Miracle-dz/UniFAR-FTUs huggingface dataset:
The Perception of Students about the use of Social Media as an Alternate Learning Platform

The advent of Web 2.0 brought about a new medium of communication, such as social media. Today’s generation of students have been greatly influenced by the Web and hence, have become active users of the different available social media platforms. The influence of social media platforms on students, is causing a shift in learning, and as a result, educational institutions are increasingly creating alternative learning platforms, using social media to facilitate student-centred learning. This paper presents the findings from a study that was conducted to understand the perception of students regarding the use of social media as an alternative learning platform. The quantitative research method was adopted, and 331 questionnaires were administered at the Pietermaritzburg campus of University of KwaZulu-Natal (UKZN). The findings reveal that students are comfortable with the use of social media as a learning platform, because it enhances their ability to be interactive and it improves their chances of accessing educational information, even when not within the academic environment. The findings, however, also revealed that some features of social media could cause distraction to students, but when adequate measures are introduced, social media could become an additional means of interaction for students, especially for academic purposes.
Analysis of Multipactor Effect in a Partially Dielectric-Loaded Rectangular Waveguide

This paper presents a study of the multipactor effect in a partially dielectric-loaded rectangular waveguide. To obtain the simulations presented in this paper, a detailed analysis of the dynamics of the electron inside this waveguide has been performed, taking into account the radio frequency electromagnetic fields propagating in the waveguide and the dc electric field that appears because of the charging of the dielectric layer. This electrostatic field is obtained by computing the electric potential produced by an arbitrary charge distribution on the dielectric layer in a dielectric-loaded waveguide. The electron trajectory is then found by numerically solving the equations of motion. The results obtained show that multipactor discharges do turn off by themselves under certain circumstances when they occur in such dielectric-loaded waveguide.
Machine Learning Based Skill-Level Classification for Personal Mobility Devices Using Only Operational Characteristics

Some electric-powered wheelchairs are recently redefined as personal mobility devices. Their users are not only elderly or handicapped people, but also passengers with large baggage or pedestrians going from station to destination, i.e., last-mile transport. Consequently, people with different operation skills and expectations on personal mobility would become new users of this kind of devices. Safe and comfort travel in human co-existing environment such as sidewalks and airports is a social expectation for personal mobility. In order to realize this, understanding the operation skill of each user by a practical and simple method is essential. This paper thus introduced a skill level classification method by machine learning using only joystick data as input. In order to determine the number of skill level clusters, basic 26 features of joystick operation data are used for unsupervised clustering (single-linkage). We then made evaluation indexes by using speed, speed control, and direction control. For a five-level classification by using gradient boosting as supervised learning, we achieved a 67% accuracy (tolerance: 0) and a 98% accuracy (tolerance: 1). Further analysis of the feature importance of gradient boosting revealed key features to a good operation. Results also show that skill level differed among people with different driving experiences.
A High-Speed, Scalable, and Programmable Traffic Manager Architecture for Flow-Based Networking

In this paper, we present a programmable and scalable traffic manager (TM) architecture, targeting requirements of high-speed networking devices, especially in the software-defined networking context. This TM is intended to ease the deployability of new architectures through field-programmable gate array (FPGA) platforms and to make the data plane programmable and scalable. Flow-based networking allows treating traffic in terms of flows rather than as a simple aggregation of individual packets, which simplifies scheduling and bandwidth allocation for each flow. Programmability brings agility, flexibility, and rapid adaptation to changes, allowing to meet network requirements in real-time. Traffic management with fast queuing and reduced latency plays an important role to support the upcoming 5G cellular communication technology. The proposed TM architecture is coded in C++ and is synthesized with the Vivado High-Level Synthesis tool. This TM is capable of supporting links operating beyond 40 Gb/s, on the ZC706 board and XCVU440-FLGB2377–3-E FPGA device from Xilinx, while achieving 80 Gb/s and 100 Gb/s throughput, respectively. The resulting placed and routed design was tested on the ZC706 board with its embedded ARM processor controlling table updates.
Semantic Monocular SLAM for Highly Dynamic Environments

Recent advances in monocular SLAM have enabled real-time capable systems which run robustly under the assumption of a static environment, but fail in presence of dynamic scene changes and motion, since they lack an explicit dynamic outlier handling. We propose a semantic monocular SLAM framework designed to deal with highly dynamic environments, combining feature-based and direct approaches to achieve robustness under challenging conditions. The proposed approach exploits semantic information extracted from the scene within an explicit probabilistic model, which maximizes the probability for both tracking and mapping to rely on those scene parts that do not present a relative motion with respect to the camera. We show more stable pose estimation in dynamic environments and comparable performance to the state of the art on static sequences on the Virtual KITTI and Synthia datasets.
Any-Time Trajectory Planning for Safe Emergency Landing

Loss of thrust is a critical situation for human pilots of fixed-wing aircraft which force them to select a landing site in the nearby range and perform an emergency landing. The time for the landing site selection is limited by the actual altitude of the aircraft, and it may be fatal if the correct decision is not chosen fast enough. Therefore, we propose a novel RRT* -based planning algorithm for finding the safest emergency landing trajectory towards a given set of possible landing sites. Multiple landing sites are evaluated simultaneously during the flight even before any mechanical issue occurs, and the roadmap of possible landing trajectories is updated permanently. Thus, the proposed algorithm has the any-time property and provides the best emergency landing trajectory almost instantly.
LIPS: LiDAR-Inertial 3D Plane SLAM

This paper presents the formalization of the closest point plane representation and an analysis of its incorporation in 3D indoor simultaneous localization and mapping (SLAM). We present a singularity free plane factor leveraging the closest point plane representation, and demonstrate its fusion with inertial preintegratation measurements in a graph-based optimization framework. The resulting LiDAR-inertial 3D plane SLAM (LIPS) system is validated both on a custom made LiDAR simulator and on a real-world experiment.
A Novel Soft Elbow Exosuit to Supplement Bicep Lifting Capacity

This paper investigates the design of a soft elbow exosuit capable of providing supplemental lifting assistance by reducing muscle activity of the bicep muscle. The aim is to improve the efficiency and endurance of workers who are tasked with repetitive lifting. The design consists of an array of pneumatically pressurized soft actuators, which are encased in nylon fabric that allows for a high force-to-weight ratio of 211.SN/g. An analytical model governing the bending behavior of two consecutive actuators and torque generated by the exosuit is developed, with test results showing less than 10% error from the theoretical model. An elbow joint torque value of 27.6N. m is achieved at 300kPa, which is comparable to the 30N.m maximum set by OSHA requirements in the USA. Further testing with a healthy participant is performed using surface electromyography (sEMG) sensors and a motion capture system to assess the capabilities of the exosuit to provide active assistance to the bicep during isometric and concentric contractions. Measurable assistance to lifting is observed with minimal obstruction to the user's range of motion for all experiments.
Reliability Modeling of Two-Component System with Degradation Interaction Based on Copulas

The stochastic dependency exists in many complex systems, which cannot be ignored in reliability modeling. The stochastic dependency is analyzed from the degradation perspective in this paper. For a two-component system, a degradation interaction model is built to describe the dependency between the degradations of two components based on nonlinear Wiener process and Copula function. Then, the reliability functions of components and system are derived, and the parameter estimation approach of the developed models is given. Finally, a numerical example about fatigue crack development is presented to validate and illustrate the performance of the developed models.
Teaching a Robot to Grasp Real Fish by Imitation Learning from a Human Supervisor in Virtual Reality

We teach a real robot to grasp real fish, by training a virtual robot exclusively in virtual reality. Our approach implements robot imitation learning from a human supervisor in virtual reality. A deep 3D convolutional neural network computes grasps from a 3D occupancy grid obtained from depth imaging at multiple viewpoints. In virtual reality, a human supervisor can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large synthetic training data set consisting of 100 000 example grasps of fish. Using this data set for training purposes, the network is able to guide a real robot and gripper to grasp real fish with good success rates. The newly proposed domain randomization approach constitutes the first step in how to efficiently perform robot imitation learning from a human supervisor in virtual reality in a way that transfers well to the real world.
Gender Influences on Preference of Pedagogical Interface Agents

The purpose of this paper was to examine gender influences on the preference of Pedagogical Interface Agents (PIAs) amongst a group of male and female adult computer literacy trainees. This was a comprehensive study with participants drawn from a township community in Bloemfontein, South Africa and matched with descriptors such as little or no formal post-school education, little or limited previous experience in computers and able to understand and speak English. Therefore, this study sought to investigate whether there is an association between the gender of adult computer literacy trainees and the preferred gender of PIAs. A total of 72 adults, specifically 41 females and 31 males participated. They were trained and given tasks to perform using Simulator Word Processor System (SMOS) which incorporated 10 PIAs that differ in terms of their human-like features such as of appearance, gender, voice and reality. A post-test questionnaire was used to measure participants’ personal opinions about their preferences regarding each PIA that they interacted with. Similarly, Fischer’s exact test was done to determine if there was an association between the gender of the participants and the preferred gender of the PIA. The post-test questionnaire findings indicated that there were only slight differences regarding the number of participants (females and males) who preferred one type over another one. It was also noted that human-like features of PIAs could influence the manner in which participants related to a particular PIA and their preference level of a particular PIA. Additionally, a Fischer’s exact test found no statistically significant effect between participants’ gender (male or female) and the preferred gender of PIA. It is, therefore concluded that the gender of participants (i.e. male or female) has no role on their preferences regarding the types, features, or gender of a PIA, neither on their preference level of a PIA.
Optical Classifications of Poyang Lake Water and Long-Term Dynamics Based on MERIS Observations

Using observations from the Medium Resolution Imaging Spectrometer (MERIS) and an unsupervised (Iterative Self-Organizing Data Analysis Technique) clustering method, the class centers for four different optical classes (i.e., moderate turbid or Class I; turbid or Class II; extremely turbid or Class III; and clear waters or Class IV) of Poyang Lake water were determined. A squared-Mahalanobis distance-based classification scheme was then used to classify 320 MERIS data between 2003 and 2012 (∼2.7 images/month on average). Significant dynamics of the probability of occurrence (POO) for different classes were observed through long-term analysis. The extremely turbid and clear water classes dominated Poyang Lake, accounting for ∼70% of the total surface area within the entire time series. Class III and IV waters showed out-of-phase fluctuation patterns, while a large POO was observed in dry seasons for the extremely turbid water class (Class III), and the clear water class (Class IV) represented most of the lake in the wet seasons. The northern narrow water channel of Poyang Lake was generally characterized by Class II and III waters. In contrast, the high POO of Class IV was demonstrated in the southern main lake and some eastern and southern isolated sublakes. The hydrological analysis shows that the south-to-north water level gradients were positively correlated with the combined POOs of Classes II and III (R2 = 0.4) and that the gradients were negatively correlated with the POO of Class IV (R2 = 0.36). Significant relationships existed between the POOs of different classes and local precipitation. This study provides critical information for future environmental conservation planning.
Application of PI/MWCNT Nanocomposite for AC Corona Discharge Reduction

Electric-field (<inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-field) and radio influence voltage (RIV) from high voltage transmission line occur due to corona discharge. In this proposed work, the effect of corona discharge on aluminum conductor steel reinforced (ACSR) conductor was deeply investigated and mitigated by coating the polyimide/multiwalled carbon nanotubes (PI/MWCNTs) on the surface of the conductor. Bare and coated ACSR panther conductors were tested for <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-field on the surface of the conductor, corona inception voltage (CIV), corona extinction voltage (CEV), and RIV. PI/MWCNT was synthesized and characterized by X-ray diffraction, thermogravimetric analysis, and permittivity and conductivity tests. The impact of MWCNT on corona discharge parameters were realized by loading it in different weight percentages as 1 wt%, 3 wt%, and 10 wt% in PI. It has been observed from the measured value (as per the standard IEC 60060-2) that the <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-field was reduced by 8%–23%, RIV was suppressed with a difference of 7–18 dB, and CIV/CEV was improved for nanocomposite coated conductor. To comprehend the reason for the <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-field and RIV suppression, permittivity and conductivity tests were performed as per the standard ASTM D5568. Lightning impulse voltage test was also conducted on plane–plane electrode configuration and affirmed that PI/MWCNT material has the capability to withstand lightning discharge plasma.
Real-Time Bayes Estimation of Residual Life Based on Multisource Information Fusion

Residual life estimation is significant in reliability engineering. In this paper, a Bayes model is proposed to estimate the residual life of components by fusing expert knowledge, lifetime and degradation data, which provides a new method for residual lifetime estimation of components characterized as small sample, high reliability and long life. The linear Wiener process is used to model the degradation data and the lifetime data is described by the inverse Gauss distribution. The joint maximum likelihood function can be obtained by integrating the lifetime data and degradation data. With the Maximum Entropy Method (MEM), the prior distribution can be determined by the expert knowledge, which is different from the non-informative prior in existing study. Due to the complexity of computation, Monte Carlo Markov Chain (MCMC) is applied to estimate the parameters. Therefore, the probability density function (PDF) of residual life can be determined. Also, the parameters of distribution can be updated in real time. Finally, an illustrative example is presented to validate the proposed method. The results prove the effectiveness and accuracy.
Test System Upgrade for ITER Current Lead Series Production at ASIPP

The Institute of Plasma Physics, Chinese Academy of Sciences (ASIPP), is in charge of the manufacturing and testing of the 60 high-temperature superconducting current lead series to be completed at the end of 2020 for the International Thermonuclear Experimental Reactor (ITER). This paper first summarizes the testing items and testing results of the ITER lead prototypes in 2015/2016, then, based on the prototypes experiences, discuss the final test piping and instrument diagram for the ITER lead series production testing. A new 5-K test platform to expedite the series testing is also introduced, and a new feature, the 30-kV high-voltage (HV) class insulation for the cryogenic temperature and voltage measurements to allow in situ HV testing, is introduced. The detailed test procedures and test items for the series are discussed finally.
A Mobility Model Based on Improved Artificial Potential Fields for Swarms of UAVs

A combination of several autonomous UAVs can be used to perform collaborative tasks. Such a combination is referred to as a swarm of drones. The use of multiple platforms can extend the system global capacities thanks to the resulting variety of embedded sensors and to information sharing. In this case, path planning and thus obstacles avoidance is still a major task. To deal with this issue, mobility models have to be implemented. Our contribution presented in this paper is a mobility model for swarms of UAVs based on the Artificial Potential Fields (APF) principle. In our model, the involved UAVs collaborate by sharing data about the obstacles that they detected. By doing so, a UAV which is not close enough to an obstacle to detect it thanks to its own sensors will still have the proper data to take this obstacle into account in its path planning. To validate our mobility strategies with realistic constraints we simulate the performances of existing sensors and transmitters, and consider real-world environment.
Adaptive Human-Agent Multi-Issue Bilateral Negotiation Using the Thomas- Kilmann Conflict Mode Instrument

Automated negotiation is an important class of problems that has wide reaching application in the real world. While a lot of work has been done in Agent-Agent negotiations, Human-Agent negotiations have been relatively unexplored. Human-Agent multi issue bilateral negotiations deals with autonomous agents negotiating with humans over more than one item. Designing agents which can engage in such negotiations requires estimating the preferences of the human opponent in real time and proposing offers which are likely to be accepted before the session timeout. We design an agent that estimates the human opponent's preferences using two new heuristics, Most Changed Least Preferred and Most Offered Most Preferred. Also, the agent utilises the Thomas- Kilmann Conflict Mode Instrument to judge the negotiation strategy of the opponent and then adapts its own strategy to reach agreement faster. The agent does so without the use of historical data, therefore, remaining free from the problems that arise from lack of or biased historical data. Our results show that the agent reaches good agreements against a wide variety of human negotiators. The agreements fall on or near the Pareto-Optimal frontier with the probability of an agreement resulting in an optimal distribution being 97.7 %.
A Generic Spatiotemporal UAV Scheduling Framework for Multi-Event Applications

In this paper, a generic scheduling framework to manage a fleet of micro unmanned aerial vehicles (UAVs) is proposed. The objective is to employ multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in a geographical area of interest over a long period of time. The proactive scheduling framework considers several constraints and challenges, including the technical specifications of the UAVs and the limited battery capacities. In addition, the platform considers the necessity to regularly send back the UAVs to a docking station for battery recharging. A mixed integer nonlinear programming problem aiming at minimizing the total energy consumption and the number of employed UAVs is formulated to guarantee the non-redundant exploitation of the resources. Afterward, a series of linearization steps are introduced to convert the problem into a mixed integer linear programming one so that it can be optimally solved. To reduce the complexity of the problem, a dynamic time horizon discretization approach adapted to the characteristics of the problem is performed beforehand. The proposed UAV scheduling framework is formulated in a generic manner and can be applied in multiple domains comprising short- and/or long-term UAV missions while ensuring uninterrupted service.
A Model of Factors Affecting Cyber Bullying Behaviors Among University Students

This paper attempts to mitigate this gap within the literature concerning the use of social media for cyber engagement (CE) among students. Since students often become upset when network providers intervene, this paper aims to develop a model to measure ethics issues related to engagement with social media. The conducted survey examines social media use with regard to cyber engagement, cyberbullying behaviors, and being bullied, harassed, and stalked. To achieve the objective, this paper employed a questionnaire as the main data collection method and distributed it to 242 students, all of whom use social media. The findings were obtained via a quantitative research method, structural equation modeling, and partial least squares. The findings from our empirical study indicate that the assessment of discriminant validity has become an extensively acknowledged requirement for the analysis of latent variables’ relationships. Goodness of fit indices demonstrates a good fit of the model. Roughly more than half of students indicated they had been bullied, harassed, and stalked online. The proposed model will help campus administration and decision makers to formulate strategies that can significantly reduce cyber harassment among students.