Miracle-dz/UniFAR-FTUs huggingface dataset:
System for S-transform Realization

The properties of both the short-time Fourier transform (STFT) and the wavelet transform are combined in the S-transform. On the one hand, preservation of the phase information of a signal as in the STFT is achieved, while, on the other hand, the variable resolution as in the wavelet transform is provided. To extend areas of its practical applications, in this paper, we propose an efficient hardware realization of the S-transform. The proposed solution is implemented on a field-programmable gate array (FPGA) circuit and is designed in a multiple-clock-cycle manner. The design is verified on a test signal corrupted by white Gaussian noise.
Narrative-Driven Immersion and Students' Perceptions in an Online Software Programming Course

Learning software programming is challenging for software engineering students. In this paper, students' engagement in learning software engineering programming is considered under the SimProgramming approach using the OC2-RD2 narrative technique to create an immersive learning context. The objectives of this paper are twofold: presenting a narrative-driven immersive learning approach to introduce software engineering concepts and coding techniques to online undergraduate students; and analyzing the students' feedback on this approach. Thematic analysis of the metacognitive tasks was performed on the students' fortnightly reflections about their learning progress. Content analysis was based on interest categories, students' perceptions, metacognitive challenges, narratives, examples and aspects to be kept or to be improved. Data from the content analysis were organized into categories, subcategories, indicators, and recording units and their categorization was peer-reviewed. The narratives were considered by the students as interesting, appealing, akin to professional reality and promoting interaction. Most students thought the approach was helpful for learning software programming.
PUMPT: An e-Textbook platform based on a Personal User Model for Learning (short paper)

While classic textbooks are increasingly available online, future e-textbooks have the potential to ﬁt within a rich ecosystem of electronic learning tools commonly used in schools and universities. We describe work towards that vision, based on the Platform for User Model Personalized Textbooks (PUMPT) platform. This integrates learning data from multiple e-textbook and other learning tools, notably a learner management system with its data from class assignments, quizzes and exams. All such data can be used to provide an Open Learner Model (OLM) to support learners in self-monitoring, reﬂection and planning that is based on a holistic view. Our core contribution is the new PUMPT platform for e-textbooks that are integrated with other learning tools. 1
DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images

Alzheimer’s Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one’s ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer’s disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer’s disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen’s Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.
Model-based vs. Data-driven Approaches for Anomaly Detection in Structural Health Monitoring: a Case Study

Modern Structural Health Monitoring (SHM) systems are becoming of pervasive use in civil engineering because they can track the structural condition and detect damages of critical and civil infrastructures such as buildings, viaducts, and tunnels. Although noticeable work has been done to improve anomaly detection for ensuring public safety, algorithms that can be executed on low-cost hardware for long-term monitoring are still an open issue to the community. This paper presents a new framework that exploits compression techniques to identify anomalies in the structure, avoiding continuous streaming of raw data to the cloud. We used a real installation on a bridge in Italy to test the proposed anomaly detection algorithm. We trained three compression models, namely a Principal Component Analysis (PCA), a fully-connected autoencoder, and a convolutional autoencoder. Performance comparison is also provided through an ablation study that analyzes the impact of various parameters. Results demonstrate that the model-based approach, i.e., PCA, can reach a better accuracy whereas data-driven models, i.e., autoencoders, are limited by training set size.
Optimal Formation Configuration Analysis for Cooperative Localization System of Multi-AUV

Aiming at the problem that the quality of measurement information directly affects the performance of cooperative localization of multiple autonomous underwater vehicles (multi-AUV), a design method for the optimal formation configuration of multi-AUV is proposed to select the best measurement information. First, for the two cases of whether the measurement information is correlated with the measurement error, the performance evaluation functions of formation configuration of multi-AUV are established based on the information ellipse area. Second, through in-depth analysis of these functions, the design methods of the optimal formation configuration in two cases are proposed respectively. Subsequently, taking the multi-AUV cooperative localization systems with four-master and five-master as examples, based on the proposed design method, the optimal formation configuration for cooperative localization system of multi-AUV in two cases is studied deeply, and the results show that it is dynamic. Besides, a special formation configuration is proposed for the cooperative localization system of multi-AUV, which is easier to be implemented compared with the optimal formation configuration when the localization performance is almost the same. At last, the simulation experiments verify the correctness of the proposed method and the effectiveness of the proposed special formation configuration.
Brain-Computer Interface System for Monitoring Biomedical Parameters Integrated in Virtual Reality

Many people have been trying to get aid and support in conducting day to day tasks, especially when they are fresh employees and they must learn new skills, when encountering very stressful situations or even when repetitive tasks were assigned to them. The state of mind is a very important factor which influences the learning or working efficiency of a person. Psychological studies have shown that if the mind is positively stimulated in appropriate situations and also aware of its own state, it will allow a better and fruitful learning than the cases where it is pushed and forced to accept information. This paper aims to bring its contribution to such situations by proposing a platform, powered by Virtual Reality and fed with brainwave readings of the user, which shall be able to assist and provide feedback when a person has to execute and run certain tasks that require a certain mind state and focus.
Utilization of Coconut Milk Processing Waste as a Low-Cost Mercury Sorbent

Desiccated coconut waste sorbent (DCWS), a byproduct of coconut milk processing, was studied as a sorbent for Hg(II) sorption. Energy dispersive X-ray analysis indicated that the DCWS mainly contained C, N, and O, while the Fourier transform infrared measurements confirmed the existence of hydroxyl, carboxyl, and amine groups on the DCWS surface. The point of zero charge (pHpzc) and cation-exchange capacity (CEC) values were 6.05 and 2.02 meq/100g, respectively. The batch equilibrium data were fitted well by the Langmuir isotherm model with a maximum sorption capacity, qmax of 500 mg/g, while the kinetic sorption data were found to follow a pseudo-second-order kinetic model. A column sorption study showed that the sorption capacity increased and the breakthrough time decreased with the increase in the initial Hg(II) concentrations. The regeneration studies revealed that the DCWS could be regenerated and reused.
6G Opportunities Arising from Internet of Things Use Cases: A Review Paper

The race for the 6th generation of wireless networks (6G) has begun. Researchers around the world have started to explore the best solutions for the challenges that the previous generations have experienced. To provide the readers with a clear map of the current developments, several review papers shared their vision and critically evaluated the state of the art. However, most of the work is based on general observations and the big picture vision, and lack the practical implementation challenges of the Internet of Things (IoT) use cases. This paper takes a novel approach in the review, as we present a sample of IoT use cases that are representative of a wide variety of its implementations. The chosen use cases are from the most research-active sectors that can benefit from 6G and its enabling technologies. These sectors are healthcare, smart grid, transport, and Industry 4.0. Additionally, we identified some of the practical challenges and the lessons learned in the implementation of these use cases. The review highlights the cases’ main requirements and how they overlap with the key drivers for the future generation of wireless networks.
Double-Layer Ku/K Dual-Band Orthogonally Polarized High-Efficiency Transmitarray Antenna

This paper presents a double-layer, dual-band, orthogonally polarized, highly efficient transmitarray antenna (TA) operated in the Ku/K band (14 GHz/20 GHz) for millimeter wave (MMW) wireless communication systems (WCS). The proposed TA consists of two types of multi-resonant dipoles with metal vias, operating in the Ku and K bands. The Ku and K elements are interlaced with each other independently to ensure dual-band orthogonally polarized operation. The proposed double-layer elements obtained a 360° phase shift range with transmission amplitude greater than −2 dB. They also achieved independent phase responses in both bands by two orthogonally arranged elements. The proposed TA has been designed, manufactured and measured. The measurement results, which are consistent with simulation results, indicate that maximum gains of 25.2 and 27.4 dBi corresponding to aperture efficiencies of 55% and 49% are obtained at 13.75 and 20 GHz, respectively. Moreover, 1-dB gain bandwidths of 10% and 6% are also achieved in the Ku and K frequency bands, respectively. Therefore, the proposed double-layer Ku/K dual-band TA with high efficiency and simple design is a good candidate for MMW WCS.
Validation of dynamic subjective quality assessment methodology for holographic coding solutions

In preparation of the Call for Proposals on JPEG Pleno Holography, multiple exploration studies are ongoing to define the general procedure for performance assessment of proposed coding solutions. The performance of proposals will be compared against JPEG 2000 and H.265/HEVC intra coding, both serving as anchor codecs. In this paper, we report on the results of a dynamic subjective visual quality assessment procedure. A subset of 8 holograms, selected from the JPEG Pleno Database, is compressed both in hologram and object plane at three different bit rates. Chosen bit rates vary per hologram depending on their content characteristics. Then, for each hologram and its compressed versions, pseudo-video sequences are generated from the reconstructed views along a scan path that involves focus and viewing angle changes and that is hologram specific. A double stimulus simultaneous test, combined with a 5-level impairment-scale scoring protocol is deployed where videos created from the reference and the decoded holograms are visualized side by side on a professional 4K display. Results demonstrate that this test procedure requires deep scenes and sufficient scene complexity throughout the depth stack to allow for adequate stress testing of the codecs under test, particularly those solutions that adhere to compression in the object plane.
Path Planning of UAV Based on Improved Adaptive Grey Wolf Optimization Algorithm

Aiming at the three-dimensional path planning of unmanned aerial vehicle (UAV) in the complex environment of material delivery in earthquake-stricken areas, this paper proposes an improved adaptive grey wolf optimization algorithm (AGWO) based on the grey wolf optimization algorithm (GWO). There are two main contributions of the proposed method. Firstly, we propose an adaptive convergence factor adjustment strategy and an adaptive weight factor to update the individual’s position. The effectiveness of the improved algorithm is verified by the convergence analysis and the test function simulation experiment. Secondly, the improved algorithm is applied to UAV path planning, the environmental map model is established by integrating digital elevation map and equivalent mountain threat model, and the performance evaluation function is established by fitting the calculated track length. The simulation results show that the improved AGWO is superior to the traditional intelligent algorithm in convergence precision, speed and stability performance, and it is effective for 3D trajectory optimization in complex environment.
A CNN Approach to Micro-Expressions Detection

Machine Learning and Convolutional Neural Networks (CNN) have significantly increased the performance in image recognition and are being widely adopted to analyze faces based on availability of very large databases of Figure and postures. A hot research interest of the the Face Recognition community is the recognition of different types of facial expressions. Among these, Facial Micro-Expressions (ME’s) are of big interest due to subtle movements which can show deep or suppressed emotions of an individual. These micro-expressions are quite prominently being used in security, psychotherapy, neuroscience and other related disciplines. The major challenge encountered while detecting these expressions are their low intensity and short duration. Previous works have used Eulerian Video Magnification (EVM) in conjunction with haar-cascades for face detection which gave misleading results. In this paper, we have proposed a special Convolutional Neural Network (CNN) model for face detection on which EVM is applied for amplifying the micro-expressions to a calculated threshold. Following that, a separately trained CNN is used to classify the formerly detected micro-expression into one of the seven universal micro expressions. Results obtained during the test experiment are presented at the end of the paper.
User Driven FPGA-Based Design Automated Framework of Deep Neural Networks for Low-Power Low-Cost Edge Computing

Deep Learning techniques have been successfully applied to solve many Artificial Intelligence (AI) applications problems. However, owing to topologies with many hidden layers, Deep Neural Networks (DNNs) have high computational complexity, which makes their deployment difficult in contexts highly constrained by requirements such as performance, real-time processing, or energy efficiency. Numerous hardware/software optimization techniques using GPUs, ASICs, and reconfigurable computing (i.e, FPGAs), have been proposed in the literature. With FPGAs, very specialized architectures have been developed to provide an optimal balance between high-speed and low power. However, when targeting edge computing, user requirements and hardware constraints must be efficiently met. Therefore, in this work, we only focus on reconfigurable embedded systems based on the Xilinx ZYNQ SoC and popular DNNs that can be implemented on Embedded Edge improving performance per watt while maintaining accuracy. In this context, we propose an automated framework for the implementation of hardware-accelerated DNN architectures. This framework provides an end-to-end solution that facilitates the efficient deployment of topologies on FPGAs by combining custom hardware scalability with optimization strategies. Cutting-edge comparisons and experimental results demonstrate that the architectures developed by our framework offer the best compromise between performance, energy consumption, and system costs. For instance, the low power (0.266W) DNN topologies generated for the MNIST database achieved a high throughput of 3,626 FPS.
Cloud-Based Architectures for Model-Based Simulation Testing of Embedded Software

Model-based testing (MBT) generates many test cases for validating a system under test against the user-defined requirements. Cloud computing provides powerful resources that can be utilised to execute these many test cases that would otherwise take much resources locally. Other benefits of utilizing cloud-based resources are elastic and on-demand, rapid provisioning and release of new, potentially value-adding services. Although cloud providers such as Amazon Web Services (AWS) have provided the necessary technologies for successful cloud-based operation, it remains difficult to migrate and hence achieve the realisation of MBT as a service for traditional in-house testing operations, especially for embedded software. In this paper, we present a series of cloud-based architectures powered by AWS and an open-source MBT tool, GraphWalker. These architectures are realized at simulation testing stage for real-world embedded software and particularly cater for online MBT, whereby the model-based tool is deployed as a RESTful web service, accessible through a number of REST API commands. The presented architectures as well as their realization through AWS can be adopted in future for more advanced levels of simulation testing of embedded software.
Monitoring Cyber SentiHate Social Behavior During COVID-19 Pandemic in North America

With communications being shifted to online social networks (OSNs) as a result of travel and social restrictions during COVID-19 pandemic, the need has arisen for discovering emerging trends and concerns formed during the pandemic as well as understanding the corresponding online social behavior that reflects its offline settings. The online connectivity of devices through social media is one example of Internet of Things (IoT) in which a two-way communication between societies and officials, could be created. Therefore, it is possible to monitor people’s behavior through OSNs, especially during pandemics, to prevent potential social and psychological instabilities that might lead to undesired consequences. This is particularly crucial for governmental and non-governmental organizations to ensure the stability and well-being in societies. In response, we propose a pandemic-friendly real-time framework for monitoring cyber social behavior by utilizing unsupervised and supervised learning approaches. Two BERT-based supervised classifiers are trained and constructed to analyze two types of online social behaviors, hate and sentiment. Unsupervised framework is proposed for OSNs data exploration and coherent interpretation that is used as a complementary tool to facilitate the analysis of online social behaviors during pandemics. Extensive experimentation and evaluation have been conducted to validate the effectiveness of the proposed work. Our results have shown superior performance of our BERT-based models in two classification tasks: 1) binary classification for hate behavior detection and 2) multi-class classification for sentiment behavior detection. In addition to our experimentation results, our large-scale analysis of COVID-19 pandemic has illustrated the capability of our unsupervised framework for concerns and trends discoveries using OSNs data, along with reliability in automatically and dynamically providing phrase-based interpenetration of the inferred trends and concerns. This paper provides a twelve-month comparison analysis of data discoveries and online social behavior between Canada and USA during COVID-19 pandemic.
Causal Semantics for BPP Nets with Silent Moves

BPP nets, a subclass of finite Place/Transition Petri nets, are equipped with some causal behavioral semantics, which are variations of fully-concurrent bisimilarity [3], inspired by weak [28] or branching bisimulation [12] on labeled transition systems. Then, we introduce novel, efficiently decidable, distributed semantics, inspired by team bisimulation [17] and h-team bisimulation [19], and show how they relate to these variants of fully-concurrent bisimulation.
Progress in Real-Time Capacitance Tomography of Multiphase Flows Based on the Maxwell-Wagner-Sillars Effect

Multiphase flow tomography involves the decomposition and continuously monitoring of different phase components in a multiphase flow mixture. This work summarizes recent progress on electrical capacitance tomography (ECT) exploiting the Maxwell-Wagner-Sillars (MWS) effect at various frequencies. The MWS effect is used to distinguish and monitor different phases of a water-dispersed multiphase flow. Alternatively, it enables ECT to monitor non-conducting phases in water-continuous multiphase flows. We illustrate the application of the method in a three-phase flow of air and water dispersed in oil (water-dispersed case) and in a two-phase flow of oil dispersed in water (water-continuous case) using a conventional ECT measurement sensor hardware. It is observed that the proposed method inherits the high speed of acquisition of ECT, including the potential for real-time monitoring.