Miracle-dz/UniFAR-FTUs huggingface dataset:
PatentQuest: A User-Oriented Tool for Integrated Patent Search

Patent Search is a well-established research field. In existing Patent Search systems, a user needs to explicitly enter a set of keywords to retrieve a set of ranked results. Conventional patent search systems lack the capability to run directly from the user’s text editor. Moreover, to the best of our knowledge, most practical systems do not leverage explicit user feedback and domain-specific context to enhance the quality of search results. In this paper, we describe a system that offers a single point of access for patent information coming from different sources as well integrates the capability for user feedback and the ability to search from the text editor itself without the need to switch applications. To explore the viability and effectiveness of such a system, we created and deployed it as a web service plug-in for Microsoft Word®and conducted both system and user evaluation on a benchmark dataset.
A New Learning Path Model for E-Learning Systems

This work presents a new approach to the learning path model in e-learning systems. The model uses data from the database records from an e-learning system and uses graphs as representation. In this work, the authors show how the model can be used to represent visually the learning paths, behavior analysis, help to suggest group formation for collaborative activities, and thus assist the teacher in making decisions. To validate the practical utility of the model, the authors created two tools, one to visualize the learning paths and another to suggest groups of students for collaborative activities. Both tools were tested in a real environment, presenting useful results. The authors carried experiments with students from three programs: physics, electrical engineering, and computer science. Experiments show that it is possible to use the proposed learning path to analyze student behavior patterns and recommend group formation with positive results.
"This research is funded by...": Named Entity Recognition of Financial Information in Research Papers

Customised Named Entity Recognition is an interesting, yet challenging task. The focus of our paper is the extraction of the named entities that provide financial information about research projects and programs. We introduce AckNER, a tool which extracts financial information from the “Acknowledgments” or “Funding” sections of research articles and dissertations. The results show that AckNER outperforms generic NLP libraries such as SpaCy, Stanza, FLAIR and DeepPavlov. The improvements found can be attributed to the ability to identify non-capitalised parts of the named entities in combination with the addition of patterns to extract information about contracts and grants.
Easy Spark

Today’s data deluge calls for novel, scalable data handling and processing solutions. Spark has emerged as a popular distributed in-memory computing engine for processing and analysing a large amount of data in parallel. However, the way parallel processing pipelines are designed is fundamentally different from traditional programming techniques, and hence most programmers are either unable to start using Spark, or are not utilising Spark to the maximum of its potential. This study describes an easier entry point into Spark. We design and implement a GUI that allows any programmer with knowledge of a standard programming language (e.g., Python or Java) to write Spark applications effortlessly and interactively, and to submit and execute them to large clusters.
EosFit7-GUI: A new graphical user interface for equation of state calculations, analyses and teaching

EosFit7-GUI is a full graphical user interface designed to simplify the analysis of thermal expansion and equations of state (EoSs). The software allows users to easily perform least-squares fitting of EoS parameters to diffraction data collected as a function of varying pressure, temperature or both. It has been especially designed to allow rapid graphical evaluation of both parametric data and the EoS fitted to the data, making it useful both for data analysis and for teaching.
Morphological and Rheological Identification of Cocci Lactic Acid Bacteria

Lactic Acid Bacteria (LAB) widely distributed in nature and play an important role in many food fermentations. Bacterial cell morphology is useful and valuable parameters used in characterization of any organisms. Therefore, cell morphological shape and size of six strains of cocci LAB grown in two different media (M17 and RSM) were studied using scanning electron microscope (SEM) and image analysis technique. Topographical images reveal spherical or pseudo-spherical structures with height profiles and variation in size which occurred singly or in pairs, chains and tetrads. Cocci cells had a wide range of diameters (0.79-1.52 μm) with wide cells perimeters of 2.69 up to 5.54 μm. Cells growing in broth medium (M17) had higher surface areas and volumes being from 0.26 μm3 to 1.82 μm3. Some cocci cells were far away from being a perfect circle (0.639) while others had close to circle shapes. Cocci cells had a different orientation angles and surfaces seemed to have heights or extrudes with external boundary which were either a sharp outline or a rough surface. Cells hardness and wall thickness had an effect negatively or positively depending on the strain and growing medium. Rheological properties (viscosity, shear stress and torque) recorded different values. Shear stress followed the same trend as viscosity with maximum reached at different times (12 up to17 h). Clotting time was 11 up to17.7 h depending on the strain. According to acid production development, the fastest strain reached the iso-electric point (pH 4.6) was Str. thermophilus (12 h).
Anthocyanins and phenolics in Cabernet Sauvignon and Pinot Noir wines.

Cabernet Sauvignon and Pinot Noir are among the most wellknown red wines in the world. Grape variety and chemical composition of the wine are important in oenology. Phenols give to the wine the color, astringency and bitterness. Certain researches confirm the antioxidant activity and health benefits of phenols, including anthocyanins. Wine samples were collected from a set of 36 bottles of red wine (Cabernet Sauvignon and Pinot Noir) from two wineries, sold in supermarkets at the same commercial value, with a vintage range from 2011 to 2013. The lowest concentration in anthocyanins was found in Pinot noir wine from Minis winery in 2012 (214.2±14.0 mg/l) while the highest value was registered in the 2013 in the Cabernet Sauvignon wine obtained in Recas winery (479.3±46.64 mg/l). Cabernet Sauvignon wines from both wineries contains anthocyanins concentrations of moderate values with limits among 258.8±10.29 mg/l (2012 vintage/ Minis) and 479.3±46.64 mg/l (2013 vintage/ Recas). The highest total phenolics content of 2758±149.9 mg/l were found in the wine obtained in the Recas winery in 2013. The lowest phenolics content was registered in Pinot noir wine from 2012 in Minis winery (1752±94.9 mg/l). Overall, in the two wineries, Cabernet Sauvignon wine has a higher content in anthocyanins and phenols. Climatic events, extended drought and rainfall and soil conditions make the difference between phenolic content in the two red wines analyzed. The aim of the study was to compare the total anthocyanins and phenolics content of Cabernet Sauvignon and Pinot Noir wines from two wineries from the west of Romania.
A Lightweight Authentication Protocol for UAV Networks Based on Security and Computational Resource Optimization

The widespread use of Unmanned Aerial Vehicles (UAV) has made the security and computing resource application efficiency of UAV a hot topic in the security field of the Internet of Things. In this paper, an optimized lightweight identity security authentication protocol, Optimized Identity Authentication Protocol (ODIAP) is proposed for Internet of Drones (IoD) networks. The protocol is targeted to the security risks faced by IoD networks, and proposes the security authentication mechanism consisting of 3 phases and 7 authentication processes, which enables the protocol has both forward and backward security, and can resist mainstream network attacks. Meanwhile, this paper fully considers the computational load and proposes the identity information generation and verification method based on the Chinese residual theorem, which reduces the computational load of resource-constrained nodes and shifts the complex computational process to server nodes with abundant computational resources. Moreover, after security protocol analysis and tool verification based on the automated security verification tool Proverif, the protocol in this paper has complete security. At the same time, the performance analysis and comparison with other mainstream protocols shows that this protocol effectively optimizes the use of computing resources without compromising security.
PMED-Net: Pyramid Based Multi-Scale Encoder-Decoder Network for Medical Image Segmentation

A pyramidical multi-scale encoder-decoder network, namely PMED-Net, is proposed for medical image segmentation. Different variants of encoder-decoder networks are in practice for segmenting the medical images and U-Net is the most widely used one. However, the existing architectures for segmenting medical images have millions of parameters that require enormous computations which results in memory and cost-inefficiency. To overcome such limitations, we come up with the idea of training small networks in a cascaded form for coarse-to-fine prediction. The proposed adaptive network is extended up to six pyramid levels, and at each level, features are extracted at different scales of the input image. Each lightweight encoder-decoder network is trained independently to minimize loss, where succeeding level networks further refine the prior predictions. Evaluation and comparison of our architecture were performed on four different publicly available medical image segmentation datasets: International Skin Imaging Collaboration (ISIC) challenge 2018 dataset, brain tumor dataset, nuclei dataset, and X-ray dataset. The experimental results of the PMED-Net are either better or on par with other state-of-the-art networks in terms of IoU, F1-Score, and sensitivity metrics. Moreover, PMED-Net is efficient in terms of parameterized complexity as it has 1/21.3, 1/21.1, 1/14.0, 1/11.6, 1/11.2, 1/6.64, and 1/4.95 times fewer parameters than SegNet, U-Net, BCDU-Net, CU-Net, FCN-8s, ORED-Net, and MultiResUNet respectively. The pre-trained models, datasets information, and implementation details are available at https://github.com/kabbas570/Pyramid-Based-Encoder-Decoder.
The Use of UTAUT Model for Understanding Academicians’ Perception Towards Online Faculty Development Programs (FDP)

This study wishes to examine the adequacy of Unified Theory of Acceptance and Use of Technology (UTAUT) in understanding academicians’ adoption and perception towards learning through online faculty development programs (FDP) in higher education in the Indian context. The emergence of information and communication technologies and cheaper internet usage charges have led to the penetration of computing devices and smartphones, leading to learning through online portals. In the prevailing covid-19 situation, countries experienced lockdown, work from home, and online learning has emerged in a big way. From traditional classroom teachers to an online learning environment, the role of academicians has changed drastically. Hence, Faculty Development Programs became an important component in this lockdown period for skill and knowledge up-gradation. This study used a quantitative methodology in which a theoretical model UTAUT was tested using hypotheses to assess the interaction between the study variables, performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC) with behavioral intentions (BI). Data from academicians were collected through a self-administered questionnaire created using Google forms. Multiple linear regression was used for data analysis using SPSS software 20. It was found that performance expectancy and effort expectancy were significant influencers in academicians’ adoption of online FDPs. However, facilitating conditions (FC) was found not a significant predictor for online FDPs. Gender as a moderating variable was significantly impacting the interaction between PE and BI.
Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals

Dehydration is a widespread problem in older adults. For this age group, dehydration can be fatal, however, there is a lack of accurate and valid methods to assess the level of hydration in healthcare settings. In this study, the use of surface electro-myographic (sEMG) sensors to detect active swallowing events (saliva vs liquid swallows) and to estimate fluid intake volume is investigated to corroborate the possibility of integrating these in a fluid intake tracking device. Eleven subjects ($28\pm 13$ years of age) were recruited, and sEMG recordings of single and continuous swallows from cups, bottles, and straws were analysed and four features were extracted. Results showed that the mean detection accuracy between noise and swallows was $99\pm1.31\%$ using a Fine Gaussian SVM classifier; and the mean classification accuracy between saliva and liquid swallows was $86.69\pm5.52\%$ using Fine KNN. Fluid volume was estimated using a feedforward artificial neural network, and results for single swallows presented a low mean RMSE value ($2.01\pm 1.39\ \text{ml}$), however the RMSE increased significantly for the estimation of continuous swallows ($25.82\pm 26.39\ \ \text{ml}$). These results suggest that while further validation is needed, especially in regards to volume estimation, the use of surface EMGs to track fluid intake seems promising.
A Convolutional Neural Network Model for Early-Stage Detection of Autism Spectrum Disorder

Autism is a developmental handicap of children that gets worse as they age. An autistic child has problems with interaction and communication, as well as limited behavior. If autistic children are diagnosed early, they can have a quality life by providing thorough care and therapy. However, in many developed countries, it is too late to diagnose children with autism. Besides, a trained medical expert is required to identify autism as there are no direct medical tests. Medical practitioners also take enough time to detect it because the children have to be monitored intensively. In this research, artificial intelligence algorithms have been utilized for detecting autism in children from images that are not viable for ordinary people. We have employed five different algorithms that are Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting Machine (GBM), AdaBoost (AB) and Convolutional Neural Network (CNN) for classifying Autism Spectrum Disorder (ASD) in children. Comparing classification performances among those algorithms, we have achieved the highest accuracy of 92.31 % on CNN, which outperformed the other conventional Machine Learning (ML) algorithms. Therefore, we proposed a prediction model based on CNN, which can be used for detecting ASD, especially for children.
Deep Learning based Fault Classification Algorithm for Roller Bearings using Time-Frequency Localized Features

The paper proposes an algorithm to classify different conditions of a bearing based on vibration data using a deep convolutional neural network. Spectrograms of vibration data are generated by means of Short-time Fourier Transform and then provided as input to a convolutional neural network. The network is successful in predicting the health condition of the bearing from the spectrograms and achieves a classification accuracy of 97%. The trained model is then tested on a different dataset and the model is able to predict the classes with an accuracy of 96%. The proposed model is finally compared with pre-existing models to evaluate its performance and the results demonstrate the state of the art performance of our proposed algorithm.
MODELING SORPTION ISOTHERMS AND ISOSTERIC HEAT OF SORPTION OF MANGO PULP cv. TOMMY ATKINS

El mango es un producto agricola de gran importancia para diferentespaises en vias de desarrollo, el cual ha incrementado su demanda porsus propiedades nutricionales y caracteristicas exoticas. La relacion entreel contenido de humedad y la actividad de agua suministra informacionutil para el procesamiento del mango, especialmente en las operacionesde secado y almacenamiento. La actividad de agua y el contenido dehumedad de la pulpa de mango (Mangifera indica L. cv. Tommy Atkins)fue analizada para determinar las isotermas de sorcion. La actividad deagua fue medida utilizando un higrometro electrico a cinco diferentestemperaturas (10, 20, 30, 40 y 50 oC) y amplios rangos de contenido dehumedad (2.829–0.031 kg kg-1b.s.) y actividad de agua (0.977–0.215). Unmodelo teorico (GAB) y cuatro ecuaciones empiricas (Oswin, Henderson,Halsey y Ratti) fueron utilizados para representar las isotermas de sorcion.Despues de considerar diferentes criterios, el modelo de GAB fue evaluadocomo la mejor opcion para representar el equilibrio higroscopico de lapulpa de mango (RMSE = 0.057, VAR = 0.996). Las isotermas mostraronun comportamiento acorde a los materiales agroalimentarios y similar alreportado para pulpa de mango de otras variedades. El calor isosterico desorcion fue calculado utilizando la ecuacion de Clausius-Clapeyron a partirdel modelo de GAB. Se observo una disminucion en el calor isosterico desorcion al aumentar el contenido de humedad de equilibrio, con gran efectode la humedad a valores por debajo de0.8 kg kg-1 (b.s.).
Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic

The dangerous COVID-19 (SARS-CoV-2) is rising steadily and globally, with more than 72,851,747 confirmed cases observed to WHO including 1,643,339 deaths till 17 December 2020. The country’s economy is now almost fully halted, people are stuck up and investment becomes deteriorating. So, this is turning to worry of the government for a development and health. Health organizations are often desperate for evolving decision-making innovations to overcome this viral virus and encourage people to receive rapid and effective responses in real-time. Thus, it is important to create auto-mechanisms as a preventive shield to ensure healthy humanity against SARS-CoV-2. Advanced analytics methods and other strategies could also empower researchers, learners and the pharmaceutical industry to acknowledge the hazardous COVID-19 and speed it up care procedures by efficiently testing vast volumes of research data. The prevention method consequence is being used to effectively manage, calculate, forecast and monitor current infected people and future potential cases. Therefore, we proposed CNN and VGG16 based deep learning models to incorporate and enforce AI-based precautionary measures to detect the face mask on Simulated Masked Face Dataset (SMFD). This technique is capable of recognizing masked and unmasked faces to help monitor safety breaches, facilitate the use of face masks, and maintain a secure working atmosphere.
Tracking Control of a Miniature 2-DOF Manipulator With Hydrogel Actuators

Due to the nature of the complex spatiotemporal dynamics of stimuli-responsive soft materials, closed-loop control of hydrogel-actuated mechanisms has remained a challenge. This letter demonstrates, for the first time, closed-loop trajectory tracking control in real-time of a millimeter-scale, two degree-of-freedom manipulator via <italic>independently-controllable</italic>, temperature-responsive hydrogel actuators. A linear state-space model of the manipulator is developed from input-output measurement data, enabling the straightforward application of control techniques to the system. The Normalized Mean Absolute Error (NMAE) between the modeled and measured displacement of the manipulator's tip is below 10%. We propose an Observer-based controller and a robust <inline-formula><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula>-optimal controller and evaluate their performance in a trajectory tracking output-feedback framework, compared with and without sinusoidal disturbances and noise. We demonstrate in simulation that the <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula>-optimal controller, which is computed using Linear Matrix Inequality (LMI) methods, tracks an elliptical trajectory more accurately than the Observer controller and is more robust to disturbances and noise. We also show experimentally that the <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula>-optimal controller can be used to track different trajectories with an NMAE below 15<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, even when the manipulator is subject to a 3 g load, 12.5 times an actuator's weight. Finally, a payload transport scenario is presented as an exemplar application; we demonstrate that an array of four manipulators is capable of moving a payload horizontally by applying the proposed <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula>-optimal trajectory-tracking controller to each manipulator in a decoupled manner.
Speech Emotion Recognition Using Convolution Neural Networks

This study aims to recognize the deep features in speech for the emotion recognition task, with less complex architecture and fewer learnable parameters. We have proposed a simple CNN (convolutional neural network) architecture, based on log-mel-spectrograms of segmented speech utterances. The proposed architecture is used to extract the emotion-related features for two principally used databases in speech emotion recognition applications, Interactive emotional dyadic motion capture (IEMOCAP) and the Berlin database of emotional speech (EmoDB) databases. Several extensive experiments on these datasets demonstrate the performance of the proposed model and the results are compared with the recent CNN architectures. For speaker-independent analysis, the proposed CNN network achieves classification accuracies of 59.33% and 65.47% on IEMOCAP and improvised IEMOCAP utterances respectively for four emotional classes, and 72.02% for the Berlin EmoDB databases for seven classes.
Tensor RNN With Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition

To deal with the temporal dependence between range cells in high resolution range profile (HRRP), dynamic methods, especially recurrent neural network (RNN), have been employed to extract features for target recognition. However, RNN has difficulty in complex and diverse sequence modeling problems as it ignores non-stationary sequential relationship between time-steps by sharing same parameters among all time-steps. Given this issue, we propose tensor recurrent neural network with Gaussian mixture model (GmTRNN) for HRRP, not only making use of temporal characteristic but also modeling the variation among its patterns. Specifically, a novel tensor RNN is developed by extending all the parameters in the form of tensor to explore diverse temporal dependence between range cells within an HRRP sample, where a mixture model is introduced to determine the parameters of each time-step in tensor RNN. Moreover, to take advantage of Bayesian nonparametrics in handling the unknown number of mixture components, we further propose the tensor recurrent neural network with Dirichlet process mixture (DPmTRNN). For scalable and joint training of clustering and recognition, we present effective hybrid online variational inference and stochastic gradient descent method. Experiments on benchmark data, measured and simulated HRRP data demonstrate the the effectiveness and efficiency of our models and its robustness to HRRP shift.