Miracle-dz/UniFAR-FTUs huggingface dataset:
Vulnerability Analysis of Cyber-physical System Based on Improved Structural Entropy

With the continuous integration of the power grid and the information network, the vulnerability analysis of cyber-physical system considering the interaction between networks become more important. Based on the complex network theory, a topology model of power cyber-physical network is established, and improved structural entropy is proposed, which considering node differences, edge differences, and node dependency differences. Taking the Hunan Electric Power Cyber-Physical System as an example, the simulation results show that the improved structural entropy can identify important nodes in the power grid. the dependent network is more vulnerable than a single network, nodes with high-degree and high-betweenness play a key role in maintaining the operation of the system, and the robustness of system will also be reduced when a few nodes with low-degree and low-betweenness fail.
Conventional and Metaheuristic Optimization Algorithms for Solving Short Term Hydrothermal Scheduling Problem: A Review

Short term hydrothermal scheduling (STHTS) is a non-linear, multi-modal and very complex constrained optimization problem which has been solved using several conventional and modern metaheuristic optimization algorithms. A number of research articles have been published addressing STHTS using different techniques. This article presents a comprehensive review of research published for solving the STHTS problem in the last four decades.
Integration of Sentinel 1 and 2 Observations for Mapping Early and Late Sowing of Soybean and Cotton Crop Using Deep Learning

The main objective of this paper is mapping of Soybean and Cotton crop area using the integration of Sentinel-1 and 2 observations and Deep Neural Networks (DNN). The present research also attempted the identification of early and late sowing of Soybean and Cotton using time-series of Normalized Difference Vegetation Index (NDVI) and VH backscatter. The study was carried out in Wardha district of Maharashtra, India during Kharif 2019. The Sentinel-1 observations available during 15 Jun. to 30 Nov. 2019 and Sentinel-2 maximum NDVI and Normalized Difference Water Index (NDWI) composites during Aug. to Sept. and Oct. to Nov. 2019 were used for Cotton and Soybean area mapping. We evaluated the performance of tuned Random Forest (RF) and DNN for classification of Soybean and Cotton. Results showed that DNN performs better with an overall accuracy of 89.15% and the F-score of 0.856. Further, identification of early and late sown Soybean and Cotton was performed using short time-series of VH backscatter and NDVI. Local minima along with subsequent increase/decrease based approach was developed for the separation of early and late sown crop. Visual comparison with geo-tagged crop growth stage images shows good agreement between estimated and actual sowing time window.
Giant Magnetoresistance in Bulk Samples of La1-xAxMnO3 (A = Sr or Ca)

Magnetoresistance measurements have been carried out on bulk samples of several members of the $La_{1-x}A_xMnO_3$ family (A = Sr and Ca) with varying x or $Mn^{4+}$ content. The magnitude of magnetoresistance (MR) is highest at the insulator-metal (I-M) transition when the transition temperature is relatively low $(\leq 250 K)$, The relative MR (%MR) is essentially the same (40-50%) at 4.2 K for all the compositions showing the I-M transition, namely $\sim 15\%$ at $T \geq 300 K$. Insulating compositions (small or large x) show smaller %MR than the compositions that undergo the I-M transition.
Coexistence of LTE-Unlicensed and WiFi: A Reinforcement Learning Framework

The proposal of LTE-unlicensed, whereby LTE operators are allowed to transmit in the unlicensed WiFi spectrum, is seen as a promising solution to meet the increasing demand for mobile data. Implementing LTE-unlicensed however requires novel techniques to enable LTE to coexist with the already existing WiFi technology. In this work we propose a detailed queueing theory based framework to model the problem of achieving coexistence between the LTE and WiFi technologies using a time-division duplexing method referred to as CSAT (Carrier Sense Adaptive Transmission). Then, using techniques from Markov decision processes and Q-learning we propose an online algorithm that can enable LTE and WiFi to share the unlicensed spectrum in an efficient manner. Finally, we conduct an extensive simulation study to demonstrate the efficacy of the proposed solution.
In Situ Characterization of Self-Assembled Butanethiol Monolayers on Au(100) Electrodes

We have studied butanethiol self-assembled monolayers on Au(100) using cyclic voltammetry and in situ scanning tunneling microscopy (STM). The butanethiol adlayer shows ordered domains with a striped structure, the stripes running parallel to the main crystallographic axes of the substrate. After modification the surface reveals a 50% coverage of monoatomic high gold islands, but no vacancy islands were observed. Reductive and oxidative desorption of the film, previously studied by electrochemistry, were monitored by STM.
Transmit Power Minimization in Bidirectional Tag-to-Device Communications for IoT

This paper explores a bidirectional communication scenario between a backscatter tag and an energy harvesting user in the presence of a multi-antenna energy transmitter. This hybrid user scenario provides the flexibility of optimizing our performance metric as the two techniques complement each other in achieving the desired objective. We measure the performance of the system in terms of the transmit power with an aim of minimizing it. This objective is achieved by solving a minimization problem over a joint set of constraints in energy transmit time, transmit power of the energy source, and a beamforming vector. Further, we analyze a special case where the source node is equipped with only a single antenna and leverage its analysis to achieve our overall aim, that is, the generalized case with a multi-antenna source node. Finally, the numerical results demonstrate a consensus with the analysis carried out for both the aforementioned cases.
Effect of Phosphate Precursor on the Production of Hydroxyapatite from Crocodile Eggshells

The hydrothermal reaction at 250°C between crocodile eggshells and three phosphate precursors, (NH4)2HPO4, Ca3(PO4)2 and H3PO4 were studied. Only (NH4)2HPO4 and Ca3(PO4)2 precursors gave monophasic hydroxyapatite within 25 and 8 h, respectively. Pure hydroxyapatite could not be produced when using H3PO4. The characteristic vibration for PO43- are observed at 566-603, 1043-1093, 962-963 and 466-474 cm-1 and for OH- group at 3570-3572 and 633-634 cm-1. The morphology of the prepared hydroxyapatite is agglomerated plate-like crystals. The Ca/P ratio by ICP method is 1.62-1.64.
Nuclei Cell Semantic Segmentation Using Deep Learning Unet

Semantic Segmentation has benefited a lot from the advancement of Deep Learning technologies. Image Segmentation has been a boon for medical field in both application as well as research areas where identification and localization of infected/abnormal parts of the organs can be extracted. Study of cell images from infected organs with the help of high definition medical images taken from MRI, CT, etc to provide treatment as well as make it possible for researchers to develop vaccines for such diseases. This study is on to train a network model tuned using different hyper-parameters so that the network learns to automatically segment nuclei from the medical images available from standard database library. Segmentation is the key problem before the any other type of problems can be encountered like that of nuclei detection and classification. This study also looks at the performance of the network build through evaluation matrix.
Neuromorphic optical sensor chip with color change-intensity change disambiguation

In this paper, we describe the development of a novel, retina-like neuromorphic chip that has an array of two types of retina 'cells' arranged to mimic the fovea structure in certain animals. One of the two retina cell types performs irradiance detection and the other can perform color detection. Together, via the two parallel pathways the retina chip can perform color change intensity change disambiguation (CCICD). The irradiance detection cell has a wide-dynamic detection range that spans almost 3 orders of magnitude. The color detection cell has a buried double junction (BDJ) photodiode as the photoreceptor followed by two parallel logarithmic I-V convertors. The output from this is a color response which has at least a 50nm resolution for wavelengths from 400nm to 900nm. With these two cells, the array can perform color change -intensity change disambiguation (CCICD) to determine if a change in the output of the irradiance pathway is because of irradiance change, color change, or both. This biological retina-like neuromorphic sensor array is implemented in ON-SEMI 0. 5μm technology, a standard CMOS fabrication process available at MOSIS.
Spatial-temporal prediction of vegetation index with a convolutional GRU network

Normalized difference vegetation index (NDVI) is a key parameter in land use/cover change and terrestrial modelling studies. With the accumulation of satellite records in the past few decades, the spatial-temporal prediction of vegetation index becomes feasible. In this paper, we established a convolutional GRU network (ConvGRU) to predict the short-term vegetation index considering the spatial and temporal patterns in the NDVI records. The predictive performance of the proposed method is evaluated for several vegetation types in different regions globally. The results demonstrate that the proposed model has sufficient ability in predicting satellite recorded NDVI. The validation on the MODIS NDVI datasets achieved an average RMSE around 0.06. And, the model performs better in regions of Australia while worse in North Europe.
Deep Networks Under Block-Level Supervision for Pixel-Level Cloud Detection in Multi-Spectral Satellite Imagery

Cloud cover hinders the usability of optical remote sensing imagery. Existing cloud detection methods either require hand-crafted features or utilize deep networks. Generally, deep networks perform better than hand-crafted features. However, deep networks for cloud detection need massive and expensive pixel-level annotation labels. To alleviate that, this paper proposes a weakly supervised deep learning-based cloud detection method using only block-level labels, with a new global convolutional pooling operation and a local pooling pruning strategy to improve the performance. For evaluating, we collect a training dataset containing over 160,000 image blocks with block-level labels and a testing dataset including ten large image scenes with pixel-level labels. Even under extremely weak supervision, our method performed well with the average overall accuracy reached 97.2 %. Experiments demonstrate that our proposed method obviously outperforms the state-of-the-art methods.
Reinforcement Learning-Based Routing Protocols for Vehicular Ad Hoc Networks: A Comparative Survey

Vehicular-ad hoc networks (VANETs) hold great importance because of their potentials in road safety improvement, traffic monitoring, and in-vehicle infotainment services. Due to high mobility, sparse connectivity, road-side obstacles, and shortage of roadside units, the links between the vehicles are subject to frequent disconnections; consequently, routing is crucial. Recently, to achieve more efficient routing, reinforcement learning (RL)-based routing algorithms have been investigated. RL represents a class of artificial intelligence that implements a learning procedure based on previous experiences and provides a better solution for future operations. RL algorithms are more favorable than other optimization techniques owing to their modest usage of memory and computational resources. Because a VANET deals with passenger safety, any kind of flaw is intolerable in VANET routing. Fortunately, RL-based algorithms have the potentials to optimize the different quality-of-service parameters of VANET routing such as bandwidth, end-to-end delay, throughput, control overhead, and packet delivery ratio. However, to the best of the authors’ knowledge, surveys on RL-based routing protocols for VANETs have not been conducted. To fulfill this gap in the literature and to provide future research directions, it is necessary to aggregate the scattered works on this topic. This study presents a comparative investigation of RL-based routing protocols, by considering their working procedure, advantages, disadvantages, and applications. They are qualitatively compared in terms of key features, characteristics, optimization criteria, performance evaluation techniques, and implemented RL techniques. Lastly, open issues and research challenges are discussed to make RL-based VANET routing protocols more efficient in the future.
Trust in Social Networks: A Computational Model Based on the Trust Features of Language Use

From the perspective of utterance and calculation, this paper mainly studied the establishment for the trust of nodes in social networks. First, on the basis of listing the five features, the principle of utterance use for trust establishment is proposed. Based on this, we build the calculation model of trust in social networks. The calculation model is mainly composed of three parts: feature score, relation score and post score, which involves three aspects: the nature of the node itself, the relationship between the node and neighbor nodes, and the works published by the node, respectively. Based on this model, the influence of each node in social networks can be calculated better.
Technology Developments for an Advanced L-Band Radiometer Mission

ESA's Soil Moisture and Ocean Salinity (SMOS) mission was launched 2 Nov 2009 and, to date, is still in good health, providing valuable L-band observations of the Earth surface [1]. A number of products are obtained from these, including thin sea ice [2], frost/thaw soils [3], high winds [4], ocean surface wind [5] and Sun brightness temperature [6], besides the main mission measurements of soil moisture and sea surface salinity [7] [8]. This paper deals with the description and early results of some technology activities conducted by ESA applying the lessons learnt by SMOS and in preparation of an advanced L-band radiometer mission.
Underwater Calibration in Near Real Time: Focus on Detection Optimized by AI and Selection of Calibration Patterns

The 3D reconstruction of underwater scenes from pairs of stereoscopic images requires to model the sensor. To take into account the refraction in the environment and to be compatible with the operational field constraints, we use the compensated pinhole model (the diopter is considered as an additional lens) whose parameters are estimated by an in situ calibration with a pattern. In this article, we propose an optimization by AI of the pattern detection to have real-time feedback and a process which selects on the fly the shots allowing to improve the estimation quality of the model so the manipulation can be stopped when sufficient number of relevant images has been reached. We present the results obtained on a database made up of 60,000 images taken in swimming pools and at sea.
Machine Learning to Improve Numerical Weather Forecasting

This paper presents a brief overview of trends in numerical weather prediction, difficulties, and the nature of their occurrence, the existing and promising ways to overcome them. The neural network architecture is proposed as a promising approach to increase the accuracy of the 2m temperature forecast given by the COSMO regional model. This architecture allows predicting errors of the atmospheric model forecasts with their further corrections. Experiments are conducted with different histories of regional model errors. The number of epochs after which network overfitting happens is determined. It is shown that the proposed architecture makes it possible to achieve an improvement of a 2m temperature forecast in approximately 50% of cases.
Classification of Hyperspectral Images based on Intrinsic Image Decomposition and Deep Convolutional Neural Network

In this paper, a new simple spatial-spectral method is proposed to classify hyperspectral images. It is based on the combination of a deep convolutional neural network (DCNN) and intrinsic image decomposition (IID). First, the dimensionality of the hyperspectral image is reduced based on a band grouping technique and mean operator. Afterward, albedo and shading components of these reduced features are recovered. Finally, stacked albedo and shading components are classified by DCNN. Experiments are applied to Pavia University's hyperspectral image from an urban area. Classification accuracy of the proposed method with only 1% of training data can reach about 99%, which is prominent according to state-of-the-art methods.
Non-orthogonal Multiple Access assisted Mobile Edge Computing via Device-to-Device Communications

Mobile edge computing (MEC) has been considered as a promising approach for enabling computation-intensive Internet services in future wireless systems. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted MEC, in which edge-computing users (EUs) adopt NOMA to simultaneously offload part of their computation-workloads to the edge-server (ES). To improve the spectrum-efficiency, we consider a paradigm of underlaying device-to-device (D2D) communications, namely, the EUs reuse a cellular user's (CU's) licensed channel for offloading transmission. We firstly characterize the transmit-powers of EUs and CU in this D2D approach, and then formulate a joint optimization of the EUs' computation- workloads offloading and the ES's computation-resource allocation, with the objective of minimizing the latency in completing the EUs' tasks. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose an efficient algorithm for computing the optimal solution. Numerical results are provided to validate the effectiveness and efficiency of our proposed NOMA assisted MEC via the D2D sharing 1.
Joint Optimization of CycleGAN and CNN Classifier for COVID-19 Detection and Biomarker Localization

The COVID-19 has caused mass casualties due to its rapid spread and lack of vaccines. Pathological lesions in lungs caused by COVID-19 is the main symptom which are easier to be detected than COVID-19 test kits. Therefore, the automatic COVID-19 detection methods based on medical images are essential for timely treatment. Considering that manual diagnosis and analysis of chest medical images require a large number of qualified experts, deep learning can effectively diagnosis and locate critical biomarkers. In this paper, we present a novel model by jointly optimizing the cycle generative adversarial network (CycleGAN) and the convolutional neural network (CNN) to detect COVID-19 and localize lesion areas with limited training data. The CycleGAN with cycle consistency can generate more realistic and reliable images. The CNN classifier can further help the generator to focus on lesion areas, and obtain the prediction results. The discriminator and the classifier can assist the generator to achieve accurate and reliable generation tasks. The discriminator and the generator achieve a local optimal solution in an adversarial manner, and the generator and the classifier are in a cooperative manner to distinguish the domain of input images. Our model can obtain an accuracy of 95.9% on chest CT dataset and an average accuracy of 87.6% on chest X-ray dataset. With clear localization for lesion areas, the competitive results reveal great potentials of the joint optimization network.
Remote Sensing Images Feature Learning based On Multi-Branch Networks

Remote sensing (RS) images feature learning, plays a crucial role in many RS images application, and attracts scholars' attention. However, since RS images contain complex contents, how to extract robust features that can fully represent RS images becomes an important and tough task. In this paper, we develop a feature learning method based on multi-branch networks, named M-Net, which consists of fine-grained branch and coarse branch. Considering the objects within RS images are diverse in type and resolution, the fine-grained branch is developed to capture rich object-level information. First, the RS images convolutional features are extracted by fine-grained branch. Second, through encoding the score maps which can highlight the important regions, the fine-grained structure mapping are obtained. Finally, the object-level features are generated by transforming the convolutional features through mapping. The coarse branch is developed to transform the obtained object-level features into global structure for representing images. The positive experimental results counted on RS benchmark data set demonstrate that the proposed M-Net can learn more powerful features.
Intelligent Edge Learning for Personalized Crowdsourced Livecast: Challenges, Opportunities, and Solutions

Recent years have witnessed the expeditious development of crowdsourced livecast, also referred to as crowdcast, breeding such industry upstarts as Youtube Live, Twitch Tv, Mixer, Douyu, and so on. Unlike traditional TV-based livecast that provides uniform services, in crowd-cast, service providers are racking their brains to satisfy each viewer's personalized QoE demands since different viewers usually have quite diverse preferences. To achieve this goal, the key challenges lie in the accurate prediction of viewers' personalized QoE preferences and the cost-ef-fective viewer serving at the network edge. We argue that the confluence of edge computing and recent advance in deep learning shed light on accommodating these challenges, enabling more intelligent personalized QoE provision. In this article, we first provide an overview of today's crowdcast solutions, highlighting the challenges and opportunities therein from uniform crowdcast to personalized crowdcast with a large-scale measurement. We then present an intelligent edge learning framework ELCast as a case study, which leverages convolutional neural network and deep reinforcement learning in an edge computing architecture for personalized crowdcast. Trace-driven experiments demonstrate the superiority of ELCast over state-of-the-art approaches.
Machine Learning-Based Threat Identification of Industrial Internet

In order to improve production and management efficiency, traditional industrial control systems are gradually connected to the Internet, and more likely to use advanced modern information technologies, such as cloud computing, big data technology, and artificial intelligence. Industrial control system is widely used in national key infrastructure. Meanwhile, a variety of attack threats and risks follow, and once the industrial control network suffers maliciously attack, the loss caused is immeasurable. In order to improve the security and stability of the industrial Internet, this paper studies the industrial control network traffic threat identification technology based on machine learning methods, including GK-SVDD, RNN and KPCA reconstruction error algorithm, and proposes a heuristic method for selecting Gaussian kernel width parameter in GK-SVDD to accelerate real-time threat detection in industrial control environments. Experiments were conducted on two public industrial control network traffic datasets. Compared with the existing methods, these methods can obtain faster detection efficiency and better threat identification performance.