Miracle-dz/UniFAR-FTUs huggingface dataset:
MeLSeC: A Method for Lightweight and Secure Communication in Internet of Vehicles

Autonomous message propagation in Internet of Vehicles (IoV) need to be secure to guarantee safety. In fact, a malicious user propagating false information may cause accidents impacting on the safety of both vehicles and drivers. Authentication should however account for the time-critical nature of messages impacting the vehicles behavior. Furthermore, it should account for the resource-limited nature of vehicles. Currently available authentication protocols fail in providing a secure and lightweight solution in terms of computation time, communication cost, and storage cost. Furthermore, as we later show, they are vulnerable to multiple attacks.In this paper, we propose Method for Lightweight and Secure Communication (MeLSeC), a novel lightweight and secure authentication scheme for IoV. Compared to state-of-the-art authentication solutions, MeLSeC is resistant to a larger number of attacks. We test MeLSeC against multiple attacks using Automated Validation of Internet Security Protocols (AVISPA), and show its higher security guarantees against other relevant state-of-the-art solutions. By comparing it with other relevant state-of-the-art solutions, we show that MeLSeC provides up to 700 times smaller computation costs on a desktop computer and up to 4 times smaller computation cost on Raspberry Pi. Furthermore, MeLSeC reduces the joint communication and storage costs by up to 7 times.
A Compact CPW fed Band-notched Antenna with Semi-Elliptical Radiating Structure and a Slot of U-shape for Ultra-wideband Applications

A compact planar antenna with a semi-elliptical radiating element and a slot of U-shape is etched out from the radiating element. This planar antenna is having a compact dimension of 20x20 mm2. The spectrum band between 3.1 GHz and 10.6 GHz is covered having VSWR ≤ 2. A semi-elliptical patch is printed within an elliptical slot on the plane of the ground. The feeding mechanism for an antenna is accomplished by a 50-Ώ transmission line on the same plane. To obtain a band notch at the WLAN band, a slot of a U-shape is carved on the patch. This particular antenna shows the required performance in terms of operating bandwidth, desired gain stability, and suitable radiation pattern.
Aggregate Drone Monitoring of Wildfires

Wildfires are an ever-increasing problem, exacerbated by the current global warming trends. Accordingly, it is becoming more and more relevant to monitor factors influencing their outbreaks and spreading, in order to both preemptively act on the most risky areas, and guide interventions in case an outbreak occurs. Different approaches have been proposed during the decades tackling this issue, which however require large datasets that are difficult and expensive to gather.In this paper, we propose to address the management of wildfires by empowering existing centralised models with a decentralised component. Leveraging dedicated monitoring drones together with smartphones held by experts and intervention corps, a decentralised system could both enhance data collection and assist interventions. As conditions near wildfires require strong fault-tolerance guarantees, we propose to develop such an application through aggregate programming, a novel approach to the resilient programming of decentralised systems.
SELECT: Secure and Lightweight Context-Aware Mechanism for Smart Vehicular Networks

Intelligent vehicular networks offer a powerful platform for traffic control and data processing. However, reciprocal verification between vehicular devices is essential before initiating the connection, as exchanged messages are used in real-time vehicular applications and services. Though many communication protocols are designed to convey sensitive data, recently developed methods are still unable to withstand critical vehicular communication threats. Moreover, they require significant computational resources in a mobility environment. This paper presents a SEcure and LightwEight vehicular ContexT-aware (SELECT) mechanism applicable to vehicular networks. SELECT withstands critical security attacks for public channel communications. The test-bed findings show that SELECT outperforms relevant message communication approaches in computation time, energy consumption, communication overhead, and storage cost on the Raspberry Pi 3B device.
Convolutional Neural Networks for Analysis and Recognition of Facial Expressions

Facial expression recognition is one of the foundations that gives enormous aid and simplicity for people working in the fields of artificial intelligence, gaming, marketing, and healthcare; it is also one of the foundations that is at the centre of many recent advancements. People are able to communicate their feelings to one another primarily through their facial expressions, which is significant given the significance of emotions as a tool for effective communication. Facial expressions are the primary means through which people convey their sentiments to one another. The process of identifying the feelings that are being sent on a person's face during social encounters is one that is both necessary and challenging. It is feasible to utilise the identification of facial expressions in conjunction with other security measures in order to raise the level of protection that is given to an individual. In the case that a consumer uses an automated teller machine and displays indicators of anxiety, it is possible that the computer will be programmed to refuse to dispense cash to that person. A person who is blind may also benefit from this since it may assist them in understanding the sentiments and facial expressions of the person who is standing in front of them. Discovering others on social media who are experiencing depression via their accounts might also be useful. This may be used in games to assess a player's strong and weak places, and the difference between the two can be formed by analysing the player's expressions at various moments throughout the game. This can be used in games to analyse a player's strengths and weaknesses. The technique known as "feature extraction" was used so that the areas of the faces that were determined to be the most important may be concealed.
Decoupled Simultaneous Complex Power Sharing and Voltage Regulation in Islanded AC Microgrids

In islanded ac microgrids, when multiple voltage source inverters (VSIs) operate in parallel, it is challenging to simultaneously obtain both accurate complex (active and reactive) power sharing and voltage regulation (in terms of frequency and amplitude). State-of-the-art strategies that target these two objectives often rely on hierarchical primary droop plus communication-based secondary controls and adaptive virtual impedance methods. However, control dynamics and/or accuracy are still affected by inverters line impedances mismatches. To overcome these limitations, this article presents for balanced ac microgrids a distributed control strategy that simultaneously guarantees both voltage regulation and accurate power sharing. The distributed strategy is based on having at each VSI a single controller that merges two control loops that can be designed in isolation, thus decoupling the design of the power sharing control from the design of the voltage control. The cooperative operation of all VSI controllers, which requires exchanging control data over a communication network, ensures meeting both control objectives with the independence of the line impedance mismatches or the connection and disconnection of inverters and loads. And these properties are achieved without changing the controller structure and (easy-to-tune) parameters nor increasing the control effort. Experimental results corroborate the control proposal.
Automatic Road Extraction from Very High Resolution Orthophoto Using DeepLab V3+

Road extraction, one of the processes in map-making, is widely used by various services such as intelligent transportation systems, disaster navigation and urban planning. So far, road extraction is done manually, which takes a long time, costs a lot, and needs to be carried out by a team of experts. Automated semantic segmentation can speed up the road extraction process. The author proposes the application of Deeplab V3+ model for road extraction from very high resolution orthophoto with the Indonesian study area. From the study, the model achieved mean Intersection Ratio Union value 88% and Mean Dice loss 6.8%.
Digital Business Transformation Framework: Mavens Studio Case Study

Digital business transformation is the use of technology that creates new business models, processes, software, and systems to generate increased revenue, competitive advantage, and efficiency of an Organisation. The emergence of digital business transformation is triggered by dynamic market changes and changes in consumer behavior in the use of technology and is triggered by the Coronavirus Disease 19 (Covid-19) pandemic. The impact of factors mentioned above affects medium-sized enterprises, in this case, the creative agency that is the research object. To assist creative agencies in maintaining their business, a guideline in the form of a digital business transformation framework is needed because there has not been a framework that can provide measurable guidance for digital business transformation in the sector. Researchers developed this framework using Design Science Research Methodology (DSRM). Evaluation of the proposed framework using a questionnaire with internal company respondents resulted that the aspect of people stating that 92% helped the digital business transformation in the scope of people's involvement in it. However, there was a percentage of 8% stated that they had not measurably minimized the impact of the change. While the results of the technology aspect stated that 100% had been able to help form a technology change team. Finally, the results of the process aspect stated that 100% had been able to explain and achieve the company's KPIs. The conclusion is that all of these aspects have digital business transformations, and the proposed framework can be used as a guide to influence in a structured and measurable way.
Resistance to Ohm's Law

It is argued that the usual account of the discovery and subsequent rejection, or criticism, of Ohm's law is both a misleading and an inadequate explanation. A close logical examination of Ohm's experimental work reveals a conceptual structure quite different from that of the electrical science of his time. As a result of this analysis, it is claimed that the conceptual shift in Ohm's experimental work was the basis for the reaction of his contemporaries.
Switching and modulation of light in magneto‐optic waveguides of garnet films

We report for the first time switching and modulation of light in a magneto‐optic waveguide that is a single‐crystal epitaxially grown iron‐garnet film. These experiments involve the Faraday rotation of the magnetic film and the motion of magnetization in the plane of the film. We have modulated light from a 1.152‐μm laser up to 80 MHz. We were also able to switch light between two waveguide modes by applying a magnetic field as small as 0.2 Oe.
Machine Learning vs Deep Learning for Anomaly Detection and Categorization in Multi-cloud Environments

Detecting intrusions is a critical issue in cyberse-curity. One way to overcome this issue is to build efficient and robust Network Intrusion Detection Systems (NIDS) using existing Machine Learning (ML) algorithms. Such an approach has been proposed in the literature and has been shown to perform well. However, a comparative analysis of the performance of ML and Deep Learning (DL) based NIDS for both detection and categorization of intrusions is still needed. This paper investigates the performance of ML and DL models for both intrusion detection and categorization. We use the publicly available Canadian Institute of Cybersecurity Intrusion Detection System 2017 (CICIDS-2017) dataset to train and test ML and DL models. We apply three traditional ML models, namely, Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), and three DL models − 1-D Convolutional Neural Network (ConvlD), Recurrent Neural Network (RNN), and a two-staged model that combines an unsupervised Dense Autoencoders (DAE) for pre-training and an Artificial Neural Network (ANN) for classification. Our results demonstrate that RF is the best performing ML model with a detection accuracy of 99.5% and DAE-ANN is the best performing DL model with a detection accuracy of 98.7%. We also show the advantages of using a stepwise multi-classification over a classical single-stage multi-classification. Finally, we observe that RF outperforms DAE-ANN in categorization with detection rates of 91.35 % and 84.66 %, respectively.
Discovering an S-Coverable WF-net using DiSCover

Although many algorithms exist that can discover a WF-net from an event log, only a few (if any at all) can discover advanced routing constructs. As examples, the Inductive miner uses process trees and cannot discover complex loops, or situations where choice and parallel behavior is mixed, and the Hybrid ILP miner cannot discover certain complex routing constructs because it cannot discover silent transitions. This paper introduces the DiSCover miner, a discovery algorithm that can discover these more complex constructs and that is implemented in ProM. The DiSCover miner discovers from the event log a WF-net that corresponds to a collection of state machines that need to synchronize on the visible transitions (that is, on the activities from the event log). As such, it discovers a WF-net that is S-Coverable but not necessarily sound. Initial results show that it can discover complex routing constructs and that it performs well on the data sets of the different Process Discovery Contests. It even preformed better than winners of the 2020 and 2021 contests.
Comparison between dry and wet EEG electrodes in an SSVEP-based BCI for robot navigation

Electroencephalography-based brain computer interfaces (BCIs) have been widely used in assistive applications for patients suffering from quadriplegia, or even the locked-in syndrome, to promote autonomy and control. Steady-state visual evoked potentials (SSVEPs) is a BCI stimulation protocol that has been employed widely in navigation applications, due to their efficiency and fast response time. In the current study, we use a previously developed SSVEP-based BCI for robotic car navigation with a low-cost EEG recording device, to compare the performance of the easier-to-use dry EEG-electrodes and the commonly-used wet electrodes. We also employ two different stimulus scenarios, a fixed one and an adaptive one optimized for each participant. We tested our system on 23 healthy participants in both an offline session and an online navigation task on a predefined route. The results indicate that our system with the combination of the low-cost EEG device and the dry electrodes achieves comparable performance to the same system using wet electrodes. Moreover, it ensures a more user-friendly and affordable alternative that could be adopted by a larger part of potential users.
Lessons Learned in Omnidirectional Co-Design of Neuromorphic Systems

Neuromorphic computing is a broad, interdisciplinary field that spans across all levels of the compute stack, from applications and algorithms to devices and materials. When designing a new computing system from the ground up, co-design is key. Here, we discuss different types of co-design for neuromorphic systems, and we advocate specifically for omnidirectional co-design. Finally, we discuss recommendations for performing omnidirectional co-design, based on our own experience in the realm of neuromorphic computing.
Performance Analysis of Three Phase Induction Motor Using Solar PV Array Fed Modular Voltage Source Inverter

Induction motors are most commonly used in industries in various applications because of higher efficiency, lower cost, reliability, simple and rugged construction and lower maintenance. For controlling induction motor, electric drive is required. Induction motor drive (IMD) normally employs conventional voltage source inverter (VSI), but this converter has limitations such as the current taken from the supply generally contains significant harmonics and it has voltage drops in the converter which limits the maximum voltage to less than the supply voltage. In order to overcome this limitation and improve the performance, a modular voltage source inverter (VSI) is proposed to drive induction motor. Present work deals with analysis of solar photovoltaic (PV) fed modular VSI induction motor drive. This proposed system is modelled in MATLAB/Simulink. The results of the proposed system are compared with existing conventional VSI fed induction motor drive. The proposed system offers better results in terms of lower total harmonics distortion(THD). The proposed system also offers smooth speed curve and hence may be preferred for the water pumping.
Design and Implementation of UFRJ Nautilus’ AUV Lua - A TinyML Approach

The UFRJ Nautilus is a student-driven engineering project team at the Federal University of Rio de Janeiro focused on building and designing AUVs to compete in the AUSVI RoboSub Competition. The team focused on the making of a brand new autonomous underwater vehicle (AUV) called Lua. There are several challenges on building a software system for an AUV. The localization problem is a major challenge. Among the different proposals for localization, the beamforming algorithms have proved to be a reliable option. However, they present some limitations. The beamforming algortihms have two sources of errors: (i) the noise typical of sensors and signals and (ii) the arrangement of the sensors, which is a function of the true azimuth (horizontal angle from an observer - the AUV - to the object of interest) and elevation angles. Therefore, the beamforming has an error factor which is the sum of both errors described. In order to overcome this issue, we propose using a TinyML-based approach - an embedded Machine Learning algorithm that is able to learn about angles combination that reduces the error. To reduce the noise we will combine the TinyML algorithm with a traditional Kalman Filter. Experimental results showed that our proposal has a better performance reducing the errors from previous approaches.
Switching techniques for capacitor arrays in Digital to Analog Converters

Data converters like ADCs and DACs have many uses in fields like data collecting, signal processing, biomedical instrumentation, and more. High-speed, medium-resolution applications are ideal for SAR Analog-to-Digital converters. Current efforts to reduce SAR ADC power consumption have been the subject of extensive study. In SAR ADCs, capacitor DACs absorb the majority of the total power during capacitor switching. This paper provides a framework for future research into the use of switching strategies to develop energy-efficient Digital to analogue converters for medium to high resolution applications by categorising existing switching techniques for capacitor arrays in D/A converters.
Uncovering Product Vulnerabilities with Threat Knowledge Graphs

Threat modeling and security assessment rely on public information on products, vulnerabilities and weaknesses. So far, databases in these categories have rarely been analyzed in combination. Yet, doing so could help predict unreported vulnerabilities and identify common threat patterns. In this paper, we propose a methodology for producing and optimizing a knowledge graph that aggregates knowledge from common threat databases (CPE, CVE, and CWE). We apply the threat knowledge graph to predict associations between threat databases, specifically between products and vulnerabilities. We evaluate the prediction performance based on historical data, using precision, recall, and F1-score metrics. We demonstrate the ability of the threat knowledge graph to uncover many associations that are currently unknown but will be revealed in the future.
BoidVR: An Agent Simulation Environment Based on Freehand and Virtual Reality

Freehand-based interaction techniques (FITs) are an emerging technology of importance for effective virtual reality (VR). In this regard, most modern head-mounted displays, such as the Oculus Quest, are equipped with inside-out cameras for egocentric hand tracking and natural hand gestures recognition. Following this technological evolution, we have developed an agent-based modeling and simulation VR application, called BoidVR, focused on freehand-based interaction with simulated agents. Our system enables users to view and interact with both the agents and virtual environment using FIT-based tools. We illustrate a number of specific gestures that allow users to manipulate the virtual environment and affect agent behaviors. Finally, we conduct a user experiment in which we evaluate the usability and user sentiment of our BoidVR application.
Predicting Text-To-Speech Quality using Brain Activity

The perceived audio quality is one of the key factors that may determine a text-to-speech system’s success in the market. Therefore, it is important to conduct audio quality evaluation before releasing such system into the market. Evaluating the synthesized audio quality is usually done either subjectively or objectively with their own advantages and disadvantages. Subjective methods usually require a large amount of time and resources, while objective methods lack human influence factors, which are crucial for deriving the subjective perception of quality. These human influence factors are manifested inside an individual’s brain in forms such as electroencephalograph (EEG). Thus, in this study, we performed audio quality prediction using EEG data. Since the data used in this study is small, we also compared the prediction result of the augmented and the non-augmented data. Our result shows that certain method yield significantly better prediction with augmented training data.
Modified CNN for Multi-class Brain Tumor Classification in MR Images with Blurred Edges

Brain tumors are caused by the hyperproduction of specific cells. Only sixty percent of patients with a malignant brain tumor survive for more than a year. As there is no treatment for tumor, the strategy implemented to provide relief to the patient is preventing tumor cells from spreading through chemotherapy or surgery. Only early stages of the cancer can be successfully treated with these approaches, making early cancer detection crucial. However, the doctors with exceptional domain knowledge need not be occupied with this task as it is linear enough to program computers to achieve adequate success in predictions, and hence, the doctors can be involved only when there is an uncertainty in the computer predictions. In order to determine the type of tumor on Magnetic Resonance Images (MRI), this article suggests a novel strategy that combines deep learning and image transformation techniques using Fourier transformation, and classifies brain tumors into four categories, including gliomas, meningiomas, pituitary tumors and no-tumor. The results show that, in comparison to the existing deep learning models, proposed strategy yields a 5% gain in accuracy simultaneously classifying and providing results more quickly.
Fractals: A User's Guide for the Natural Sciences

PART I. INTRODUCTION: OUR VIEW OF NATURE PART II. THE MATHEMATICS OF RANDOM FRACTALS: FRACTALS AND POWER LAW SCALING 1. Dimension of graphs of functions 2. The Fourier transform PART III. THE BRIDGE TO APPLICATIONS: MODELLING SPATIAL AND TEMPORAL PATTERNS 3. Alternative models 4. Examples 5. Fractal analysis of time series PART IV. CASE STUDIES: PATTERN AND PROCESS IN VEGETATIVE ECOSYSTEMS 6. Scaling behaviour of density-dependent populations under random noise PART V. THE TOOLBOX: PROGRAMS/ANNOTATED REFERENCES Index