as generalized Bloch vectors. We study the properties of our bound and present its form for closed and open evolution, with the latter in both Lindblad form and in terms of a memory kernel. Our speed limit is provably robust under composition and mixing, features that largely improve the effectiveness of quantum speed limits for open evolution of mixed states. We also demonstrate that our bound is easier to compute and measure than other quantum speed limits for open evolution, and that it is tighter than the previous bounds for almost all open processes. Finally, we discuss the usefulness of quantum speed limits and their impact in current research.
A New DBSCAN Parameters Determination Method Based on Improved MVO

Density-based spatial clustering of applications with noise (DBSCAN) is a typical kind of algorithm based on density clustering in unsupervised learning. It can cluster data of arbitrary shape and also identify noise samples in the dataset. However, an unavoidable defect of the DBSCAN algorithm exists since the clustering performance is quite sensitive to the parameter settings of MinPts and Eps, and there is no theory to guide the setting of its parameters. Therefore, a new method is proposed to optimize the DBSCAN parameters in this paper. Multi-verse optimizer algorithm, a special variable updating method with excellent optimization performance, is selected and improved for optimizing the parameters of DBSCAN, which not only can quickly find out the highest clustering accuracy of DBSCAN, but also find the interval of Eps corresponding to the highest accuracy. In order to search the range of Eps more quickly and efficiently, we design a new mechanism for the variable update of MVO. The experimental results show that the improved MVO is used to optimize DBSCAN, which not only can quickly find out its highest clustering accuracy but also can search the parameters of MinPts and Eps corresponding to the highest clustering accuracy efficiently.