Miracle-dz/UniFAR-FTUs huggingface dataset:
F-NAD: An Application for Fake News Article Detection using Machine Learning Techniques

Nowadays the Internet and Social Media are flooded with fake accounts, fake posts and misleading news articles. The intention of these are often to mislead the common people and/or manipulate them into believing something that is not real. Misinformation or fake news can leave negative impact on a person or society as a whole that can last forever even if they get corrected afterwards. This work proposed here is to tackle this issue and it aims to identify a news articles whether it is real or misleading. This is achieved using an ensemble technique of state of the art recurrent neural networks (LSTM and GRU). An android application has also been developed for determining the sanctity of a news article. The proposed model is tested on a large dataset which is prepared in this work by collecting news from various fake and real news sources. It has also been tested using different standard datasets available in the literature and it is found that the proposed model performs better.
RKA Security for Identity-Based Signature Scheme

Related-key attack (RKA) is a kind of side-channel attack considered for kinds of cryptographic primitives, such as public key encryption, digital signature, pseudorandom functions etc. However, we note that the RKA-security seems to be not considered for identity-based signature (IBS), which is an important primitive for identity-based cryptography and proposed by Shamir in 1984. In this paper, for the first time, we introduce the RKA security into IBS schemes and try to define the security model for it. More specifically, we consider the RKA occurs in the users’ signing key or the master key of the key-generation center (KGC), which derives two kinds of RKA securities for IBS. Meanwhile, we illustrate that the most efficient Schnorr-like IBS scheme proposed by Galindo and Garcia is RKA-insecure by launching a simple RKA. However, a slight modification of it yields a RKA-secure IBS scheme, for which we give the detailed security proof in the random oracle. Finally, the performance analysis shows that the modified scheme is still extremely efficient but has higher security.
MagWi: Practical Indoor Localization with Smartphone Magnetic and WiFi Sensors

Geomagnetic field and WiFi networks have attracted much attention in indoor localization, since they are pervasive, and their signals are easy to collect by off-the-shelf smartphones. However, there are still challenges in practice: 1) GFI (Geomagnetic Field intensity) read by magnetometer in smartphones is hard to be used directly, since it is in phone coordinate system and varies with phone attitude. On the other hand, the magnitude of GFI lacks location diversity; 2) WiFi RSS (Received Signal Strength) suffers from fluctuations due to multipath fading effect indoors. To address the above issues, we first propose Mag2D, a high quality feature of GFI. Mag2D is robust to phone attitude and has better spatial discrimination than the magnitude. We then study the complementary properties of Mag2D and RSS, and design MagWi, a practical fingerprinting indoor localization system fusing Mag2D and RSS. MagWi adjusts fusion weights of Mag2D and RSS according to their capabilities of discriminating locations. To do so, we propose LCD (Local Clustering Degree) to quantify the location-discriminating capability and build a LCD-Error model. The fusion weights are thus assigned dynamically based on the LCD-Error model during localization. We have implemented MagWi to provide location service on an Android phone in three typical indoor environments, covering a total area size over 600m2. Our experiment results show that MagWi achieves high accuracy and is easy to deploy in practice.
An AI driven Genomic Profiling System and Secure Data Sharing using DLT for cancer patients

In the pharmacogenomic and theranostic approach of treating melanoma, a continuous monitoring of the disease and the mutations associated with the disease is essential. Such a monitoring system has been designed and developed based upon the concept ‘One-shot learning’, a machine learning technique adapted to work with a relatively small number of training images. The samples have been exhaustively studied through genomics, epigenomics, metagenomics and environmental genomics, finding the genetic signature behind proneness of these attributes. The mutations CDK4, CDKN2A, BRAF and KIT have been included in the analysis. The prediction accuracy of the machine is found to substantially high suggesting the device for the theranostic and pharmacogenomic strategies of controlling melanoma. A Distributed Ledger Technology (DLT) based system has been proposed for real time data sharing, training and analysis enabling hospitals and research labs to communicate with each other and conduct a cost-effective diagnostic workflow.
Scalable Small-Signal Modeling of AlGaN/GaN HEMT Based on Distributed Gate Resistance

This paper reports on scalable small signal modeling of AlGaN/GaN high-electron-mobility transistors (HEMTs) based on distributed gate resistance model. A distributed gate resistance model (DGRM) is used to model large periphery of GaN HEMT with various gate widths. A fully scalable analytical small signal model is developed with the experimental results. Intrinsic parameters, independent of the frequency, have been shown. Furthermore, S-parameters are obtained from the modeling and measurements to verify the model. The good agreement between the measured and the simulated results indicate that this model is accurate, stable and comparatively more representative of the real evolution of local resistance in the gate. The proposed scalable DGRM would be useful for accurate scalable large signal modeling of large periphery GaN HEMTs for high power RF application.
Emission Uniformity of an Annular Graphite Cathode With a Focusing Electrode in a High Power Vacuum Diode

The emission uniformity of an annular knife-edged cathode in a coaxial diode with a focusing electrode and operating in a low macro electric field has been investigated. It was found that the emission uniformity deteriorated as the cathode width increased from 0.5 mm to 2.0 mm. To improve the emission uniformity, a grooved graphite cathode was prepared by a laser micro-etching treatment. Experimental results demonstrated that the emission threshold for the grooved cathode may be reduced and thus the delay in explosive emission can be decreased. Visual inspection of photographs of the cathode plasmas operating under different guiding magnetic fields (GMFs) was used to confirm improvements in emission uniformity. Even under a low GMF of 0.21 T, good emission uniformity was realized.
GAN-Based Fine-Tuning of Vibrotactile Signals to Render Material Surfaces

The design productivity of fine-tuning for vibrotactile stimuli becomes important as consumer devices equipped with vibrotactile actuators will become wide-spread. The fine-tuned vibrotactile stimuli output by vibrotactile actuators allows the end-users to feel the surface of the virtual material. However, there is no suitable tool for fine-tuning while there are existing tools suitable for initial designing. In this paper, we test whether we can use GAN (Generative Adversarial Network)-based vibrotactile signal generator at the tuning phase. The generator provides a material-level interface to designers. Designers can define any intermediate materials among pre-defined 108 materials and obtain corresponding intermediate signals that the generator generates. We showed the applicability of the generator to the fine-tuning of vibrotactile signals from the viewpoints of principal component analysis and a user test.
Broadband VHF/UHF Antenna Using a Multi-Resonance Meanderline Element

A compact and broadband VHF/UHF antenna operating from 270 MHz to 1.56 GHz for $\mathbf{VSWR}\leq 3$ has been designed. Radiating element of the antenna is a planar meander line which exhibits multiple narrowband resonances and has been built on FR4 substrate. When the meanderline radiating element is loaded by an additional FR4 layer on top, the resonances shift down in frequency. If the top dielectric is metallized at one end, these resonances shift further down. This effect is enhanced if a metallized bottom dielectric is also employed. And for this case, the resonances eventually merge and form a broadband response.
Development of wearable dry sEMG sensor and selection of optimal location through anatomical consideration for the prosthetic hand

The aim of this study is to design a wearable sensor device and to optimize the position of the sensor attachment, which is one of the important factors for the prosthetic hand control based on the surface electromyography (sEMG). Through anatomical examination of the arm, the independence of the surface electromyogram acquired in each channel is increased and the signal to noise ratio (SNR) is also increased by signal processing. We introduce a study on the research trend and sensor position of the surface EMG-based prosthetic hand in Introduction. In body, the design and characteristics of the developed sensor are explained, and correlation analysis between each channel and finger force is performed by selecting the position of the sensor through anatomical examination of the arm. In conclusion, we use the correlation analysis result and find the optimal sensor location.
A Novel Decoding Scheme for Polar Code Using Convolutional Neural Network

Polar code is the first capacity-achieving channel coding scheme which has been selected for the next generation of wireless communication standard(5G). However, the traditional decoding schemes such as CA-SCL and BP algorithms cannot take into account both BER and latency performance. Inspired by the deep learning technology, we propose a scheme that combine convolutional neural network (CNN) with traditional Belief Propagation (BP) decoding. By exploiting noise correlation for channel decoding under colored noise environment, the proposed scheme can obtain a more accurate estimation of channel noise. We simulate the performance of the proposed scheme under BPSK and 4QAM modulation. For QAM modulation, a scheme that extend the conventional real-valued CNN technique to the complex-valued one is given. The results demonstrate that bit error rates (BER) performance can be improved after removing the estimated noise. Compare to the traditional Polar decoding schemes, the proposed scheme has better trade-off between BER and latency performance under colored noise environment.
Predicting Solar Performance Ratio Based on Encoder-Decoder Neural Network Model

Fault diagnosis of photovoltaic (PV) arrays is an important task for improving the reliability and safety of the overall photovoltaic system. In the traditional fault diagnosis, most of the past researches focus on the I-V curve to determine the PV arrays state. However, most inverters as smart meters cannot obtain data in high frequency and high volume in order to calculate the I-V curve, and the information usually cannot be retrieved conveniently. In this paper, we evaluate various Recurrent Neural Network Models’ from the recent advances in the deep learning field and their abilities in predicting PV arrays power generation. The results show the Encoder-Decoder model using Convolutional Neural Network (CNN) as the decoder and Temporal Convolutional Network (TCN) are more effective in predicting the PV arrays solar output production and can be used as the foundation for fault diagnosis and prediction.
An Improved Fault Tolerant Converter Topology for Field Oriented Controlled Induction Motor Drive

A fault tolerant converter topology to mitigate open-circuit and short-circuit faults in the inverter fed induction motor drive using field oriented control (FOC) is proposed in this paper. The proposed converter consists of a current controlled 3-level boost converter to increase the dc-link voltage at the 2-level inverter during pre-fault and post-fault to meet rated load conditions of the induction motor and also to balance the capacitor voltages at the dc-link. Experimental results are presented for open-circuit and short-circuit fault of power devices in a 2-level inverter leg and also post-fault operation. The results of the 3-level boost converter fed induction motor drive combination shows the merits and also compared with fault-tolerant converter (B4) topology and conventional converter (B6) topology.
A review on Soft Robotics: Modeling, Control and Applications in Human-Robot interaction

Nature inspires engineers to build robots with soft and flexible materials. Advancements in technology have opened doors for designing compliant and soft robots. Such robots find applications in designing soft surgical tools, soft wearable, implantable devices, etc. which can be used in collaboration with humans to execute a variety of non-conventional tasks. These robots have the ability to achieve any kinematic configurations through deformations. Unlike conventional robots, soft robots interact with an unknown environment with improved dexterity. However, this comes at the cost of increased complexity within existing modeling frameworks which motivated researchers to come up with specialized modeling techniques for soft robots. When a reasonable model is available, the next challenge for soft robots is to design a suitable control law to achieve some desired performance. This article gives some insights into soft robots with a focus on existing control methodologies. It also discusses the state of the art: biological inspirations, modeling, simulation, actuation methods, control and applications in human-robot interaction.
Using Optical IoT Sensing for Detrended Fluctuation Analysis of Skin Blood Pulsation during Visual Stimulation Task

This paper considers optical IoT sensing for the analysis of cardiovascular state. It is used for skin blood pulsation, i.e. photoplethysmography (PPG) signals acquisition and analysis using DFA (detrended fluctuation analysis) approach. Visual stimulus has been performed during the acquisition process. The results showed that DFA based spectra can be useful in further monitoring of the PPG signals. Analysis was performed using signals recorded from eight young volunteers.
An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity

The Internet has become an important tool for modern terrorist groups as a means of spreading their propaganda messages and recruitment purposes. Previous studies have shown that the analysis of social signs can help in the analysis, detection, and prediction of radical users. In this work, we focus on the analysis of affect signs in social media and social networks, which has not been yet previously addressed. The article contributions are: (i) a novel dataset to be used in radicalization detection works, (ii) a method for utilizing an emotion lexicon for radicalization detection, and (iii) an application to the radical detection domain of an embedding-based semantic similarity model. Results show that emotion can be a reliable indicator of radicalization, as well as that the proposed feature extraction methods can yield high-performance scores.
Reference Architectures and Standards for the Internet of Things and Big Data in Smart Manufacturing

This study aims to identify the main Internet of Things and big data reference architectures (RAs) available for the manufacturing industry. Besides open RAs developed by national and international consortia, proprietary RAs and frameworks are also discussed. The main building blocks of RAs are governed by viewpoints finding their root in systems and software engineering literature and standards. However, in most RAs, only technical viewpoints are addressed, and business architecture and customer context aspects are usually missing. In this paper, RAs are discussed in terms of their functionality and viewpoints addressed. Additionally, the standards developed by international and European institutions to complement RAs in the manufacturing industry are identified and presented. The RAs, standards and underlying models given can be useful for system architects, service providers, and other users as a guideline in the digital transformation of industry.
Molecular Property Prediction Based on a Multichannel Substructure Graph

Molecular property prediction is important to drug design. With the development of artificial intelligence, deep learning methods are effective for extracting molecular features. In this paper, we propose a multichannel substructure-graph gated recurrent unit (GRU) architecture, which is a novel GRU-based neural network with attention mechanisms applied to molecular substructures to learn and predict properties. In the architecture, molecular features are extracted at the node level and molecule level for capturing fine-grained and coarse-grained information. In addition, three bidirectional GRUs are adopted to extract the features on three channels to generate the molecular representations. Different attention weights are assigned to the entities in the molecule to evaluate their contributions. Experiments are implemented to compare our model with benchmark models in molecular property prediction for both regression and classification tasks, and the results show that our model has strong robustness and generalizability.
Innovative approach to Wireless Sensor Networks: SD-WSN

Traditional Networks are dominated by hardware constraints and have inflexible architectural design, thus restricting research and innovation. SDN is an innovation in networking which provides administrators to centrally manage and conFigure entire network. SDN simplifies and improves network management. Intelligent SDN controllers conFigure network elements and cooperate with applications to enhance the network. Primary objective of SD-WSN is that existing WSN can profit by making use of SDN. WSN’s deployment can be developed to improve transmission performance. In this survey, the basics of SDN, WSN, SD-WSN are explained. Also developments in WSN through SDN and its challenges have been discussed. Besides, the paper also summarizes the architecture of SDN and WSN. Future research and related challenges have been discussed towards the end.
Energy Storage Strategy in a Non-Agent Energy Trading Platform: Energy Bank System

This paper addresses a strategy for distributed energy storage system (DESS) in a non-agent energy trading platform. This platform is based on the peer-to-peer (P2P) trading method. It is termed as energy bank system (EBS). The trading mechanism of EBS refers to the banking system and the BitCoin trading system. There is no requirement for users to participate in EBS. To make EBS suit various kinds of DESS, a coefficient of energy charging is defined as figure of distributed energy charging (FDEC). FDEC is processed in participant-side for the optimization energy management strategy. The conditional Value-at-Risk (CVaR) is used for analyzing the risk of FDEC management. The trading method of EBS is the call action method, and the trading mechanism follows the rule of maximum transaction volume. Case study is based on the real historical data in Australia electricity market considering the renewable distributed generation (RDG) system. Also, the economic analysis is discussed base on the simulation results.
An Active Method to Mitigate the Long Latencies for Host-Aware Shingle Magnetic Recording Drives

Shingled Magnetic Recording (SMR) is one of the most promising techniques that satisfy the ever-growing storage volume demands. By overlapping tracks, SMR enormously improves the storage area density, which in turn brings higher storage volumes. However, SMR sacrifices the random write performance for better storage volumes. Current SMR drives propose to remedy this problem by employing an in-drive persistent cache to temporally store incoming writes and migrate them to their disk destinations later on. Unfortunately, cleaning processes for the persistent cache takes up to tens of seconds, and the unpredictable timing of these time-consuming operations chokes normal requests and drastically degrades SMR drive performance. In this paper, we propose to remedy this issue by proactively freeing the persistent cache space so that keeping these lengthy processes transparent with regards to normal requests, therefore reducing the long tails and delivering steady and predictable performance for SMR drive-based storage systems. We prototype our design as a Host-Aware SMR drive aware userspace file system, AM FS, and evaluate it on the real HA-SMR drive with libzbc. Evaluations results show that AM FS reduces the long tails of HA-SMR drives.
Parametric optimization of the CJFET Buffer Amplifier with Low Offset Voltage within the Range of Low Temperature

A new design of a buffer amplifier (BA) on complementary junction field-effect transistors (CJFET) is investigated and optimized for practical use as a part of IP modules and analog ICs operating in severe conditions (exposure to low temperatures and penetrating radiation). The optimal parameters of the BA elements are chosen in the LTspice simulation software, with low temperature models CJFET were creating and using multi-criteria optimization algorithm NSGA-II from the DEAP library. Besides, the offset voltage of the BA is selected as a priority parameter to be improved with limit on current consumption. It is important, for example, for constructing operational amplifiers (OA) with current negative feedback
Magneto-Optical Spectroscopy of (CoFeB)x-(Al-O)100-x Nanocomposites: Evidence of Superferromagnetism

We present results of magneto-optical investigations of (CoFeB)<italic><sub>x</sub></italic>-(Al-O)<sub>100-</sub><italic><sub>x</sub></italic> film nanocomposites in transverse Kerr effect (TKE) geometry in the spectral range 0.5–4.0 eV and magnetic field up to 3.1 kOe. Nanocomposites were deposited onto a glass-ceramic substrate by ion-beam sputtering. According to the data obtained from resistivity measurements, the percolation threshold is about <italic>x</italic><sub>per</sub> ≈ 57 at. %, and the critical concentration for metal–insulator transition is about <italic>x</italic><sub>c</sub> ≈ 48 at. %. The TKE response at room temperature strongly depends on the wavelength of light, applied magnetic field <italic>H</italic>, and the volume metallic fraction. The TKE field dependence is almost linear to <italic>H</italic> = 3.1 kOe and identical for 1.57 and 3.06 eV at <italic>x</italic> ≤ 30 at. %. It means that at <italic>x</italic> ≤ 30 at. %, nanocomposites consist of only superparamagnetic granules. With increasing concentration <italic>x</italic>, the TKE field dependence at 3.06 eV becomes more and more similar to the field dependence of ferromagnetic material, but it is not the case at 1.57 eV. For example, the TKE signal for <italic>x</italic> = 43 at. % measured at 3.06 eV saturates at <italic>H</italic> = 100 Oe, which indicates the presence of large ferromagnetic regions below <italic>x</italic><sub>c</sub>. For the same composition, the TKE field dependence measured at 1.57 eV does not saturate and resembles more a superparamagnetic response. The same difference is observed also at <italic>x</italic> = 49 at. %. The presence of large ferromagnetic regions in completely insulating samples together with separate superparamagnetic granules can be considered as an evidence of superferromagnetic ordering in the vicinity of <italic>x</italic><sub>c</sub>.