Miracle-dz/UniFAR-FTUs huggingface dataset:
How the Lack of Knowledge Management Policies affects Employees Behavior on Educational Institutions: A Study about Petitions in a Brazilian Federal Institute

Federal Institute of Paraiba (IFPB) is an educational institution in Brazil. To enable its operation, it defines and adopts hundreds of organizational processes described by resolutions proposed and approved by its various superior boards. However, unfortunately, the reality is that many employees do not know how the institute works and see everything as complex and unnecessary bureaucracy that leads to more work and delays due to petitions with problems, such as being unclear, or it is missing information. We conducted a survey to identify how employees of IFPB react when they need to make a petition and to measure the current state of Knowledge Management (KM) practices in this unit. Our results suggest that most of employees do not know how every type of petition works, they prefer to use the Village Paradigm to find information, and they believe that IFPB does not promotes KM practices.
The Relationship of Higher Education Access with Final Marks in a Core Chemical Engineering Topic

This present work cracks to understand the relationship between final marks in a topic with competences that are core of Chemical Engineering and students’ access grades to a Portuguese Higher Education Institution (HEI). The study is based on data gathered through a questionnaire concerning students’ perceptions about assessment and teaching-learning methodologies in four academic years (2016-2017 to 2019-2020). The student´s access grades were included in the questionnaire since 2018-19 academic year allowing to establish a relationship with the final marks obtained in a core Chemical Engineering Topic, Fluid Mechanics. From the analysis made, it can be said that students who obtained higher access grades to HEI may have also a higher Chemical Engineering curricular unit final mark, although the low linear relationship obtained (r =0.45) for the year 2019-20.
Wetting Silicon Carbide with Nitrogen: A Theoretical Study

In order to model the early stages of nitride growth on cubic silicon carbide, we have studied the deposition of atomic nitrogen on the Si-terminated SiC(001) surface, using first-principles molecular dynamics. For many configurations with coverages less than 1/2 monolayer, N is preferably adsorbed at zinc blende sites and forms nitridized Si pairs. At one monolayer, N forms instead hydrazinelike N{sub 2}Si {sub 4} complexes, yielding a fully coated, inert substrate which inhibits growth. In this configuration, full nitridization of the surface is energetically favored. Interface mixing and formation of C-N bonds are observed when mimicking the deposition of positively charged nitrogen ions. {copyright} {ital 1999} {ital The American Physical Society}
Introduction for the TEEM 2020 Doctoral Consortium track

Once again, the doctoral consortium track plays a central role in TEEM Conference. This track exists since the first edition on the conference in 2013 and still looks for Ph.D. candidates present the advances of their theses and do networking with other peers and more experienced researchers. This track was born linked to the Ph.D. Programme on Education in the Knowledge Society of the University of Salamanca (Spain), but we are happy to receive exciting proposals from others Ph.D. Programmes around the world, enriching the debates and the networking.
NLP-based Enhancement of Information Security in ITO - A Diffusion of Innovation Theory perspective

Information technology outsourcing (ITO) has grown significantly in recent decades and is now over a USD trillion-dollar industry. Service provider organisations are striving to improve the efficiencies of their service deliveries. Natural language processing (NLP) provides an opportunity to bring efficiencies through automation in understanding and processing information. Since information security risk management (ISRM) in ITO is a growing concern of both, client and service provider organisations, they are adopting to improve ISRM in ITO using NLP. This paper explores those ISRM improvement scenarios. It also investigates the information security risks (ISRs) that result from the use of NLP in ITO and proposes strategies to manage those ISRs. To gain insights into the problem, a qualitative research approach is followed using the case study method. Six semi-structured interviews were conducted from participants in three organisations in the ICT industry, engaged in an ITO relationship. To the best of our knowledge, it is the first study to investigate the use of NLP for enhancing ISRM in ITO.
Predicting Software Design Patterns from Text using Neural Embedding

Software design patterns are solutions to common software problems that are proven to work adequately in particular scenarios. Deciding which design pattern to use for a given software problem often requires practical knowledge acquired with experience in a similar domain and can be highly subjective and error-prone. Further, for novice programmers, an automated approach would be of tremendous help as it would provide practical knowledge required for deciding which design pattern to use for a particular software problem. The majority of research in software design pattern prediction involves using software structure and features in determining which design pattern to implement. However, there are circumstances where software engineers would prefer to know which design pattern to be used by looking at the design problem during or before the implementation phase. Existing design pattern prediction tools cannot be utilized in this scenario due to the absence of code and class structures. To address this issue, this paper proposes a new approach that analyzes the context of the software problem from text and predicts a suitable design pattern for the given problem context using feature learning, neural embedding, and classification. We evaluate our approach on a case study from Stack Overflow with more than 66,000 questions that discuss problems and consequences related to 23 design patterns. Results show that our approach can predict design patterns from the text with 82% overall accuracy.
Technique of Classification, Organization, Creation, and Use of Collective Knowledge

During the teaching of a subject, engineering in this particular case, students acquire knowledge through different learning activities that are guided by the teaching staff. In this process, students work with the resources and activities provided by the faculty, acquiring knowledge and demonstrating it through an exam. In this work, students have been asked to share their learning experience and to create knowledge resources that can facilitate learning. Thus, collective knowledge has been created in the subject, which can be used by anyone. The work has shown that students can create useful knowledge for the subject, as well as establish an ontological classification of all the knowledge necessary for learning the subject. The results of this analysis show the ontology defined by the students that is applicable to any subject. This study also describes the process carried out for the creation and management of knowledge by the students themselves, as well as the perception of the use of the collective knowledge created.
Kinetic and Thermodynamic Analysis During Dissimilatory γ-FeOOH Reduction: Formation of Green Rust 1 and Magnetite

In laboratory experiments, lepidocrocite reduction by the dissimilatory iron reducing bacteria, Shewanella putrefaciens , is known to generate extra-cellular iron (II-III) minerals as green rust (GR) or magnetite. However, the parameters controlling the formation of these minerals remain unclear. In order to identify these parameters, reduction experiments were designed to obtain either GR or magnetite with methanoate as electron source and lepidocrocite (γ-FeOOH) as the electron acceptor. The mineral products were monitored by XRD analyses, and the rate of reduction and E h /pH evolution were assessed during GR and magnetite formation. The only difference between the two conditions lies with the inoculum size: magnetite is systematically produced by the treatments containing the lowest cell density (5 × 10 8 CFU mL −1 ) and GR1(CO 3 2− ) precipitated in the highest cell density (2 × 10 9 CFU mL −1 ). We showed that 100 μ M of anthraquinone 2,6 disulfonate (AQDS) did not influence the nature of the biogenic minerals. In addition, based on thermodynamic calculations, we observed that the E h /pH paths in the Pourbaix diagram depend on the nature of the minerals formed, i.e. GR1(CO 3 2− ) or Fe 3 O 4 . However, the Pourbaix diagram cannot be used to forecast unambiguously the nature of these minerals. We propose that a close association of bacterial cells and γ -FeOOH particles occurs during the Fe(III) mineral reduction. We hypothesis that this aggregation influences the bio-reduction rate and is related to the nature of the biogenic mineral precipitated in the reduction media.
Modeling Low-risk Actions from Multivariate Time Series Data Using Distributional Reinforcement Learning

In recent years, investment strategies on financial markets using deep learning have attracted a significant amount of research attention. The objective of these studies is to obtain investment action that has a low risk and increases profit. On the other hand, Distributional Reinforcement Learning (DRL) expands the action-value function to a discrete distribution in reinforcement learning, which can control risk. However, DRL has not yet been used to learn investment action. In this study, we construct a low-risk investment trading model using DRL. This model is backtested on Nikkei 225 dataset and compared with Deep Q Network (DQN). We evaluate performance in terms of final asset amounts, their standard deviation, and the Sharpe ratio. The experimental results show that the proposed DRL-based method can learn low-risk actions with increasing profit, outperforming the compared method DQN.
Laser x-ray Conversion and Electron Thermal Conductivity

The influence of electron thermal conductivity on the laser x-ray conversion in the coupling of 3ωo laser with Au plane target has been investigated by using a non-LTE radiation hydrodynamic code. The non-local electron thermal conductivity is introduced and compared with the other two kinds of the flux-limited Spitzer-Harm description. The results show that the non-local thermal conductivity causes the increase of the laser x-ray conversion efficiency and important changes of the plasma state and coupling feature.
Improved Spiking Neural Networks with multiple neurons for digit recognition

For more than a decade Deep Learning, a subset of machine learning have been using for many applications such as forecasting, data visualization, classification etc. However, it consumes more energy and also takes longer training periods for computation, when compared to human brain. In most cases, it is difficult to reach human level performance. With the recent technological improvements in neuroscience and thanks to neuromorphic computing, we now can achieve higher classification efficacy for producing the desired outputs with considerably lower power consumption. Latest advancements in brain simulation technologies has given a breakthrough for analysing and modelling brain functions. Despite its advancements, this research remains undiscovered due to lack of coordination between neuroscientists, electronics engineers and computer scientists. Recent progress in Spiking Neural Networks(SNN) led towards integration different fields under one single roof. Biological neurons inside human brain communicate with each other through synapses. Similarly, bio-inspired synapses in the neuromorphic model mimic the biological neuro synapses for computing. In this novel research, we have modelled a supervised Spiking Neural Network algorithm using Leaky Integrate and Fire (LIF), Izhikevich and rectified linear neurons and tested its spike latency under different conditions. Furthermore, these SNN models are tested on the MNIST dataset to classify the handwritten digits, and the results are compared with the results of the Convolutional Neural Network (CNN).
Robust Integral Sliding Mode Control of Non-minimum Phase DC-DC Converters

In this paper, a modified integral sliding mode controller (ISMC) is proposed for non-minimum phase dc-dc converters. A boost dc-dc converter operating in continuous conduction mode (CCM) is chosen as a representative case study. The converter’s bilinear model and its effect on the controller design necessitate the need for a nonlinear controller. Unlike a recently introduced ISMC, the proposed controller does not need a priori knowledge of initial conditions such as inductor current or capacitor voltage, nor does it implement a sign function to ensure a fast response or reachability. A novel modified sliding surface which ensures reachability, stability and reduces chattering at the steady-state operation point is proposed. Moreover, the proposed controller in conjunction with a disturbance observer (DO) enables the converter to maintain stable operation under disturbances and uncertainties. The system’s robustness against drastic, discontinuous, autonomous, or non-zero derivative disturbances are enhanced by including an invariant saturation function. The performance of the proposed ISMC is tested and compared with a well-established linear controller to prove its validity under different types and levels of disturbances and uncertainties.
Vision-based Autonomous Tracking Control of Unmanned Aerial Vehicle

During the past decades, the field of aerial robotics has become the hot-spot for researchers, especially control of autonomous robots navigating in indoor environments. A lot of work has been published but still needs further efforts. In this paper, the problem of marker-based tracking control of a ground moving target and its solution is discussed. The main motivating application of this work is tracking a ground moving target while observing the environment at the same time. So the whole work mainly consists of two parts: Object recognition and tracking control. In the object recognition part, recognizing the marker and calculating the position of the target using the identified marker is discussed. While tracking control part, mainly focused on applying a control algorithm to track the moving target. A drone with a full HD camera is utilized for marker tracking and using it as a bird's eye view. With a larger field of view, surrounding can be observed. No external tracking system is used. In the end, experiments (including indoor and outdoor environments) and results are discussed, which show the effectiveness of the proposed approach. For tracking of the target, markers of different sizes and numbers (including single and multiple markers) are used. The performance of multiple marker-based tracking control in terms of states versus time plots is compared with the single marker tracking approach to elaborate our presented scenario.
Allystar low power/small chip size dual-frequency RTK solution

The lowest power and smallest size, dual-frequency GNSS SoC chip with embedded Real Time Kinematic (RTK) solution available on the market developed by Allystar is introduced in this paper. RTK engine performance is evaluated using GPS L1/L2C and BeiDou B1I/B2I and alternatively using GPS L1/L5 and Galileo E1/E5a signals. It is shown that having dual-frequency observations will provide significant benefits over single-frequency RTK in terms of reliability, fixing rate and accuracy. RTK is tested in both static and kinematic environments, under high-multipath and signal blockage conditions. Results are obtained using RTK engine implemented on a GNSS chip with - only 39 mA current draw at 3.3V. In addition, future development is discussed.
Review Paper on Door Lock Security Systems

Security is becoming an important issue everywhere nowadays. Every person wants his house, factory, bank etc. to be secured. House security is becoming necessary as the possibilities of intrusion are increasing day by day. Here in this paper, we are trying to present research made on various door locking systems that have been used at homes, offices, shops, industries, etc. The research will also put some light on the pros and cons of the various door lock systems and the technology used in the respective systems. The research will include traditional door lock system, RFID based system, gesture-based systems, Bluetooth and GSM technology- based and various other technologies used in the door lock security systems. The advance in technology in each passing day can help us come across various technologies that can be used in the door lock security system. Since security is the major concern of the majority of the people at present, we feel research made on the various system relevant to the topic can help people to choose the best technology for their upcoming projects or make changes in the past projects and use effective door lock systems for their needs.
K-Nearest Neighbor and Naive Bayes Classifier Comparison for Individual Character Classification on Twitter

Twitter is one of the most commonly used social media, especially in Indonesia. People use Twitter social media to express their opinions every day. This allows the DISC method to be applied to find out Twitter user's character. By knowing their characters using the DISC method and Twitter, this can help parties like the HRD without spending more effort in selecting employees. Three main processes are performed in this study, data acquisition, pre-processing, and calculation process. Classification methods, namely Text Naïve Bayes Classifier algorithm and K - Nearest Neighbor, are used to classified and mapping tweet data to the DISC method. As a result, the accuracy of the Naïve Bayes Classifier algorithm is 34.16%, while the K – Nearest Neighbor is 28.33%. So it can be concluded that the Naïve Bayes Classifier algorithm has a higher accuracy of 5.83% compared to K – Nearest Neighbor in classifying a Twitter account with TF-IDF Weighting into DISC method.
Challenges in developing collaborative IoT systems

The Internet of Things (IoT) represents the next significant step in the evolution of the Internet. It will allow “things” to be connected anytime, anywhere, with anything and anyone, providing a myriad of novel applications and augmented services to citizens, governments, and enterprises. We believe that for the IoT to reach its full potential, it will be necessary to advance the investigation of techniques and technologies to build systems in such a brand-new scenario. In the IoT, the collaborating entities encompass both physical and virtual resources, interactions occur both in an active and programmed way as well as by chance, and therefore it is necessary to deal with expected but also emerging behaviors of the collaborating parties. In this paper, we first discuss the main features that make IoT a unique ecosystem, and as such, calls for new software development solutions. We claim that there is a need to rethinking the techniques and methodologies for developing IoT systems and applications. Novel models, architectural approaches and techniques should be proposed, or existing ones should be adapted to deal with the high heterogeneity, dynamism, serendipity and interdependencies that are typical of the IoT ecosystem. We then analyze and discuss potential key design solutions that deserve a deeper understanding in order to pave the way for the building of this new generation of systems. Our discussion is presented from a bottom-up perspective: from the modeling of devices that make up the $\mathrm{IoT}$ to the representation of requirements that must be addressed when engineering IoT systems.
The Monitoring System of Aquaculture Environment

According to the needs of the aquaculture industry, this article proposes to design an aquaculture environment monitoring system for collecting temperature, PH, and dissolved oxygen parameters that affect fishery water quality environment, real-time tracking and monitoring, remote transmission and abnormal handling, etc. The system has played an important role in the application of fish breeding industry, providing technical support for the development of the breeding industry, and bringing greater economic profits to farmers.
Bayesian Optimization Based ANN Model for Short Term Wind Speed Forecasting in Newfoundland, Canada

Wind power capacity around the world is increasing day by day, but the production of wind energy greatly depends on the wind speed, where the wind speed has stochastic nature over time. In this paper, an artificial neural network (ANN) technique to forecast wind speed for the next hour in Newfoundland, Canada is proposed. As, deep learning models are combined with different hyperparameters, in our study, the selection of important hyperparameters are conducted by applying the Bayesian optimization algorithm. The wind speed forecasting performance of the proposed model is compared with other recognized models like support vector machine (SVM), random forest (R.F.) and decision tree (D.T.), where it is observed that our proposed model performs better than the other models in terms of mean absolute error (M.A.E.) and root mean squared error (R.M.S.E.). The proposed Bayesian optimized artificial neural network is fed with five input features and delivers M.A.E. and R.M.S.E. of 1.09 and 1.45.
High Slew-Rate Quadruple-Voltage Mixed-Quenching Active-Resetting Circuit for SPADs in 0.35-μm CMOS for Increasing PDP

An optical sensor IC in 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS is presented containing a single-photon avalanche diode (SPAD) and a fast quadruple-voltage quenching circuit (QVQC). The QVQC features a fast active quenching time of 0.93 ns, a total quenching time of 1.9 ns, and an adjustable total dead time (8.6–200 ns) to reduce the afterpulsing probability (APP). To verify the quenching performance, the circuit was integrated with a 40-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> diameter SPAD. Experiments show the reduction of afterpulsing by a low detection threshold and by fast quenching with a slew rate of 13.8 GV/s. Thus, an APP of 3.2% at 27-ns dead time, a peak photon detection probability (PDP) of 67.6% at 652 nm, and a PDP of 34.7% at 854 nm were measured at 13.2-V excess bias.
Matrix Inverter: A Multilevel Inverter Based on Matrix Converter Switch Matrix

This paper presents a new concept of matrix converter application as an inverter. A matrix converter is commonly used as an AC-to-AC conversion device. Hypothetically the AC sources of the matrix converter can be replaced with DC sources. By using a switching strategy similar to a multilevel inverter switching strategy, and some modification on the matrix converter topology, the matrix converter is able to produce a stepped output waveform similar to a multilevel inverter output. The proposed matrix inverter is fundamentally built on the basis of the combination of matrix converter topology with the DC-to-AC conversion technique of a multilevel inverter. The number of rows in the switches matrix represents the number of output voltage level, while the number of columns represents the number of output phases. The matrix inverter topology version variations can be synthesized for single-phase, two-phase, three-phase, four-phase, five-phase, six-phase and up to any number of output phases – not limited by the number of the input sources, with each output phase frequency can be set independently. A 41-level, three-phase matrix inverter has been constructed and tested in order to validate the matrix inverter concept.