Miracle-dz/UniFAR-FTUs huggingface dataset:
Speaker Identification Using Small Artificial Neural Network on Small Dataset

Speaker recognition provides an answer to the question “Who is speaking? ”. Most of the research in the field of speaker recognition focuses on models for recognizing thousands of speakers and uses large datasets for doing so. In this paper, we explore the possibility of using small neural networks that could quickly be trained on small datasets for doing speaker recognition for a small number of speakers with high accuracy. To investigate this matter experimental analysis was conducted by using hyperparameter optimization to find the optimal combination of the structure and parameters for multiple configurations of neural networks. Furthermore, the investigation was performed with multiple preprocessing methods to find the effect of preprocessing on the size and accuracy of the resulting models. As the result of the experiment, we found a residual neural network with 387,461 parameters that was able to classify speakers with high accuracy. In comparison state of the art speaker recognition uses 4.2 million parameters.
Electric Utility Customer Segmentation from Advanced Metering System Data Using K-Shape Clustering — A Norwegian Case Study

In this paper, a framework was developed for the segmentation of the customer base of Norwegian Distribution System Operators (DSO), based on Advanced Metering System (AMS) time series data of the electricity consumption of DSO customers. A computer programme for customer segmentation was synthesised in the programming language Python, using shape-based clustering, and a Cluster Validation Index (CVI) algorithm. Additionally, an option to perform a simple outlier analysis based on user input of the AMS input data was included. The assessment of the developed customer segmentation programme and the underlying methodology was first done through tests on a known data set to verify the results. Following this, an assessment was made on the basis of two actual AMS-data sets provided by the Norwegian DSO Lnett AS. AMS-data was more challenging for the algorithm to cluster than the known data set, possibly because the AMS-data set was more homogeneous with more similarly shaped and less discernible time series groups. Outlier analysis was shown to improve the programme performance by removing irregular (i.e. flat) time series. Based on the second AMS-data set, a comparison with the current standard method of customer segmentation utilised by Norwegian DSOs was performed. The developed customer segmentation method, when measured with a CVI, was shown to produce a better partition compactness and distinctness of the AMS-data set than with the standard DSO method.
Airgap Flux-based Estimation of Permanent Magnet Temperature for Thermal Protection of PMSMs

Airgap flux monitoring has recently received a lot of attention as a low cost means of providing reliable fault detection for permanent magnet synchronous motors (PMSM). In this paper, the feasibility of using an airgap search coil for estimating the temperature of the PM, TPM, for thermal protection is evaluated. Thermal and electrical model- or signal injection-based methods proposed for TPM estimation can provide satisfactory estimates of the average TPM for thermal protection. However, the complexity in the compensation required for non-ideal effects and/or the invasiveness of high frequency signal injection can be limiting factors for implementation. A new method for TPM estimation based on the airgap search coil voltage measurement is proposed in this paper. The proposed method does not require compensation for inverter non-linearity or resistance variation, or injection of high-frequency signals. In addition, it can provide thermal protection based on the TPM estimate of the hottest PM since individual PMs can be monitored. Experimental results on a 2.2 kW IPMSM verify the claims and show that the existing methods can be simplified and enhanced with the proposed method.
Benefits and Challenges of a Graduate Course: An Experience Teaching Systematic Literature Review

This research to practice full paper observed that graduate subjects (or courses) are commonly offered in graduate programs and can provide specialized knowledge of different topics that are important for the formation of Ph.D. and Master’s students. At the same time, Systematic Literature Review (SLR) has been increasingly adopted in the computing area as a research method to synthesize the state of the art of a given research topic, identify research groups working on that topic, understand the existing limitations and research gaps, and also identify new research directions. However, it is still not well understood the real benefits and challenges of offering a subject that addresses SLR for graduate students. Moreover, it is not known the difficulties faced by professors (i.e., educators) to teach this subject. The main goal of this paper is to present an experience report of teaching SLR, in particular, the benefits and challenges of this subject for computer science graduate students. In addition, this paper also presents the essential topics of SLR that we recommend to be taught and a better way to teach them, aiming at supporting graduate courses to offer it. For this, we surveyed computer science graduate students who attended the SLR subject that was taught for almost ten years in our institutions. In particular, we collected the lessons learned, findings, and insights; following, we summarized the benefits and challenges for students, the difficulties for professors, and also those essential topics to be taught. As a main result, the SLR subject can be considered a valuable opportunity for graduate students that could use this subject to conduct the required deep literature review of their research topic and have a better comprehension of their research area. Besides and more importantly, this subject can improve important research skills of students, including the ability to recognize research problems, analyze and synthesize data, think critically, and write papers. Therefore, we believe that graduate courses should analyze the possibility of offering the SLR subject.
OPEN: Order-preserving Pointcloud Encoder Decoder Network for Body Shape Refinement

Image-based 3-D human body shape estimation and reconstruction have shown significant improvement by using deep neural networks. Compared with reconstructing from a single image, reconstructing 3-D human body shapes from video or image sequences requires high precision and dense correspondences between the keypoints of the reconstructed shape sequence. Existing methods cannot achieve both high accuracy and keep the dense correspondence between different shapes after reconstruction. In this paper, we propose a method named Order-preserving Point cloud Encoder-decoder Network to refine the reconstructed human body shape from SMPL with the assistance of RGB images while preserving its original dense correspondence. We further introduce using 2-D RGB images as weak supervision when 3-D labels are not available. We assess our methods on the public dataset and show improved results compared with the baseline methods.
Intrusion Detection Method Based on Wasserstein Generative Adversarial Network

An intrusion detection method based on Wasserstein generative adversarial network and deep neural network was proposed to solve the problem of low accuracy and high false positive rate caused by data imbalance in traditional intrusion detection system based on machine learning. By learning the distribution of the original data set and generating new samples, the method can improve the class imbalance of the data set, so as to train the intrusion detection model and improve the detection efficiency of the model. First, the training set is balanced by combining Wasserstein GAN of the Dropout regular network to generate new data. Then the balanced data set is used to train DNN, and the DNN model obtained after training is used for intrusion detection. Experimental results on NSL-KDD data set show that this method is more effective than the traditional method.
An Improved Model-free Predictive Power Control for Three-Phase AC/DC Converters

Model predictive power control (MPPC) is a powerful and popular control method for the control of three-phase AC/DC converters due to its merits of quick response and simple principle. However, conventional MPPC suffers from the high steady power ripples due to the application of only one voltage vector during one control period. Furthermore, the performance will deteriorate when there are parameter variations due to saturation, temperature, and so on. Recently, model-free predictive current control (MFPCC) based on current difference detection has been proposed to solve the problem of parameter robustness for the control of ac/dc converters. However, it cannot be directly applied to the power control of ac/dc converters due to the complicated relationship between the complex power and converter voltage. This paper proposes an improved model-free predictive power control (MFPPC) for three-phase ac/dc converters. Instead of using accurate mathematical model, the proposed method uses an ultra-local model to predict the complex power. Different from the conventional ultra-local model, both the gain of the input voltage and the other parts of the ultralocal model are estimated and updated online based on the input and output information of system. Hence, the proposed MFPPC is not affected by the parameter variations and exhibits strong parameter robustness. The effectiveness of the proposed method is confirmed by the presented simulation and experimental results.
A Flexible-Combined Heat and Power System Interface Converter's Benefits in Increasing Stability Margin of a Microgrid with High Renewable Penetrations

The flexible combined heat and power (F-CHP) is a concept proposed by the Department of Energy, United States. It enables cogeneration plants in small to mid-size manufacturing facilities as microgrid-controlled distributed energy resources (DER) for operation in local areas with high aggregated renewable energy penetration. At the same time, because of the higher number of grid-tied power electronics-based converters for renewable energy integration, the grid is becoming more vulnerable in terms of stability. In this paper, it is found that the F-CHP converter can also avoid the onset of dynamic interactions thus increasing the allowed renewable integration amount. Different control schemes and the power of the F-CHP converters' impact on these benefits are also discussed.
Vertical CdTe:PVP/p-Si-Based Temperature Sensor by Using Aluminum Anode Schottky Contact

The vertical Schottky barrier diode (SBD)-based temperature sensors with the drive modes are a significant issue with more advantageous than the on-chip sensor. The sensitivity (<inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>) and the current conduction mechanisms (CCMs) of the vertical cadmium telluride (CdTe):polyvinyl pyrolidone (PVP)/<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-Si SBD were studied experimentally over the range of 80–340 K and compared with that of the lateral and vertical sensors. It is shown that the low and moderated voltages of the CdTe:PVP/<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-Si corresponding two linear regions of the current–voltage (<inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>) outputs are around 0.1–0.3 and 0.4–0.65 V, respectively. The variation of Schottky barrier height (BH; <inline-formula> <tex-math notation="LaTeX">${\sf \Phi } _{\text {Bo}}$ </tex-math></inline-formula>) and ideality factor (<inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>) with temperature was obtained according to two linear regions. Energy dispersion of the interface traps (<inline-formula> <tex-math notation="LaTeX">${N}_{\text {ss}}$ </tex-math></inline-formula>) with changing temperature is additionally analyzed quantitatively. It is concluded that the thermionic-emission (TE) theory with double-Gaussian distribution (GD) is the dominant mechanism resulting the <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> characteristics of the vertical CdTe:PVP/<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-Si SBD in this study. Moreover, in the constant current, the <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> values at the drive current of 10, 20, and <inline-formula> <tex-math notation="LaTeX">$50~\mu \text{A}$ </tex-math></inline-formula> were resulting in a range of −1.6 to −1.8 mV/K.
Temperature Annealing Knowledge Distillation from Averaged Teacher

Despite the success of deep neural networks (DNNs) in almost every field, their deployment on edge devices has been restricted due to the significant memory and computational resource requirements. Among various model compression techniques for DNNs, Knowledge Distillation (KD) is a simple but effective one, which transfers the knowledge of a large teacher model to a smaller student model. However, as pointed out in the literature, the student is unable to mimic the teacher perfectly even when it has sufficient capacity. As a result, the student may not be able to retain the teacher's accuracy. What's worse, the student performance may be impaired by the wrong knowledge and potential over- regularization effect of the teacher. In this paper, we propose a novel method TAKDAT which is short for Temperature Annealing Knowledge Distillation from A veraged Teacher. Specifically, TAKDAT comprises of two con-tributions: 1) we propose to use an averaged teacher, which is an equally weighted average of model checkpoints traversed by SGD, in the distillation. Compared to a normal teacher, an averaged teacher provides richer similarity information and has less wrong knowledge; 2) we propose a temperature annealing scheme to gradually reduce the regularization effect of the teacher. Finally, extensive experiments verify the effectiveness of TAKDAT, e.g., it achieves a test accuracy of 74.31 % on CIFARI00 for ResNet32.
Student performance impacted from modifying a first-year/semester engineering core course during a global pandemic

This Research Full Paper considers the effects of abruptly migrating a first-semester engineering core course into a hybrid, partially online as a response to a global pandemic. This study combines both quantitative and qualitative methods to perform a retrospective analysis to identify and explain the effects of the sudden changes on students’ performance in a course at a large research university in the southwestern continental United States. The qualitative portion of this study focuses on student performance as an important factor that directly impacts and concerns many students. The study first compares student performance on similar course materials provided in Fall of 2019 and 2020, pre- and post-pandemic respectively. The Fall 2020 course transitioned to a hybrid format, combining online content delivery with in-room class meetings synchronously, as part of precautions due to the pandemic. We utilize statistical analyses methods such as t-tests on existing student performance data. Preliminary results indicated that the Fall 2020 students performed worse on the first exam than those of their 2019 counterparts, leading to closer examinations of specific assignments that contribute to the exam performance. Using particular assignments we broke down which topics contributed most to the contrasting performance on each exam and what specific adjustments to make so that Fall 2020 students’ performance became up to par with Fall 2019 students by the second exam.
Faculty Experience with a Novel Remote Testing Protocol for Online Courses

In this work, which is intended to be a Work in Progress Paper in the Research to Practice Category, a novel remote testing protocol based on Cognitive Load Theory and the Theory of Situated Cognition is described. The proposed protocol reduced students’ cognitive overload during online exams by addressing student privacy, unreliable internet connections, accessible accommodations, all-or-nothing automatic grading, and false accusations of plagiarism. The described protocol relies on finite exam times and a sophisticated question randomization system. The protocol has been used successfully for five semesters, for two courses (20 sections).
A Novel COVID-19 Detection Model using CT Scan

Presently, COVID-19 is considered to be the most dangerous and deadly disease to human caused by the new Coronavirus. Early discovery of COVID-19 through precise diagnosis, especially for cases without clear symptoms, can reduce the patient's mortality rate. In this work, using CT scans images, we present a new Level Set evolution method (NSDRLS), in which we propose to utilize both color intensity and the salience map as the external energy of the region to motivate an initial evolution of the Level Set function (LSF). Herein, a complete comparative quantitative study of the considered approaches was established. Various criteria were calculated to evaluate the methods of segmentation.
Measuring Cognitive Loads while Learning Computational Statistics

This work-in-progress paper is part of a project that aims to analyze how the implementation of different instructional designs through complete and incomplete examples influence the cognitive load that students perceive when learning statistics and computer programming. For this first stage, we conducted a pilot study aimed at conducting a differentiated measurement of cognitive loads through secondary tasks and a subjective scale. The pilot study consisted of three moments. The first one focused on measuring their working memory capacity and their prior knowledge of statistics. Then, students worked on a learning activity focused on using Chi-square and Pearson correlation to conduct inferential analyses. The study included two different instructional designs in the form of computational notebooks. Design 1 consisted of correct and incomplete examples, while Design 2 included correct, incomplete, and incorrect examples. Simultaneously, students had to attend to a secondary task, which consisted of responding to an auditory stimulus, after which they had to press a key on their keyboard. Finally, students completed a naïve rating scale of cognitive loads involved in the task. The results highlight the important role of prior knowledge in different instructional designs and how it connects to students' perceived cognitive effort. Likewise, the differences in the instructional designs influenced students' cognitive loads, suggesting a differentiated measure of extraneous loads. Using incorrect examples in learning statistics and programming may affect students' perceived difficulty without resulting in a better learning outcome.
Design of Magnetic Hall Sensor for Current Sensing in a Standard CMOS Process

This paper presents a magnetic Hall sensor for current sensing in a standard CMOS process, which that does not include any special fabrication process steps for the sensor implementation. Based on the measurement results for a previously fabricated sensor device, we employ a square-shaped Hall plate as a sensor device and design a novel bridge instrumentation amplifier with chopping and auto-zero functions. The prototype of the sensor is implemented in a standard 180 nm CMOS process with a die area of $450\mu m\times 450\mu m$.
Computation-Efficient Variable Angle Phase-Shifting PWM Method for Cascaded H-Bridge Converters

When unbalances happen among the cells of a cascaded H-bridge (CHB) converter, variable angle phase-shifting (VAPS) pulse-width modulation (PWM) methods can be employed to improve the total harmonic distortion (THD) performance. However, as conventional methods are based on optimization algorithms which require a huge amount of evolutionary computations, the optimal carrier phase-shifting (PS) angles cannot be timely updated with a common standard digital signal processor (DSP) or microcontroller. During the period when the optimization is not finished, the THD of the total output voltage may significantly increase, bringing about higher power losses. Thus, a computation-efficient VAPS PWM method is proposed in this paper. With multiple calculation units (CUs) implemented in field programmable gate arrays (FPGAs), multiple calculations can be executed in parallel, significantly improving the optimization speed of the VAPS PWM method. Experimental results on a 4-cell CHB converter have demonstrated the effectiveness of the proposed method.
Safe Multi-Agent Learning Control for Unmanned Surface Vessels Cooperative Interception Mission

This paper aims to develop a multi-agent reinforcement learning control method for the unmanned surface vessels (USVs) interception mission. A safe proximal policy optimization (SPPO) method is developed to control defender vessels blocking the invading ship. SPPO employs a joint state-value function to strengthen the cooperation between defender vessels. A safety constraint is also introduced into action and state value estimation to reduce irresponsible actions, and we develop a safety constraint on motion boundaries and convert the safety problem into a constraint in the learning-based control method. Simulation results on defender USVs cooperative control ultimately illustrate the effectiveness of the safe proximal policy optimization method. With high performance in reward, SPPO can control USVs achieving cooperative interception missions.
Low Loss Non Air Gap Multi-Permeability Planar Inductor Design for Totem-Pole PFC

A optimized magnetic core structure without air gaps and made of two different permeability materials for planar inductors is proposed. In this paper, the effect of filling the magnetic yokes with different permeability magnetic materials on improving the magnetic field distribution around the PCB windings is discussed. In order to optimize the magneto motive force distribution in planar inductors, the magnetoresistance distribution of the multi-permeability magnetic core is designed. The theoretical analysis based on Ampere's loop theorem and 3D FEA simulation results demonstrate the effectiveness of the proposed optimized magnetic core structure in reducing copper losses. And the efficiency of CRM PFC can be improved by about 0.41% by using the proposed optimized magnetic core structure.
EmotionAlBERTo: Emotion Recognition of Italian Social Media Texts Through BERT

Social networks are perceived by users as a natural environment for publicly sharing their thoughts and emotions about different subjects. In these platforms, despite the availability of various forms of communication, text is the most widespread way of communication. Therefore, the development of automatic techniques for emotion recognition in social media texts, such as tweets or Facebook posts, gives the opportunity of extracting information that could be valuable for many application fields, ranging from the analysis of customer satisfaction to the optimization of political campaigns. Nowadays, several emotion classifiers and datasets have been built for English texts while few resources are available for other languages. For this reason, in this work we present a deep learning algorithm for emotion recognition in Italian social media texts. The algorithm was named EmotionAlBERTo as it is based on AlBERTo, a BERT-based language understanding model for the Italian language. We trained and evaluated EmotionALBERTo on two different Twitter datasets: the MultiEmotions-It dataset and TwIT, a novel Italian dataset for emotion recognition that we built by collecting and manually labelling a corpus of about 3100 Italian tweets. Experiments show that the models achieve remarkable performance on both 4- class and 6-class emotion classification, by respectively obtaining F1 = 0.91 and F1 = 0.83 on MultiEmotions-It, F1 = 0.92 and F1 = 0.86 on TwIT.