Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial–Spectral Fusion of HY-1C COCTS/CZI Data for Coastal Water Remote Sensing Using Deep Belief Network

The remote sensing monitoring of coastal waters with dramatic changes requires images with high spatial and temporal resolutions and adequate spectral bands. However, a single sensor is limited to meet these requirements. Image fusion is, therefore, widely adopted. In this article, a deep belief network (DBN) is developed to fuse images from the Chinese ocean color and temperature scanner (1000 m, eight bands) and coastal zone imager (50 m, four bands) onboard HaiYang-1C satellite to generate 50-m, eight-band, and three-day observations for coastal waters. The DBN is compared with the existing prevailing Gram–Schmidt transformation (GS) and inversion-based fusion (IBF) algorithms over the Bohai Sea at the top-of-atmosphere reflectance and product [e.g., chlorophyll-a (chl-a)] levels. Results indicate that for the spatial aspect, DBN can avoid the block effect and maintain details. The average structural similarity index of DBN is approximately 22.08% and 3.30% better than that of GS and IBF, respectively; for the spectral aspect, the mean relative errors for eight bands of DBN range from 3.15% to 21.54%. The errors are less than 50% and 80% of those of GS, while less than 80% and 110% of those of IBF, at bands 1–6 and bands 7 and 8, respectively; for chl-a retrieval, DBN yields better results with the coefficient of determination R2 of 0.78 and root-mean-square error (RMSE) of 0.10 mg/m3 compared with those of IBF (R2 = 0.59 and RMSE = 0.16 mg/m3). DBN outperforms GS and IBF at reflectance and product levels, displaying great potential for the remote sensing monitoring of coastal waters.
Auditing Cache Data Integrity in the Edge Computing Environment

Edge computing allows app vendors to deploy their applications and relevant data on distributed edge servers to serve nearby users. Caching data on edge servers can minimize users’ data retrieval latency. However, such cache data are subject to both intentional and accidental corruption in the highly distributed, dynamic, and volatile edge computing environment. Given a large number of edge servers and their limited computing resources, how to effectively and efficiently audit the integrity of app vendors’ cache data is a critical and challenging problem. This article makes the first attempt to tackle this Edge Data Integrity (EDI) problem. We first analyze the threat model and the audit objectives, then propose a lightweight sampling-based probabilistic approach, namely EDI-V, to help app vendors audit the integrity of their data cached on a large scale of edge servers. We propose a new data structure named variable Merkle hash tree (VMHT) for generating the integrity proofs of those data replicas during the audit. VMHT can ensure the audit accuracy of EDI-V by maintaining sampling uniformity. EDI-V allows app vendors to inspect their cache data and locate the corrupted ones efficiently and effectively. Both theoretical analysis and comprehensively experimental evaluation demonstrate the efficiency and effectiveness of EDI-V.
Visual Human–Computer Interactions for Intelligent Vehicles and Intelligent Transportation Systems: The State of the Art and Future Directions

Research on intelligent vehicles has been popular in the past decade. To fill the gap between automatic approaches and man-machine control systems, it is indispensable to integrate visual human–computer interactions (VHCIs) into intelligent vehicles systems. In this article, we review existing studies on VHCI in intelligent vehicles from three aspects: 1) visual intelligence; 2) decision making; and 3) macro deployment. We discuss how VHCI evolves in intelligent vehicles and how it enhances the capability of intelligent vehicles. We present several simulated scenarios and cases for future intelligent transportation system.
Sparse Regularization Tensor Robust PCA Based on t-product and Its Application in Cancer Genomic Data

Genetic information becomes more and more important in the process of biological research. Gene analysis is an effective mean in biological research, especially the analysis of differentially expressed genes. Robust principal component analysis (RPCA) is an effective method to identify differentially expressed genes. But tensor robust principal component analysis (TRPCA) performs better than RPCA when processing multi-dimensional data. The traditional TRPCA method also has limitations in restoring low-rank sparse components. To further improve the accuracy of the TRPCA method in restoring low-rank components and sparse components, we propose a novel TRPCA method to obtain high-order correlations information of multi-dimensional data. It uses a new nuclear norm based on t-product operator to approximate the rank function. The $L_{2,1}$ -norm is used to improve the sparsity of tensors and reduce the negative effects caused by noises and outliers. At the same time, the introduction of $L_{2,1}$ -norm enhances the sparsity of error components, and improves the accuracy of low-rank component recovery. The low-rank sparse components are obtained by solving the convex problem of the new tensor nuclear norm. It can well preserve the spatial structure and make full use of complementary information to improve the clustering effect. Alternating direction method of multiplier (ADMM) is used to solve the optimization problem of this method. Experimental results on different cancer genomic datasets indicate that our method is superior to other methods.
Coverage Path Planning with Proximal Policy Optimization in a Grid-based Environment

We present an on-line approach for coverage path planning in 2D grid environments based on reinforcement learning. We used actor-critic architecture with convolutional layers to learn an agent’s policy from simulated limited-range sensor observations. We experimented with different reward functions and network architectures to get a minimal repetition rate. Our results show that model generalizes well to unseen environments with complex geometry and dynamic obstacles and demonstrates the ability to learn some optimal trajectory patterns like circular and boustrophedon motion. An approach may as well be suitable for a multi-agent setting with minor adjustments, as shown by our simulations.
Distributed Optimization for Linear Multi-Agent Systems Subject to DoS Attacks

This paper discussed the distributed optimization problem where interagent communication is subject to DoS attacks. An event-based communication strategy is adopted to determine the signal transmission time, and then a resilient control algorithm is put forward to achieve consensus and meanwhile minimize the global objective function. By means of a positive invariant set and a quadratic Lyapunov functional, the convergence of the developed algorithm is guaranteed. The effectiveness of the theoretical result is illustrated by a simulation example.
Model of Cathode Spots Crater Formation and Deuterium Desorption Process on Zirconium Deuterium Electrode

In this article, the model including cathode spot process and deuterium desorption process on zirconium deuterium cathode is developed. Five kinds of particle fluxes on cathode surface and three mechanisms affecting deuterium diffusion in cathode are considered. The model not only describes the deformation of cathode metal, but also describes the variation of deuterium concentration with time. On pure zirconium cathode, the mean charge state is larger but on zirconium deuterium cathode, the total ion number density is larger since there are additional deuterium ions, and simulation results show the discharge currents are very close on the two cathodes, the energy flux density on ZrD0.66 cathode is larger so cathode spot crater develops faster on ZrD0.66 cathode. Ablation rates caused by different mechanisms are calculated and the results show in cathode spot development process, mass loss caused by evaporation is ignored, most of the liquid metal will resolidify on cathode surface, and droplets are the main reason for cathode ablation. The ablation rates due to the ejection of droplets are close to experimental results. The ratios of deuterium ion from different mechanisms in this process are also calculated and it is 93.5% in simulation. Simulation results are in agreement with the other researchers’ works.
IoT based Smart Water Quality Monitoring and Flow Control System

Quality of water may be an advanced exploration and production fundamental concept. The water quality is based on so many factors. Water infection is surely one of the essential crucial fears for the green globalization. With a view to deliver safe and secure water, real-time quality has to be monitored. The current system comprises a sensor network which is utilized to gauge both physical and synthetic boundaries of the water that are temperature, PH, turbidity, water glide sensor can be measured. These measured values from the sensors are processed via the Arduino UNO controller. Finally, the sensor values may be regarded using Wi-Fi module. This paper presents a cost efficient system for real time quality monitoring using Internet of Things (IoT).
Policy Gradient Reinforcement Learning-based Optimal Decoupling Capacitor Design Method for 2.5-D/3-D ICs using Transformer Network

In this paper, we first propose a policy gradient reinforcement learning (RL)-based optimal decoupling capacitor (decap) design method for 2.5-D/3-D integrated circuits (ICs) using a transformer network. The proposed method can provide an optimal decap design that meets target impedance. Unlike previous value-based RL methods with simple value approximators such as multi-layer perceptron (MLP) and convolutional neural network (CNN), the proposed method directly parameterizes policy using an attention-based transformer network model. The model is trained through the policy gradient algorithm so that it can achieve larger action space, i.e. search space. For verification, we applied the proposed method to a test hierarchical power distribution network (PDN). We compared convergence results depending on the action space with the previous value-based RL method. As a result, it is validated that the proposed method can cover ×4 times larger action space than that of the previous work.
Constructing a Relevance-oriented Dataset for Training Transformer Rankers for Medical Search

As there is usually sparse relevance assessment data for training, state-of-the-art transformer-based rankers may under-perform when directly employed in the medical domain. In particular, the lack of human-annotated labels has become a bottleneck of learning the data-hungry deep IR models. In this work, we introduce an effective approach to generating data from existing medical corpora for training transformerbased re-ranking models. Inspired by the fact that the title is usually closely relevant to the abstract in a scientific article, we create a dataset by regarding titles as queries and the corresponding abstracts as relevant documents, and use an unsupervised IR model (e.g., BM25) to sample negative examples. Thereafter, we train the re-ranking model on this relevanceoriented dataset before training the model on the target collection. Experimental results on the standard TREC Precision Medicine track benchmark show that our approach, despite its simplicity, is able to outperform strong recent baselines. The dataset is available for download 1.
Practical Real-Time Implementation of a Disturbance Rejection Control Scheme for a Twin-Rotor Helicopter System Using Intelligent Active Force Control

This paper centers around an experimental investigation into the effectiveness of an innovative hybrid control approach based on an intelligent active force control (IAFC) strategy to stabilize a twin-rotor helicopter model and improve its ability to reject external disturbances efficiently. The intelligent algorithm was based on an iterative learning (IL) method integrated into the main control loop to estimate control parameters automatically while on-line. A mechatronic test rig with the IAFC-based control algorithm was incorporated into a Quanser Aero twin-rotor model in a laboratory setting as a verification platform to evaluate the applicability and efficacy of the proposed control algorithm via a practical real-time implementation. The hybrid IAFC-based control design was rigorously examined to test its feasibility and durability in countering various forms of external disturbances while executing the trajectory tracking tasks. Notably, the efficiency of the IAFC-based control unit was mainly studied and compared with other control plans under different operating conditions for benchmarking. The experimental results show the ability of the controller based on the IAFC strategy to effectively improve the disturbance rejection capability compared to the other control schemes considered in the study. About 27% improvement of the system performance in terms of lowering the root mean square error (RMSE) was observed compared to the other control systems counterparts.
Gradient Masking of Label Smoothing in Adversarial Robustness

Deep neural networks (DNNs) have achieved impressive results in several image classification tasks. However, these architectures are unstable for adversarial examples (AEs) such as inputs crafted by a hardly perceptible perturbation with the intent of causing neural networks to make errors. AEs must be considered to prevent accidents in areas such as unmanned car driving using visual object detection in Internet of Things (IoT) networks. Gaussian noise with label smoothing or logit squeezing can be used to increase the robustness against AEs in the training of DNNs. However, from a model interpretability aspect, Gaussian noise with label smoothing does not increase the adversarial robustness of the model. To resolve this problem, we tackle the AE instead of measuring the accuracy of the model against AEs. Considering that a robust model shows a small curvature of the loss surface, we propose a metric to measure the strength of the AEs and the robustness of the model. Furthermore, we introduce a method to verify the existence of the obfuscated gradients of the model based on the black-box attack sanity check method. The proposed method enables us to identify a gradient masking problem wherein the model does not provide useful gradients and exploits false defenses. We evaluate our technique against representative adversarially trained models using the CIFAR10, CIFAR100, SVHN, and Restricted ImageNet datasets. Our results show that the performance of some false defense models decreases by up to 32% compared to the previous evaluation metrics. Moreover, our metric reveals that traditional metrics used to measure the robustness of the model may produce false results.
Analysis and Prediction of Glove Quality Based on Manufacturing Factors

This paper considers the effects of manufacturing factors on glove quality. Four factors are considered, namely the curing temperature profile, temperature of latex, percentage of calcium nitrate and the oven temperature after coagulant dip. The glove quality is measured based on the weight of the gloves, tensile strength after ageing, number of gloves with pinholes and finger thickness. The analysis is carried out using Wilcoxon signed rank tests where the significance of each factor on the glove quality can be determined. The glove quality is subsequently predicted using neural networks. It is shown that the weight of the gloves can be predicted quite well using a simple neural network structure. However, the other quality measures cannot be predicted equally well. More complicated input-output relationships are likely to exist for these. The results from this work are important for aiding decisions on process changes in glove manufacturing.
Transport properties of the Widom–Rowlinson hard‐sphere mixture model

The thermal conductivity, mutual diffusion coefficient, and viscosities of the hard‐sphere Widom–Rowlinson Model are studied in quantitative detail within the framework of revised Enskog theory. It is found that the thermal conductivity and viscosities behave smoothly at the critical point, whereas the mutual diffusion coefficient vanishes strongly at the critical point and along the spinodal curve. (We note that the latter coefficient does not appear to vanish at the critical point, when analyzed in the framework of the less satisfactory standard Enskog theory.)
Analysis of Hollow Cathode Aperture Wall Profile on Pseudospark Discharge-Based Sheet Electron Beam Source

In this article, an investigation has been performed to analyze the role of hollow cathode aperture wall profile on pseudospark (PS) discharge-based single gap sheet electron beam source. A simulation study using COMSOL Multiphysics has been carried out for different hollow cathode aperture wall profile parameters, such as thickness and radius of curvature of aperture wall. The role of hollow cathode aperture wall thickness “<inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>” and the radius of curvature of aperture wall “<inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>” for different gas pressures, 16–20 Pa, and the corresponding applied gap voltage 16–20 kV have been studied. The hollow cathode aperture wall thickness has been analyzed for the range of ~1–3 mm, keeping the constant aspect ratio 12:1, while the radius of curvature of aperture wall has been analyzed for the range of 0–3 mm for different wall thicknesses. Experimental studies have also been performed to analyze the role of the thickness of the aperture wall on PS discharge. A good correlation has been obtained between the simulation and experimental studies.
A Hybrid Community-based Simulated Annealing Approach for Influence Maximization in Social Networks

Influence maximization (IM) in social networks aims to figure out the best subset of seed nodes which have maximum cascading influence under a diffusion model. This paper proposes a hybrid Community-based Simulated Annealing (ComSA) approach for the IM problem. A community detection algorithm is employed to segregate the entire social network structure into some more deeply clustered communities. Thereafter, a degree-based metric has been used to select the candidate pool from each community by excluding less influential nodes at the preliminary data preprocessing phase. A community-based seed initialization and neighborhood search technique have been proposed. To speed up the convergence of stable solutions in Simulated Annealing approach, a greedy hill climbing strategy is also implemented instead of using probabilistic based solution acceptance processes. Experimental results on four real-world datasets show that our proposed algorithm has comparable solution with greedy and outperforms the other existing meta-heuristic approaches.
The Multimodal Deep Learning for Diagnosing COVID-19 Pneumonia from Chest CT-Scan and X-Ray Images

Due to the COVID-19 Pandemic, doctors need to make medical decisions for their patients based on many examinations (e.g., polymerase chain reaction test, temperature test, CT-Scans, or X-rays). However, transfer learning has been used in several researches and focuses on only a single modality of biomarkers (e.g., CT-Scan or X-Ray) for diagnosing Pneumonia. In recent studies, a single modality has its own classification accuracy and every different biomarker may provide complementary information for detecting COVID-19 Pneumonia. The COVID-19 virus can be detected by CT-Scan and X-Ray imaging of the chest. In this work, we propose to use concatenation of two different transfer learning models using an open-source dataset of 2500 CT-Scan images and 2500 X-ray images for classifying CT-Scan images and X-ray images into two classes: normal and COVID-19 Pneumonia. We have used DenseNet121, MobileNet, Xception, InceptionV3, ResNet50, and VGG16 models for image recognition in our work. As a result, we achieve the best classification accuracy of 99.87% of the concatenation of ResNet50 and VGG16 networks. We also achieved the best classification accuracy of 98.00% when using a single modality of CT-Scan ResNet50 networks and classification accuracy of 98.93% for X-Ray VGG16 networks. Our multimodal fusion method shows a better classification accuracy compared to the method of using a single modality of biomarkers.
Optimal Motion Control for Connected and Automated Electric Vehicles at Signal-Free Intersections

Traffic congestion is one of the major issues for urban traffic networks. The connected and autonomous vehicles (CAV) is an emerging technology that has the potential to address this issue by improving safety, efficiency, and capacity of the transportation system. In this paper, the problem of optimal trajectory planning of battery-electric CAVs in the context of cooperative crossing of an unsignalized intersection is addressed. An optimization-based centralized intersection controller is proposed to find the optimal velocity trajectory of each vehicle so as to minimize electric energy consumption and traffic throughput. Solving the underlying optimization problem for a group of CAVs is not straightforward because of the nonlinear and nonconvex dynamics, especially when the powertrain model is explicitly modelled. In order to ensure a rapid solution search and a unique global optimum, the optimal control problem (OCP) is reformulated via convex modeling techniques. Several simulation case studies show the effectiveness of the proposed approach and the trade-off between energy consumption and traffic throughput is illustrated.
Investigation of the gas-phase emitter effect of dysprosium in ceramic metal halide lamps

The behaviour of electrodes operated with ac-currents in ceramic metal halide lamps containing Hg + NaTlDy iodide has been investigated experimentally. Using transparent YAG lamp tubes with the so-called Bochum model lamp as an outer bulb phase resolved measurements were performed of the electrode temperature and Dy density in dependence on the cold-spot temperature of the salt filling. The electrode tip temperature and electrode power loss are deduced from the temperature profile measured along the electrode axis. The Dy density in front of the electrode is determined by spatially resolved spectroscopic measurements of absolute line intensities. It is found that doping of a mercury lamp only with Dy iodide generates at low operation frequencies a pronounced emitter effect at the cathode but it declines with increasing frequency. In a lamp doped with NaTlDy iodide the formation and movement of Dy ions are hampered by Na ions accumulated in front of the cathode due to cataphoresis. As a consequence the lowering of the power loss by Dy is in part diminished. It is shown that a gas-phase emitter effect of Dy is effective for standard operation conditions of lamps in spite of the counteracting effects of a long time constant of the emitter effect and Na accumulation.
A Contract Service Provider Model for Virtual Assets

With the recent rise in the cost of transactions on blockchain platforms, there is a need to explore other service models that may provide a more predictable cost to customers and end-users. We discuss the Contract Service Provider (CSP) model as a counterpart of the successful Internet Service Provider (ISP) model. Similar to the ISP business model based on peered routing-networks, the CSP business model is based on multiple CSP entities forming a CSP Community or group offering a contract service for specific types of virtual assets. We discuss the contract domain construct which encapsulates well-defined smart contract primitives, policies and contract-ledger. We offer a number of design principles borrowed from the design principles of the Internet architecture.