Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Cavity Hybrid Raman-Erbium Random Fiber Laser With Common Pump

A symmetrical 80-km open cavity erbium-integrated hybrid random distributed feedback fiber laser (HRFL) was proposed and experimentally demonstrated. A variation of pumping schemes and cavity lengths was first investigated prior to the integration of the EDF. The impact of Raman and EDF hybrid amplification was then investigated through EDF length variation. The proposed scheme used a single common pump to incite both Raman and erbium gain to produce a single peak at a 1567-nm wavelength with maximum OSNR of 62.37 dB. A maximum total output power generation of 1420 mW was achieved with high-slope efficiency of 38%. The proposed hybrid setup has shown improved performance despite using open-ended cavity sustained by only a single pump in contrast to previous more complex hybrid schemes. Prolonged chaotic regime manifesting spontaneous pulse burst was also observed before the stable regime. The simple operation with the high performance of the proposed configuration offers a great potential for long distance or remote access applications such as heavy metals sensing or even for biological hazard sensing.
An Efficient Successive Cancellation List Decoder for Polar Codes

Achieving a high decoding throughput using a successive cancellation list (SCL) decoder for polar codes is difficult due to its sequential decoding architecture. In this work, combining the local sorter from a single parity check (SPC) node with a shift-based path memory, a modified fast simplified successive cancellation list (Fast-SSCL) decoder is proposed, in order to provide a high-throughput using a low-complexity implementation. The proposed modified Fast-SSCL decoder can be operated at 470 MHz and was synthesized with an area of 5.26 mm<sup>2</sup> using a TSMC 90 nm CMOS process. The decoder presented in this work is able to improve the throughput to area ratio (TAR) by more than 30% compared with the previous designs.
Evaluation of Environmental Assessments Made with EcoReport and GaBi 4

EU directive 2005/32/EC for Energy using Products (EuP) was developed as a framework for the adoption of implementing measures concerning eco-design requirements. For selected priority product groups directive requires preparatory studies to identify the environmental load and the need for implementing measures. All preparatory studies are based on special methodology (MEEuP) and part of this methodology is a simple Excel-based LCA-tool: EcoReport. Thus results from EcoReport affect future implementing measures. The aim of this paper is to present example cases, in which we compared the results from EcoReport with the results gained from the more complex LCA-tool: GaBi 4.
A Framework For Memory Performance Prediction From Brain Volume In Preterm-Born Adolescents

With advances in medical care, higher numbers of extremely preterm-born babies are now surviving, however the rate of neurodevelopmental and neurological complications has not improved at the same rate and the relative rate of disabilities and health problems is increasing, with associated high costs for health care systems and education. Understanding brain development after early birth is of great importance to be able to make informed decisions. Many studies have associated different areas of the preterm brain with poor cognitive performance, however it is less clear whether these associations persist into adult life. In this study, we investigate how well cortical volumes describe memory performance in 133 19 year-old adolescents, 61% of whom were born extremely preterm. We employ LASSO to identify brain regions that better explain memory performance. The brain regions identified by LASSO explained 27% and 32% of the variance in the visual working memory scores and the visual short term memory respectively. Furthermore, the correlation between the predicted scores and validation scores is statistically significant and it is 58% for the visual working memory task and 56% for the visual short term memory task.
Investigation Study of the Influence of Pole Numbers on Torque Density and Flux-Weakening Ability of Fractional Slot Concentrated Winding Wheel-Hub Machines

Fractional slot concentrated winding machines (FSCWMs) with the low operation speed and large diameter usually have a large number of poles and slots; thus, numerous pole/slot combinations are feasible. The common practice to choose pole/slot combinations by multiplying the basic combinations may neglect some competitive candidates. Taking 36-slot FSCWMs as examples, this paper investigates the influence of pole numbers on torque density and flux-weakening ability, the two most vital performances of wheel-hub machine. It is shown that the machines with pole number slightly less than the slot number have the highest torque densities. Each component of synchronous inductance is separately analyzed, and its variation against pole numbers shows obvious regularity. Machines with pole numbers larger than the slot numbers have an excellent flux-weakening ability due to the large inductance and small permanent magnet flux linkage. The measurements together with the finite element analysis results confirm that the stator leakage inductance contributes the most to the superior flux-weakening ability. The identical analysis is also performed on 54-slot and 81-slot FSCWMs, with similar regularities observed.
Robust Remote Heart Rate Estimation from Face Utilizing Spatial-temporal Attention

In this work, we propose an end-to-end approach for robust remote heart rate (HR) measurement gleaned from facial videos. Specifically the approach is based on remote photoplethysmography (rPPG), which constitutes a pulse triggered perceivable chromatic variation, sensed in RGB-face videos. Consequently, rPPGs can be affected in less-constrained settings. To unpin the shortcoming, the proposed algorithm utilizes a spatio-temporal attention mechanism, which places focus on the salient features included in rPPG-signals. In addition, we propose an effective rPPG augmentation approach, generating multiple rPPG signals with varying HRs from a single face video. Experimental results on the public datasets VIPL-HR and MMSE-HR show that the proposed method outperforms state-of-the-art algorithms in remote HR estimation.
Fully End-to-End Composite Recurrent Convolution Network for Deformable Facial Tracking In The Wild

Human facial tracking is an important task in computer vision, which has recently lost pace compared to other facial analysis tasks. The majority of current available tracker possess two major limitations: their little use of temporal information and the widespread use of handcrafted features, without taking full advantage of the large annotated datasets that have recently become available. In this paper we present a fully end-to-end facial tracking model based on current state of the art deep model architectures that can be effectively trained from the available annotated facial landmark datasets. We build our model from the recently introduced general object tracker Re3, which allows modeling the short and long temporal dependency between frames by means of its internal Long Short Term Memory (LSTM) layers. Facial tracking experiments on the challenging 300-VW dataset show that our model can produce state of the art accuracy and far lower failure rates than competing approaches. We specifically compare the performance of our approach modified to work in tracking-by-detection mode and showed that, as such, it can produce results that are comparable to state of the art trackers. However, upon activation of our tracking mechanism, the results improve significantly, confirming the advantage of taking into account temporal dependencies.
Characterizing the State of Apathy with Facial Expression and Motion Analysis

Reduced emotional response, lack of motivation, and limited social interaction comprise the major symptoms of apathy. Current methods for apathy diagnosis require the patient’s presence in a clinic, and time consuming clinical interviews and questionnaires involving medical personnel, which are costly and logistically inconvenient for patients and clinical staff, hindering among other large scale diagnostics. In this paper we introduce a novel machine learning framework to classify apathetic and non-apathetic patients based on analysis of facial dynamics, entailing both emotion and facial movement. Our approach caters to the challenging setting of current apathy assessment interviews, which include short video clips with wide face pose variations, very low-intensity expressions, and insignificant inter-class variations. We test our algorithm on a dataset consisting of 90 video sequences acquired from 45 subjects and obtained an accuracy of 84% in apathy classification. Based on extensive experiments, we show that the fusion of emotion and facial local motion produces the best feature set for apathy classification. In addition, we train regression models to predict the clinical scores related to the mental state examination (MMSE) and the neuropsychiatric apathy inventory (NPI) using the motion and emotion features. Our results suggest that the performance can be further improved by appending the predicted clinical scores to the video-based feature representation.
Simple solutions for modelling the non-uniform substrate doping

Present-day CMOS processes use ion implantation to adjust the threshold voltages and to avoid the punch through effect. The substrate doping in the transistor can thus no longer be considered as uniform, and accurate modelling of this effect is required for precise analog circuit simulation. Two new solutions taking this effect into account are proposed and compared with other existing approaches. The results of these new models are compared with 2-D device simulations and measurements of a 2 /spl mu/m CMOS low voltage process.
Online Targetless End-to-End Camera-LIDAR Self-calibration

In this paper we propose an end-to-end, automatic, online camera-LIDAR calibration approach, for application in self driving vehicle navigation. The main idea is to connect the image domain and the 3D space by generating point clouds from camera data while driving, using a structure from motion (SfM) pipeline, and use it as the basis for registration. As a core step of the algorithm we introduce an object level alignment to transform the generated and captured point clouds into a common coordinate system. Finally, we calculate the correspondences between the 2D image domain and the 3D LIDAR point clouds, to produce the registration. We evaluated the method in various different real life traffic scenarios.
Modeling and Computation of Vernacular Regions

A vernacular region is a region without a precise or administrative boundary. A formalization is needed to model these regions for automated usage. This allows for more intelligent treatment of search queries like “hotels in the British Midlands”. A common approach is to use the internet as a source of information, using search engines to relate exact geographical locations to vernacular regions. The question arises how to find the (approximate) boundary of a region, given a set of points that are likely to be inside or near the region. However, current models are not satisfactory. In this thesis, a new geometric model is described and algorithms are developed to compute this model. The model’s performance is evaluated in an experimental study.
Challenges of testing deep word knowledge of vocabulary: which path to follow?

The present research explores the challenges of testing deep word knowledge of the vocabulary of students of English as a Foreign/Second Language (EFL/ESL) at higher education. A productive test modelled on the Lex30 test developed by Meara and Fitzpatrick (2000) was presented to the participants. Results indicate that (i) ESL students outperform their EFL counterparts of comparable class level, (ii) aspects of deep word knowledge among both higher education EFL and ESL students develop in the order of analytic relations, paradigmatic relations, and collocations; and (iii) aspects of deep word knowledge among both higher education EFL and ESL students grow alongside one another and correlate significantly with overall deep word knowledge; the strength of which may reflect the extent to which they contribute to it. On the basis of these findings, we conclude that Lex30 may constitute a good measure of deep word knowledge at the productive level, which is the path we suggest should be followed in order to test deep word knowledge of vocabulary at higher education. Furthermore, teaching implications aimed to foster deep word knowledge growth are discussed. Keywords: Vocabulary Deep Word Knowledge, Testing Deep Word Knowledge Productively, Vocabulary Dimensions, English as a Foreign/Second Language (EFL/ESL)
One-Stage Five-Class Polyp Detection and Classification

The detection and classification of anatomies from medical images has traditionally been developed in a two-stage process, where the first stage detects the regions of interest (ROIs), while the second stage classifies the detected ROIs. Recent developments from the computer vision community allowed the unification of these two stages into a single detection and classification model that is trained in an end to end fashion. This allows for a simpler and faster training and inference procedures because only one model (instead of the two models needed for the two-stage approach) is required. In this paper, we adapt a recently proposed onestage detection and classification approach for the new 5class polyp classification problem. We show that this onestage approach is not only competitive in terms of detection and classification accuracy with respect to the two-stage approach, but it is also substantially faster for training and testing. We also show that the one-stage approach produces competitive detection results compared to the state of the art results on the MICCAI 2015 polyp detection challenge.
Security-Aware Scheduling for TTEthernet-Based Real-Time Automotive Systems

TTEthernet is a deterministic, congestion-free, and high-bandwidth communication protocol based on the Ethernet standard that provides a powerful network solution for developing safety-critical distributed real-time automotive systems. With the development of intelligence and networking of vehicles, such systems are becoming increasingly connected to external environments; thus, security has become a pressing issue in system design. However, TTEthernet-based architecture does not have direct support for secure communication. When deploying the security mechanisms on these architectures, a major challenge is to guarantee the schedulability of systems, given the tight resource constraints and strict timing constraints. In this paper, we apply an authentication mechanism based on the delayed exposure of one-way key chains to protect the authenticity of messages on TTEthernet and make a slight modification to reduce the authentication delay. On that basis, we propose a mixed integer linear programming formulation for solving the scheduling problem of the TTEthernet-based real-time automotive systems subject to both authentication mechanism constraints and other traditional design constraints. The extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method.
Regularisation With a Dictionary of Lines for Medical Ultrasound Image Deconvolution

Lines and boundaries are important structures in medical ultrasound images as they can help differentiate between tissue types, organs, and membranes. A typical example is in lung ultrasonography, where the presence of so-called B-lines is indicative of lung status in ventilated critically ill patients or of fluid overload in patients on dialysis. In order to be able to quantify such linear features, deconvolution is typically necessary, in order to enhance the generally poor ultrasound image quality. This paper presents a novel deconvolution technique for restoring ultrasound images. Our approach employs a standard inverse problem formulation involving a penalty term for enforcing a sparse solution, but augmented with an additional term aimed at promoting linear features. Specifically, we regularise our solution using the Radon transform, which effectively acts as a dictionary of lines. The resulting optimisation problem can then be addressed using both con-vex and non-convex techniques. We evaluated our approach on real B-mode ultrasound images and our results show that the proposed method outperforms existing techniques by up to 30% in terms of contrast-to-noise ratio.
GitHub as backbone in Software Engineering course: Technology acceptance analysis

In the field of software engineering education, continuous adjustment of course content and technologies with real-working environment is crucial for developing students’ skills that meet the needs of the labor market. The main goal of this paper is to present the research result regarding the use of GitHub in Software Engineering course which has characteristics of real-working environment: students are divided into up-to-five-member teams with adequate development roles and during the semester are working on project which encompasses full software development life-cycle. A pilot study was conducted in the end of the summer semester of academic year 2017/2018 on the sample of undergraduate students (N=78) whose study field is related to informatics. New measuring instrument was developed and consisted of scales related to the UTAUT (Unified Theory of Acceptance and Use of Technology) model, but also those related to learning value, learning satisfaction and ease of use to investigate in detail students’ acceptance of GitHub in context of software engineering learning. Results of the study outlined following remarks: students find GitHub useful in team coding projects, its ease of use has positive effect on students’ acceptance, as well as on learning satisfaction and learning value of software engineering in practice.
Demonstration of Electron/Hole Injections in the Gate of $p$-GaN/AlGaN/GaN Power Transistors and Their Effect on Device Dynamic Performance

In this work, we demonstrated the electron/hole injections in the gate of $\boldsymbol{p}$-GaN/AlGaN/GaN power transistors at forward gate bias by capturing the electroluminescence (EL) emission from the gate region. The EL included both visible and ultraviolet (UV) luminescence. The dynamic switching tests were carried out to investigate the effect of electron and hole injections on device performances. The electron injection and trapping at the forward gate bias caused positive threshold voltage shift, while the injection of holes into the GaN channel induced the emission of the UV light and the resulting leakage current increase in the device.
English teachers' barriers to the use of computer-assisted language learning

Kuang-wu Lee Johnny [at] hcu.edu. tw Hsuan Chuang University (Hsinchu, Taiwan) Computers have been used for language teaching ever since the 1960's. This 40-year period can be divided into three main stages: behaviorist CALL, communicative CALL, and integrative CALL. Each stage corresponds to a certain level of technology and certain pedagogical theories. The reasons for using Computer-assisted Language Learning include: (a) experiential learning, (b) motivation, (c) enhance student achievement, (d) authentic materials for study, (e) greater interaction, (f) individualization, (g) independence from a single source of information, and (h) global understanding. The barriers inhibiting the practice of Computer-assisted Language Learning can be classified in the following common categories: (a) financial barriers, (b) availability of computer hardware and software, (c) technical and theoretical knowledge, and (d) acceptance of the technology.
Decision-Augmented Generative Adversarial Network for Skin Lesion Segmentation

Skin lesion is regarded as an illness, which is often detected and diagnosed by utilizing dermoscopy imaging. The essential step of skin lesion analysis is to segment the lesion region accurately. However, many variations in view and scale of lesion areas make segmentation quite challenging. In this paper, we propose an effective skin lesion segmentation architecture based on a modified generative adversarial network. As the network can learn great feature representation of dermoscopic images automatically in an unsupervised way, supervised learning could make it more promising. In particular, we explore an encoder-decoder network concatenating residual blocks and atrous convolution as the generator to generate the segmentation masks. This generator can obtain deep representation and preserve fine-grained information. The decision of the discriminator is very important to optimize a network. To enhance the reliability of the discrimination, two discriminators are applied to distinguish generated masks from ground truth jointly. The model is trained and evaluated using ISIC skin lesion challenge datasets in 2017. Our experiments demonstrate that the proposed network achieves the favorable segmentation performance compared with the other CNN-based supervised methods.
Spatio-Temporal Partitioning And Description Of Full-Length Routine Fetal Anomaly Ultrasound Scans

This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2D and 2D + t CNN, LSTM, and convolutional LSTM are investigated and compared. The contributions of short-term and long-term temporal changes are studied, and a multi-stream framework analysis is found to achieve the best top-l accuracy =0.77 and top-3 accuracy =0.94. Automated partitioning and characterisation on unlabelled full-length video scans show high correlation (ρ=0.95, p=0.0004) with workflow statistics of manually labelled videos, suggesting practicality of proposed methods.
Google's PageRank and Beyond

Why is Google so good at what it does? There ate a variety of reasons, but the fundamental thing that distinguishes Google and has put them so far ahead of other search engines is their patented PageRank concept. PageRank has revolutionized Web search to the extent that it has been charged in Federal Court with driving the direction of commerce on the Internet. Many mathematicians are therefore surprised when they learn that a technology of such consequence is predicated on the same mathematics that is available to undergraduate students. This talk will survey some of these concepts.
Partial discharge characteristics of inverter-fed motor coil samples under ac and surge voltage conditions

We discussed the partial discharge (PD) characteristics of inverter-fed motor coil samples under surge voltage condition and compared with those under ac voltage application. PD light emission images and PD light intensity waveforms were simultaneously measured to obtain the PD inception characteristics. The differences in the PD characteristics under ac and surge voltage conditions were investigated for the insulation design of inverter-fed motors. It was found that the PDIV under surge voltage condition was 1.6-1.8 times and 2.3-2.7 times higher than those under ac voltage application for the used and virgin samples respectively, and the PD inception mechanisms were clarified.