Miracle-dz/UniFAR-FTUs huggingface dataset:
Academic Guidance for Undergraduate Students in a South African Medical School: Can we guide them all?

Higher education institutions, including medical schools, still grapple with the challenge of poor academic performance of students. Some studies report the positive results of providing academic guidance for common challenges such as poor and/or ineffective time management, study methods,test- and exam-taking techniques and management, and the high academic workload of undergraduate medical students. However, limited detailed insights and understanding of medical students who experience more complex challenges are available. This study was conducted at a medical school in South Africa to determine undergraduate medical students’ perceptions of factors affecting their academic performance. A total of 89 semi-structured interviews were held with undergraduate medical students who were identified as having academic problems between 2012 and 2015. According to the results, more blacks, males and first- and secondyear students experienced poor academic performance. Prominent findings included the harsh realities and implications of lack of accommodation for black students; how poor academic performance can lead to an array of other social and psychological problems, such as withdrawal of bursaries and negative achievement emotions that some students experience. Compared to the usual objective measures of individual ability, the rich qualitative data of cases presented in this study reveal critical, real insights and understanding of students’ challenges from their own perspective.
Honeycomb Texturing of Hierarchical Nanoflowers of WO3 as an Efficient Route to Improve Repeatability and Stability of Room Temperature Vapor Sensor

Hierarchical nanostructures of WO3 was synthesized by a hydrothermal method at 180°C using sodium tungstate dihydrate (Na2WO4.2H2O), sodium bisulfate dihydrate (NaHSO4.2H2O) and ethylene glycol (having different molar concentrations) with Titanium as the substrate. Molar concentrations of ethylene glycol was taken to be 2.241 M, 8.274 M and 15.693 M, which eventually produced, (i) nanoneedles with sparsely distributed nanosphere clusters (S-I) (ii) nanospheres (S-II) (iii) honeycomb textured hierarchical nanoflowers (S-III), respectively. After detailed morphological and structural characterizations, like field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD), these different nanostructures were employed as the gas sensing layer targeting the alcohol vapors in the concentration range of 10–400 ppm over a temperature range of 27°C (room temperature) to 200°C. Among the hierarchical nanoforms, S-III offered the best sensing performance in terms of response magnitude (~29.35%/~91.59%), response time (~31 s/~19 s) and recovery time (~42 s/~27 s), for 10/400 ppm ethanol at 27°C. Moreover, improved stability and repeatability were also achieved by the same structure. Such improvement in the sensing performance was duly correlated with the corresponding morphology of the nanostructure employing a semi-quantitative geometrical model.
NOMA Based UAV Relay Communication Protocol in Cellular Network

Introducing unmanned aerial vehicles (UAVs) into wireless communication systems has recently gained a lot of attention. UAV provides many advantages such as shorter communication distance and a higher probability of having line-of-sight (LoS) condition due to its dynamic positioning. In this paper, UAV is deployed as a relay station because it can give superior performance due to the high probability of having LoS channel compared to a fixed ground relay station. However, the achievable throughput is decreased as relay communication requires twice of time resources in direct communication. To tackle this problem, non-orthogonal multiple access (NOMA) based communication protocol is proposed in this paper. Furthermore, as a specific problem for UAV, interference from neighbouring base stations (BSs) is large due to LoS channel between UAV and neighbouring BSs. In order to eliminate the interference from neighbouring BSs, UAV relay is equipped with array antenna. The simulation results elucidate that the throughput improvement of the proposed protocol over the conventional protocol.
Planning and Execution using Inaccurate Models with Provable Guarantees

Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives. However, robots operating in the real world often face situations not modeled by these models before execution. This imperfect modeling can lead to highly suboptimal or even incomplete behavior during execution. In this paper, we propose CMAX an approach for interleaving planning and execution. CMAX adapts its planning strategy online during real-world execution to account for any discrepancies in dynamics during planning, without requiring updates to the dynamics of the model. This is achieved by biasing the planner away from transitions whose dynamics are discovered to be inaccurately modeled, thereby leading to robot behavior that tries to complete the task despite having an inaccurate model. We provide provable guarantees on the completeness and efficiency of the proposed planning and execution framework under specific assumptions on the model, for both small and large state spaces. Our approach CMAX is shown to be efficient empirically in simulated robotic tasks including 4D planar pushing, and in real robotic experiments using PR2 involving a 3D pick-and-place task where the mass of the object is incorrectly modeled, and a 7D arm planning task where one of the joints is not operational leading to discrepancy in dynamics. The video of our physical robot experiments can be found at this https URL
SHNU Anti-spoofing Systems for ASVspoof 2019 Challenge

This paper presents an experimental analysis of SHNU anti-spoofing systems for the ASVspoof 2019 challenge. This challenge focused on countermeasures for three major attack types, namely those stemming from the advanced technology of TTS, VC and replay spoofing attacks. According to the type of attacks, the challenge was divided into two independent sub-challenges, the logical access (LA) and physical access (PA). Results of different anti-spoofing technologies on both sub-challenges were reported. Furthermore, the same countermeasures were also evaluated on two previous challenges, the ASVspoof 2015 and 2017. Experiments on cross-databases showed that, it appeared hard to generalize the classifiers trained from ASVspoof 2019 LA and PA databases to the previous challenges. The generalization ability of anti-spoofing technologies to different, new and unknown conditions was still very challenging. In addition, the effectiveness of different acoustic features were also examined and reported. Finally, we investigated the linear and an interfusing score-level fusion methods to individual systems to achieve better performance.
A Distributed Learning Simulation Platform for Edge Hierarchies

We develop a distributed learning simulation platform that allows users to create multi-level Edge hierarchy for a given application by simulating resource constrained Edge devices and communication links amongst them. The resulting Edge computing hierarchy is used to run a given DNN for the application in a data distributed fashion, rolling up learned parameter values up a hierarchy of parameter servers that merge parameters received from the lower levels. The root of this hierarchy has the latest model, which is then pushed lazily back down the tree to the Edge servers. The platform can be used to study cost vs accuracy analysis of a given application for different Edge hierarchy configurations. We use handwritten digit recognition problem as a case study to show the usefulness of our platform.
Process and Design Consideration for Wafer-to-Wafer Hybrid Bonding

In this paper, we evaluate the surface condition with consideration of different process and designed pattern after chemical mechanical polishing (CMP) process in 12-inch platform for high density hybrid bonding application. After the CMP process, the surface topology was carried out by atomic force microscopy (AFM) measurement. We study the impact on the surface topology of the wafer after CMP process, and the results showed that the surface topology including surface roughness and Cu pad dishing/protrusion is dependent on the design of the pad size and spacing and may further make impact on bonding quality, which is needed to be considered carefully.
An Approach of Support Capability Evaluation for Unmanned Aerial Systems

With the continuous development of science and technology, the application scope of unmanned aerial system (UAS) is more and more wide, and the combat importance is higher. The existing evaluation index system of manned aircraft support capability cannot be fully applied to UAS. In this paper, through the decomposition of system dimension, process dimension and characteristic dimension, the evaluation index system of support capability of UAS is established, and the measurable parameters are used to express the unmeasurable parameters to evaluate the support capability of UAS, and the basic flow of applying SEM method is given.
Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey

Assigning bands of the wireless spectrum as resources to users is a common problem in wireless networks. Typically, frequency bands were assumed to be available in a stable manner. Nevertheless, in recent scenarios where wireless networks may be deployed in unknown environments, spectrum competition is considered, making it uncertain whether a frequency band is available at all or at what quality. To fully exploit such resources with uncertain availability, the multi-armed bandit (MAB) method, a representative online learning technique, has been applied to design spectrum scheduling algorithms. This article surveys such proposals. We describe the following three aspects: how to model spectrum scheduling problems within the MAB framework, what the main thread is following which prevalent algorithms are designed, and how to evaluate algorithm performance and complexity. We also give some promising directions for future research in related fields.
Snoring sound classification using multiclass classifier under actual environments

The problem with conventional snoring sound identification methods is that their performance declines when the snoring sound is identified in the actual environment. Therefore, it is necessary to cope with the stationary and nonstationary environmental sounds that cause the decrease. In this research, we tried to cope with stationary environmental sounds by spectrum subtraction method for noise suppression. Non-stationary environmental sounds were regarded as one class for each type of environmental sound. We tried to identify the snoring sounds by multikernel learning, which is a multiclass extension of a support vector machine and by multilayer perceptron, which is a kind of neural network.
A One-Dimensional Convolutional Neural Network Model for Automated Localization of Epileptic Foci

Intracranial electrocorticogram (iEEG) is often used by clinical experts to determine the location of the epileptic focal in the treatment of epilepsy. However, assess the location of epileptic foci by using iEEG is time-consuming and strenuous for clinical experts. Technology for automated localization of the channel of epileptic focal is indispensable. Hence, we developed a one-dimensional convolutional neural network (ID-CNN) model, which can directly extract features and train model by the raw signals without preprocessing, and performed the classification of focal and nonfocal epileptic iEEG signals. Compared with other machine learning methods, the amount of parameter reduced significantly. Our developed model has yielded the classification accuracy of 85.14% in classifying the focal and nonfocal epileptic iEEG signals.
Structure Growth for Small-Footprint Speech Recognition

Modern speech recognition (ASR) is based on large-scale deep neural nets (DNNs) with various architectures. For small-footprint applications running on low-power chips, however, the size of the DNNs must be extremely constrained. In this case, training a generalizable acoustic model is not feasible, especially when the acoustic conditions are diverse. Most of existing approaches to small-footprint networks start from a large net and reduce its scale by pruning. In this paper, we investigate a reverse idea: starting from a small net and increasing it gradually. This structure-growth approach follows a ‘general to specific’ principle and grows the net gradually. We start from the AdaBoost algorithm that builds specific nets for error-prone data, and then propose a new ConBoost that builds specific nets for specific conditions. Our experiments on a small-footprint ASR task demonstrated that both AdaBoost and ConBoost outperform the baseline and other comparative methods including bagging and double-net retraining. Furthermore, ConBoost performs better than AdaBoost.
Geolocation with Graph-Based Model Fitting

We address the problem of geolocation in a search and rescue mission where a person with a hand-held transmitter is to be located by a sporadic set of receivers, which measure the received power of the transmitted distress beacon. As simple propagation models are insufficient to correctly predict the received powers, we suggest to use the region topology in order to derive a graph-based structure. The latter is then exploited for simultaneous model adjustment and geolocation. Simulations demonstrate the effectiveness of the suggested method.
Visualization of Focal Cues for Visuomotor Coordination by Gradient-based Methods: A Recurrent Neural Network Shifts The Attention Depending on Task Requirements

For an autonomous robot to flexibly move in response to various tasks or environmental changes, an attention mechanism is required that is based on the robot's behavioral experience. In this paper, we visualize how attention is acquired inside a neural network learned using supervised learning and describe how to acquire a suitable representation for performing a task. Our experimental evaluation shows that the attention was automatically acquired for objects that are needed to perform tasks by learning the time-series of both vision and motor information rather than only vision information. By multimodal learning, the attention is robust against unlearned conditions which background changes or obstacles.
Practical Server-side Indoor Localization: Tackling Cardinality & Outlier Challenges

In spite of many advances in indoor localization techniques, practical implementation of robust device-independent, server-side Wi-Fi localization (i.e., without any active participation of client devices) remains a challenge. This work utilizes an operationally-deployed Wi-Fi based indoor location infrastructure, based on the classical RADAR algorithm, to tackle two such practical challenges: (a) Low cardinality, whereby only the associated AP generates sufficient RSSI reports and (b) outlier identification, which requires explicit identification of mobile clients that are attached to the Wi-Fi network but outside the fingerprinted region. To tackle the low-cardinality problem, we present a technique that uses cardinality changes to demarcate periods of stationary behaviour, and then augment the RSSI reports with useful but apparently “stale” RSSI readings from neighbouring APs. To tackle the filtering of clients with outlier locations, we propose a model that combines a weighted path-loss propagation model with a Voronoi tessellation of the fingerprint map to define suitable boundary values for RSSI readings. We experimentally show how these two approaches improve the stability and robustness of location tracking, and consequently, the accuracy of overall occupancy estimation.
Detection of Blur and Non-Blur Regions using Frequency-based Multi-level Fusion Transformation and Classification via KNN Matting

In digital images, blur wraps significant information and makes automatic image analysis a challenging task for computer vision algorithms. Hence, accurate blur detection and classification becomes essential to understand the information wrapped up in blurry images. In this paper, we proposed a novel automatic blur and non-blur region detection, and classification technique “Frequency-based Multi-level Fusion Transformation” (FMFT) to detect the unwanted blurry region and classify the blur and non-blur regions using single image processing. Our proposed approach mainly works with frequency sub-bands in patch wise manner, without having any prior information regarding camera configuration, type of blur and intensity of blur. Moreover, the detected Tri-Map from FMFT is further processed to perform blur and non-blur regions classification along with sharp object detection from blurry images using KNN-Matting. The average F1-score of 0.98 signifies the effectiveness of the proposed method in terms of blur detection and classification. Additionally, the proposed method also outperforms existing state-of-the-art techniques.
Learning-Based Spatial Reuse for WLANs With Early Identification of Interfering Transmitters

In this paper, a reinforcement learning-based spatial reuse scheme for wireless local area networks (WLANs) is proposed and analyzed. In this scheme, when an access point (or a station) overhears an on-going transmission, it decodes the information in the frame header to identify the transmitter and decides whether or not to exploit spatial reuse accordingly. Specifically, it decides whether to stop receiving the remaining part of the frame and start its own transmission or to refrain from channel access until the detected transmission finishes. Through the repeated update Q-learning (RUQL) algorithm, the agent learns the optimal decision in the sense of reducing the media access control layer delay. Moreover, we compare the proposed scheme with the spatial reuse operation in IEEE 802.11ax, which makes the spatial reuse decision only based on a binary identification of the detected interferer, i.e., whether it is in my cell or neighboring cells. The proposed scheme, however, treats different interferers differently for exploiting spatial reuse. From a theoretical perspective, we derive a theoretical bound on the gains in the value function, i.e., the discounted sum of delay, due to making non-binary identifications. Simulation evaluations confirm that the proposed scheme achieves high throughput by reducing the time of freezing backoff counter while not increasing the time of failed transmissions.
Event-Triggered Synchronization for Mobile Distributed Transmit Beamforming

Distributed transmit beamforming offers $N^{2}$ -fold increase of the received power with $N$ transmitters forming a virtual antenna array, however, it requires the transmitters to be synchronized in-phase without sharing a common wired clock reference. One popular approach to establish such synchronization is based on the receiver periodically providing feedback on phase and possibly frequency offsets of each individual transmitter through a low-rate feedback channel. In this work, we propose a new phase- and frequency-synchronization scheme performing synchronization in an aperiodic manner, when the receiver detects the asynchrony has exceeded allowed limits. We then prototype the proposed scheme in software-defined radio and evaluate its performance with field trials. The proposed scheme significantly reduces the demand for feedback channel bandwidth even when a receiver is mobile, while keeping the phase and frequency synchronization sufficient to achieve the received signal-to-noise ratio (SNR) improvement predicted theoretically.
Modeling, Optimization, and Experimentation of the ParaGripper for In-Hand Manipulation Without Parasitic Rotation

Recently, underactuated robotic hands have been exploited for dexterous in-hand manipulation, after having been proven efficient in performing versatile adaptive grasps. However, the reported in-hand manipulation skills are usually associated with parasitic motion, which may complicate control and application of the hand. This paper presents the modeling, optimization and experimentation of the ParaGripper, an underactuated gripper capable of performing in-hand manipulation without parasitic rotation. The underactuated finger uses two serially connected parallelograms to ensure pure translation of the fingertips. If the object remains stationary within the fingertips, the gripper can translate the object without parasitic rotation. The kinematics and kinetostatics of the hand–object system are derived and the manipulation workspace is optimized. The ParaGripper is designed and fabricated according to suitable optimal parameters. Experiments show that the ParaGripper could perform non-parasitic in-hand manipulation and versatile adaptive grasps.
Optimal Control of Lane Keeping System Using Simulated Annealing and Linear Quadratic Regulator

Advanced driver-assistance systems (ADAS) are currently trending in automotive industry requiring optimal designs for achieving sensitive performance. In this paper, an optimal controller is proposed for lane keeping system (LKS) based on linear quadratic regulator (LQR). Simulated Annealing (SA) algorithm is applied to effectively provide an optimal solution for the weighting matrices that aids obtaining an optimal gain matrix for the state feedback loop. The proposed approach has been evaluated and tested as a software-in-the-loop (SIL) on a The Open Source Racing Vehicle Simulator (TORCS) which is a high-fidelity nonlinear car simulator. The developed controller has achieved better performance and proven reliability over manually tuned LQR controllers.
ResLap: Generating High-Resolution Climate Prediction Through Image Super-Resolution

In recent years, many models based on the convolutional neural network have achieved high-quality reconstruction for single image super-resolution. Meanwhile, many researches on image super-resolution have been applied to various fields. However, only a few research works have been applied to climate prediction. In this paper, we present ResLap to achieve high-resolution climate prediction. ResLap is a spatial downscaling method that converts low spatial resolution climate data into high-resolution regional climate forecasts. This method mainly introduces a novel residual dense block (RDB) into the Laplacian pyramid super-resolution network (LapSRN). Among them, we use LapSRN to achieve upsampling image reconstruction, and adopt RDB to fully extract the hierarchical features from all the convolutional layers. Extensive experimental results on benchmark climate datasets show that our new proposed model performs better than many super-resolution methods. Besides, the climate data are more complicated than the general image, because of its dynamic and chaotic nature. To facilitate model training, we integrate original climate data provided by the China Meteorological Administration, then convert it into trainable climate images. We also publish some climate image datasets online for research. Finally, we avoid the checkerboard artifacts in the generated high-resolution climate images.