Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Website for COVID-19 Detection on Chest X-Ray Images

Control of the spread of COVID-19 must be encouraged, even though this is a new normal era. Rapid screening for COVID-19 detection must be carried out to control the spread of COVID-19. This research develops a website for COVID-19 detection based on chest X-Ray images and compares the CNN-BiLSTM model. This study divides X-ray images of the chest into three categories: COVID-19, Normal, and Viral Pneumonia. When compared to other models, the Resnet50-BiLSTM model produces the highest accuracy. The accuracy of the Resnet50-BiLSTM model was 98.51%. Then, in order, the following models were used: Resnet50, VGG19-BiLSTM, VGG19, AlexNet-BiLSTM, and AlexNet. The comparison of Precision, Recall, and F1-Measure findings also demonstrate that Resnet50-BiLSTM has the highest score when compared to other approaches. The website was also developed using the Flask framework for automatic COVID-19 detection.
Measurement of sub-GHz Band LPWA Propagation at Half-Wavelength Intervals

In this paper, we investigate the radio wave prop-agation of sub-GHz band low power wide area (LPWA) communications. To reveal the spatial propagation characteristics of LPWA signals for wireless sensor networks in detail, we measured the propagation at half-wavelength intervals in a square area of approximately 5 m for both line-of-sight (LOS) and non-line-of-sight (NLOS) environments. The measurement results show that the differences between the maximum and minimum average received signal strength indicator (RSSI) values were 30 dB for the LOS signal and 20 dB for the NLOS signal. Furthermore, frequency analysis results of the measured RSSI in both environments quantitatively showed a spatial correlation of the measured RSSI.
Joint Design of User Clustering, Beamforming, and Power Allocation for NOMA

This paper investigates the application of downlink beamforming along with non-orthogonal multiple access (NOMA) in a multiple-input multiple-output (MIMO) system. The joint design of user clustering, downlink beamforming and power allocation is formulated as a mixed-integer non-linear programming (MINLP) model, aiming to minimize the total transmission power while satisfying quality-of-service (QoS) and power constraints. To tackle this challenging problem, we reformulate it into a more tractable form and conceive a low complexity algorithm based on the penalty dual decomposition technique for its solution. The performance of the proposed NOMA scheme with joint design of user clustering, beamforming and power allocation is validated by means of simulations over millimeter-wave (mmWave) channels. The results show the advantages of the proposed scheme in terms of total transmit power and spectral efficiency over competing multiple access schemes.
DOA Estimation for Joint Sensing and Communication with Nonorthogonal Waveform

In this paper, a virtual sensing matrix-based direction-of-arrival (DOA) estimation method is proposed for the joint sensing and communication system. To maintain high communication rate, fully nonorthogonal communication waveforms are employed, which however, limit the virtual array derivation in the existing DOA estimation methods to achieve accurate estimation. To address this problem, we formulate a virtual sensing matrix from the estimated sensing channels, rather than directly constructing the virtual array from the received signals. As such, the reliance on the waveform orthogonality during virtual array derivation can be avoided. As a result, the DOAs of non-cooperative targets can be estimated by treating the virtual sensing matrix as the equivalent first-order observation matrix of an augmented virtual array and incorporating the subspace processing. Simulation results validate the effectiveness of the proposed method in both estimation accuracy and angular resolution.
Design of a 3DoF Passive Hip Exoskeleton for Rehabilitation

In recent years, many mechanisms have been developed to help people with reduced mobility, especially for people with injuries that impede the mobility of the lower part with respect to the hip. That is why this work presents the design of a passive hip-based exoskeleton of a standard size so that it can be used in the rehabilitation area. Most hip exoskeletons make a strong call on walking assistance rather than on the stability of the user, so the design is adjusted to the stability, the objective of this work is to design a self-balancing exoskeleton with control by sensors that detect the hip rotation and send a signal to the microcontroller which assists the movement of the hip, allowing the servos to move according to the degrees detected by the sensor. In addition, the exoskeleton has been designed using CAE software, using aluminum elements to provide lightness and robustness to the exoskeleton and, at the same time, an external PLA plastic shell is made, in order to provide lightness, flexibility and limited volume, when performing a stress test shows optimal results that allows us to know the durability and comfort of the exoskeleton.
An Iterative Discrete Least Square Estimator with Dynamic Parameterization via Deep-Unfolding

We propose a new dynamic parameterization approach via deep unfolding as an extension of the recently-introduced iterative discrete least square (IDLS) scheme, shown to elegantly generalize the conventional linear minimum mean squared error (LMMSE) method to enable the solution of inversion problems in complex multidimensional linear systems subject to discrete inputs. Configuring a layer-wise structure analogous to a deep neural network, the new approach enables an efficient optimization of the iterative IDLS algorithm, by finding optimal hyper-parameters for the related optimization problem through backpropagation and stochastic gradient descent techniques. The effectiveness of the proposed approach is confirmed via computer simulations.
Synergizing Digital Competence and Team Work for Accelerating Workforce Agility

COVID-19 has brought disruptive changes to many businesses. The ability of employees to be agile in dealing with changes is a key for surviving in difficult times. Workforce agility as an organizational behavior is influenced by internal and external factors. This article is interested to elaborate and examine the impact of team working as external factor and digital competence as internal factor of field workers toward workforce agility in a construction service company. The study was conducted by using a cross-sectional survey which has involved 378 construction workers. By using PLS SEM as an analysis method on obtained data, the results conclude that workforce agility of a construction company is directly and significantly influenced by team working and digital competence. Digital competence plays as a mediating role that strengthens the impact of team working toward workforce agility. For being survival in dealing the crises such as the COVID-19 pandemic, the company is recommended to accelerate workforce agility by endorsing field workers working collaboratively as a team for leveraging digital competence development.
Design of a High-Resolution Multifocal LIDAR: Enabling Higher Resolution Beyond the Laser Pulse Rise Time

The 3-D imaging is increasingly impacting areas such as space, defense, automation, medical, and automotive industries. The most well-known optical 3-D imaging systems are LIDAR systems that rely on time of flight (ToF) measurement. The depth resolution of the LIDAR systems is limited by the rise time of the illumination signal, bandwidth of the detector systems, and the signal-to-noise ratio. The focal gradients can be utilized to obtain 3-D images from 2-D camera captures. In this study, we combine the basic LIDAR techniques with a physical measurement of focal gradient changes that are caused by the depth of field, to obtain a high-resolution 3-D image using spatially separated single-pixel detectors. The system that we introduce here enables depth resolution that goes beyond the rise time of the incident laser pulse.
“It Sets Boundaries Making Your Life Personal and More Comfortable”: Understanding Young People’s Privacy Needs and Concerns

Children and young people are prolific digital users, making up a third of the world’s online users <xref ref-type="bibr" rid="ref1">[1]</xref> and engaging with the digital world in different and distinct ways. However, their unique understandings and perspectives are often not considered in debates and discussions around privacy and security <xref ref-type="bibr" rid="ref2">[2]</xref>. This article outlines a youth-centric notion of digital privacy and guiding principles around privacy developed by young people from Antigua and Barbuda, Australia, Ghana, and Slovenia.
AFARN: Domain Adaptation for Intelligent Cross-Domain Bearing Fault Diagnosis in Nuclear Circulating Water Pump

Domain adaptation can transfer cross-domain diagnosis knowledge by minimizing the divergence of labeled source and unlabeled target data. However, the model neglects to maximize physics prior knowledge during feature extraction and distribution alignment, resulting in a noninterpretable model even negative transfer. Hence, a physics-informed domain adaptation network, termed adaptive fault attention residual network (AFARN), is proposed. First, an adaptive fault attention mechanism is designed to refine features guided by bearing fault characteristics, suited to generating diagnosis-relevant features. Then, several metrics are applied to minimize the marginal and conditional distribution discrepancy of features, thus, generalizing the model from source to target domain. The AFARN utilizes the fault characteristics and label information simultaneously to train the model, which can enhance the distribution alignment of diagnosis-relevant features, thus, providing an interpretable knowledge transfer. Finally, experiments on public and circulating water pump datasets show that AFARN can enhance fault feature learning and diagnosis accuracy.
Time-Series Forecast-Based Endpoint Thickness Compensation for Thinning of Sapphire Wafer

The grinder consults measurement modules to control the endpoint thickness of the sapphire wafer thinning process, but these modules have shortcomings. We propose a forecast-based endpoint thickness and online error compensation approach for grinding hard, brittle material. We leverage forecasts to improve grinding efficiency, and address the shortcomings of conventional probe gauges, which can be used only when grinding is paused, as well as those of contact gauges, which require correction from time to time due to wear. We construct a multi-signal time-series forecast model and compare the prediction performance using features from various signals. We implement the forecast model in a vertical grinder for actual grinding to evaluate the effectiveness of the proposed approach. The results show that online error compensation reduces contact times for the contact gauge and maintains precise thickness, greatly facilitating the wafer thinning process. The proposed measurement approach applies not only to wafer thinning but can also be extended to other grinding processes.
Waiting Experience: Optimization of Feedback Mechanism of Voice User Interfaces Based on Time Perception

Waiting is an indispensable and inevitable part of man-machine voice interaction. The voice user interface (VUI) feedback mechanism is a key factor affecting voice interaction’s waiting experience. The feedback time of most available voice interfaces is fixed or decided by the processing time of hardware and software, which has not been designed and cannot offer users a good interaction experience. In this paper, the speech rate of user-machine voice interaction is collected through prototype experimentation. Besides, users’ time perception of different voice interfaces’ feedback time settings is studied based on time psychology theories. Moreover, users’ emotional changes are described after a specific feedback time with the distribution of two-dimension arousal-valence emotion space. Users’ time perception and subjective emotions are differently influenced by different VUI feedback times. The experimental results show that 750 ms is the optimal VUI feedback time point at which the best users’ subjective feelings and psychological experiences are reached, and the threshold limit time spent by users in waiting for the VUI feedback is 1,850 ms which will lead to user emotions with low levels of arousal and valence after being exceeded. Based on that, a linear regression model is proposed to define the optimal feedback time of VUI. The user experience VUI research results show that the calculated feedback time parameters can make users produce time perception in line with their expectations in interacting with voice interfaces.
Research on Pollution Degree Evaluation Method of Insulation Equipment Integrating Hyperspectral Imaging Technology and Ultraviolet Imaging Technology

Insulator polluted seriously affects the safe and stable operation of power system, in order to put forward a kind of pollution level detection method with high confidence level, this paper combines the hyperspectral imaging technology and ultraviolet imaging technology, and has set up a multiple classifier fusion classification method and the filth of the achieved 96% accuracy level detection model, provides reference for the pollution level of accurate detection.
Message Alignment for an Information Bottleneck Optimized FFT in OFDM Receivers

The trend towards larger bandwidths to accommodate higher data rates is unbroken in mobile communications. High sampling rates resulting from these large bandwidths can lead to critical energy consumption in the analog-to-digital converter (ADC) particularly in mobile devices. As the ADC energy demand depends exponentially on the bit resolution, an increase in energy consumption can be avoided by coarse quantization. The ADC can be designed to maximize the mutual information between its output and the channel input using the information bottleneck (IB) method. This approach can also be used to replace computationally expensive arithmetic operations with mutual information maximizing look-up tables (LUTs). We consider an orthogonal frequency division multiplexing communication link and substitute arithmetic computations inside the fast Fourier transform (FFT) with LUTs. Replacing every FFT butterfly operation with an individual LUT quickly becomes infeasible for large FFT sizes due to exploding memory requirements. Instead, a technique called message alignment is used ensuring similar meanings of cluster indexes at different positions in the IB-FFT butterfly network. In the proposed message aligned IB-FFT, butterfly nodes are replaced by separately optimized LUTs for multiplication with twiddle factors and for addition. Message alignment allows to reuse these LUTs saving valuable memory resources.
Adversarial Example Attacks Against Intelligent Malware Detection: A Survey

With the advancement of information technology and the increasing prevalence of the internet, the number of malware has experienced an exponential, and the security threat to users' property and privacy is becoming more serious. With the great success of artificial intelligence especially deep learning technology in fields such as computer vision and speech processing, deep learning are gradually introduced into the field of malware detection, to process large amounts of malware more efficiently. However, in recent years, researchers have discovered that the deep learning model itself also has security risks. While malware detection based on deep learning brings convenience and technical progress, it also introduces some new threats, such as adversarial examples. This paper investigates adversarial example attacks against deep learning-based malware detectors. First, We reviewed research on malware detection techniques. Then we analyze the works related to adversarial example attacks against intelligent malware detectors. Finally, we discuss the challenges and prospects in future research.
A Trading Strategy Based on LSTM

To make a profit from the complex stock market, we are now launching our model FD- Forecast. It has experienced a six-year simulation test, raising the original principal of $1000 to $32455, which is magnified to 32 times and has an excellent performance of an average profit of $9156, which is increased by 9 times. Our Model FD is consisted of two modules, the Forecast module, and the Decision Module. The Forecast module attempts to predict the new market data in the next stage by inputting historical data. By training an LSTM based neural network, we will be able to learn the historical stock data distribution from the data, which is a popular method in the field of today's economics. And with the help of adversary training, and self-forecast learning, we will be able to overcome the challenge brought by the lack of training data. The Decision module takes the predicted data from the Forecast module, and tries to make the best decision given the above information. We manage to solve this problem by converting the decision problem into a Linear Programming Problem. By solving this Linear Programming Problem, we shall be able to present a solution that can profit from the given historical stock data. To demonstrate our model performs the BEST strategy, we formally proved this problem. Firstly, the proposition is clarified, that is, the optimality we discussed here is the expected optimality in the statistical sense. Second, the mathematical expectation of the return brought by any portfolio investment strategy in the strategy space will not exceed the mathematical expectation of the return brought by our method. We will be able to show that if we can prove the above two points, our method will be the best.
Quantitative analysis of diseased horse tendons using Fourier-transform-second-harmonic generation imaging

Fourier transform-second-harmonic generation (FT-SHG) imaging is used to quantitatively assess the structural organization of collagen fibers in tendonitis-induced horse tendons. Fiber orientation, isotropy, and the ratio of forward to backward SHG signal (F/B ratio) are used to differentiate the fiber organization between the normal and diseased horse tendons. Each second-harmonic generation (SHG) image is divided into several smaller regions of interest (ROI) and the aforementioned quantitative metrics are calculated across the whole grid. ROIs are further labeled as dark (no or minimal presence of fibers), isotropic (random fiber organization), or anisotropic (regular fiber organization) regions. Results show that the normal tendon possesses minimal isotropic regions and small standard deviations in the histograms of orientation and F/B ratio, indicating an intact and highly regular fiber organization. However, the tendonitis-induced horse tendons possess higher number of dark and isotropic regions, and larger standard deviations of the measured parameters, suggesting significantly disoriented and disorganized collagen fibers. This type of quantification would be highly beneficial in diagnosing and determining the stage of tendonitis in clinical settings. Not limited to tendonitis, the technique could also be applied to other diseases that structurally affect collagen fibers. The advantage of FT-SHG over the conventional polarization microscopy is also discussed.
Autonomous Path Follow UAV to Assist Onshore Pipe Inspection Tasks

Unmanned Aerial Vehicles (UAVs) are being deployed in different applications due to their reduced time execution to perform tasks, more extensive coverage area, and more risk minimization to humans. In the Oil & Gas industry, its use for inspection activities is even more attractive due to the large structures in these facilities. Therefore, this research proposes deploying an autonomous UAV system to inspect unburied pipelines of onshore O&G facilities. The proposed UAV guiding system is based on image processing techniques Canny edge detection and Hough Transform to detect the line and on a path follower algorithm to generate the trajectory. The proposed strategy was developed in Robot Operating System (ROS) and tested in a simulated environment considering the practical operational. The same controller was tested on a physical UAV to validate the results obtained in previous simulations. The results demonstrated the effectiveness and feasibility of deploying the proposed strategy for this specific task and the cost reduction potential for real-life operations, as well as reduced potential risks to the physical integrity of the workers.
Vasculature Detection in Tympanic Membrane

Tympanic membrane, an essential part of the human ear whose function is to help in hearing. In this paper, novelty-based characterization of the tympanic membrane using its vasculature is proposed. In part (1) digital image processing techniques were used for the detection of the blood vessels. In this green channel extraction followed by pre-processing techniques like CLAHE and OTSU thresholding were performed. Vessel detection was done by using Morphological Operation combined with Canny Edge Detection. In part (2) Histogram and pixel-based techniques were used for quantization of blood vessels. Result analysis was done using percentage of vasculature and speed of the algorithm used in the finding the percentage of vasculature. Comparison between Sobel edge detection and Canny edge detection was performed to choose the best method.
Multi-class Motor Imagery Classification Method Using One-dimensional Convolutional Neural Networks

Motor Imagery-based Brain-computer interface (MI-BCI) enables communications between the human brain and external machines, which is considered a valuable frontier technology. Motor imagery (MI) identification is a fundamental problem for these applications. However, because of the non-stationary and nonlinear properties of EEG motor imagery signals, it is very hard to get a high accuracy multi-class method. In this study, a multi-class classification method of MI EEG signals using wavelet packets and a one-dimensional convolutional neural network is presented. Data normalization and wavelet threshold denoising were performed for signal preprocessing. Then EEG signals were decomposed and reconstructed by wavelet packets transform. According to the features between different channels of EEG signal, a multilayer one-dimensional convolutional neural network model was adopted to get a multi-class classification model for MI. This model was validated on the public datasets BCI competition IV 2a. The result shows that the mean accuracy of this model is 93.05%. This study is beneficial to MI-BCI research applications in the future.
Graph Neural Networks for Timely Updates of Short Packets in Interference-Limited Networks

This paper investigates the timely updates of short packets in interference-limited networks. Specifically, we minimize a performance metric, Age-of-Information (AoI), by optimizing the power control policy that is scalable to the number of wireless links. Since the AoI minimization problem in the interference-limited network is a sequential decision-making problem, we develop a deep reinforcement learning algorithm, graph-based deterministic policy gradient (GDPG), to find the optimal policy. The actor and critic of the GDPG are represented by two types of graph neural networks (GNNs). The actor is a random edge GNN that maps the AoI and network topology to the transmit power. The critic is a message-passing GNN for estimating the long-term reward of state-action pairs visited by the policy, where the received interference is the aggregated message of a node in the message-passing GNN. Simulation results show that even when the number of wireless links in the testing stage is different from that in the training stage, the average AoI achieved by the GDPG is 30% lower than the weighted minimum mean square error (WMMSE) algorithm. Therefore, the proposed GDPG is scalable to the number of wireless links in the communication network.