Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course

In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large-scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post-course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content.
Output‐feedback‐based sliding mode control for networked control systems subject to packet loss and quantization

In this paper, observer‐based sliding mode control is considered for networked control systems subject to quantization, in which packet dropout may happen when the measurement output is transmitted from the sensor to the controller. Firstly, a compensating scheme is proposed to deal with the effect of packet loss modelled as a Bernoulli process. Then, by means of available output information and state observer, the desirable sliding mode controller is designed. Furthermore, both the reachability and the stochastic stability of sliding mode dynamics can be attained and the corresponding sufficient conditions are derived. Finally, numerical simulation results are provided to illustrate the proposed control law.
Direct Numerical Simulation of Combined Turbulent Channel Flow

Wave-current processes are often observed in many different physical problems such as the flow in estuaries, beaches, etc. In these cases the pure current flow is modified due to the presence of waves. The propagation of waves can increase or decrease the streamwise current intensity depending on the direction of waves as it has been observed in laboratory experiments. The purpose of this work is to analyze the nonlinear effects of wave amplitude and wave period on turbulence characteristics of combined flow by means of Direct Numerical Simulation of the 3-D Navier-Stokes Equation. The effect of a wide range of frequencies and amplitude of the driving pressure gradient is reported and discussed.
An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data

High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly seen in industrial applications, such as recommender systems. They can be modeled into an HiDS matrix, from which non-negative latent factor analysis (NLFA) is highly effective in extracting useful features. Preforming NLFA on an HiDS matrix is ill-posed, desiring an effective regularization scheme for avoiding overfitting. Current models mostly adopt a standard <inline-formula> <tex-math notation="LaTeX">${L} _{2}$ </tex-math></inline-formula> scheme, which does not consider the imbalanced distribution of known data in an HiDS matrix. From this point of view, this paper proposes an instance-frequency-weighted regularization (IR) scheme for NLFA on HiDS data. It specifies the regularization effects on each latent factors with its relevant instance count, i.e., instance-frequency, which clearly describes the known data distribution of an HiDS matrix. By doing so, it achieves finely grained modeling of regularization effects. The experimental results on HiDS matrices from industrial applications demonstrate that compared with an <inline-formula> <tex-math notation="LaTeX">${L} _{2}$ </tex-math></inline-formula> scheme, an IR scheme enables a resultant model to achieve higher accuracy in missing data estimation of an HiDS matrix.
Synaptic Information Transmission in a Two-State Model of Short-Term Facilitation

Action potentials (spikes) can trigger the release of a neurotransmitter at chemical synapses between neurons. Such release is uncertain, as it occurs only with a certain probability. Moreover, synaptic release can occur independently of an action potential (asynchronous release) and depends on the history of synaptic activity. We focus here on short-term synaptic facilitation, in which a sequence of action potentials can temporarily increase the release probability of the synapse. In contrast to the phenomenon of short-term depression, quantifying the information transmission in facilitating synapses remains to be done. We find rigorous lower and upper bounds for the rate of information transmission in a model of synaptic facilitation. We treat the synapse as a two-state binary asymmetric channel, in which the arrival of an action potential shifts the synapse to a facilitated state, while in the absence of a spike, the synapse returns to its baseline state. The information bounds are functions of both the asynchronous and synchronous release parameters. If synchronous release facilitates more than asynchronous release, the mutual information rate increases. In contrast, short-term facilitation degrades information transmission when the synchronous release probability is intrinsically high. As synaptic release is energetically expensive, we exploit the information bounds to determine the energy–information trade-off in facilitating synapses. We show that unlike information rate, the energy-normalized information rate is robust with respect to variations in the strength of facilitation.
Performance enhancement of electric vehicle traction system using FO‐PI controller

This work improves the performance of an electric vehicle (EV) traction system by using non-linear fractional-order proportional–integral (FO-PI) controller. The functionality of FO-PI controller is benchmarked against integer-order proportional–integral (IO-PI) controller. A 400 V, 6.6 Ah Li-ion battery bank is designed to power an indirect field-oriented induction motor driven prototype EV traction system. The appeal of this study is to reveal the simplicity, robustness, and effectiveness of FO-PI speed regulator for EV traction system. The efficacy of FO-PI speed regulator for EV applications is validated through rigorous experimentation. The FO-PI controller due to its non-linear nature proves to be more robust and effective under variation of a system's parameters and external disturbances as compared to IO-PI controller. Furthermore, the experimental results show that FO-PI control strategy provides higher torque per amp output while maintaining better voltage and state-of-charge profiles of the Li-ion battery bank.
Sidewall effects on heat transfer in narrow backward facing step in transitional regime

Abstract In this work, we study numerically with large eddy simulation, the effects induced by the three-dimensional geometry of the channel on the flow topology that exists when the three-dimensional intrinsic instabilities appear in a backward facing step flow with low aspect ratio for Reynolds in the transitional regime (Re = 1,000–1,600), and its impact on the heat flux in the lower wall. Under the transitional regime, the three-dimensional instabilities begin to appear, but they can be masked by the flows due to the presence of the side walls. The study is carried out with two boundary conditions in the sidewalls, slip, and no-slip, to discriminate between the three-dimensionality induced by the geometry and the intrinsic three-dimensional instabilities. The results obtained are compared between the two boundary conditions, establishing what type of flow prevails and its influence on time-averaged mean Nusselt number for all Reynolds.
Turning a Television Into a GNSS Receiver

Dr. Carles Fernández–Prades serves as Head of the Communications Systems Division and Senior Research Associate at the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). He received the PhD degree in Electrical Engineering from Universitat Politècnica de Catalunya (UPC) in 2006. His primary areas of interest include signal processing, estimation theory, GNSS and the design of RF front-ends.
New insights into the lightning discharge processes

This review covers selected results of recent observations of lightning discharges performed across the entire electromagnetic spectrum (radiofrequency, optical, and energetic radiation) at the Lightning Observatory in Gainesville, Florida. The most important results include (a) characterization of the preliminary-breakdown, stepped-leader, and return-stroke processes in high-intensity (⩾50 kA) negative lightning discharges, (b) the first high-speed video images of bidirectional leader that made contact with the ground and produced a return stroke, (c) discovery of negative stepped leader branches colliding with the lateral surface of neighboring branches of the same leader, (d) new data on the occurrence context and properties of compact intracloud discharges, and (e) observation of a terrestrial gamma-ray flash that occurred during a bipolar cloud-to-ground lightning discharge. The results serve to improve our understanding of the physics of lightning with important implications for lightning modeling, lightning protection, and high-energy atmospheric physics studies.
Further Improvement of Debayering Performance of RGBW Color Filter Arrays Using Deep Learning and Pansharpening Techniques

The RGBW color filter arrays (CFA), also known as CFA2.0, contains R, G, B, and white (W) pixels. It is a 4 × 4 pattern that has 8 white pixels, 4 green pixels, 2 red pixels, and 2 blue pixels. The pattern repeats itself over the whole image. In an earlier conference paper, we cast the demosaicing process for CFA2.0 as a pansharpening problem. That formulation is modular and allows us to insert different pansharpening algorithms for demosaicing. New algorithms in interpolation and demosaicing can also be used. In this paper, we propose a new enhancement of our earlier approach by integrating a deep learning-based algorithm into the framework. Extensive experiments using IMAX and Kodak images clearly demonstrated that the new approach improved the demosaicing performance even further.
STUDENTS’ ERRORS IN TRANSLATING NARRATIVE TEXT BASED ON DULAY’S SURFACE STRATEGY TAXONOMY

This study is purposed to find out the errors in translating narrative text based on Dulay‟s Surface Strategy taxonomy. In addition, this study is also aimed to describe the possible causes of those errors. This study applied the descriptive qualitative research which the data were obtained from the translation texts that were written by the second- year students of SMA Tunas Pelita Binjai. The data were collected by using the students‟ translation text. Then the data were analyzed by identifying the errors, describing the errors, and the last explaining the errors. The errors that occurred in students‟ translation texts are omission error, addition error, misformation error, and misordering error. The result of the research indicates that the students made 230 errors which classified into: 22,61% omission error, 11,30% addition error, 65,22% misformation error, and 0,87% misordering error. The most dominant error that occurred in the students‟ translation text is misformation errors and the causes of errors were influenced by the interlingual transfer and intralingual transfer.
Lift force acting on a pair of clean bubbles rising in-line

In this study, we experimentally observed the motion of a pair of bubbles initially positioned in line, especially focusing on the intermediate Reynolds number case, i.e., 20 < Re < 60. We observed three types of motion at different Reynolds numbers. At a low Reynolds number (Re < 20), the trailing bubble collided with the leading bubble like a pair of rigid spheres. At a high Reynolds number (100 < Re), the trailing bubble moved out from the original vertical line joining the two bubbles. At intermediate Reynolds numbers (20 < Re < 60), small differences in bubble size affected the motion. When the leading bubble was larger than or equal to the trailing bubble, the trailing bubble first approached the leading bubble and later moved out from the initial vertical line owing to a lift force. When the leading bubble was smaller than the trailing bubble, the trailing bubble first approached the leading bubble, and then a repulsive force acted on both bubbles so that both of them moved out from the vertical line in opposite directions. These motions are attributed to two effects, the first is potential effects at short distance between bubbles, and the second is the wake of the leading bubble.In this study, we experimentally observed the motion of a pair of bubbles initially positioned in line, especially focusing on the intermediate Reynolds number case, i.e., 20 < Re < 60. We observed three types of motion at different Reynolds numbers. At a low Reynolds number (Re < 20), the trailing bubble collided with the leading bubble like a pair of rigid spheres. At a high Reynolds number (100 < Re), the trailing bubble moved out from the original vertical line joining the two bubbles. At intermediate Reynolds numbers (20 < Re < 60), small differences in bubble size affected the motion. When the leading bubble was larger than or equal to the trailing bubble, the trailing bubble first approached the leading bubble and later moved out from the initial vertical line owing to a lift force. When the leading bubble was smaller than the trailing bubble, the trailing bubble first approached the leading bubble, and then a repulsive force acted on both bubbles so that both of them moved out from the vertical li.
Assessing Learning Strategies with an Educational Video

ASSESSING LEARNING STRATEGIES WITH AN EDUCATIONAL VIDEO David James Harrison Old Dominion University, 2016 Director: Dr. Ginger S. Watson Even though educational films and videos have been in used for a century, there remains insufficient research into efficacious learning strategies that can be used alongside them. This study sought to investigate active learning strategies as a method to improve learning from video. This true experimental study, supported and informed by qualitative data, examined three active learning strategies utilized within video-based instruction: Guided Notetaking, Personal Notetaking, and Guided Summaries. Outcome measures included three dimensions of learning (factual, conceptual, and procedural) on an immediate posttest, perceptions of extrinsic cognitive load, likelihood that participants would use the strategies again, how often participants paused the video, time-on-task, quality of strategy usage, and commonly shared experiences. While there were no significant differences between groups on general measures of learning, when the scores of only those participants who crafted high quality products (notes or summaries) were compared, the Guided Notetaking group scored significantly higher than the Guided Summaries on factual learning. These results suggest that quality of strategy usage is a factor that should be included in research examining active learning strategies with educational videos. The Guided Notetaking group experienced significantly higher perceived extrinsic cognitive load than the other groups. Participants in the Personal Notetaking group reported significantly higher likelihood that they would use these strategies again compared to the other groups. Participants in the Guided Notetaking strategy paused the video significantly more often than participants in the other groups. Analysis of commonly shared subjective experiences indicated that Guided Notetaking was difficult for several reasons: matching of the video content with the notes, switching back and forth between the video and notes (which some perceived as detracting from their learning), and the constant pausing of the video this strategy required. Personal Notetaking was perceived as the easiest of the three strategies, only slightly easier than the Guided Summaries. Suggestions for implementation of strategies, future research, and production of educational videos are also provided.
Phylogenetic relationships within Apocynaceae s.l. based on trnl intron and trnL-F spacer sequences and propagule characters

Relationships within Apocynaceae s.l. (Apocynaceae s. str., Periplocaceae, and Asclepiadaceae) were investigated by maximum parsimony analysis of morphological and molecular sequence data. Sequences of the plastid trnL intron and trnL-F spacer for 152 accessions for representatives of all major tribes were included in this study; 96% of these sequences represent new data. Two outgroups were selected from the closely related Loganiaceae. The total evidence matrix incorporated trnL intron and trnL-F spacer sequences, insertion/deletion information, and propagule characters. The phylogenetic hypothesis derived from this data set was used to evaluate the most current classification systems and was also used to investigate trends in seed dispersal. Apocynaceae s.l. are a strongly defined monophyletic group, a finding that should be reflected in taxonomic treatments. The recognition of three of the five subfamilies proposed by Endress and Bruyns in 2000 is supported by the monophyletic Periplocoideae, Secamonoideae, and Asclepiadoideae, whereas the paraphyletic Rauvolfioideae and Apocynoideae are not supported. The precise position of the Periplocoideae, however, remains unclear. Traditional tribal delimitations were less congruent with our total evidence phylogeny. The evolution of seed comas and enhanced long-distance dispersal within the Apocynaceae s.l. probably contributed to accelerated cladogenesis, ultimately giving rise to the majority of the extant genera within the family. Phylogeographic analysis of our data provides some evidence for a Gondwanan origin of the family.
New materials physics

This review presents a survey of, and guide to, new materials physics (NMP) research. It begins with an overview of the goals of NMP and then presents important ideas and techniques for the design and growth of new materials. An emphasis is placed on the use of compositional phase diagrams to inform and motivate solution growth of single crystals. The second half of this review focuses on the vital process of generating actionable ideas for the growth and discovery of new materials and ground states. Motivations ranging from (1) wanting a specific compound, to (2) wanting a specific ground state to (3) wanting to explore for known and unknown unknowns, will be discussed and illustrated with abundant examples. The goal of this review is to inform, inspire, an even entertain, as many practitioners of this field as possible.
Robotic Tasks Acquisition via Human Guidance: Representation, Learning and Execution

This thesis presents a framework that permits intuitive learning, compact representation, and flexible execution of robotic tasks. Task demonstrations are used to intuitively transfer novel skills to the robot. A learned task is represented at two different levels. At the lower level, each segmented motion unit is compactly encoded into a stable dynamical system. At the higher level, the task is represented in a symbolic manner. The symbolic representation is also learned from demonstration and used to generate the task plan that the robot has to execute.
Design of a broad‐band microstrip loop antennas with less‐dispersive group velocity for accurate direction finding

This study proposes the design of broad-band microstrip loop antennas with less-dispersive group velocity of radiation patterns for accurate direction of arrival (DoA) estimation. The proposed structure consists of radiating and feeding microstrip loops, and the radiating loop is electromagnetically coupled to the feeding loop. This feeding mechanism helps to suppress phase variations of radiating patterns by avoiding dual resonances within a wide frequency band over 60 MHz, which allows to obtain the less-dispersive group velocity for accurate DoA estimation. The effectiveness of the less-dispersive behaviour is validated by implementing the proposed structure into a four-element array to estimate vertical and horizontal placements using dual-axis phase interferometry. The results confirm that the proposed antenna has less-dispersive group velocity compared to a conventional microstrip patch antenna and is more suitable for accurate DoA estimation.
Parametric Robust Control of the Multivariable 2 × 2 Looper System in Steel Hot Rolling: A Comparison between Multivariable QFT and H∞

Two robust mutlivariable controllers, H∞ and a decentralized quantitative feedback theory (QFT), are designed in the frequency domain for the 2 × 2 looper system in a steel hot rolling mill to keep stability in the presence of parametric uncertainties. The H∞ controller is designed by using the mixed sensitivity approach, while the multivariable decentralized QFT is designed by the extension of the sequential loop closing method presented elsewhere. Stability robustness conditions are verified in the frequency domain, while simulations in time domain are carried out to evaluate the controllers and compare their performance along with that of proportional + integral (PI) and single input single output (SISO) QFT controllers designed earlier. The QFT controller shows the best balance among the performance indicators analyzed here; however, at the expenses of using higher power in one of the control inputs.
LSCDroid: Malware Detection Based on Local Sensitive API Invocation Sequences

Malware detection is an important and challenging issue in the Android ecosystem. Many approaches have been proposed to distinguish malicious applications from benign ones, but few of them can represent the behavior patterns of malicious applications and help understand their intention. In this paper, we propose LSCDroid, a malware detecting approach that cannot only detect malware but also help understand the malware's intention by analyzing its behavior patterns. LSCDroid uses local sensitive application programming interface (API) invocation (LSAI) sequences as features to detect malware and represent different malicious behavior patterns. We first extract LSAI sequences of malicious applications based on their function-call graphs. After removing redundant sequences and merging fragmented ones, we obtain a set of LSAI sequences that can be used to effectively detect malicious applications. We further manually analyze the semantic of the obtained sequences and find that a large fraction of them can be used to characterize different behavior patterns of malware and help understand their intention, e.g., sending SMS message stealthily, obtaining geographical information, remote control, and root privilege. We design a machine learning based malware detection and classification algorithm by taking the obtained sequences as input features. Experimental results show that the accuracy and recall of LSCDroid on multiple datasets are both higher than 0.98. Meanwhile, LSCDroid can classify malware families with an accuracy higher than 0.96. Moreover, LSCDroid can represent the behavior patterns and help understand intention of malware by mapping their LSAI sequences to some typical malicious behaviors.