Miracle-dz/UniFAR-FTUs huggingface dataset:
From Supervised to Unsupervised Learning for Land Cover Analysis of Sentinel-2 Multispectral Images

Sentinel-2 provides a large volume of the multi-spectral multi-resolution dataset. Training deep convolutional architecture with such a dataset is still a challenging task in land cover classification due to the absence of ground truth. Also, the selection of appropriate deep architecture for handling the Sentinel-2 dataset is another challenging task. In this paper, we propose a convolutional neural network (CNN) architecture to extract the information from various combinations of bands in Sentinel-2 imagery. We use a loss function, proposed in an earlier work, to train our model in an unsupervised manner. Recent advances in deep learning allow for transferring knowledge from one data set to another. Thus, in our study, we aim at analyzing the “transfer learning” capabilities of our proposed network to land cover classification in Sentinel-2 images. Pre-trained weights of a deep neural architecture, which is trained with very high-resolution optical satellite imagery from Aviris is transferred to a network of almost similar architecture for processing Sentinel-2 data. We used Salinas, a publicly available hyper-spectral dataset, to train this architecture in a supervised fashion and, finally, use transfer learning and fine-tuning on the architecture handling Sentinel-2 data for clustering. Experiments show that bands of 60m resolution have a positive combining effect with bands of 20m resolution for segregating waterbody, urban settlement, and tree canopies distinctly.
Adaptive Secure Transmission Strategy for Industrial Wireless Edge-Enabled CPS

For real-time industrial wireless edge-enabled cyber-physical systems, this paper proposes an adaptive secure transmission mechanism under the large scale path loss to fight against randomly distributed illegal sensing devices. According to changes in the communication environment, the proposed scheme adaptively obtains the corresponding optimal power allocations and wiretap code rates. Specifically, by injecting artificial noise, an edge controller provides confidential control information for a legitimate wireless sensing device and achieves non-confidential data service for a service subscriber, simultaneously. Considering whether the secrecy and non-confidential service rate constraints are met, a dynamic secrecy transmission mechanism is introduced to minimize secrecy outage probability (SOP) based on random channel realizations. The optimal power allocation for minimizing the SOP is derived through the efficient alternating optimization algorithm with numerical methods. Numerical results show that the proposed adaptive secure transmission strategy can efficiently utilize transmit power to achieve non-confidential information traffic without compromising the secrecy performance.
Unsupervised Extraction of Workplace Rights and Duties from Collective Bargaining Agreements

This paper describes an unsupervised legal document parser which performs a decomposition of labor union contracts into discrete assignments of rights and duties among agents of interest. We use insights from deontic logic applied to modal categories and other linguistic patterns to generate topic-specific measures of relative legal authority. We illustrate the consistency and efficiency of the pipeline by applying it to a large corpus of 35K contracts and validating the resulting outputs.
A Combined Cluster‐Dynamic and Deterministic Description of Decomposition Kinetics in Binary Alloys with a Tendency for Clustering

A novel numerical model is devised to describe the decomposition kinetics in metastable binary alloys with a positive pair exchange parameter. The stochastic process of nucleation and growth of precipitates is simulated on grounds of a novel cluster-dynamical model, which for a description of the later stages is linked to a deterministic model. The discussion of the precipitation kinetics in a Cu-Co type alloy yields the interpretation of kinetic experiments in terms of classical theories to be inadequate. Eine neue numerische Beschreibung von Entmischungsreaktionen in metastabilen binaren Legierungen mit positiver Paaraustauschenergie wird vorgestellt. Hierin wird der stochastische Keimbildungsprozes und das Wachstum von Ausscheidungen im Rahmen eines neuen clusterdynamischen Modells simuliert, das zur Beschreibung spaterer Stadien mit einem deterministischen verbunden wird. Die Diskussion des zeitlichen Verlaufs einer Entmischungsreaktion in einer Cu-Co-ahnlichen Legierung zeigt, das entmischungskinetische Experimente nur unzulanglich im Rahmen klassischer Entmischungstheorien interpretiert werden konnen.
A Robustness Comparison of Measured Narrowband CSI vs RSSI for IoT Localization

Received Signal Strength Indicator (RSSI)-based fingerprinting is currently viewed as an important technique for the positioning capabilities in the Internet of Things (IoT). However, in the case of practical measurement, the localization methods based on RSSI are easily affected by the temporal and spatial variation, which contributes to most of the estimation errors in current systems. In this paper, the feasibility of utilizing the Channel State Information (CSI) for localization is studied, after knowing that the CSI contains information about the channel between the sender and receiver at the level of individual data subcarriers. Unlike most of the previous work, the intended approach is to use the entire subcarrier magnitudes without averaging or any reduction of the obtained narrowband CSI. Moreover, the frequency hopping in the LoRa systems should be a profit for localization by getting access to a wider band. In order to obtain a reliable basis for this approach, an outdoor measurement campaign is performed in the area of the Campus Beaulieu in Rennes to estimate the CSI of transmitted LoRa signals from different locations. For this, it is necessary for the individual channels from each different position to be appropriately different from one another to achieve significant localization gain. Hence, a comparison is done investigating the attainable evolution in the CSI at each location based on the CSI slope versus its average amplitude. In the given results, the feasibility of using the proposed technique is asserted by the drastic stability of the CSI slope over time and space, in contrary to the CSI average amplitude. This manifests the robustness of the CSI to the signal fluctuations and its more valuable rendering than the RSSI.
Learning Enabled Adaptive Multiple Attribute-based Physical Layer Authentication

In this paper, we propose an adaptive multi-attributes based physical layer authentication framework for enhanced authenticity provisioning. Instead of optimizing the "threshold" for a preset PHY-layer signature, this paper resort to exploiting and selecting multiple historical better performed PHY-layer attributes for authentication enhancement. In particular, the authenticator of the proposed scheme is designed to be capable of recording the historically performance of each potential attribute. Based on which, the most effective PHY-layer attributes (MEA) would be chosen to improve the reliability of the PHY-layer authentication. This paper experimentally proves that the dimension extension on PHY-layer signature attributes effectively enhances authenticator’s capability in signal discrimination. However, with more attribute to observe, it also complicates the predicting and authenticating procedure. Therefore, a learning-based search algorithm is then formulated to facilitate the MEA selection procedure. Both theoretical analysis and experiment results are given to demonstrate the efficiency and superiority of the proposed scheme.
Amphiphilic copolymer membranes promote NADH:Ubiquinone Oxidoreductase activity : towards an electron-transfer nanodevice

Nanoscale devices for energy conversion require the transfer of electrons from one compartment to another. The enzyme complex I, which in vivo mediates electron transfer from NADH to ubiquinone, is an intriguing candidate for this role in nanodevices. However, complex I normally requires the presence of lipids to remain active, potentially limiting its application. Here we demonstrate for the first time that complex I can be actively reconstituted in the synthetic membrane of amphiphilic triblock copolymer vesicles. The functionality of the reconstituted protein was characterized by EPR and activity assays. Its activity is strongly influenced by the molar mass and the block length of the membrane-forming polymers, and increases with increasing membrane thickness.
Automating Car Park Management With Blockchain And Computer Vision

Most current semi-automated parking lots are not able to cater for fully autonomous vehicles since traditional systems assume a human is driving the vehicle and making a payment physically. This empirical research aims to determine whether a system combining computer vision with blockchain technology, can be used to document parking occupancy in a transparent and efficient manner whilst facilitating payment autonomously. There are three main components to the proposed solution, which are: Identifying and Authorisation of vehicles; Occupancy Detection; and Payment handling. This study investigates the accuracy, scaling, and efficiency of all three components. Two parking lots, indoor/outdoor, and 5 different vehicles were used to simulate interactions that may occur whilst making use of a parking lot. Whilst noting that the identification and authorisation of vehicles is possible with an accuracy around 96%, the detection of occupancy and vacating of a parking space is also possible with an accuracy around 94%, we do not that the use of blockchain suffers from a volatile price index and limited transaction speeds. Analysis of the results showed that making use of a permission based blockchain, resulted in faster and cheaper transactions whilst sacrificing on decentralisation. The main components of the proposed solution are executed at the source, meaning a low-cost computer attached at each camera point is sufficient, resulting in a system which can scale upwards by design.
An Efficient Intrusion Detection Model Combined Bidirectional Gated Recurrent Units With Attention Mechanism

In recent years, various types of network attacks emerge in endlessly, the protection of network security has been paid more and more attention by our society. Network Intrusion Detection System (NIDS)is used to protect computer systems from malicious attacks and intrusions, thus has also become a hot research field. Due to the great success of deep learning in industry and academia, there is an increasing interest in the application of deep learning methods for feature representations and classification. In this paper,we propose a intrusion detection model based on time-related deep learning approach with attention mechanism. Firstly, we build a stacked sparse autoencoder(SSAE) to extract high-level feature representations of intrusion information. Then we design a two-layer bidirectional gated recurrent unit(BiGRU) network with attention mechanism to classify traffic data. We perform experiments on a benchmark dataset UNSW-NB15, the results in binary classification indicate that using high-dimensional sparse features extracted by SSAE can significantly accelerate the classification progress. Our model can detect network intrusions effectively and outperform other related methods with reduction of false alarm rate, high accuracy rate, reduced training and testing time.
Secure Transmission in Underlay D2D Communications Using Optimal Relay Selection

This paper investigates the physical layer security of inband underlay Device-to-Device (D2D) communication, where there is no direct link between D2D users. In this respect, optimal relay selection is utilized to secure the D2D transmission. The eavesdropper uses either maximal-ratio combining or selection combining to improve the wiretapped signals. The D2D secrecy performance analysis is performed regarding the secrecy out-age probability (SOP) and the probability of non-zero secrecy capacity, and closed-form expressions are provided. To validate the correctness of our analysis, numerical and simulation results are presented. Furthermore, the asymptotic analysis of the SOP is provided, where new insights into the system parameters are revealed.
Polynomial Expansion-Based MMSE Channel Estimation for Massive MIMO-GFDM Systems

In this paper, low-complexity channel estimators are proposed for massive multiple-input multiple-output generalized frequency division multiplexing (MIMO-GFDM) systems. In order to combat the effect of non-orthogonality in GFDM, interference-free pilots are used in frequency-domain minimum mean square error (MMSE) channel estimation. Polynomial expansion is used to approximately compute the matrix inverse in conventional MMSE estimation, consequently reducing the cubic computational complexity to square order. The degree of the matrix polynomial can be properly selected to get a required trade-off between complexity and estimation performance. Different weights can be assigned to the terms in the polynomial expansion and be optimized to achieve a minimal mean square error (MSE). Performance limits on the MSE of the proposed estimators are derived. The computational complexity of the proposed MMSE estimators is analyzed and the impacts of the polynomial degree and the pilot subcarrier spacing are also investigated. Numerical results show the accuracy of the proposed channel estimators.
Adaptive Filtering for (Soil Moisture) Data Assimilation

Data assimilation (DA) techniques provide the means to integrate observational information into dynamical models. The success of such methods requires accurate knowledge of model and observation uncertainties, which are seldom available. Adaptive DA techniques estimate these uncertainties as part of the system. In this study, we apply the recently developed Monte Carlo based adaptive Kalman Filter (MadKF) to assimilate SMOS brightness temperature (Tb) measurements into the Catchment Land Surface Model for soil moisture state updating. The MadKF yields robust Tb uncertainty estimates and significant skill improvements relative to model-only soil moisture estimates, when evaluated with in situ measurements.
DNN-based Direction Finding by Time Modulation

In this paper, novel direction finding method based on deep neural network (DNN) and time modulation is proposed for resolution improvement. The proposed method has fast estimation time and simple system compared to the conventional method. The resolution improvement by the time modulation and DNN is three times higher than the previously studied method. In addition to, through an experiment, the proposed method has been validated to have higher resolution and similar accuracy.
Transparent Circularly-Polarized Antenna with Tilted Beam for Vehicular Platforms

In this paper, a transparent microstrip CPW-fed antenna which could be used in Global Navigation Satellite Systems (GNSS) bands has been proposed. The antenna is designed on a glass substrate which can be mounted on the windshield of a car. The beam of the antenna is tilted for 30 degrees as lots of cars' windshields are inclined around 30 degrees. This design has 59% 10-dB return loss bandwidth (0.97-1.78 GHz) and 3-dB axial ratio of 12% (1.14-1.3 GHz) and 14% (1.43-1.65 GHz) over lower and upper frequency bands of interest, respectively. The obtained optical transparency of the proposed antenna is 88%.
Improving ML Detection of IoT Botnets using Comprehensive Data and Feature Sets

In recent times, the world has seen a tremendous increase in the number of attacks on IoT devices. A majority of these attacks have been botnet attacks, where an army of compromised IoT devices is used to launch DDoS attacks on targeted systems. In this paper, we study how the choice of a dataset and the extracted features determine the performance of a Machine Learning model, given the task of classifying Linux Binaries (ELFs) as being benign or malicious. Our work focuses on Linux systems since embedded Linux is the more popular choice for building today’s IoT devices and systems. We propose using 4 different types of files as the dataset for any ML model. These include system files, IoT application files, IoT botnet files and general malware files. Further, we propose using static, dynamic as well as network features to do the classification task. We show that existing methods leave out one or the other features, or file types and hence, our model outperforms them in terms of accuracy in detecting these files. While enhancing the dataset adds to the robustness of a model, utilizing all 3 types of features decreases the false positive and false negative rates non-trivially. We employ an exhaustive scenario based method for evaluating a ML model and show the importance of including each of the proposed files in a dataset. We also analyze the features and try to explain their importance for a model, using observed trends in different benign and malicious files. We perform feature extraction using the open source Limon sandbox, which prior to this work has been tested only on Ubuntu 14. We installed and configured it for Ubuntu 18, the documentation of which has been shared on Github.
Decision Problems for Joint Transmission in Multi-AP Coordination Framework of IEEE 802.11be

For the new joint transmission paradigm being discussed under the IEEE 802.11be standardization efforts, we report the findings of our attempt at addressing the possible gains, and strategies, of using such a scheme. We look at the best possible rate achievable under the Joint Transmission approach, and then use these findings to address the problem of performing joint scheduling in a common practical deployment scenario.
DR-KNN: A Hybrid Approach for Dimensionality Reduction of EO Image Datasets

The two Sentinel-2 satellites provide, since March 2017, high-resolution worldwide images every five days, freely distributed, generating terabytes of high-dimensional data. An intuitive manner to summarize the main characteristics of the data and gather knowledge is visual exploratory analysis, which is often based on dimensionality reduction methods to represent high-dimensional data. From previous research and the state-of-the-art literature, turned out that t-distributed Stochastic Neighbour Embedding is one of the most appropriate technique to reduce the dimensionality of a dataset, but it requires very high computational power. To overcome this inconvenience, we proposed two hybrid DR algorithms, which combine the DR with the nearest neighbour technique or random forest regression. The main conclusion is that our approaches reduce computational power without compromising the representation quality.
Local Privacy-Preserving Dynamic Worker Locations in Spatial Crowdsourcing

An outsourcing service named spatial crowdsourcing (SC) becomes popular, whereby the SC-server allocates nearby tasks to the workers based on the outsourced task and worker locations. Exposing real locations can cause serious privacy leakage. However, traditional differential privacy (DP) and encryption methods do not consider the dynamic worker location and correlation privacy. Here, a Local DP-based dynamic worker location protection (LDPDW) scheme is proposed to achieve high-quality task allocation and locally protect the correlation and location privacy of dynamic workers. Specifically, LDPDW generates noisy high correlated graph classes and obfuscates the worker locations in a static case by adopting a LDP-based correlation graph (LDPCG) algorithm and distance score-based LDP (DSLDP) algorithm, thereby achieving controlled noise addition and ensuring the correlation and location privacy. To support the privacy-preserving dynamic locations, a dynamic correlation graph-based location obfuscation (DCGLO) algorithm is proposed to allocate reasonable privacy budget $\epsilon $ , which ensures the data utility. Finally, a linear acceptance model-based task allocation (LAMTA) algorithm is used to allocate tasks to the workers with high acceptance rates. Privacy analysis and the extensive experimental results show that our LDPDW scheme follows $\epsilon $ -LDP while allocating tasks with high data utility.
Deep Adaptive Proposal Network in Optical Remote Sensing Images Objective Detection

It is difficult to distinguish complicated distribution characteristics of objects, which limits the performance of two-stage detectors in the field of optical remote sensing images object detection. In this paper, we propose a deep adaptive proposal network (DAPNet), which is a new category prior network (CPN) on the basis of the existing faster region convolutional neural network (Faster RCNN) architecture. The adaptive candidate boxes for each image is obtained by combining the candidate regions and the object number, which are generated by the fine-region proposal network (F-RPN) and the CPN respectively. These adaptive candidate boxes can satisfy the detection tasks in sparse and dense scenes. A set of experimental results verify the superiority of the proposed approach.
Multi-user MIMO using ZF-based Multiplexing Coordinated with User-wise Spatial Diversity

In this paper, a multi-user MIMO using zeroforcing (ZF)-based multiplexing coordinated with user-wise spatial diversity is proposed assuming time-division duplex (TDD). The downlink and uplink signal-to-noise ratios (SNRs) are analyzed for the given multi-user MIMO channel. It is shown that the downlink capacity is the same for all users while the uplink capacity is not necessarily the same. The closed-form expressions for the downlink and uplink SNRs conditioned on the 2-user virtual MIMO channel are derived. It is shown that the uplink sum capacity is higher than the downlink sum capacity. The downlink and uplink capacities are numerically evaluated by Monte Carlo simulation in a Rayleigh fading environment. It is shown that the link capacity can be improved by increasing the number of user antennas while keeping the ZF matrix size in the same level.
A Novel Gain Control Method Based On Extremum Envelope For High Speed Array GPR

To find out the area that road collapse may occur, the ground penetrating radar (GPR) is utilized to detect the underground structure. And a high speed array impulse GPR mounted on a high-speed vehicles is specially designed for the detection in the city. The gain control method is an important step to make the useful signal more visible in the data processing. To process the cross-section of three-dimension data collected by the high speed array impulse GPR, two kinds of methods based on the envelope of extremum are proposed in this paper. Compared with the conventional method, the first kind of extremum envelope method has a good performance in compensating the signal attenuation but it enhances the interfering signals as well. So another improved extremum envelope method is introduced to keep a balance. It is proved that the second kind of extremum envelope method is appropriate for the high speed array impulse GPR system.
Achievement of the Highest Performance of Disk MHD Generator: Isentropic Efficiency of 60 % and Enthalpy Extraction Ratio of 30 %

CCMHD power generation experiments using a shock-tube driven disk generator have been conducted to achieve high performance. Despite of a small-scale channel having a generating volume of 4.1 liter, outstanding performance: an isentropic efficiency of 63 %, an enthalpy extraction ratio of 30.8 % are achieved at a stagnation temperature of 2250 K and a stagnation pressure of 0.14 MPa, where an applied magnetic flux density is 3.0 T. Furthermore, a maximum electrical power output of 1.23 MW and a maximum power density of 297 MW/m 3 are obtained. The following facts contribute to a world’s highest performance of CCMHD power generation, a small area ratio of the disk channel; an employment of a cesium seeded helium gas: an introduction of an inlet swirl; and a production of homogeneous plasma.