Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low Cost Optically Transparent and Frequency-Reconfigurable Seawater Antenna

A low cost optically transparent and frequency-reconfigurable water antenna is proposed in this paper. The proposed water antenna is mainly made of plexiglass (εr = 3.4) and seawater. Thus, the most distinctive feature of this water antenna is that its whole structure is almost completely optically transparent and its cost is very low. Another attractive feature of the proposed antenna is that it can achieve frequency reconfiguration. The water ground is divided into four sections, in which four work states are realized by injecting seawater into different section. Simulation results show that impedance bandwidth for| S11| < -10 dB under four different states is stable at about 22% and the tuning range is from 1.46GHz to 1.64GHz. Besides, radiation patterns and efficiency are able to maintain stability even if at different work states. Due to these features such as high optical transparency, low cost and excellent frequency reconfiguration, the proposed water antenna is very suitable for the integration of various solar cells and solar panel.
Face presentation attack detection with passive imaging at 250 GHz

Systems for detecting face presentation attacks are facing great challenges since new 3D facial masks are used. Passive terahertz imaging offer specific physical properties that may improve presentation attack detection capabilities. The aim of this paper is to present outcomes of study on using novel 3D facial masks for face presentation attack detection in passive imaging at 250 GHz. A thorough analysis of presentation attacks for facial recognition systems using custom 3D-printed and custom flexible 3D-latex masks is provided together with spectral characterization of various items. A set of experiments with various spoofing items is described and discussed. Finally, a presentation attack detection method with passive imaging at 250 GHz is presented.
YaskSite: Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures

The landscape of multi-core architectures is growing more complex and diverse. Optimal application performance tuning parameters can vary widely across CPUs, and finding them in a possibly multidimensional parameter search space can be time consuming, expensive and potentially infeasible. In this work, we introduce YaskSite, a tool capable of tackling these challenges for stencil computations. YaskSite is built upon Intel's YASK framework. It combines YASK's flexibility to deal with different target architectures with the Execution-Cache-Memory performance model, which enables identifying optimal performance parameters analytically without the need to run the code. Further we show that YaskSite's features can be exploited by external tuning frameworks to reliably select the most efficient kernel(s) for the application at hand. To demonstrate this, we integrate YaskSite into Offsite, an offline tuner for explicit ordinary differential equation methods, and show that the generated performance predictions are reliable and accurate, leading to considerable performance gains at minimal code generation time and autotuning costs on the latest Intel Cascade Lake and AMD Rome CPUs.
Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer’s Disease from Clinical Notes

Due to the absence of medications on Alzheimer’s disease (AD), lifestyle exposures that could improve cognitive functionality have become extremely important. Thus, the objective of the study was to show the feasibility of using natural language processing (NLP) methods to extract lifestyle exposures from clinical texts. The proposed named-entity recognition (NER) task’s results indicate that NLP models can detect lifestyle information (i.e., excessive diet, physical activity, sleep deprivation and substance abuse) from clinical notes, which has the potential for improving efficiency in information acquisition and accrual for AD clinical trials.
Visualising the Structure of 18th Century Operas: A Multidisciplinary Data Science Approach

Data visualisation is an effective strategy to communicate information. From a multidisciplinary perspective, applying this methodology (typical of computer science) to humanistic purposes (such as visualising musical, narrative and dramaturgical structure) has shown very promising results. Opera is a complex form of art, whose structure is strongly influenced by dramaturgical and musical aspects, both present in all operatic librettos. Although visualisation methods have been successfully applied in the understanding of music and narrative individually, a combined approach, aimed to jointly visualise both aspects together—by this enhancing opera comprehension—has not yet been developed. With this in mind, through a cooperative methodology of musicology and computer science disciplines, we carry out a data science project aimed to graphically represent the structure of 18th century operas’ libretto structure. The presented approach, based on XML librettos from the Progetto Metastasio, automatically generates a comprehensive graphical representation of the opera structure, based upon musical and dramaturgical information. The schemes developed in this work show all the elements relevant to the 18th century opera structure, and graphically synthesise its complex shape, in order to encourage opera understanding across a large set of users, from general listeners to musicians and finally to musicologists.
ORCHESTRA 2.0: Enhancing a multi-access solution to address 5G challenges

New challenges in communications are being raised as autonomous vehicles are gaining popularity. Higher data rates, connection reliability and real-time responses are challenges to be undertaken on the next years and, although they have been researched over the past decade, no solution that addresses all of them simultaneously has been widely accepted. In this paper, we reintroduce ORCHESTRA, an all-in-one solution to manage devices with multi-connectivity capabilities, while also enabling the use of techniques such as load balancing, seamless handovers and package duplication to address the challenges aforementioned. Furthermore, we also present a list of additions and improvements that would enable this solution to respond to the newest communication challenges.
Thermal and Electromagnetic Design of DC HTS Cables for the Future French Railway Network

The use of High Temperature Superconducting (HTS) cables as a technology is considered as key to increasing the power of the electrical grid while reducing the volume of installation. In addition, when transmission currents exceed a few kA, DC HTS cables significantly reduce power losses, rights-of-way and total system mass. This article describes the various studies that need to be carried out in order to correctly size HTS DC cables for the new railway network envisaged by the French company SNCF, which must consider ultra-urban needs. The process used to design DC cables for operating powers of 10, 20 and 30 MW at a nominal voltage of 1.5 kV using commercial (RE)BCO tapes is presented. In this sizing process, the critical current density Jc(B, θ, T) dependence of the superconducting tapes, the thermal properties of the materials and the different cooling modes are taken into account. Several solutions are suggested to ensure that the cable can withstand fault currents and reduce recovery time in the initial state. Finally, with the help of analytical models and industrial data, once the cables have been designed, the various system losses are reported as well as the power or size of the various auxiliary components.
Resource Efficient Boosting Method for IoT Security Monitoring

Machine learning (ML) methods are widely proposed for security monitoring of Internet of Things (IoT). However, these methods can be computationally expensive for resource constraint IoT devices. This paper proposes an optimized resource efficient ML method that can detect various attacks on IoT devices. It utilizes Light Gradient Boosting Machine (LGBM). The performance of this approach was evaluated against four realistic IoT benchmark datasets. Experimental results show that the proposed method can effectively detect attacks on IoT devices with limited resources, and outperforms the state of the art techniques.
A study on the acceptance and influencing factors of online teaching in China - Taking mosoteach cloud class as an example

The theory of Decomposed Theory of Planned Behavior (DTPB) was applied to explore students’ acceptance of online classes. Taking “Mosoteach cloud class” in China universities as an example, the research model was constructed and the research hypothesis was put forward, and 298 valid questionnaires were collected through questionnaires. Using SPSS & Smartpls3.29 software to verify the model. Overall, the results indicate that the acceptance of students to cloud classes is mainly affected by compatibility, subjective norms and perceived usefulness. The effects of compatibility and subjective norms exceed perceived usefulness, and the acceptance varies on different genders or different grades. Finally, the future research direction and suggestions are put forward.
Cell dynamics simulations of block copolymers

Cell dynamics simulations are a powerful tool to simulate kinetic processes in phase separating systems. Here we review the technique and its application to block copolymers. Its advantages and disadvantages compared to other simulation methods for block copolymer structure and dynamics are reviewed. Results on the dynamics of microphase separation and interface propagation, and on the rate of order-order phase transitions are reviewed. The use of the method to model certain shear-induced structural and flow effects is also summarised. Lamellar pattern from cell dynamics simulations in two dimensions using Eq. (17) with μ = 0.5, λ= 1.5, D = 0.5, c = 0.02, a = 1.5, r = -0.35 and f = 0.5 (figure: courtesy of Dr. S. R. Ren).
Event-based Viewing Tool for Learning Illustrations

Creators can learn the art of their craft through different types of learning materials. In this study, we focus on learning materials of two-dimensional illustrations. Expressions for learning materials have various forms, many of which are posted as articles on illustration posting sites and magazines and others are posted as movies on video sharing services. They have their own advantages and disadvantages. In terms of data, illustration learning materials can be regarded as spatio-temporal data. The authors of this paper have considered applying illustration learning materials as spatio-temporal data consisting of events that occur during the creation process, and they proposed the use of learning materials in an event-based form. A viewing tool developed by the authors provides functions to display the learning materials in an event-based form using space-based keys. The effectiveness of the proposed form is also shown through an evaluation experiment.
COVID-19 Topic Modeling and Visualization

The World Health Organization announced the novel coronavirus (COVID-19) outbreak as a global pandemic on March 11, 2020. To date, this virus has infected over 13 million people worldwide, causing a significant impact on human societies. More than 570,000 people have lost their lives. This study tries to understand the concern and experiences of the general public in this pandemic via social media data analysis. Twitter is a very popular social media platform where people share their ideas and express their views in a timely manner. It is also one of the most comprehensive sources of public conversation. In this work, we collected COVID-19 related Tweets during this pandemic, analyzed the volume, location, frequent words, main topics and people’s emotion. We further visualized the evolution of the main topics along with the development of this pandemic. The analysis and visualization can help us to construct a picture of people’s concerns and to understand their behaviors. It would also be helpful to the public health systems to devise proper policies to better guide and support the pubic.
Machine Learning and Deep Learning Approaches to Quantify Respiratory Distress Severity and Predict Critical Alarms

Respiratory distress (RD) is often the premonition and accompanying symptom of many critical conditions that may eventually lead to mortality among patients admitted in intensive care units (ICUs). The contemporary monitoring and alarm systems often fail to give timely and assertive alert of dangerously soaring RD, thus obscuring accurate prognosis. These systems do not capture the information provided by multiple parameters, which is important to predict and track patient health deterioration due to RD over time. Also, the repeated occurrence of false alarms in present systems leads to alarm fatigue. Our method addresses these issues by quantifying RD condition with a ‘Severity Index (SI)’ based on trend and value of respiratory rate (RR) and peripheral capillary oxygen saturation (SpO2) for 24 hours segment in a streaming monitor arrangement. We work on 912 records extracted from MIMIC-III Clinical and Waveform Database. We mapped our task as a classification problem and explored multiple machine learning and deep learning models in order to propose the best solution. The trend and value features were used for classification to train logistic regression, decision tree, support vector machine and multi-layer perceptron for quantification of RD severity on segment. We also use convolutional neural network (CNN) and long-short term memory (LSTM) for segment classification since they have capability to capture the temporal pattern of RD. All the models gave AUC (Area Under Curve for Receiver Operating Characteristic (ROC)) either close to or above 0.90 for classification task. We later used these models for raising clinical alarm for RD. We conclude that CNN model is marginally better than other models if we aggregate performance on all metrics. We can trigger an alarm based on SI of RD as evaluated by our CNN model instead of contemporary threshold based alarms of RR and SpO2. This RD alarm gives sensitivity and specificity of 86% and 85% respectively and achieves an average lead time of 5.5 hours without contributing to alarm fatigue. The dataset, code, trained models and the GUI are available at https://github.com/rohit-pardasani/RDQuantization.
E-tinerary: A decision support approach for tourist trip planning

This paper presents an approach to support the planning of tourist trips. E-tinerary, provides information on possible points of interest to visit, allowing the expression of preferences and limitations on various aspects. The main objective is to maximize the number of visits in the time available, while reducing the travel time between locations. E-tinerary allows to discover new places to visit, plan sightseeing trips and interact with other users, particularly through viewing and using shared travel plans. Additionally, it is possible to customize travel plans using information about preferred places to visit, available budget, etc. A user-centered design approach was used. Firstly, the profiles of several types of target users, their usage contexts, needs and motivations were characterized, based on which relevant requirements were identified in support of tourism travel planning. Low-fidelity prototypes of an application were created where the developed concepts were demonstrated and evaluated with users. This allowed validate the proposed functionalities and the user interface conceptual model. The application was then developed, providing information needed for travel planning by obtaining external data from APIs such as Google Maps Platform, OpenWeatherMap, and Wikipedia, and aims to propose tours based on route optimization methods, taking into account preferences and limitations expressed by the user. Finally, usability tests were carried out with participants with different profiles; results suggest that E-tinerary was positively evaluated and they would recommend its usage.
Using Machine Learning to Explore the Relation Between Student Engagement and Student Performance

Engagement in learning activities is an important factor that affects student performance in education. According to research, student engagement involves the degree of passion, interest and attention that they exhibit in their educational environment. In the traditional learning system, educators encourage students to engage in their learning activities through various teaching strategies such as making them pay attention, take notes, ask questions and participate actively in the learning processes. Sometimes, educators call on a specific student to answer a question as a means of encouraging the student to participate in learning processes. Nowadays, engagement strategies for learning are changing, especially with the use of technology-enhanced learning systems (TELS) in education. As a result, improving the engagement level of students in online learning environments remains an open research question that needs to be explored. This research is part of a preliminary study on discovering ways of increasing student engagement in an online learning system through data-driven interventions. Student engagement in this research is determined using objective data (activity logs of a specific undergraduate course in a TELS). Activity log is unbiased data and a reflection of students' actual learning behaviours (uncontrolled). In this study, we mined the log of students’ learning activities from a TELS used for an undergraduate course to explore differences between students’ learning behaviours as they relate to their engagement level and academic performance (measured in terms of final grade points in a course). We employed supervised (Random Forest) and unsupervised (Clustering) machine learning approaches in exploring the relations. The approaches identified an interesting pattern on student engagement and show that engagement and assessment scores are good predictors of student academic performance. Assessment scores are measured with results of quizzes and assignments performed by the students in the TELS, while academic performance is measured with the final grade of the student in the course. The implications of our findings are discussed.
Towards 6G Networks: Ensemble Deep Learning Empowered VNF Deployment for IoT Services

The prospective Internet of Things (IoT) vertical use cases demand latency perception, privacy preservation, and scalability intelligence equipped Virtual Network Function (VNF) orchestration in a dynamic context. With the massive growth of IoT connectivity, smart VNF orchestration with real-time deployment abilities is vital for the ubiquitous digital network environment. Hence, this paper collaboratively considers all the future service orchestration specifications. Moreover, we urge the necessity to go beyond the traditional service deployment framework and introduce VNF allocation at edge cloudlet small scale data-centers. Extensive simulation results manifest the applicability and potential of our proposed deep learning models with the twist of ensemble techniques for automated VNF orchestration. Additionally, our proposed ensemble deep learning aided approach inspires the employment of intelligent orchestrator to address 6G network era challenges for perpetual telecommunication research enigmas.
Probing the Low-Temperature Phase Transition in $\text{Er}_{x}\mathrm{Y}_{1-x}\text{FeO}_{3}$ by Terahertz Magnetospectroscopy

We have studied the terahertz response of the rare-earth orthoferrite $\text{Er}_{x}\mathrm{Y}_{1-x}\text{FeO}_{3}$ as a function of magnetic field, temperature, and yttrium composition ($x$). Through theoretical modelling based on an extended Dicke Hamiltonian, we provide new insight into the role of cooperative, “superradiant” interactions between Er spins and Fe magnons in the low-temperature phase transition that this magnetic system exhibits.
Effect of Target Density on the Surface Morphology of Y-Ba-Cu-O Thin Films Prepared by Ionized Jet Deposition

Ionized jet deposition (IJD) is a pulse electron deposition method. It can be used for the preparation of thin films from a wide spectrum of materials on different substrates. The main benefits of IJD are high flexibility and a possibility to change many of the deposition parameters. Some of them even during the deposition such as acceleration voltage, working gas, substrate temperature, etc. The wide variability of the deposition parameters allows finding the ideal conditions for the preparation of thin films of almost any material. This deposition method has a great potential for a cost-effective scale-up of HTS (High-temperature Superconductors) tapes fabrication. This research is focused on the study of the influence of target density on the microstructure of the deposited YBCO thin layers. The target density has an important role in the morphology of thin films. In this work, the deposition parameters were fixed (except for target density). The distance between substrate and target was 110 mm and substrate temperatures were set to 650 °C. The targets and samples were examined by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and LED Confocal profilometer. Nine different YBCO targets with densities from 3.8 g·cm−3 to 5.6 g·cm−3 were prepared in total. All targets were prepared from a single batch of precursor powder at ambient atmosphere and different sintering temperatures and times.
An oscillating blade microtome for rubber

Abstract In a study of the equilibrium swelling of rubber in thick oils, thin samples, about 0.1 mm thick, were required so that equilibrium could be reached within a reasonable time. Thin sheets can be molded, but they are frequently very anisotropic and therefore undesirable. Although it is possible to microtome very thin samples of rubber at sufficiently low temperatures where it is relatively stiff, only small areas are obtainable, and it is a difficult and inconvenient process if several square centimeters are required. An oscillating blade microtome of simple design was therefore constructed, capable of cutting sufficiently thin slices, about 2 cm square, at room temperature. Because of the sawing action of the blade the cutting force is greatly reduced and deformation of the sample is minimized.
Performance Analysis of Dynamic Downlink PPP Cellular Networks Over Generalized Fading Channels With MRC Diversity

This paper proposes novel and generalized expressions to characterize the performance of modern cellular networks under realistic user mobility behavior. The <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> distribution is employed to derive the received power probability density function, the average bit error rate for different modulation schemes, and the coverage probability assuming a Poisson point process spatial distribution of base stations in downlink. The user is assumed to experience fading with Maximum Ratio Combining (MRC) and move according to a random way-point mobility model. The proposed expressions are applicable to different widely-used fading environments, such as Rayleigh and Nakagami-m as particular cases, by an appropriate selection of the <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> parameters. Monte Carlo simulation was used to show the validity of the proposed expressions. The generalized expressions allow the system designer to quantify the effects of user mobility on the cellular network performance, in different propagation environments, and network topologies as a function of the number of base stations and MRC branches.