Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognizing Music Mood and Theme Using Convolutional Neural Networks and Attention

We present the UAI-CNRL submission to MediaEval 2020 task on Emotion and Theme Recognition in Music. We make use of the ResNet34 architecture, coupled with a self-attention module to detect moods/themes in music tracks. The autotagging-moodtheme subset of the MTG-Jamendo dataset was used to train the model. We show that the proposed model outperforms the provided VGG-ish and popularity baselines
HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table Tennis Strokes Classification Task

The Sports Video Classification Tasks in the Multimedia Evaluation 2020 Challenge focuses on classifying different types of table tennis strokes in video segments. In this task, we - the HCMUS Team - perform multiple experiments, which includes a combination of models such as SlowFast, Optical Flow, DensePose, R2+1, Channel-Separated Convolutional Networks, to classify 21 types of table tennis strokes from video segments. In total, we submit eight runs corresponding to five different models with different sets of hyper-parameters in each of our models. In addition, we apply some pre-processing techniques on the dataset in order for our model to learn and classify more accurately. According to the evaluation results, one of our team’s methods out-performs all other teams. In particular, our best run achieves 31.35% global accuracy.
Detecting Conspiracy Tweets Using Support Vector Machines

This paper summarizes the contribution of our team UIBK-DBISFAKENEWS to the task "FakeNews: Corona virus and 5G conspiracy" as part of MediaEval 2020. The goal for this task is to classify tweets as "5G corona virus conspiracy", "other conspiracy", or "non conspiracy", based on text analysis and based on the retweet graphs. We achieved our best results using a calibrated linear SVM with word and character n-grams for the text classification task and a non-calibrated linear SVM with graph statistics for the graph classification task. © 2020 Copyright 2020 for this paper by its authors. All Rights Reserved.
HCMUS at MediaEval 2020: Image-Text Fusion for Automatic News-Images Re-Matching

Matching text and images based on their semantics has an important role in cross-media retrieval. Especially, in terms of news, text and images connection is highly ambiguous. In the context of MediaEval 2020 Challenge, we propose three multi-modal methods for mapping text and images of news articles to the shared space in order to perform efficient cross-retrieval. Our methods show systemic improvement and validate our hypotheses, while the best-performed method reaches a recall@100 score of 0.2064
Ensemble U-Net Model for Efficient Polyp Segmentation

This paper presents our approach developed for the Medico automatic polyp segmentation challenge 2020 1 . We used a U-Net model with two different encoder backbones: ResNet-34 and EfficientNet-B2. The two models were trained separately, and trained for en-sembling using Tversky loss. We performed CutMix and standard augmentations for data pre-processing. For ensembling, we chose the hyperparameter of the loss function in the range that makes individual models have high recall while relaxing the precision. We evaluated the individual models and the ensemble model on validation data. ResNet-34 backbone model and the ensemble model were submitted to the challenge website for further evaluation on the test data. Our ensemble model improved performance on metrics compared to the single networks by achieving a Dice Coefficient of 0.8316, Intersection Over Union of 0.7550, Precision of 0.8851, and Overall Accuracy of 0.9583.
Enriching Content Analysis of Tweets Using Community Discovery Graph Analysis

This paper describes the proposed solutions of the Texas State University Data Lab team to the MediaEval 2020 FakeNews benchmark. We have responded to the text-based and structural-based tasks with lexical, graph, and community labeling approaches. Our lexical analysis approach using logistic regression produces the best results at 0.43 MCC, we describe a promising community labeling model, and discuss our attempts to find predictive structural features in retweet graphs of conspiracy promoting tweets.
Calprotectin: from biomarker to biological function

The incidence of inflammatory bowel diseases (IBD) emerged with Westernisation of dietary habits worldwide. Crohn’s disease and ulcerative colitis are chronic debilitating conditions that afflict individuals with substantial morbidity and challenge healthcare systems across the globe. Since identification and characterisation of calprotectin (CP) in the 1980s, faecal CP emerged as significantly validated, non-invasive biomarker that allows evaluation of gut inflammation. Faecal CP discriminates between inflammatory and non-inflammatory diseases of the gut and portraits the disease course of human IBD. Recent studies revealed insights into biological functions of the CP subunits S100A8 and S100A9 during orchestration of an inflammatory response at mucosal surfaces across organ systems. In this review, we summarise longitudinal evidence for the evolution of CP from biomarker to rheostat of mucosal inflammation and suggest an algorithm for the interpretation of faecal CP in daily clinical practice. We propose that mechanistic insights into the biological function of CP in the gut and beyond may facilitate interpretation of current assays and guide patient-tailored medical therapy in IBD, a concept warranting controlled clinical trials.
Cognitive Motor Dual Task Costs in Older Adults with Motoric Cognitive Risk Syndrome

The purpose of this study was to characterize Cognitive Motor Dual Task (CMDT) costs for a community-based sample of older adults with Motoric Cognitive Risk Syndrome (MCR), as well as investigate associations between CMDT costs and cognitive performance. Twenty-five community-dwelling older adults (ages 60–89 years) with MCR performed single and dual task complex walking scenarios, as well as a computerized cognitive testing battery. Participants with lower CMDT costs had higher scores on composite measures of Working Memory, Processing Speed, and Shifting, as well as an overall cognitive composite measure. In addition, participants with faster single task gait velocity had higher scores on composite measures of Working Memory, Processing Speed, and overall cognition. Taken together, these results suggest that CMDT paradigms can help to elucidate the interplay between cognitive and motor abilities for older adults with MCR.
Phytoextraction of lead and its relationship with histidine in six plant species using ICP-MS and HPLC-MS

ABSTRACT High-performance liquid chromatography-mass spectrometry and inductively coupled plasma mass spectrometer (ICP-MS) methods were used for determination of histidine and lead in leaves of six plant species taken from industrial areas, including Gaziantep and Bursa cities, Turkey. For extraction of histidine from plant samples, ultrapure water was used at 90°C for 30 min. Using optimum conditions of 0.2 mL min−1, 70 V, 15 µL and 20°C, concentrations of histidine (in mg kg−1) were found to be between 2 and 9 for Morus L. , 6 and 13 for Robinia pseudoacacio L. , 2 and 10 for Populous nigra L. , 3 and 10 for Thuja, 1 and 11 for Cupressus arizonica and 4 for Cedrus libani. Concentrations of lead were in the ranges of 4–378 mg kg−1 for Morus L. , 1–122 mg kg−1 for R. pseudoacacio L. , 1–14 mg kg−1 for P. nigra L. , 1.6–224 mg kg−1 for Thuja (Cupressaceae), 1.5–57 mg kg−1 for C. arizonica and 1.8 mg kg−1 for C. libani. Related with correlation coefficient, significant linear correlation for Thuja (Cupressaceae) (r = 0.81) and insignificant linear correlation for P. nigra L. (r = 0.50) were seen. Further, the leaves of Morus L. , Thuja and R. pseudoacacio L. have a potential as biomonitor and/or hyperaccumulator for Pb because the rates of their maximum/minimum concentrations were found higher than 90.
Enhancement of Optical Nonlinearity in the Triangular Gold Nanoplates on Indium Tin Oxide

The nonlinear optical materials with small footprint, low operating energy, ultrafast and large nonlinear optical response are highly required for the signal processing. Here, we investigated the broadband optical nonlinear response of the triangular gold nanoplates on indium tin oxide (ITO) coated glass, and obtained a large optical nonlinear enhancement at its localized surface plasmon resonance (LSPR) region at the wavelength of 780 nm, far from the ENZ (epsilon-near-zero) region of ITO. The measured nonlinear refractive index and nonlinear absorption coefficient can reach up to 8.39×10-15 m2/W and 5.81×10-8 m/W, respectively. The experimental results highlight the noble metals’ potential for nonlinear optics and nanophotonics.
Analysis of methods to improve energy storage arbitrage benefit considering capacity degradation

The addition of energy storage systems into the grid will allow to mitigate the effects of imbalances caused by the uncertainties of renewable energy sources. Energy storage facilities would give the grid more reliability and efficiency. One of the applications of these facilities is energy arbitrage, which is the trade of energy with the grid using price variability through the day to obtain a benefit. To impulse the investments on energy storage systems connected to the grid, they must be economically viable. The arbitrage operation can be optimized to ensure that viability. The optimization can improve the device’s profitability by controlling its degradation. This degradation management can be done in different perspectives attending to economical considerations which may change the way that profitability is viewed. In this paper, the mitigation of this deterioration process is done with two approaches by whether considering it as an operational or as an opportunity cost. Both approaches are compared, with simulation results of a Li-ion Battery Energy Storage System (BESS) performing arbitrage on the Spanish day-ahead electricity market. The main contribution of this work is the opportunity cost concept, which uses a more realistic calculation of degradation impact on arbitrage schedule.
Releasing ARP Data with Differential Privacy Guarantees For LAN Anomaly Detection

Anomaly detection has emerged as a popular technique for detecting malicious behaviors in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual users or their relationship in LAN has not been thoroughly explored in the prior work. In some realistic cases, the anomaly detection analysis needs to be carried out by an external party, located outside the LAN. Thus, it is important for the LAN admin to release LAN data to this party in a private way in order to reveal no information about LAN users; at the same time, the released data must also preserve the utility of being able to detect anomalies. This paper investigates the possibility of privately releasing ARP data that can later be used to identify anomalies in LAN. We present two approaches and show that they satisfy different levels of differential privacy – a rigorous and provable notion for quantifying privacy loss in a system. Our real-world experimental results confirm practical feasibility of our approaches. With a proper privacy budget, both of our approaches preserve more than 90% utility of the released data.
Dial-a-Ride Problem With Meeting Point Feature Known-as Express-Pool

This study extends a dial-a-ride problem by considering (1) Express-Pool and (2) Friend-Only. Uber provides an advanced feature called Express-Pool where passengers are willing to walk a short distance to be picked up and be dropped off somewhere close to destination in exchange for a reduced fare. Tesla’s 2020 Robotaxi plan includes that vehicle owners will be able to determine if their vehicles can be shared with friends and co-workers only. For this evolving transportation problem, the contributions of this paper are threefold. First, we define DARP with Express-Pool and Friend-Only. Second, a novel constraint programming approach is proposed. Third, a two-phase approach based on warm-start method is devised to improve the performance of the base model.
Semantic Segmentation of Cerebellum in 2D Fetal Ultrasound Brain Images Using Convolutional Neural Networks

Cerebellum measurements of routinely acquired ultrasound (US) images are commonly used to estimate gestational age and to assess structural abnormalities of the developing central nervous system. Investigating associations between the developing cerebellum and neurodevelopmental outcomes post partum requires standardized cerebellum measurements from large clinical datasets. Such investigations have the potential to identify structural changes that can be used as biomarkers to predict growth and neurodevelopmental outcomes. For this purpose, high throughput, accurate, and unbiased measurements are necessary to replace existing manual, semi-automatic, and automated approaches which are tedious and lack reproducibility and accuracy. In this study, we propose a new deep learning algorithm for automated segmentation of the fetal cerebellum from 2-dimensional (2D) US images. We propose ResU-Net-c a semantic segmentation model optimized for fetal cerebellum structure. We leverage U-Net as a base model with the integration of residual blocks (Res) and introduce dilation convolution in the last two layers to segment the cerebellum (c) from noisy US images. Our experiments used a 5-fold cross-validation with 588 images for training and 146 for testing. Our ResU-Net-c achieved a mean Dice Score Coefficient, Hausdorff Distance, Recall, and Precision of 87.00%, 28.15, 86.00%, and 90.00%, respectively. The superiority of the proposed method over the other U-Net based methods is statistically significant (p < 0.001). Our proposed method can be leveraged to enable high throughput image analysis in clinical research fetal US images and can be employed in the biometric assessment in fetal US images on a larger scale.
A Design Pattern for Monitoring Adapter Connections in IEC 61499

Today’s software developments are frequently structured into different components with well defined interfaces. IEC 61499 comes with well defined interface mechanisms such as adapters that group data and event exchange between different subsystems. Compliance with interface specifications can be monitored at run-time. In this paper we present a design pattern to monitor adapters thereby observing whether communication specifications are fulfilled. We present an example demonstrating monitoring of a handshaking mechanism used between control application components.
Lightweight and Low-Cost Deployable Origami Antennas—A Review

The art of paper folding also known as Origami plays an important role in various research areas of different scientific fields. Origami structures possess many useful characteristics such as reconfigurability, flexibility, deployability, compactness, and multi-functionality. These attributes are inevitable for modern communication systems. Additionally, they provide antenna engineers an extra degree of freedom while blueprinting next-generation designs. One of the desirable features of origami is its folding capability. Using this property, it can be transformed into a two-Dimensional (2D) sheet from a three-Dimensional (3D) structure and vice versa through external stimulus. In recent years, researchers have proposed numerous smart materials to optimize folding behavior. These smart materials extend the functionality of origami technology for designing self-folding structures required in space systems, small scale devices, and self-assembly systems. Origami structures feature many useful characteristics pertinent to modern day design challenges of communication systems such as reconfiguration, flexibility, compactness, and multifunctionality. In this work, a state of the art review is presented on conceptualization, design challenges, and fabrication of light-weight and low-cost deployable origami antennas. Furthermore, to provide critical insights deployment challenges and possible solutions are also provided. It is believed that this work will not only help peers to understand the basic working principles of different origami structures but will also lay the foundation for future research in the evolving field of origami-based antennas. Moreover, the design methodology proposed in this paper will also provide pragmatic solutions for origami antennas suitable for the harsh and rigid environments.
Validated Undergraduates’ Misconceptions about Software Engineering

(Pre-)Conceptions are of central importance when it comes to learning in a (social) constructivist meaning. This can be explained by the fact that new information gets linked with existing knowledge and prior experiences. Therefore undergraduates do not enter university or a specific course as “blank sheets” or “empty vessels”. This is why lecturers should take prior knowledge into consideration for designing as well as implementing teaching and learning arrangements. To focus especially on Misconceptions 1 as potential learning obstacles, the first step is to identify these (Mis-)conceptions. Therefore, the paper covers a first catalogue of Misconceptions in Software Engineering, as the result of a mixed-methods approach enclosing Grounded Theory Methodology to elicit undergraduates’ statements about Software Engineering, its tools, methods, and processes and reconstruct the (Mis-)conceptions included; as well as a subsequent expert validation of the Misconceptions found using qualitative data.
Design and Manufacturing of a Wireless Temperature Monitoring System for Gas Insulated Switchgear

The design, manufacturing, and characterization of a temperature monitoring system is presented in this work. The system is used to measure the temperature of critical locations on the Gas Insulated Switchgear (GIS) including bus bar, contact, and switches. A compensation algorithm is developed to eliminate the influence of glass window, SF6 gas and the surface profile of the bus bar. The system is also capable of provide warning information on the overheating issues at the early stage of different equipment failures. The proposed temperature monitoring system is capable of measuring temperature of the GIS key locations in the range of 30-100°C, and an accuracy of ±2°C. The temperature monitoring system and its integration to GIS are intended to be the initial stage of the development of new GIS product line and its standard with temperature monitoring and warning function.
Comparison of Different Approaches for Identification of Radar Ghost Detections in Automotive Scenarios

This paper focuses on the frequently occurring issue of automotive radar sensors: ghost detection. Three data-based approaches, namely random forest, convolutional neural network (CNN), and PointNet++, are adopted to identify ghost detection. Evaluated with the same dataset, random forest and PointNet++, with more than 95% accuracy, are evidently better than CNN in not only city but also motorway scenarios. Furthermore, the influence of various features for each classifier is also analyzed.
The Effects of Brightness Difference on Visual Perception of Characters

The visual perception of characters depends on the color contrast difference between the foreground and the background. In the human-machine interface, the black characters on a white background or white characters on a black background are the most commonly used color combinations. However, with the increase in the amount of information and the requirement of information layering, interface elements need to be presented through multiple foregrounds and backgrounds. Therefore, it is significant to ensure the overall optimization of multiple information in the human-machine interface. The study was conducted from the perspective of brightness and aimed to explore the influence of color combinations between characters and backgrounds on visual perception from the dimensions of polarity, background, and brightness difference. By analyzing the comparison time and accuracy in the visual comparison experiment, the study found that black background had shorter comparison time and better visual performance than grey and white backgrounds on the whole. Besides, visual perception of negative polarity was better than positive polarity. Furthermore, the layering of visual perception at different brightness levels was also obtained, which was beneficial to improve the efficiency of information cognition in the human-machine interface.
The Performances of Pre-trained Convolutional Neural Networks in Clothing Sketch Classification

Sketch is a unique source of information. Unlike an image (photograph), a sketch contains mostly edges and fewer textures; thus, it has fewer visual cues. Besides, it contains inherent intra-class variations – the geometric and shape variations between sketches of the same object – which lead to a more challenging recognition/classification (R/C) task. The sketch R/C is required in many areas; however, the explorations in this direction – particularly those using deep neural networks – are still limited. This work, thus, aims to present and compare the performances of four pre-trained convolutional neural network (CNN) architectures, namely ResNet101, DenseNet, MobileNetv2, and ShuffleNetv2, in classifying clothing sketches that have intra-class variations and fewer visual cues. We measured the training accuracy, training time, validation accuracy as well as testing accuracy. The simulation results showed that the CNN models had good performances in classifying clothing sketches, with the overall testing accuracy being over 94%. All of the architectures set at epoch=20 resulted in the highest accuracy.
Performance Evaluation of Downlink Multi-user OFDMA Scheduling in 5G New Radio (NR)

A fifth-generation system (5G) mobile networks has been developed to realize high throughput, low latency, high scalability, the provision of ubiquitous connectivity solution for end-users, and efficient energy consumption technique. The benefits of 5G New Radio (NR) access technology in the physical layer design accommodate to be flexible and scalable that can support many use cases such as orthogonal frequency division multiple access (OFDMA) at its medium access control (MAC) layer and numerology. The efficiency of OFDMA depends on how the access point (AP) schedules channel resources among STAs (stations). The common schedulers used in OFDMA are round - robin (RR), maximum rate (MR), and proportional fair (PF). This study evaluates the downlink access with varying scheduling mechanisms, the number of user equipment (UE), and numerology with an additional proposed scheduler mechanism called Optimized PF to maintain high throughput and fairness using 5G-NR LENA (LTE/EPC Network Simulator) module in the ns-3 environment. Based on the study, the RR scheduler has stable packet delivery performance from numerology 0 and 4 which can still send packets to all UEs. It caused the RR scheduler does not use a channel quality indicator (CQI) to discriminate the user and the used scenario uses the same data rate for every UEs. Besides, the additional scheduler mechanism, optimized PF, performs better than the PF scheduler for sending packets in numerology 4 (higher numerology). Also, increasing numerology will result in increasing the mean throughput and packet delivery ratio for each scheduler.