Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Based Impostor Detection By Invariant Features From Nir Finger Vein Imaging

Biometrics mostly used as personal identification accordingly securing a client against the unapproved utilization of his or her identity. Acquiring biometric information is getting to be simpler. Smartphones and other advanced technologies exist from which biometric data can collect easily without the knowledge of others. Finger vein authentication is a method for biometric verification that depends on a vein pattern, which is located beneath the human finger’s skin. Veins are covered with skin that cannot be copied by others. In this research, our focus is on these invariant features of finger veins. We have collected invariant features from various recent features extraction techniques and then classified with state-of-the-art machine learning classifiers. For this purpose, we have used publicly available finger vein image databases. The performance has been evaluated by various evaluation metrics and comparative analysis of various machine learning classifiers has been presented to describe the performance of these classifiers on the said data set.
Experimental Survey’s results for IoT Projects with Tinkercad Circuits Prototypes for Virtual Classes

This document presents experimental results about an applied survey on Internet of Things (IoT) projects, applying resources and developing solutions for create prototypes using Tinkercad Circuits application for computing classes. The results of this experiment were able to prove the capability of online solutions for share the development and to managing the operations and process while working with IoT applications. The results of this studies allowed to see and show the viability to build online solutions, which can contribute for a physical solution, practical and applicable for real projects, the prototype allowed to verify the functionality of a physical solution, avoiding issues as device damages, costs, other devices attachment and loss of time for solutions development. These results will allow the development of the solutions with no cost, fast, practical and with a professional point of view, helping professionals, students, experts, and others individual which need to develop IoT solutions and with low code and remotely. Qualitative and quantitative variables used for comparisons and result analysis on the participants’ opinion, which pointed to consistent and valuable results for the development of solutions using the tool.
Towards a model driven approach for integrating NWN models in CINCO

In this paper, we propose a model driven approach to facilitate the integration of Nets within Nets (NWN) modeling language in CINCO modeling tool. Despite that, NWN has a sophisticated modeling-simulation tool called Renew, moving NWN models from a platform to another has been a challenge for programmers. The development of our tool is heavily benefits from being implemented with CINCO; a full generation of the application code is provided, a powerful tool is developed with a few efforts and facilities are provided to add semantic to the platform. Combine formal method with a sophisticated model driven tool simplifies the prototypical of domain specific systems, promotes the interoperability capabilities between different technologies and assists designer in the validation of the correctness of the modeled system.
An Application of IoT in Programmed Tidal Energy Observation System

According to the Internet of Things (IoT), internet objects will play an important role as computer products in every part of our daily life. With the use of the current network infrastructure, IoT provides devices to be monitored or directly controlled, optimized, precision enabled, and economic improvement and also by reducing human participation. This innovation has a broad spectrum of applications, such as solar energy, tidal power stations, smart villagers, microgrids, and tidal-powered appliances. As renewable energy had its greatest success ever throughout this time, with a growth that was recorded considerably higher than ever. The problem and solution described here involve displaying tidal power's electricity usage as a sustainable power source on the IoT-enabled controller. Using suitable sensing devices and raspberry pi, this approach has been formed. Through a monitoring dashboard, every renewable electricity use is monitored. The user will find it simpler to comprehend how much electricity the system is generating daily. Both the utilization of sustainable energy and power generation issues are touchedon by this research work.
Códigos de peso constante

Let Fq be a field with q elements, Cn be a cyclic group of order n and suppose that gcd(q, n) = 1. In this work conditions are given to ensure that a code in FqCn is a one weight code, inspired in the work of Vega [19]. As a consequence of this result we showed that a one weight code in FqCn is of the form {rge|r ∈ Fq, 0 ≤ i ≤ n}. With this, we determined the number of one weight codes in FqCn, and constructed examples of two weight codes in Fq(Cn×Cn). After this, we gave conditions to ensure that a code had constant weight in FqA, for A a finite abelian group. We also analyzed the one weight codes in RG, R a chain ring and Cn a cyclic group with n elements with gcd(n, q) = 1. Moreover, we analyzed the case when the elements of an ideal in RA, for R an infinite integral domain and A a finite abelian group, have constant weight.
A Futures Quantitative Trading Strategy Based on a Deep Reinforcement Learning Algorithm

Deep reinforcement learning (DRL) is a type of machine learning algorithm that has gained a lot of attention for its application in the financial field. Based on the proximal policy optimization algorithm (PPO) in deep reinforcement learning, this paper designs a trading strategy for the Chinese futures market, and realizes the end-to-end decision-making process from futures data to trading actions. Afterwards, using domestic rebar futures data, multiple historical data were selected for backtesting, and compared with traditional trading strategies. The results show that in the 12 selected test periods, 83.3% of the test periods are profitable, which is better than 33.3% of mean reversion (MR) and 25% of trend following (TF). It shows that the strategy proposed by us shows good adaptability when the futures market rises or falls compared with traditional methods, and can reduce losses through trading even when the market price changes significantly, thus increasing the return on investment.
Teaching web development courses using flipped classroom and Discord: a two-year experience in the Peruvian context during the COVID-19 pandemic

The Flipped Classroom methodology encourages students to interact with content in multiple ways and professors, who provide active learning strategies to create a super engaging group space that can extend beyond the classroom walls. The purpose of the study was to generate learning scenarios that ensure the good performance of students to achieve the skills in two programming courses at a private university in Peru, making its implementation sustainable over the years. The educational proposal presented in this research made use of the flipped classroom methodology and the Discord platform as an agile means of communication. The results are very encouraging because it allowed students to participate in their own learning in an active and self-directed way so that they self-regulate their own learning path individually and in groups; based on flipped classroom and successfully deployed on the Discord platform.
The Early Detection of Autism Within Children Through Facial Recognition; A Deep Transfer Learning Approach

Over the past years, autism rates have increased alarmingly, with 1 in 59 children, aged between 1 to 6 years, being affected globally. While treatment is available, if detected at a later stage or not detected at all, children must face lifelong consequences and even a reduced life expectancy. Therefore, an early diagnosis has the potential to enhance the children’s probability of having near-to-normal development. However, current methods of diagnosis are not accessible to everyone due to the high costs involved in clinical assessments and the time taken to reach a conclusive diagnosis thus leading majority of children being under-diagnosed. Deep learning has transformed multiple sectors thanks to its "high perform a nee" feature as opposed to traditional machine learning models and could have been long used for the early detection of autism as an attempt to reduce the affliction rates. Although autistic children have unique facial features which could be exploited using Deep Learning, not much effort has been put in that area. As such, this work takes on a Deep Transfer Learning approach for the detection of autism within children based on facial images by applying CNN-based models of ResNet50, VGG-16 and MobileNet with the latter being the most performant. After tuning, an overall accuracy of 89.5% and AUC of 0.97 were reached. Furthermore, on an endnote, the practical & ethical implications are looked at while also proposing that, as this work shows promising results, future works could look at a more real-time approach for the same.
A Systematic Review of Online Learning Platforms for Computer Science Courses

Online Learning has been in rise with Massive Open Online Courses and many other learning platforms. With nearly two decades of digital learning, the landscape of learning spaces have seen different flavors in terms of domains that are addressed for learning, styles of engaging the learner, etc. We need a mechanism to observe these learning platforms design and understand how the learning process is facilitated by the features of the platform. In this work, we have conducted a systematic review of the literature and analysis of the platforms to understand the features. We have also conducted a survey of four different types of platforms with the help of 120 participants. With the help of literature review and survey we have designed a framework to observe the learning platforms and provided an analysis of these platforms with the parameters of the framework.
Cable Intelligent Management and Control and Personnel Emergency Response Measures Based on Internet of Things Computing

With the rapid development of science and technology, people's life and production have been inseparable from electricity. As one of the main equipment of power transmission, the working state of cable directly affects the reliability of power supply. This paper mainly studies the cable intelligent management and control based on Internet of Things computing and the analysis of personnel emergency response measures. In this paper, a cable information integrated processing system based on microservice framework is designed. According to the requirements of ubiquitous power Internet of Things and the division principle of microservices, the cable information integrated processing system is vertically divided into seven independent microservices, realizing the independent design, independent development and independent deployment of each microservice. In order to solve the problem of different access protocols and data formats of terminal equipment, MQTT protocol is applied to complete the design of a dedicated gateway in the Internet of Things, which realizes the unified management of data in the system.
BeRedy (Period Tracker & PCOS Diagnosis)

Abstract - We’re on a mission to help all menstruating individuals understand their menstrual cycle and make it less of a taboo topic. Menstruating individuals often face a range of challenges related to their menstrual cycle, including difficulty keeping track of their cycles, finding appropriate sanitary products, and managing symptoms such as cramps. In addition, many individuals may feel anxious, stressed, or confused about menstruation, and may not have access to accurate information or resources to help them manage their menstrual health. BeRedy is a one-stop solution to all your menstrual problems. We plan to develop a system that solves the many issues that menstruating individuals have, through our easy-to-operate system. We believe that menstrual health is a fundamental human need. That’s why we have created our system “BeRedy” so that one can identify the problems in their period cycle and check whether the symptoms are of PCOD/PCOS, if one is diagnosed with the disease our system will suggest various exercises and changes in their diet plan that they can make which will help to overcome it. Our system also recommends support groups that will help people get through the mental distress that they might go through because of PCOS/ PCOD. With this system, menstruating individuals can track their period cycle and take precautionary measures like carrying sanitary napkins, and medications according to their needs, our system also gets insightful articles, and helpful tips on how to be healthy and happy with your periods with the help of our blogs, users can also share their period-experiences like their firstperiod experience or any other experience they have with others anonymously and read others’ experiences. Another useful feature of our system is the ability to purchase menstrual products from trustworthy websites. This ensures that users are able to access high-quality products that are safe and effective. Our system also includes a chatbot that can answer common questions about menstruation and provide users with personalized advice and support - all for free! We offer a suite of tools that are all free to use, with no ads or in-app purchases.
The Discussion on the Extension of Laplace Templet in Image Edge Detection

For the problem of the extension of Laplace operator in image edge detection, a new method of extending Laplace templet and rule of setting weight coefficients in the templet is proposed. The method is implemented in Microsoft Visual C++ programming language. Experimental results show that better performance of image edge detection can be achieved by using the method.
Misinformation classification using LSTM and BERT model

In the information age with the reckless growth in the field of artificial intelligence and knowledge discovery a lot of research work is directed to solve challenges which were not at all thought in the assessment of computer science research area. Among many such problems, one such problem in the current scenario is of misinformation spread control and detection. A combined approach of natural language processing and Machine learning models can classify and detect misinformation. In this research paper we have used LSTM and BERT models to classify misinformation on a dataset and compared their accuracy and performance.
Characterization of the LV distribution grid for the deployment of a pilot BB-PLC network

As a previous step before the rollout of a pilot Broadband Power Line Communications (BB-PLC) network, this paper describes the measurement campaign carried out for the characterization of the Low Voltage (LV) electrical grid as a transmission medium in terms of grid access impedance, attenuation, and Non-Intentional Emissions (NIEs). First, the main reasons why BB-PLC is the technology selected for the development of new Smart Grid (SG) applications over the LV distribution grid are discussed. Second, the pilot network to be rolled out is described. Then, the measurement systems designed for the assessment of the grid impedance, attenuation, and NIEs are presented, as well as a brief description of the selected locations that correspond to nine scenarios of the LV distribution grid in a town in the north of Spain. These locations represent several grid topologies and areas of different population densities. Finally, some results obtained in the field trials are shown, which demonstrate the importance of a prior characterization of the LV grid as a transmission medium for an efficient planning of a BB-PLC deployment. These results are only a representative selection of the data obtained in the measurement campaign and, thus, further analysis is required to draw accurate conclusions.
Investigating the Use and Effects of Feedback in CodingBat Exercises: An Exploratory Thinking Aloud Study

The increasing availability of online tools helps support computing students via feedback to gain more practice in programming at their own pace. Due to the lack of educators’ insights into students’ independent practice with such online tools and their feedback options, this research aims at the evaluation of tutoring feedback types offered by the exemplary online tool CodingBat. In particular, students’ use of tutoring feedback types, as well as their effects on the cognitive, meta-cognitive and motivational level are investigated. The exploratory research methodology comprises a qualitative thinking aloud study with five novice learners of programming. The transcribed protocols were analyzed by using qualitative content analysis and deductive categories that originate from previous research on feedback effects and their observable and reportable indicators. The qualitative results reveal insights into students’ use of feedback, effects of feedback types on the cognitive, meta-cognitive and motivational level, as well as the importance of tutoring feedback including hints and a sample solution. The results of this applied, qualitative research add to the exploration of recommendations for the design of tutoring feedback in the context of self-paced online exercises for novice programmers. The findings further imply that automatically generated tutoring feedback seems to be helpful even without information that is adapted to the individual learner’s input.
Resolvent operators of Markov processes and their applications in the control of a finite dam

The resolvent operators of the following two processes are obtained: (a) the bivariate Markov process W = (X, Y), where Y(t) is an irreducible Markov chain and X(t) is its integral, and (b) the geometric Wiener process G(t) = exp{B(t} where B(t) is a Wiener process with non-negative drift μ and variance parameter σ2. These results are then used via a limiting procedure to determine the long-run average cost per unit time of operating a finite dam where the input process is either X(t) or G(t). The system is controlled by a policy (Attia [1], Lam [6]).
Immunogenicity and In Vivo Protective Effects of Recombinant Nucleocapsid-Based SARS-CoV-2 Vaccine Convacell®

The vast majority of SARS-CoV-2 vaccines which are licensed or under development focus on the spike (S) protein and its receptor binding domain (RBD). However, the S protein shows considerable sequence variations among variants of concern. The aim of this study was to develop and characterize a SARS-CoV-2 vaccine targeting the highly conserved nucleocapsid (N) protein. Recombinant N protein was expressed in Escherichia coli, purified to homogeneity by chromatography and characterized by SDS-PAGE, immunoblotting, mass spectrometry, dynamic light scattering and differential scanning calorimetry. The vaccine, formulated as a squalane-based emulsion, was used to immunize Balb/c mice and NOD SCID gamma (NSG) mice engrafted with human PBMCs, rabbits and marmoset monkeys. Safety and immunogenicity of the vaccine was assessed via ELISA, cytokine titer assays and CFSE dilution assays. The protective effect of the vaccine was studied in SARS-CoV-2-infected Syrian hamsters. Immunization induced sustainable N-specific IgG responses and an N-specific mixed Th1/Th2 cytokine response. In marmoset monkeys, an N-specific CD4+/CD8+ T cell response was observed. Vaccinated Syrian hamsters showed reduced lung histopathology, lower virus proliferation, lower lung weight relative to the body, and faster body weight recovery. Convacell® thus is shown to be effective and may augment the existing armamentarium of vaccines against COVID-19.
A New Method for Solving Fuzzy Linear Fractional Programs with Triangular Fuzzy Numbers

Several methods currently exist for solving fuzzy linear and non-linear programming problems. In this paper an efficient method for FLFP has been proposed, in order to obtain the fuzzy optimal solution. This proposed method based on crisp linear programming and has a simple structure. It is easy to apply the proposed method compare to exiting method for solving FLFP problems in real life situations. To show the efficiency of our proposed method a numerical example has been illustrated with a practical problem.
Adaptive State Feedback Stabilization for High-order Stochastic Nonlinear Systems

In this paper,the adaptive state feedback stabilization is studied for a class of high-order stochastic nonlinear systems in which drift and diffusion terms depend on all the states.We design parameters by choosing an appropriate Lyapunov function,the additive a power integrator scheme,the backstepping scheme,the parameter separation lemma and some flexible algebraic techniques.A smooth adaptive controller is constructed which guarantees that the closed-loop system has an almost surely unique solution for any initial state,the equilibrium of interest is globaly stable in probability,and the states can be regulated to origin almost surely.A simulation example is given to illustrate the e? ectiveness of the control scheme.
The Ionic Liquid-Assisted Synthesis of a Novel Polyaniline/Graphitic Carbon Nitride/Zinc Tungstate (PANI/g-C3N4/ZnWO4) Ternary Nanocomposite: The Usage of Easy Double Electron Transfer Photocatalyst for Glyphosate Photocatalytic Degradation Process

In this study, a novel polyaniline/graphitic carbon nitride/zinc tungstate (PANI/g-C 3 N 4 /ZnWO 4 ) (PGZ) ternary nanocomposites (NCs) as a heterostructure photocatalys was examined during photocatalytic degradation process in the efficient removal of glyphosate herbicide from a aqueous solution. Different pH values (3.0, 5.0, 7.0, 9.0 and 11.0), increasing glyphosate concentrations (5 mg/l, 10 mg/l, 15 mg/l and 20 mg/l), increasing PANI/g-C 3 N 4 / ZnWO 4 ternary NCs concentrations (5 mg/l, 15 mg/l, 30 mg/l and 45 mg/l) and increasing recycle times (1., 2., 3., 4., 5., 6. and 7.) was operated during photocatalytic degradation process in the efficient removal of glyphosate in a aqueous solution. The characteristics of the synthesized nanoparticles (NPs) were assessed using X-Ray Difraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive X-Ray (EDX), Fourier Transform Infrared Spectroscopy (FTIR), Transmission Electron Microscopy (TEM), Diffuse reflectance UV-Vis spectra (DRS) and X-Ray Photoelectron Spectroscopy (XPS) analyses, respectively. The cyctotoxicity test was operated to the standard TBE (trypan blue dye exclusion) assay technique with Drosophila melanogaster (fruit fly). ANOVA statistical analysis was used for all experimental samples. The maximum 99% glyphosate removal efficiency was obtained during photocatalytic degradation process in aqueous solution, at 15 mg/l gylphosate, at 30 mg/l PANI/g-C 3 N 4 /ZnWO 4 ternary NCs, at pH=11.0, at 150 W UV-vis light irradiation power, after 180 min photocatalytic degradation time and at 25°C, respectively. The maximum 99% cyctotoxicity removal was observed at untreated glyphosate samples, after 180 min photocatalytic degradation time, at 150 W UV-vis light irradiation power, at pH=7.0 and at 25°C, respectively. The maximum 99% cyctotoxicity removal was observed at 5 mg/l PANI/g-C 3 N 4 /ZnWO 4 ternary NCs photocatalyst concentrations, after 180 min photocatalytic degradation time, at 150 W UV-vis light irradiation power, at pH=7.0 and at 25°C, respectively. The study revealed the excellent minimization of cytotoxicity of glyphosate after photocatalytic degradation process with the PANI/g-C 3 N 4 /ZnWO 4 ternary NCs photocatalyst. As a result, the PANI/g-C 3 N 4 /ZnWO 4 ternary NCs photocatalyst is found to be non-cytotoxic irrespective of its quantity used. Finally, the combination of a simple, easy operation preparation process, excellent performance and cost effective, makes this a novel PANI/g-C 3 N 4 /CoMoO 4