Miracle-dz/UniFAR-FTUs huggingface dataset:
An ISO-Compliant Test Procedure for Technical Risk Analyses of IoT Systems Based on STRIDE

The rising number of IoT systems deployed in production increases the risk of becoming a victim of costly attacks. The complex architecture of such systems increases their attack surface making the systems more vulnerable by attacks that can even paralyze production. For 2020, it is estimated that more than 30 billion IoT devices are in use worldwide; of which more than 5.8 billion in the industrial sector. It is therefore imperative today to have a threat modeling tool that examines all system components and assigns them a risk of being exposed. This paper proposes a novel ISO-compliant test procedure for the technical risk analysis of IoT systems since state-of-the-art methods are either not ISO-compliant or are not suitable for technical risk analyses. It is based on the generic threat model Microsoft STRIDE, which has been developed to consider and evaluate all system components. The test procedure is able to identify vulnerabilities and assigns a risk to them. It complies with ISO/IEC 27001 and implies ISO/IEC 27005 and ISO 31000. The threats are stored in a threat database and linked to countermeasures. The designed tool implementing the novel test procedure automatically proposes countermeasures for certain attacks to the user. The tool uses a learning database in which new insights can be entered during each risk analysis so that the tool is improved with each use. As proof of concept, an IoT system is analyzed for its risks and the high achievable accuracy of the proposed risk assessment is shown.
Field measurements of filtration and respiration rates in mytilus edulis L. an assessment of methods

Abstract Two methods for field measurements of filtration and respiration rates in the bivalve Mytilus edulis L. are described and compared. The methods differ only with regard to degree of physical manipulation of the mussels. Measurements of filtration and respiration rates were performed in situ via SCUBA-diving and simultaneously on the shore on collected mussels. Collection and transport of mussels prior to field measurements is shown to be a stress factor, which significantly reduce filtration rate while respiration is not affected. The biological and methodological implications are discussed in relation to field measurements in general.
Human Mesenchymal Stromal Cell Secretome Promotes the Immunoregulatory Phenotype and Phagocytosis Activity in Human Macrophages

Human mesenchymal stromal/stem cells (hMSCs) show great promise in cell therapy due to their immunomodulatory properties. The overall immunomodulatory response of hMSCs resembles the resolution of inflammation, in which lipid mediators and regulatory macrophages (Mregs) play key roles. We investigated the effect of hMSC cell-cell contact and secretome on macrophages polarized and activated toward Mreg phenotype. Moreover, we studied the effect of supplemented polyunsaturated fatty acids (PUFAs): docosahexaenoic acid (DHA) and arachidonic acid, the precursors of lipid mediators, on hMSC immunomodulation. Our results show that unlike hMSC cell-cell contact, the hMSC secretome markedly increased the CD206 expression in both Mreg-polarized and Mreg-activated macrophages. Moreover, the secretome enhanced the expression of programmed death-ligand 1 on Mreg-polarized macrophages and Mer receptor tyrosine kinase on Mreg-activated macrophages. Remarkably, these changes were translated into improved Candida albicans phagocytosis activity of macrophages. Taken together, these results demonstrate that the hMSC secretome promotes the immunoregulatory and proresolving phenotype of Mregs. Intriguingly, DHA supplementation to hMSCs resulted in a more potentiated immunomodulation with increased CD163 expression and decreased gene expression of matrix metalloproteinase 2 in Mreg-polarized macrophages. These findings highlight the potential of PUFA supplementations as an easy and safe method to improve the hMSC therapeutic potential.
Reinventing Cloth Masks in the Face of Pandemics

Because asymptomatic carriers of COVID‐19 produce respiratory droplets that can remain suspended in air for several hours, social distancing may not be a reliable physical barrier to transmission. During the COVID‐19 pandemic, however, some governments were reluctant to mandate public mask use out of concern this would worsen shortages of respirators for healthcare workers. Cloth masks with a filtering effectiveness of 70–90% can be made from widely available materials, and are a better option than respirators for the public. Countries could rapidly implement Effective Fiber Mask Programs (EFMPs) to use local resources to mass produce effective and affordable cloth masks, and to engage the public in their correct use. EFMPs could be a cost‐effective measure to ease isolation while limiting new infections during pandemics. EFMPs could also protect healthcare workers by increasing the supply of respirators for their use, reducing their risk of acquiring the illness from their communities, and by reducing the number of patients they must treat.
A novel framework for autoregressive features selection and stacked ensemble learning for aggregated electricity demand prediction of neighborhoods

Demand forecast plays an important role in the power industry, as it sets the basis for decision making in power system operation and planning. Electricity consumption forecasting of individual buildings has been widely used for energy management, planning and energy-saving potential identification in the past decade. Yet, insignificant focus has been put on aggregated demand forecast of neighborhoods. In the context of the future smart grid, short- and long-term demand forecast on a neighborhood level will be an essential task for utility providers to better plan generation and solve congestion problems of the distribution network. Based on a comprehensive literature study, an ensemble learning method is proposed for predicting short- and long-term electricity demand of a campus located in the Netherlands. The ensemble model performed better in demand forecasting of neighborhoods compared to individual models for an hour ahead, day ahead and year ahead with R2 values of 0.988, 0.951 and 0.943 respectively. Assessing the demand for cluster of buildings with distinct boundaries such as hospitals and campuses at an aggregated level would reduce the amount of data needed to be stored. The proposed technique contributes to short (single step) and long term (multi step) energy self-sufficiency planning and energy balancing systems on a neighborhood scale.
Remote health monitoring system of elderly based on Fog to Cloud (F2C) computing

The majority of older persons are challenged by chronic illnesses, so more innovations are needed for geriatric care. Therefore, technological and modern techniques have been adopted to improve the elderly's health. Different computing solutions have been deployed to store and process the health data such as cloud computing which provides powerful computing resources. However, connecting different kind of things directly to the cloud is inefficient. Fortunately, fog computing has emerged as a new computing solution to complement cloud, it allows computation and data storage closer to the IoT devices. However, managing health data stored in fog computing presents a major issue. It is necessary to consider the importance of performance, availability, storage, and privacy of data in fog computing. In this paper, we propose architecture based on Fog to Cloud computing which is a novel solution and innovative approach that contribute to enhance the synergy between the cloud and fog computing to facilitate the management of elderly health data. To analyze the performance of our model in Fog to Cloud environment, FogWorkflowSim toolkit has been used.
Cryptanalysis of “Certificateless remote data integrity checking using lattices in cloud storage”

C. Sasikala et al.’s proposed a certificateless RDIC protocol to audit the outsourced data without the requirement of PKI infrastructure and against quantum computer attacks. They argue the RDIC is correct and possesses desired security properties. However, through cryptoanalysis technique we find that their scheme is neither correct nor secure against two common attacks, that is, signature forgery attack from the malicious cloud service provider (CSP) and public key replacement attack from the curious private key generator (PKG) and other malicious users. Furthermore, we remark that their RDIC is not a rigorous certificateless protocol.
MECPerf: An Application-Level Tool for Estimating the Network Performance in Edge Computing Environments

Edge computing is an emerging architecture in 5G networks where computing power is provided at the edge of the fixed network, to be as close as possible to the end users. Computation offloading, better communication latency, and reduction of traffic in the core network are just some of the possible benefits. However, the Quality of Experience (QoE) depends significantly on the network performance of the user device towards the edge server vs. cloud server, which is not known a priori and may generally change very fast, especially in heterogeneous, dense, and mobile deployments. Building on the emergence of standard interfaces for the installation and operation of thirdparty edge applications in a mobile network, such as the MultiAccess Edge Computing (MEC) under standardization at the European Telecommunications Standards Institute (ETSI), we propose MECPerf, a tool for user-driven network performance measurements. Bandwidth and latency on different network segments are measured and stored in a central repository, from where they can be analyzed, e.g., by application and service providers without access to the underlying network management services, for run-time resource optimization.
Injection Locking in Switching Power Amplifiers

In this work, injection locking in switching power amplifiers (PAs) is studied. Traditionally, injection-locked PAs (ILPAs) have supported phase modulation, with injection locking used primarily to improve the power-added efficiency by reducing the power required to drive the ILPA. Since the output power in conventional ILPA architectures is mainly contributed by the locked oscillator in an ILPA, the amplitude modulation is difficult to achieve unless supply modulation is employed. In the ILPA presented in this work, it is shown that by injection locking a switching PA and a power oscillator, improvement in both power-added efficiency and drain efficiency can be achieved as compared to just a switching PA. Moreover, amplitude modulation at a fixed supply voltage is achieved using an RF-DAC approach to scale both the switching PA and the locked oscillator. This approach employs variable injection-strengths varying from ≤1 (weak injection locking) to >1 (strong injection locking) to achieve the required power back-off. Accordingly, new formulations are introduced to extend the existing injection locking theory for injection strengths >1 case in ILPAs. Benefits of a larger injection strength on lock-range, maximum allowable symbol rate, AM-PM distortion and phase noise performance for an ILPA is provided. An ILPA is designed to support 64-QAM and implemented in a standard 65-nm bulk CMOS process. A peak drain efficiency of 42.7% and power-added efficiency of 40% is measured at the highest output power of 23 dBm, operating from a 1.45 V PA power supply at 2.5 GHz. Modulation tests with 5/50 MSym/s 64-QAM signals achieve the measured RMS EVM of 1.9%/3.1% with the average output power, drain efficiency and power-added efficiency of 18.1 dBm, 27.9% and 25.8% at 2.5 GHz, respectively.
Nonlinear Dynamics of Optical Frequency Combs Generated by Gain-Switched Semiconductor Lasers Subject to Optical Injection

We report on an experimental study of the nonlinear dynamics appearing when an optical frequency comb (OFC) generated by a gain-switched semiconductor laser is subject to optical injection. OFCs with a frequency spacing of $f_{\mathrm{m}}=5$ GHz were generated at two values of the modulation amplitude. For low injected power two combs with the same frequency spacing are observed, one due to the gain-switched laser and the other due to the modulated injected field. When the injected power is increased for a fixed value of the frequency detuning, a rich variety of nonlinear dynamics was found. Irregular behaviour, limit cycles or locked combs with a frequency spacing of $f_{\mathrm{m}}/N$, with $N$ a positive integer up to 14 are obtained. A much more complex dynamics was found for the low modulation amplitude comb.
Human Gait Recognition Based on Integrated Gait Features using Kinect Depth Cameras

Biometrics are widely used for security authentication systems to verify a person's identity such as fingerprint, iris, face, and voice recognition. Among them, unlike other biometrics, human gait has the advantage that it can be captured in an unobtrusive manner. In our previous research, we proposed a method of modeling the body parts of a captured walking person using the Kinect depth cameras. In this paper, we propose a new human gait recognition method that uses gait features extracted from the modeled body parts to identify a walking person. The proposed method uses a combination of static and dynamic gait features to improve the accuracy of person identification. Because each gait has a different cycle length, we also use a time normalization technique to transform gait feature sequences with different lengths to those of the same length to compare them more precisely. Based on the time-normalized gait feature sequences, we build a k-NN classifier and an LSTM classifier to classify different walking persons. Our experimental results show the high potentiality of the proposed method for identifying unknown walking persons.
Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

The abundant movement information carried by the surface electromyography (sEMG) signal can comprehensively reflect the muscle movement patterns on the surface of the human body, and the characteristics of physiological signals involving movement information have promoted the vigorous development in the field of human-computer interaction in machine learning. This paper takes subtle gestures as the research objects, and proposes a subtle gestures recognition system that uses two-channel sEMG signal and utilizes Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) which are parameter estimation classifiers based on probability density. First, combining the raw sEMG signal and the envelope sEMG signal, the number of channels for sensors and corresponding position of the muscle groups are preferably selected. Then, we propose to use the Pearson correlation coefficient to optimize the four types of features extracted in the time domain, frequency domain, time-frequency domain and AR model parameters, which effectively ensures the uniqueness of each gesture and eliminates individual differences. Finally, this paper compares four classifiers of QDA, LDA, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), and explores the pattern recognition classifier suitable for subtle actions. The experiment collects 9 kinds of subtle gestures from 8 subjects. The results show that the system can use only two channels of sEMG signal to recognize 9 kinds of subtle gestures which include multi-finger clicking or pinching or holding objects, and the most suitable classifiers QDA and LDA have the average recognition rates of 95.79% and 95.01%, respectively.
Facial Expression Classification Using Vanilla Convolution Neural Network

Automatic Facial Expression Recognition (FER) is an active research area in artificial intelligence and image processing. One of the main challenges in FER is the automatic feature extraction. In this paper, the proposed 10 –layer Convolutional Neural Network (CNN) architecture can automatically detect important features without human supervision. This research uses Extended Cohn-Kanade (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset for facial expression recognition. The proposed architecture achieved an accuracy of 99.3% and 78.1% in classifying facial expressions in CK+ and JAFFE datasets.
Deep Learning Aided NOMA for User Fairness in 5G

User fairness is an important consideration for the upcoming wireless communication systems. Non Orthogonal Multiple Access (NOMA) is a potential promoter of fifth-generation (5G) wireless communication. The advantages of NOMA in 5G are to improve the efficiency of spectrum and increase the number of users. But the main disadvantage of this system is high computational complexity. To overcome this disadvantage, in our work we put forward a new technique of DNN (Deep Neural Network) aided NOMA system. User fairness in the sense of max-min fairness is attained by joint power allocation and beamforming. This is a non-convex problem, because of the higher dimensionality of variables and complexity of the problem. A substandard solution is obtained in the form of perfect power assigning and beamforming vector. From the results, we can clear that the DL aided NOMA has better performance characteristics than the conventional NOMA system.
A Simulation-based Online Evolutionary Algorithm for Combat in StarCraft II

Real-time strategy (RTS) games have become one of the hotspots in the field of artificial intelligence research due to the large search space, long-term planning, and real-time constraint. In view of the fact that most of the current research on solving the RTS combat problem uses offline training methods, and these methods may need a lot of training time, this work uses an evolutionary algorithm (EA) to directly optimize the units’ action sequences online. In order to solve the problems of repetitive actions caused by the neutral area and the size correlation caused by cooperation between units, three search operators are proposed. It is shown that this method can effectively solve the combat problem in RTS games.
Machine Learning Applied to Support Medical Decision in Transthoracic Echocardiogram Exams: A Systematic Review

The echocardiogram (ECHO) is an ultrasound of the heart used to diagnose heart diseases (DHC). The analysis and interpretation of ECHO are dependent on the doctor's experience. However, software that uses artificial intelligence to analyze ECHO images or videos is contributing to support the physician's decision. This paper aims to perform a Systematic Literature Review (SLR) on artificial intelligence (AI) techniques applied in the automation of Transthoracic Echocardiogram (TTE) processes, to support medical decisions. The study identified more than 800 articles on the topic in the leading scientific research platforms. To select the most relevant studies, inclusion and exclusion criteria were applied, where 45 articles were selected to compose the detailed study of the SRL. The results obtained with the extraction of information from the papers, identified 3 groups of primary studies, namely: identification of the cardiac vision plan, analysis of cardiac functions and detection of cardiac diseases. SRL identifies that the set of Machine learning (ML) techniques are being widely applied in the tasks of segmentation, detection and classification of images obtained from ECHO. The techniques based on Convolutional Neural Network (CNN), presented the best Accuracy rates. Research shows a strong interest in automating ECHO processes. However, it is still an open research field, with the potential to generate many publications for researchers.
A MIMO Antenna with Slotted Ground Plane for 5G Millimeter Waves Communication Systems

This paper presents a four-element multiple-input multiple-output (MIMO) antenna with slotted ground plane and patch for 5G millimeter waves communication systems. It is designed using Computer Simulation Technology (CST) software on Rogers RT/Duriod 5880 with a thickness and dielectric constant of 0.787mm and 2.2, respectively. The inset feeding technique is applied to obtain the matching impedance of 50Ω. The overall size of the antenna is 48×12×0.787mm3, and the dimensions of the slots on the ground plane and patch are determined through applying the empirical Hill Climbing algorithm to achieve an enhanced bandwidth and gain. It resonates at 26.455GHz with a reflection coefficient of -65.09dB and provides a huge bandwidth of 4.468GHz in the range of 23.37GHz – 27.838GHz at S11 ≤ -10dB. It also achieves a mutual coupling of less than -32dB without utilizing a decoupling structure or extra spacing. In addition, other MIMO antenna parameters such as Envelope Correlation Coefficient (ECC), Diversity-Gain (DG), and Channel Capacity Loss (CCL) are evaluated which satisfy 5G systems requirements.
Studying the Effects of Compression in EEG-Based Wearable Sleep Monitoring Systems

Long-term sleep monitoring through the use of wearable EEG-based systems generates large volumes of data that need to be either locally stored or wireless transmitted. Compression of data can play a vital role to reduce the power consumption of these already resource-constrained systems. While compression methods can result in significantly reduced data storage and transmission requirements, the loss in signal information can have an impact on the algorithms used to extract the key sleep parameters. This paper studies the impact of six different state-of-the-art compression methods, including wavelet, SPIHT, filter and predictor-based methods, analysing their effects on the reconstructed signal quality particularly for automatic sleep staging applications. It looks at how the overall sleep staging accuracy as well as the detection accuracy of different sleep stages is reduced as a result of different EEG compression methods. It shows that the SPIHT and predictor-based compression methods outperform wavelet and filter-based methods in preserving the relevant signal features. It also shows that compression ratios of up to 65 can be achieved using the QSPIHT method with less than 10% loss in overall sleep staging accuracy.
Performance Gains Imparted by Traffic-Awareness in an Elastic Single Link

This paper presents an analytical discussion of optimal spectrum allocation algorithms for an elastic optical single link under multiclass incremental and dynamic traffic with arbitrary rates per class. The performances of the optimal algorithms are then compared with the usual greedy algorithms (e.g. first-fit) in order to derive the performance gains imparted by traffic-awareness for incremental traffic. Both uniform and non-uniform traffic profiles, as well as greedy and non-greedy assignment policies, are considered. Finally, extension of these results to the case of dynamic traffic is briefly discussed.
EagleEYE: Aerial Edge-enabled Disaster Relief Response System

The fifth generation (5G) mobile network has paved the way for innovations across vertical industries. The integration of distributed intelligent edge into the 5G orchestrated architecture brings the benefits of low-latency and automation. A successful example of this integration is exhibited by the 5G-DIVE project, which aims at proving the technical merits and business value proposition of vertical industries such as autonomous drone surveillance and navigation. In this paper, and as part of 5G-DIVE, we present an aerial disaster relief system, called EagleEYE, which utilizes edge computing and machine learning to detect emergency situations in real-time. EagleEYE reduces training time by devising an object fusion mechanism which enables reusing existing datasets. Furthermore, EagleEYE parallelizes the detection tasks to enable real-time response. Finally, EagleEYE is evaluated in a real-world testbed and the results show that EagleEYE can reduce the inference latency by 90% with a high detection accuracy of 87%.