Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Fingerprint Biometric and RFID Time-Based Attendance Management System

Attendance documentation is a crucial metric for determining a student's dedication, qualification, and status. Several types of systems have been developed for automated attendance. The majority of these implementations were built on a single factor template, representing a line of security error. In recent years, rapid development has taken place in several applications such as authorization, data access, and access control by implementing biometrics and radio frequency identification technologies. This research work focuses on designing a smart protocol for RFID authentication in combination with fingerprint biometric information, which guarantees confidentiality, identity verification, and data protection. Reaction time and event performance evaluation were the tests performed on the system. Ten students with unique fingerprints and RFID cards are assessed for the test. The test results show an average duration of 20.61 respective seconds for each student, and zero percent of false refusal rate was recorded in the implementation. Finally, the result shows the system is reliable and efficient. Each result revealed high-speed performance due to software and hardware coordination. Biometric systems provided data integrity and security, RFID provided limited complexity, and the proposed method's technical programming pattern provided an average run time.
Lexeme : Journal of Linguistics and Applied Linguistics

A number of shadow educations (private supplementary tutoring) of English have emerged in Indonesia regarding the fulfillment of the variety of students’ needs in learning English that is not fully accommodated in conventional schooling. Apparently, the services provided in shadow educations have been transferred in online setting due to the rapid advancement of technology. This research aims at observing the EFL learners’ perspective on online English shadow education. With a descriptive qualitative design, the subjects were involving 28 EFL learners experiencing English shadow education in KII online English course. The data were collected from a questionnaire developed in closedand open-ended formats. This research reports the result of a survey research on EFL learners’ perspective on online English shadow educations that the EFL learners’ participation were not only led by the same reasons of the conventional one but the practicality also contributes on it and challenges faced by the learners were revealed as well. Technical issues such as bad internet connection and limited interaction between the tutors and the learners were the major case in online English shadow education. Moreover, EFL learners also admitted that they were suffered from self-regulation learning strategy and it relates to teaching pedagogy as well. These results have implications for EFL practitioners aiming at better understanding current EFL learners.
CUSTOMISED ITINERARY CREATION USING NEURAL NETWORK

_ Abstract : A neural network based system is designed to create at-go itineraries based on interests of the traveller. Data is collected by scrapping various websites. After pre-processing, the data is labelled. A neural network is used to predict POIs other than those given by the user. The suggested POIs are knit in an itinerary using time dependent orientation approach. An Android Application is used to make the system deliverable.
IL-25 Gene Delivery; Immunotherapeutic Approach for Cutaneous Squamous Cell Carcinoma

Mohammadzadeh A1; Bahmaie N2,3,4; Esmaeilzadeh A5,6,7* 1School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran. 2Department of Allergy and Immunology, Faculty of Medicine, Graduate School of Health Science, Near East University (NEU), Nicosia, Northern Cyprus, Cyprus. 3Clinical Diagnosis Laboratory Expert, Private Baskent Hospital, Nicosia, Northern Cyprus, Cyprus. 4Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran. 5Department of Immunology, Associate professor of Immunology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran. 6Cancer Gene Therapy Research Center (CGRC), Zanjan University of Medical Sciences, Zanjan, Iran. 7Immunotherapy Research & Technology Group, Zanjan University of Medical Sciences, Zanjan, Iran.
Hand as Sensor: Virtualizing Interactions with Everyday Object Affordances using Wrist-worn Hand Pose Sensing

We are a group of HCI researchers from a capacitive technology company, and we have been collaborating on hardware to enable sensing the posture and anatomy of hands for use in mixed reality. Through this process, we have been exploring the motion and activity of hands; after discovering that our sensor is sensitive not only to dynamic hand pose but also to tendon load (such as is caused by holding or pressing on an object), we began exploring the idea of using passive held or touched objects as support for gestural input. Specifically, we are interested less in the passive haptics of existing objects, and more in their interactive affordances . This paper presents Ubiquitous Controls: a work-in-progress input technique measuring interactions between users’ hands and objects’ affordances powered by a wristband, which senses muscle pose and motion using capacitive-based mechanomyography (MMG). We use this technology to train machine learning models that map continuous object interactions—like “twisting a bottle cap” or “cutting with scissors”—to controls like dials or sliders. Across 10 participants, our models discriminated between 6 object interactions and identified the correct range state with mean accuracies of 85.5–98.5 %. Furthermore, 8 users interactively controlled UI widgets using objects and valued their haptic feedback and enhanced state reproducibility over freehand gestures.
Intraventricular Broken Dermoid Cyst: A Case Report

Case Report Intracranial dermoid cysts are benign, slow, extra-axial, benign dysembryoplastic tumors. We report the case of a 29year-old man who visited emergency rooms for sudden onset headaches. A cerebral CT showed a left basal frontal cyst formation of homogeneous fat density. MRI confirmed the fat content of the cyst and its intraventricular rupture. Histologically, dermoid cysts have a thick wall with possible presence of different appendages of the skin explaining the possibility of rupture during growth, even if rare. Imaging, based on CT and especially MRI, allows the positive diagnosis of this rupture. Conservative treatment may be considered for ruptured cysts where there is a risk of intraoperative vascular involvement, but with a risk of recurrence of rupture.
American Journal on Intellectual and Developmental Disabilities Emotion word comprehension in Down Syndrome

We studied comprehension of emotion versus concrete/abstract words in Down syndrome (DS). Study 1 compared 26 participants with DS and 26 typically developing (TD) children matched on verbal ability. Results showed no difference between groups. Study 2 assessed whether chronological age and (non)verbal abilities predicted developmental trajectories of comprehension in 36 children with DS and 143 TD children. For the latter, these variables predicted comprehension of all three word types. For the former, receptive vocabulary predicted comprehension of all word types, but chronological age and nonverbal reasoning only predicted comprehension of concrete words. This suggests that individuals with DS have no specific emotional lexicon deficit. Supporting their general lexical development would help them access abstract and emotional meanings.
Effect of Incubation Time, CuSO4 and Glucose Concentrations on Biosynthesis of Copper Oxide (CuO) Nanoparticles with Rectangular Shape and Antibacterial Activity: Taguchi Method Approach

In nanotechnology, using of metallic nanoparticles (MNPs) has obtained major attention for over a century because of their unique properties in nano scale. In this paper, the effect of three factors (incubation time, concentrations of copper sulfate and glucose) on the biosynthesis of copper (II) oxide nanoparticles (CuO NPs) by Halomonas elongata IBRC-M 10214 and the antibacterial activity of nanoparticles were evaluated. Experiment design and analysis of Taguchi method were carried out by Qualitek-4 software. Effect of CuSO4 concentration (1.4, 2.8 and 5.6 mM), incubation time (48, 72 and 96 h) as three different levels was measured as three major factors in the biosynthesis of metallic nanoparticles. Synthesized CuO nanoparticles were characterized by utilizing ultraviolet-visible (UVVis) spectroscopy, X-ray diffraction (XRD), Fourier transform infra-red (FT-IR) spectroscopy and field emission scanning electron microscope (FESEM) techniques. Among the three factors, results illustrated that considerable effect was related to CuSO4 concentration. These analyses demonstrated that the average CuO nanoparticles crystalline size was 57-79 nm with rectangular shape. Also, for evaluating of antibacterial effects, maximum zone of inhibition, two important multidrug resistant pathogenesis bacteria, Escherichia coli ATCC 25922, and Staphylococcus aureus ATCC 43300 were used. Antibacterial assay of CuO nanoparticles showed antibacterial activity toward the pathogenic bacterial strains of E. coli by 5 mm and S. aureus by 5.5 mm for maximum zone of inhibition. In conclusion, this study presented simple, low expensive, eco-friendly and high productivity in the fabrication of CuO nanoparticles. In addition, these metallic nanoparticles had antibacterial effect that could be usable in medicinal aspect for fighting against prominent pathogen bacteria such as E. coli ATCC 25922 and S. aureus ATCC 43300.
BLIND ASSISTANCE USING ARTIFICIAL INTELLIGENGE

— To help the visually impaired and blind people to be independent, this project proposes a portable electronic assistive device based on Artificial Intelligence. This device utilizes different sensors including a GPS sensor, ultrasonic sensor, and a camera module for sensing the surroundings. Raspberry Pi, a small computer, processes all the sensor data and provides information of the surroundings through the headphone and thus makes blind people's life easier. This device has a special feature, which provides the location of the blind person to his/her caretaker with the help of a GPS tracker.
A COMPARATIVE STUDY OF CYCLE GAN AND PROGRESSIVE GROWING GAN FOR SYNTHETIC DATA GENERATION

-Modern deep learning applications require a lot of data. Most of the times the data is insufficient and can be very costly to collect. This problem can be solved by synthetic data generation using. Generative Adversarial Networks. We have performed a detailed comparative study of two most popular GANs for synthetic data generation and when each of them is used. Using the following approaches alone would solve most modern deep learning data insufficiency problems.
Lunar illumination shapes small mammal activity in lowland agricultural landscapes

The behavioural patterns of small mammalian prey species have been shown to be widely impacted by predator avoidance. Cues to avoid predation may come from the predator itself or environmental cues, such as moonlight and available vegetative cover. We investigated how the activity of the bank vole Myodes glareolus, field vole Microtus agrestis, wood mouse Apodemus sylvaticus and the common shrew Sorex araneus were impacted by changes in habitat and lunar light conditions across a range of habitats (mainly grassland protected sites) in Lincolnshire (UK). Microhabitat vegetation density as well as weather conditions were recorded across all trap sites, with Longworth traps set overnight and the successfully captured species recorded the following morning. Overnight temperature was found to positively influence capture rate across all species. The lunar phase was found to significantly impact capture rate, with the gibbous lunar phase providing the highest capture rate across species. The interaction between illumination and vegetation density was also found to impact activity levels in the bank vole and wood mouse, with the bank vole showing higher activity in thick vegetation at low light levels and across habitats at higher light levels, whereas wood mice were more often captured in intermediate cover at low and intermediate illumination but across a range of habitats at brighter illumination. In combination, it suggests that small mammal activity is altered to potentially reduce predation risk. However, in this community at least, brighter lunar illumination leads to increase activity.
High Reusability of Catalytically Active Gold Nanoparticles Immobilized in Core–Shell Hydrogel Microspheres

The reusability of hybrid core–shell microgels, whose core surfaces were decorated with gold nanoparticles, was investigated in terms of catalysis activity. Hybrid core–shell microgels composed of a rigid core and water-swollen gel shell endowed the immobilized gold nanoparticles with a high dispersion stability, which resulted in excellent catalytic activity. In contrast to free Au nanoparticles and conventional hybrid microgels, where the Au nanoparticles are randomly distributed over the entire microgel templates, the hydrogel shell part of the hybrid core–shell microgels suppressed the aggregation between the microgels and Au nanoparticles in individual microgels, which improved the reusability for the catalysis reaction. The results of this study should help to develop advanced catalyst systems that require high reusability even when the chemical reactions occur in aqueous solution and external stimuli are applied.
HPLC Profile of Bioactive Compounds Present in Olea europeae, Myrtus communis and Monotheca buxifolia and their Antibacterial Activities

Aims: In this study the aqueous extract of Olea europeae, Myrtus communis and Monotheca buxifolia were screened for antibacterial activity, total phenolic content, polyphenol, chlorophyll chlorophyll β-carotene lycopene. composed of identification and quantification of bioactive compounds in Methodology: The antibacterial activities of the selected plants extracts were determined using agar well diffusion, minimum inhibitory concentration and minimum bactericidal concentration methods. The total polyphenol contents were determined by Follin Ciocalteu reagent method. The extract was subjected to HPLC analysis for antioxidants concentration. Results: The zone of inhibition against Escherichia coli, Klebsiella pneumonia, Enterococcus faecalis and Bacillus cereus were 1.82, 1.52, 1.72 and 2.46 cm for Olea europeae extract , 1.51, 2.06, 1.98, 1.79 cm for Myrtus communis extract, 1.98, 2.28, 2.36, 1.62 cm for stem extract and 1.78, 2.05, 1.80, 1.77 cm for root extract of Monotheca buxifolia respectively. Morin, Quercetin and Chlorogenic acid were detected at retention times; 12.518, 10.022 and 6.305 minutes respectively in Olea europeae extract. Morein and Hydroxy benzoic acid were detected at 12.858 and 6.079 minutes retention time respectively in Myrtus communis extract. Chlorogenic acid, Quercetin, Morin, Pyrogallol and Phloroglucinol were detected in root extract of Monotheca buxifolia while in its stem, Chlorogenic acid, Quercetin, Morin, Rutin, Mandalic acid, Phloroglucinol and Hydroxy benzoic acid were detected . Conclusion: A number of bioactive compounds were detected in the selected plants and on the basis of findings it was concluded that selected plants could be used medicinally.
FACE RECOGNITION USING 2D GABOR FILTER WITH PCA APPROACH

-Face recognition is a broadly researched topic by researchers from differing disciplines. Several unsupervised statistical feature extraction strategies have been used in face recognition, out of these in this paper I propose a robust face recognition system using 2 D Gabor filter with PCA approach. The proposed system was tested on two famous face recognition databases, namely ORL and faces 94 having face recognition rates of 81.72% and 97.30% were obtained respectively. The parameters which we are considered are recognition rate and speed with varying number of training and testing images.
American Journal on Intellectual and Developmental Disabilities Relationship between sensory processing and Autism Spectrum Disorder-like behaviors in Prader-Willi Syndrome

5 The relationship between sensory processing and ASD-like and associated behaviors in 6 patients with Prader-Willi Syndrome (PWS) remains relatively unexplored. Examining this 7 relationship, 51 adults with PWS were administered the Pervasive Developmental Disorders 8 Autism Society Japan Rating Scale (PARS), Short Sensory Profile (SSP-J), Food-Related 9 Problem Questionnaire (FRPQ), and Aberrant Behavior Checklist (ABC-J). Based on SSP-J 10 z-scores, participants were classified into three severity groups. Analysis of variance was 11 performed to compare the behavioral scores of these three groups. Statistically significant 12 group differences were observed in PARS (p=.006, ηp =.194) and ABC-J (p=.006, ηp =.193) 13 scores. Our findings suggest that the level of sensory processing may predict ASD-like and 14 aberrant behaviors in adults with PWS, implying the importance of a proper assessment for 15 early intervention. 16 17
Heart cardiac’s sounds signals segmentation by using the discrete wavelet transform (DWT)

The presence of abnormal sounds in one cardiac cycle, provide valuable information on various diseases.Early detection of various diseases is necessary; it is done by a simple technique known as: phonocardiography. The phonocardiography, based on registration of vibrations or oscillations of different frequencies, audible or not, that correspond to normal and abnormal heart sounds. It provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography, combined with signal processing techniques, has strongly renewed researchers’ interest in studying heart sounds and murmurs. This paper presents an algorithm based on the denoising by wavelet transform (DWT) and the Shannon energy of the PCG signal, for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.
PGPM_A_274258 707..718

*These authors contributed equally to this work Purpose: Ulcerative colitis (UC) patients have an increased risk of colorectal cancer (CRC), and compared with sporadic CRC, ulcerative colitis-associated colorectal cancer (CAC) is more aggressive with a worse prognosis. This study aimed to identify a gene signature to predict the risk of CAC for patients with UC in remission. Patients and Methods: Series of quiescent UC-related transcriptome data obtained from the Gene Expression Omnibus (GEO) data set were divided into a training set and a validation set. Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and \Weighted Correlation Network Analysis (WGCNA) combined with protein– protein interaction (PPI) analysis were used to identify the pathways and gene signatures related to tumorigenesis among quiescent UC patients. A generalized linear model (GLM) of Poisson regression based on the training set was applied to estimate the diagnostic power of the gene signature in our validation set. Results: The tumor necrosis factor (TNF) signaling via NF-κB pathway was significantly augmented with the highest normalized enrichment score (NES). The genes in the brown module from WGCNA have shown a significant correlation with CAC (Pearson coefficient = 0.83, p = 6e-06). A subset of NF-κB related genes (FOS, CCL4, CXCL1, MYC, CEBPB, ATF3, and JUNB) were identified with a relatively higher expression level in CAC samples. The diagnostic value of this 7-gene biomarker was estimated by the receiver operating characteristic (ROC) curve with an area under the ROC curve (AUC) at 0.82 (p<0.0001, 95% CI: 0.7098–0.9400) in the validation cohort. Conclusion: In summary, the increased expression of this seven-NF-κB-related gene signature may act as a powerful index for tumorigenesis prediction among patients with UC in remission.
Thank you for thorough reading and revisions of our manuscript "Near Real-Time Automated Classification of Seismic Signals of Slope Failures with Continuous Random Forests". Enclosed you will find a response to all

In mountainous areas rockfalls, rock avalanches and debris flows constitute a risk to human life and property. Seismology has proven a useful tool to monitor such mass movements, while increasing data volumes and availability of real-time data streams demand new solutions for automatic signal classification. Ideally, seismic monitoring arrays have large apertures and record a significant number of mass movements to train detection algorithms. This is, however, rarely the case, as a result of cost and time constraints and the rare occurrence of catastrophic mass movements. Here, we use the supervised 5 random forest algorithm to classify windowed seismic data on a continuous data stream. We investigate algorithm performance for signal classification into noise (NO), slope failure (SF), and earthquakes (EQ) classes using data from two separate locations in the Swiss Alps: data set (i) contains signals of several dozen slope failures with low signal-to-noise ratio, data set (ii) contains only five slope failure events albeit with higher signal-to-noise ratio. The low signal-to-noise ratio of slope failure events in data set (i) leads to a classification accuracy of 70% for SF, the largest confusion being between NO and SF. Although data 10 set (ii) is highly imbalanced, lowering the prediction threshold for slope failures leads to a prediction accuracy of 80% for SF, 90% for EQ, and 99% for NO, the largest confusion being between SF and EQ. Standard techniques to mitigate training data imbalance do not increase prediction accuracy. The classifier of data set (ii) is then used to train a model for classification of 176 days of continuous seismic recordings containing four slope failure events. The model classifies eight events as slope failures, of which two are snow avalanches and one is a rock slope failure. The other events are local or regional earthquakes. 15 By including earthquake detection of a permanent seismic station at 131 km distance to the test site into the decision-making process, all earthquakes falsely classified as slope failures can be excluded. Our study shows that even for limited training data and non-optimal network geometry, machine learning algorithms applied to high-quality seismic records can be used to automatically monitor mass movements. Copyright statement. TEXT 20
Digital Teaching, Inclusion and Students’ Needs: Student Perspectives on Participation and Access in Higher Education

In this article we discuss the contribution of digitalisation for equal participation in higher education. Its potential is often postulated, but accessibility is seldom examined in this context. Despite the challenges and difficulties created in the summer term of 2020, this semester has provided a great opportunity to collect data on digital teaching, as face‐to‐face teaching needed to be transformed into digital teaching. Based on two surveys conducted in the summer of 2020, current practices and students’ needs regarding accessibility are outlined. Despite the circumstances, it can be derived from the surveys that digital teaching generally provides a variety of advantages for students with disabilities, although some tools and platforms remain not fully accessible to them. Additionally, the results indicate that not only students with sensory impairments benefit from the principles of the Web Content Accessibility Guidelines (2018). In particular, the principles ‘operable’ and ‘understandable’ are beneficial for students with mental health difficulties. Regarding the assessment of accessibility features, the study shows that the perception of students with and without impairments is very similar.