Reviewerly/exHarmony huggingface dataset:
Masked Face Recognition With Mask Transfer and Self-Attention Under the COVID-19 Pandemic

Face masks bring a new challenge to face recognition systems especially against the background of the COVID-19 pandemic. In this paper, a method used for mitigating the negative effects of mask defects on face recognition is proposed. Firstly, a low-cost, accurate method of masked face synthesis, i.e. mask transfer, is proposed for data augmentation. Secondly, an attention-aware masked face recognition (AMaskNet) is proposed to improve the performance of masked face recognition, which includes two modules: a feature extractor and a contribution estimator. Therein, the contribution estimator is employed to learn the contribution of the feature elements, thus achieving refined feature representation by simple matrix multiplications. Meanwhile, the end-to-end training strategy is utilized to optimize the entire model. Finally, a mask-aware similarity matching strategy (MS) is adopted to improve the performance in the inference stage. The experiments show that the proposed method consistently outperforms on three masked face recognition datasets: RMFRD, COX and Public-IvS. Meanwhile, a qualitative analysis experiments using CAM indicate that the contribution learned by AMaskNet is more conducive to masked face recognition.
Single- and Multiagent Actor–Critic for Initial UAV’s Deployment and 3-D Trajectory Design

This article considers a wireless network consisting of unmanned aerial vehicles (UAVs), deployed as aerial base stations, and a large number of terrestrial users randomly distributed in a dense urban area. The main objective of this work is to maximize the downlink rate of users along with clustering of users and 2-D initial placement of UAVs, which effectively minimizes the clustering error. To achieve this goal, we estimate the next users’ locations with deep echo-state network (ESN) to find the movement pattern of users with high accuracy. Then, we propose the single- and multiagent actor–critic (AC) algorithms for UAVs’ initial deployment and trajectory design, where the multiagent scheme employs an efficient bandwidth allocation. Simulation results supported by a real data set of the terrestrial users’ coordinates indicate that, when the deep ESN algorithm is used, the accuracy is 93.75% for longitude and 88.36% for latitude compared to the simple ESN performance. Moreover, the use of single- and multiagent AC algorithms display better performance in terms of downlink rate and convergence speed than value-based algorithms such as deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network schemes.
The time-varying correlation between cryptocurrency policy uncertainty and cryptocurrency returns

Purpose This study aims to analyze the time-varying correlation between the cryptocurrency policy uncertainty (UCRY Policy) and cryptocurrency returns. More specifically, it analyzes whether these correlations vary according to the uncertainty attributable to salient events such as China banning ICOs, cryptocurrency exchanges attacks, Coronavirus (Covid-19) pandemic crisis and the United States (U.S.) Security and Exchange Commission’s (SEC’s) announcement about Ripple. Design/methodology/approach To measure the dynamic relationship, it uses the dynamic conditional correlation (DCC) model of Engle (2002) to consider time variation in UCRY Policy and cryptocurrency returns. The data set encompasses the weekly frequency data of the UCRY Policy and Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), Stellar (XLM), Dash (DASH), Monero (XMR) from 4 September 2016, to 21 February 2021. Findings Empirical findings indicate that the correlations between the UCRY Policy and the BTC, ETH, LTC, XRP, XLM, DASH and XMR returns are consistently negative. Thus, an increase in the volatility of the UCRY Policy can lead to a decrease in volatility for BTC, ETH, LTC, XRP, XLM, DASH and XMR returns. Besides, these findings indicate that the estimated DCC is not only time-varying but also substantially responsive to salient events, such as China banning ICOs, cryptocurrency exchanges attacks, the Covid-19 pandemic crisis and SEC’s announcement about Ripple. Besides, empirical findings show that cryptocurrency returns are adversely impacted by UCRY Policy during the salient events (China bans ICOs, the hack of cryptocurrency exchanges, Covid-19 crisis), suggesting their failure to act as a hedge or safe-haven asset. Originality/value To the best of the author’s knowledge, this study investigates the time-varying correlation between UCRY Policy and cryptocurrency returns. Besides, this study may be useful for new studies and fill a gap in the finance literature, due to the limited number of studies on the UCRY Policy in the finance literature.
A Novel Architecture Combining Oracle With Decentralized Learning for IIoT

The rapid development of digital technology is reshaping the architecture of the Industrial Internet of Things (IIoT). The traditional architecture cannot process vast amounts of data exchanges and provide entities with trust. The future IIoT is expected to be a decentralized architecture in which blockchain and digital twin-driven IIoT can enable trusted data exchanges. However, this architecture cannot obtain huge amounts of external real-time data and isolated data. Moreover, it cannot handle complex industrial computing tasks. Therefore, we combine oracle with decentralized learning to propose a novel IIoT-oriented digital twin architecture. We also propose an effective decentralized collaboration mechanism to support external data and resources exchanges. Moreover, we propose a novel computing collaboration mechanism to expand the learning capabilities of the industrial ecology. Experiments show that our proposed paradigm has less processing time, a more stable process, and better learning ability compared to other paradigms.
IoT Device Integration and Payment via an Autonomic Blockchain-Based Service for IoT Device Sharing

The Internet of Things (IoT) incorporates billions of IoT devices (e.g., sensors, cameras, wearables, smart phones, as well as other internet-connected machines in homes, vehicles, and industrial plants), and the number of such connected IoT devices is currently growing rapidly. This paper proposes a novel Autonomic Global IoT Device Discovery and Integration Service (which we refer to as aGIDDI) that permits IoT applications to find IoT devices that are owned and managed by other parties in IoT (which we refer to as IoT device providers), integrate them, and pay for using their data observations. aGIDDI incorporates a suite of interacting sub-services supporting IoT device description, query, integration, payment (via a pay-as-you-go payment model), and access control that utilise a special-purpose blockchain to manage all information needed for IoT applications to find, pay and use the IoT devices they need. The paper describes aGIDDI's novel protocol that allows any IoT application to discover and automatically integrate and pay for IoT devices and their data that are provided by other parties. The paper also presents aGIDDI's architecture and proof-of-concept implementation, as well as an experimental evaluation of the performance and scalability of aGIDDI in variety of IoT device integration and payment scenarios.
Chase and Track: Toward Safe and Smooth Trajectory Planning for Robotic Navigation in Dynamic Environments

In this article presents a trajectory planning approach toward safe and smooth robot motion in dynamic environments. We develop a hierarchical planning framework with a global planner generating the shortest path between the robot and the navigation target. Specially, a virtual target (VT) is set to run on the global path with a designed velocity. At the local level, the robot chases the VT and tracks the global path when traveling through the dynamic environment. We employ the model predictive control (MPC) framework for the local path generation. In particular, the prediction horizon of the MPC is adaptively changed concerning the distance between the robot and the VT. It implicitly reflects the crowdedness of the environment, which helps reduce the environmental uncertainty. Besides, we develop an event-triggered mechanism that executes the local plan aperiodically to release the computational burden. Based on the local chasing and tracking performance, we develop a global path replanning scheme in response to the untraversable area emerging in the dense environment. The developed framework is validated through extensive experiments in dynamic environments, demonstrating that the robot can reach the target faster and showcase a safer and smoother trajectory in the navigation.
Drone Control in AR: An Intuitive System for Single-Handed Gesture Control, Drone Tracking, and Contextualized Camera Feed Visualization in Augmented Reality

Traditional drone handheld remote controllers, although well-established and widely used, are not a particularly intuitive control method. At the same time, drone pilots normally watch the drone video feed on a smartphone or another small screen attached to the remote. This forces them to constantly shift their visual focus from the drone to the screen and vice-versa. This can be an eye-and-mind-tiring and stressful experience, as the eyes constantly change focus and the mind struggles to merge two different points of view. This paper presents a solution based on Microsoft’s HoloLens 2 headset that leverages augmented reality and gesture recognition to make drone piloting easier, more comfortable, and more intuitive. It describes a system for single-handed gesture control that can achieve all maneuvers possible with a traditional remote, including complex motions; a method for tracking a real drone in AR to improve flying beyond line of sight or at distances where the physical drone is hard to see; and the option to display the drone’s live video feed in AR, either in first-person-view mode or in context with the environment.
Risk identification of coal spontaneous combustion based on COWA modified G1 combination weighting cloud model

To realize the scientific judgment of spontaneous combustion risk in the coal mine, the spontaneous combustion influence factors were analyzed from the three aspects of coal spontaneous combustion tendency, air leakage, and oxygen supply, heat storage and heat dissipation. And the basis for the evaluation of t spontaneous combustion grade was constructed. Combination ordered weighted averaging (COWA) calculation was introduced to optimizes G1 subjective weighting, and a COWA modified G1 combined weighting cloud model was proposed to identify the spontaneous combustion risk in the coal mine. Finally, the rationality of the model was verified with actual cases. The research results show that the spontaneous combustion risk level in the Lingquan coal mine is relatively safe, which is consistent with the actual situation. And the spontaneous combustion tendency of coal is the leading factor affecting spontaneous combustion risk.
A robust method for clustering football players with mixed attributes

Abstract A robust fuzzy clustering model for mixed data is proposed. For each variable, or attribute, a proper dissimilarity measure is computed and the clustering procedure combines the dissimilarity matrices with weights objectively computed during the optimization process. The weights reflect the relevance of each attribute type in the clustering results. A simulation study and an empirical application to football players data are presented that show the effectiveness of the proposed clustering algorithm in finding clusters that would be hidden unless a multi-attributes approach were used.
Latent to Latent: A Learned Mapper for Identity Preserving Editing of Multiple Face Attributes in StyleGAN-generated Images

Several recent papers introduced techniques to adjust the attributes of human faces generated by unconditional GANs such as StyleGAN. Despite efforts to disentangle the attributes, a request to change one attribute often triggers unwanted changes to other attributes as well. More importantly, in some cases, a human observer would not recognize the edited face to belong to the same person. We propose an approach where a neural network takes as input the latent encoding of a face and the desired attribute changes and outputs the latent space encoding of the edited image. The network is trained offline using unsupervised data, with training labels generated by an off-the-shelf attribute classifier. The desired attribute changes and conservation laws, such as identity maintenance, are encoded in the training loss. The number of attributes the mapper can simultaneously modify is only limited by the attributes available to the classifier – we trained a network that handles 35 attributes, more than any previous approach. As no optimization is performed at deployment time, the computation time is negligible, allowing real-time attribute editing. Qualitative and quantitative comparisons with the current state-of-the-art show our method is better at conserving the identity of the face and restricting changes to the requested attributes.
The Datasets Dilemma

There has been sustained interest from both academia and industry throughout the years due to the importance and practicability of recommendation systems. However, several recent papers have pointed out critical issues with the evaluation process in recommender systems. Likewise, this paper takes an in-depth look at a fundamental but often neglected aspect of the evaluation procedure, i.e. the datasets themselves. To do so, we adopt a systematic and comprehensive approach to understand the datasets used for implicit feedback based top-K recommendation. We start by examining recent papers from top-tier conferences to find out how different datasets have been utilised thus far. Next, we look at the characteristics of these datasets to understand their similarities and differences. Finally, we conduct an empirical study to determine whether the choice of datasets used for evaluation can influence the observations and/or conclusions obtained. Our findings suggest that greater attention needs to be paid to the selection process of datasets used for evaluating recommender systems in order to improve the robustness of the obtained results.
Fiber orientation evaluation in reinforced composites using digital image correlation and thermal excitation

A digital image correlation (DIC) nondestructive evaluation (NDE) method for determining fiber orientation in a composite material was developed. Fiber orientation is a crucial factor that influences the mechanical and thermal properties of fiber reinforced composites. By thermally exciting the composite surface, DIC measured the strain difference axial and transverse to the fiber axis to determine fiber orientation. A 2.7x increase in strain transverse to the fiber axis was observed compared to the longitudinal axis. The transverse direction experiences a larger strain of 0.00445 m/m due to the higher thermal expansion in the surrounding polymer matrix than the fiber direction which resists expansion and resulted a 0.00166 m/m strain. DIC provides a full-field qualitative measurement for fiber orientation while maintaining high resolution. This innovative method is in a preliminary stage and has been developed to fill the gap/limitations of complementary NDE methods.
Validation of weather reanalysis datasets and geospatial and techno-economic viability and potential assessment of concentrated solar power plants

• Validation of weather reanalysis datasets over multiple temporal aggregates. • Using thermodynamic inputs to assess concentrated solar power’s technical potential. • Multi-criteria techno-economic potential assessment and optimal site selection. • Net potential of 10 TW and levelized energy cost below 9.6 c/kWh is estimated. • Among viable areas, 6.5% of land found highly suitable for power plant development. Accelerating the decarbonization of power sector requires strategic planning and multi-aspectual feasibility analysis of renewable energy systems including, but not limited to, meteorological, land-use, techno-economic and environmental parameters. In this study, at first, the suitability of reanalysis datasets of Direct Normal Irradiance, air temperature, wind speed, relative humidity and air pressure was investigated at different timescales and locations over Pakistan. High correlation (R > 0.9) and low bias is found for Surface Solar Radiation Data records – Heliosat East, after preprocessing, among radiation datasets in consideration. Likewise, Modern-Era Retrospective analysis for Research and Applications version 2 datasets of temperature and air pressure strongly correlate with ground measurements whereas wind speed and relative humidity show peculiarity. Afterwards, resource maps from long-term timeseries are developed for each of the aforementioned parameters. In the second part, spatial feasibility and Analytical Hierarchy Process based site suitability is assessed utilizing site-specific meteorological, socio-economic and environmental parameters. Moreover, multi-parametric evaluation of techno-economic potential is performed using bias-corrected datasets. For technical potential, System Advisor Model’s Physical Trough Collector model is used for exclusively selected points. Additionally, cost of water usage is introduced in estimation of Levelized Cost of Electricity besides other parameters. Cumulative power capacity potential of 10,035 GW is identified for country, with net cost estimated below 0.096 USD/kWh. Nine sites are found to have generation potential greater than 1200 TWh/year, the individual capacity to fulfil total predicted energy demand of the country for year 2030. Around 6.5% of viable regions are found to be highly suitable for plant deployment. Land cover and lack of access to transmission network act as limiting barriers in installation of concentrated solar power plants at many potential sites across the country.
Fast and Explicit Neural View Synthesis

We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects. We propose a simple yet effective approach that is neither continuous nor implicit, challenging recent trends on view synthesis. Our approach explicitly encodes observations into a volumetric representation that enables amortized rendering. We demonstrate that although continuous radiance field representations have gained a lot of attention due to their expressive power, our simple approach obtains comparable or even better novel view reconstruction quality comparing with state-of-the-art baselines [49] while increasing rendering speed by over 400x. Our model is trained in a category-agnostic manner and does not require scene-specific optimization. Therefore, it is able to generalize novel view synthesis to object categories not seen during training. In addition, we show that with our simple formulation, we can use view synthesis as a self-supervision signal for efficient learning of 3D geometry without explicit 3D supervision.
TA-Net: Topology-Aware Network for Gland Segmentation

Gland segmentation is a critical step to quantitatively assess the morphology of glands in histopathology image analysis. However, it is challenging to separate densely clustered glands accurately. Existing deep learning-based approaches attempted to use contour-based techniques to alleviate this issue but only achieved limited success. To address this challenge, we propose a novel topology-aware network (TA-Net) to accurately separate densely clustered and severely deformed glands. The proposed TA-Net has a multitask learning architecture and enhances the generalization of gland segmentation by learning shared representation from two tasks: instance segmentation and gland topology estimation. The proposed topology loss computes gland topology using gland skeletons and markers. It drives the network to generate segmentation results that comply with the true gland topology. We validate the proposed approach on the GlaS and CRAG datasets using three quantitative metrics, F1-score, object-level Dice coefficient, and object-level Hausdorff distance. Extensive experiments demonstrate that TA-Net achieves state-of-the-art performance on the two datasets. TA-Net outperforms other approaches in the presence of densely clustered glands.
Blockchain-Enabled Online Diagnostic Platform of Suspected Patients of COVID-19 Like Pandemics

During times of pandemics, the healthcare system may collapse due to the high demand for healthcare resources. Hence, there is a need for an online-automated platform that enables remote collection of symptoms from suspected patients, accurate and fast diagnostics, and data sharing among different entities within the healthcare system. However, many privacy and scalability challenges face such a platform. To address such challenges, we propose a custom-designed blockchain enabled platform that guarantees privacy-preservation via a mixture of group signature and random numbers that support anonymity of suspected patients and unlinkability of data while enabling mutual interaction between the suspected patient and the platform; provides automatic diagnostics via a deep neural network-based detector that runs on a smart contract within the blockchain; and offers access and administrative authority of the healthcare entities to the database of symptoms and their diagnoses via a consortium-based blockchain architecture. Experimental studies demonstrate a detection accuracy of 90 percent based on a deep convolutional recurrent neural network. A case study of 500 expected patients is examined giving promising results. Every patient can know the test results after only 14 min of submitting the data. The storage requirements are as low as 0.52 MB for each suspected patient and 0.6 MB for each hospital.
YOLO-Ship: A Visible Light Ship Detection Method

YOLO detection model is a lightweight and efficient target detection algorithm. For realizing real-time accurate recognition of visible ship targets, an improved ship target detection method YOLO-Ship derived from YOLOv5 is advanced. This model uses MixConv to improve the traditional convolution operation and coordinated attention mechanism, and uses Focal Loss and CIoU Loss to optimize loss functions of the original method. On Ship7000 dataset, YOLO-Ship detection method has a more outstanding accuracy than YOLOv5 and YOLOv3 algorithms, and its average detection accuracy under different IoU thresholds is improved from 0.514 to 0.728, which can effectively meet the practical application requirements.