Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Charging System Electric Energy Metering and Its Impact on Operation

With the gradual maturity of wireless charging technology for electric vehicles, wireless charging piles have gradually been installed for trial operation. Different from the conductive charging piles, there is still a lot of controversy in the energy measurement of electric vehicle wireless charging systems. Based on the characteristics of the separation of the primary and secondary sides of the electric vehicle wireless charging system, this paper analyzes the characteristics of the electric vehicle wireless charging system and the impact on measurement. This paper discusses the two billing modes of the transmitter and receiver. And based on these two billing modes, this paper selects four different measurement points for feasibility analysis, gives the advantages and disadvantages of different measurement point selection. This paper discusses and summarizes the impact on the actual operation of electric vehicles. The research results show that the measurement of electrical energy at the transmitting coil is a more suitable measurement scheme for the commercial operation of the charging stations, but at the same time, different measurement methods can be used in different applications to meet to the current needs.
Analysis of Technological Trends in Digital Public Services: The context of Colombia.

The purpose of this study is to show the technological analysis for the development of an anticipatory Digital Public Services (DPS) public policy framework, which is a catalyst for the Digital Government Policy (DGP) of the Colombian government. The study linkage the use of field of futures studies in policymaking to contribute to the national policymaking process. The results aim to improve and to strengthen the interaction and relationship between government agencies and citizens by creating public value and innovation through the integration of digital technologies over a period of ten years. The developed process uses competitive and technological intelligence practices and integrates six types of key social actors. The results recognize the relevant role of emerging technologies, which allow the design of measures to build a forward-looking policy framework that combines an “exploratory” vision and at the same time a “regulatory” vision that integrates five dimensions: institutional, normative, economic, social and technological. The results presented here refer to the future scenario named “DPS, an emergent way of networked institutions and laboratories to promote public innovation in the systematic and growing development of digital services Therefore, it is expected that these contributions from the National Planning Department (DNP) in Colombia will help to reduce the difficulties faced by decision-makers in implementing policy initiatives to promote public digital transformation.
Enhanced Protection for Information and Network using Intrusion Detection System

The world of today has been transformed into a little community where everyone may communicate with one another. The widespread use of the internet makes this possible. People now have a huge window of opportunity thanks to the internet to broaden their perspectives. Threats to personal security were another negative, which will result in expenses for both psychological support and money. There is a lot of research being done to preserve the data in such an environment. The results of this survey could improve internetworking environment security and control current assaults like DoS and cyberattacks. As it allows for the interception of private networks, intrusion is a problem that frequently arises in internetwork. Denials of Service Attacks are a significant issue in Internet networks as it can disrupt communication between two or more authorized entities. The entire focus of this research is on examining the approaches used to develop intrusion detection systems.
A Systematic Approach for the Prediction of Personality Based on Attention Enhanced GCNN and LSTM Approach

Individuals can be distinguished from one another in significant ways, and one such way is by one's personality. Predicting someone's character traits is a perennial topic of study. Personality prediction using social media data has great potential because it eliminates the need for users to fill out lengthy questionnaires, saving both time and improving the method's trustworthiness. As a result, understanding personality is a topic that piques the curiosity of scientists. There are many real-world uses for personality prediction. The popularity of social media platforms continues to grow. Every day, more and more pictures and text are uploaded on the internet. Most recent efforts have been concentrated on feature extraction, feature selection, and model training. The proposed method employs AGCNN-LSTM for measuring performance. The suggested model performs better than the CNN and LSTM models against which it is benchmarked.
Pareto-Based Dimensionality Reduction of Parameters in DC/DC Converters

This paper studies a method to reduce dimension of parameters in DC/DC converters. The method is based on a bi-objective optimization problem characterized by approximated Pareto front. The first objective evaluates stability of the circuit operation and the second objective evaluates extracting input power. In order to evaluate the approximated Pareto front, we present a modified hypervolume indicator. Applying the method to a piecewise linear model of a boost converter, the dimensionality reduction is realized effectively.
Enabling NR Cellular Networks for UAV: Introduction to the Interference and Mobility Issues

Nowadays, the popularity of Unmanned aerial vehicle (UAV) based services is growing rapidly. For cost and deployment time, radio communications for UAVs would derive enormous benefits from widely distributed cellular network coverage. 5G New Radio (NR) support for UAVs is one of the vertical services recently addressed in the 3GPP standardization activities. This paper briefly describes the interference and mobility issues that NR needs to address in order to support UAV scenarios. Then we sketch some potential solutions for enhanced interference and mobility management. Finally, we describe the direction of future work to enhance NR to support UAV services.
Graph Regularized Low-Rank Representation for Hyperspectral Anomaly Detection

One of the most crucial uses of hyperspectral images is anomaly identification, which seeks to find items that deviate significantly from their surroundings. Many other approaches to anomaly detection have been suggested in the past. However, given the spectral connection of all the pixels, the majority of them never reach a climax. In recent years, there has been a lot of interest in Low Rank Representation (LRR) as a viable model for hyperspectral images, which frequently contain a global structure made up of a few groundcover signatures.In this research, we offer a new hyperspectral anomaly detection approach based on the Graph Regularized LRR (GLR), which we combine with the graph regularization into the LRR formulation. The combination of the global and local structure in the suggested algorithm is a key benefit.
Identification of Facial Emotions in Hitech Modern Era

At the moment, one of the most significant areas of pattern recognition research is facial expression recognition. This research study proposes a novel method for identifying people's facial expressions based on their moods. Emotions are sudden impulses accompanied by alterations in the facial muscles that result in emotions on the face. The objective of emotion recognition is to identify which of the seven basic emotions-joy, sadness, anger, contempt, surprise, dread, and no-emotion these facial expressions belong to. This topic is difficult due to the complexity of human expressions, which has several applications, including the ability to predict behavior. Deeper architectures have helped researchers understand emotions at the cutting edge of technology. In this research, Convolutional Neural Network, a Deep Learning-based technique, is utilized. Using numerous benchmarked datasets, including FER-2013, CK+ and RAF-DB, the suggested approach's overall efficacy and accuracy are assessed. Experimental results showed that the recommended method obtains an accuracy of 91.12, 92.52%, and 93.83% for Inception-V3, VGG19, and ResNet-50, respectively, on benchmarked datasets as well as our custom-made dataset. ResNet-50 outperforms the competition in real-time learning scenarios with an accuracy of 93.83% for classifying facial emotions.
Deep time series missing data from self-attention-based inference model

This study suggests a deep time series missing data inference model based on self-attention, known as AGRU-AE, to fill in the missing data in order to address the issue that data loss influences the impact of data analysis. In order to deal with various missing gaps and achieve the goal of concentrating on highly correlated sequences, the model combines a gated cyclic data unit (GRU) and an autoencoder (AE), adds a self-attention mechanism between the encoder and the decoder, and calculates the relationship weight between the partially generated sequence in the encoder and the partially known sequence in the decoder. The experimental findings demonstrate that the suggested model AGRU-AE, when compared to the conventional approach, can fill and predict the incomplete time series with more accuracy.
A Teacher-Focused Impact Assessment in Scratch4All Project

The “Scratch4All” project aims to reduce school dropout rates by encouraging and motivating students in the 1st, 2nd, and 3rd cycles of elementary schools to achieve academic success. In addition, the project promotes true equality of opportunity in terms of educational resources. A learning environment that contributes positively to the improvement of educational results is created through the use of new technologies, specifically programming in the Scratch language and robotics. Impact indicators were developed and used as project evaluation tools based on Theory of Change. This paper presents and analyzes the changes observed from the perspective of the project’s teachers’ stakeholders during the 2020/2021 school year. This group of beneficiaries is critical to the project’s success and continuation, not only because of the special relationship they have with the students, but also with their families and the school. The assessment of impact is very positive in terms of indicators such as school success and motivation, particularly in Mathematics and Portuguese Language, as well as in the reduction of inequalities, access to new technologies, and contribution to gender equality.
Are enterprises from lagging regions digitally connected?

This study focuses on how enterprises located on lagging regions are using online resources and how can they enhance their usage. Most companies have transitioned to the “online world”, however, in rural and more deprived regions, it is perceived that companies still have difficulties accessing some online resources or accessing qualified workers in ICT areas. As such, this paper focus on 29 enterprises from the northeast of Portugal, a recognized lagging region. In the context of an international project, CRECEER creation of business cooperation networks in rural cross-border regions between companies in the gourmet agri-food and tourism sectors, the use of ICT was analysed, what online tools were used and what could be done to improve their ICT use in their business.As a result, it was found that the majority of the companies only had a minimal investment in business online tools, they are not aware of the majority of cloud computing software, and they do not use it in their business. Even though the studied companies were willing to evolve digitally, they recognized the severe restrictions they had, namely the requirement of specific currently unavailable knowledge, the necessity of qualified workers that they couldn’t afford, and even the hardware necessity they couldn’t supress.It is clear that for these companies had it not been the CRECEER project, even the smallest changes and improvements, would not be achieved in a short period. It is rather crucial to have European funds dedicated to enhance the ICT competencies of these businesses.
Network Solutions and Trials of 5G Emergency Small Cell

5G is a new generation of broadband mobile communication technology with high speed, low latency and large connectivity, which has been widely deployed and applied in China. In recent years, natural disasters and emergencies have occurred frequently, and emergency communications play a very important role in the handling of disasters and emergencies, so it is necessary to study how to better use 5G for emergency communications. The article proposes a new technology to enhance emergency disaster response systems using high throughput satellites, 5G, edge computing and other new technologies to meet emergency communication needs in different application scenarios by organically combining multiple new technologies in the 5G era.
The Anthropology of Food: Core and Fringe in Diet

Tonight I should like to bring together through my theme, Core and Fringe, two remarkable changes—transformations—one could almost say revolutions—in the history of human nutrition; and to do so by describing a central feature of the eating habits of the vast majority of human beings, both alive and dead. In effect, this is a story which, while primarily historical, has, I believe, important cultural or anthro pological relevance.
Modeling and Recognizing Network Scanning Activities with Finite Mixture Models and Hidden Markov Models. (Modélisation et reconnaissance des activités de balayage du réseau à l'aide de modèles à mélange fini et de modèles de Markov cachés)

The work accomplished in this PhD consisted in building stochastic models of ZMap and Shodan, respectively, two Internet-wide scanners. More in detail, packets originated by each of the two considered scanners have been collected by the High Security Lab hosted in Inria, and have been used to learn Hidden Markov Models (HMMs). The rst part of the work consisted in modeling intensity of the two considered scanners. We investigated if the intensity of ZMap varies with respect to the targeted service, and if the intensities of the two scanners are comparable. Results showed that the answer to the first question is positive (i.e., intensity of ZMap varied with respect to the targeted ports), whereas the answer to the second question is negative. In other words, we obtained a model for each set of logs. The following part of the work consisted in investigating other two features of the same scanners: their spatial and temporal movements, respectively. More in detail, we created datasets containing logs of one single execution of ZMap and Shodan, respectively. Then, we computed di erences of IP addresses consecutively targeted by the same scanner (i.e., in each sample), and of the corresponding timestamps. The former have been used to model spatial movements, whereas the latter temporal ones. Once the Hidden Markov Models are available, they have been applied to detect scanners from other sets of logs. In both cases, our models are not able to detect the targeted service, but they correctly detect the scanner that originates new logs, with an accuracy of 95% when exploiting spatial movements, and of 98% when using temporal movements
Efficient VNF-FG Scaling Algorithm for 5G Network Slices

While meeting the needs of network traffic, dynamic deployment of VNF is the key to cost optimization of NFV system. In addition to considering server resource capacity and dynamic user traffic, the dynamic expansion strategy also needs to strike a balance between the operating costs of VNF, the cost of maintaining VNF instances, the cost of VNF deployment, and the additional cost of servers placing new VNF instances. In this paper, the problem is modeled as a 0-1 integer linear programming model with quadratic terms, and a new linear programming method is used to solve the problem. The algorithm reduces the complexity of the algorithm to a great extent by reducing the number of variables in the constraint. Through the experimental simulation, we prove the effectiveness of the algorithm and reduce the cost of VNF-FG scaling.
Research on Launch Management Control in Hybrid Vehicles

The launch management strategy of the P2.5 hybrid system is based on the creep force calculation, the traction force arbitration and launch force control. The specific contents are as below:1)The creep control. The base creep force is based on the pedal map and oil temperature. The cancellation conditions of creep force are defined.2)The traction force arbitration. The creep force is sent to this part and the traction force is based on the drive mode, the acceleration pedal map and the vehicle speed.3)The launch torque control. The purpose is to control clutch torque during launch and to send the lock up flag out when launch is finished. There is also a P-controller that controls the desired slip, this can to some extent compensate for poor engine/clutch torque accuracy. The launch control is based on the P-control of the slip speed and the drive compensation is defined.4)The torque arbitration control. There are two crankshaft torque requirements arbitration and crankshaft torque limit transmission. The slow limitation through transmission control is used to protect the gearbox from overload and temporarily reduce the crankshaft torque during gear shifting. The fast limit is only used to temporarily reduce the crankshaft torque during shifting.The vehicle results are conducted and show that launch torque strategy can also meet the driver torque by the motor and avoid the drop of the engine speed.
Smart Dashboard for Hoffmann Reflex Analysis

The Hoffmann reflex is a is a neurophysiological test that provides insight into the functioning of the human nervous system. It is commonly used in clinical and research settings to evaluate the modulation of the monosynaptic spinal reflex. This paper focus the analysis of the Hoffmann reflex in the trapezius muscle, a muscle of particular interest for researchers and clinicians due to its importance in upper limb function and dynamic stability. However, the Hoffmann reflex analysis of this muscle bring some challenges as the need of applicating burst of electrical square impulses in each current intensity. A web-based smart dashboard, implemented in Python, which allows the user to visualize and analyze the Hoffmann reflex using various signals acquired through a constant current stimulator. The dashboard provides an intuitive and user-friendly interface that facilitates the selection of muscle signals of interest, analysis cycles, and start and end points for the signals. The visualizations offered by the dashboard, including overlapped and mean signal graphics, provide valuable insights into the Hoffmann reflex and its properties. Preliminary experiments with field experts and physiotherapists have yielded positive feedback on the usefulness of this tool, as they seek to gain a deeper understanding of the Hoffmann reflex, and we plan to further improve its capabilities in the future by employing machine learning techniques to automate the reflex detection.
Compact Quad-band Filter with Bridged-T Coil Based on Through-Silicon Vias Technology

By using the lumped elements based on the bridged-T coil structure, a quad-band bandpass filter is proposed based on Through-Silicon Via (TSV) technology. By stacking multilayer bridged-T coils in 3D configuration, the proposed filter is minimized to a compact footprint of only 0.4 $\mathrm{mm}^{2}$. With the four passbands of designed at 0.665, 5.2, 10.5 and 35.8 GHz, the simulated results demonstrate a high-selectivity and wide stopband band range (up to 40GHz) performance. The filter has insertion losses less than 0.48 dB@0.665 GHz, 2.48 dB@5.2 GHz, 2.6 dB@10.5 GHz and 2.12 dB@35.8 GHz in the passbands. The suppression levels are better than 38dB in the stopband.
Ensemble Deep Learning Models for Accurate Prediction of Cardiovascular Disease Risk: A Comparative Analysis

A leading cause of death globally is cardiovascular disease (CVD). Early CVD detection is essential for successful treatment and complication prevention. Convolutional neural network (CNN), Recurrent neural networks (RNN), bidirectional recurrent neural networks (BiRNN), deep neural networks (DNN), and an ensemble model has all been used in this study's deep learning-based approach for CVD prediction. With a test size of 20%, suggested models were trained and assessed on a dataset of 303 patients. The models were assessed using a variety of criteria, including recall, sensitivity, specificity, F1-score, accuracy, and precision. The ensemble model achieved best performance, with 99% accuracy, 100% precision, 100% recall, 0.97 F1-score, 1.0 sensitivity, and 0.99 specificity. The training and validation loss vs. epoch graph for each model was also analysed to assess the model's performance. Findings from this research suggest that the proposed machine learning-based approach can effectively predict CVD, with the ensemble model outperforming individual models. The use of such models can aid in the early detection and prevention of CVD, improving patient outcomes. Future work can focus on evaluating the proposed models on a larger dataset and incorporating additional clinical variables.
QoS splitting mechanism for UE-to-UE multi hops

With the development of mobile communication technology, QoS has been widely studied and applied as an important basis for measuring network performance. To support QoS handling for UE-to-UE multi hops, a new QoS handling mechanism is presented to ensure the end-to-end QoS by adding new parameters to the confirmation parameters. By introducing Link Quality Indicator (LQI) and wireless link burstiness factor, the proposed QoS handling mechanism is modeled and evaluated from link quality and node cost. And numerical results have shown that the QoS splitting formulation can be extended to multi hops with more than two UE-to-UE relays.
Implementation of a Novel Framework for Physically Disabled Patients using Human-Computer Interface

Human-Computer Interface (HCI) refers to how a user interacts with a computer through the use of various input/output devices. Computers are now capable of seeing the world from a perspective similar to humans due to current developments in the fields of Computer Vision and Object recognition and tracking. Users automatically move their heads in the direction of their field of vision. Using the biological feature of the head's autonomous and involuntary movement, any facial expression can confirm what the user wishes to focus on and agitate the mouse cursor. The technology was created intends to address several shortcomings of present interface devices. This study extracts the user's images from the video stream using a webcam and processes them in real time to convert them into cursor motions. Physically disabled patients' datasets are collected, preprocessed using the Modified Histogram Equalization, and features are extracted using the Gabor filter bank. A bounding box is created by ResNet 101 around the recognized face. Enhanced Multi-Label Sequential convolutional neural network (EM-LSCNN) is proposed to recognize the facial landmarks of the provided face. When the system is implemented and fine-tuned following the user, it will change the way the mouse indicator moves across the screen's viewport, completely doing away with the need for a mouse. The proposed model performed with the greatest accuracy of 98.85%, a precision of 99.20%, a recall of 98.30%, and an f1-score of 98.65% when compared to existing approaches.