Miracle-dz/UniFAR-FTUs huggingface dataset:
AutoLog: Toward the Design of a Vehicular Lifelogging Framework for Capturing, Storing, and Visualizing LifeBits

Vehicular lifelogging provides us with the opportunity to digitally record life associated with our journeys on the road. Driving on the road is an integral part of life, and it should be given unparalleled attention to log life bits on the road for many purposes, including safety, entertainment, memory augmentation, and analyzing driving behaviors. Various technologies have been used to assist people in capturing live events such as wearable devices, biometric devices, fitness devices, non-visual wearable, and unwearable devices. These devices have played a vital role in capturing the life events of individuals. However, little attention has been given to capturing and recording events related to a person’s life on the road. By using a vehicle’s integrated sensors along with other auxiliary devices, a vehicle can very conveniently capture personal as well as vehicle-related data of the driver. This research aims to propose a framework, termed as AutoLog, which is implemented in the Android platform and can be installed on any android-based smartphones/tablets or a vehicle’s infotainment system. AutoLog is capable of logging the driver activities and vehicle dynamics along with several varying environmental contexts. The proposed solution is evaluated through an empirical study by collecting data from 63 drivers. Different tests, i.e., descriptive tabulation, Cronbach alpha, Kendall’s tau-b, and Principal Component Factor Analysis (PCFA) have been carried out to analyze the data. These tests are significant, and the findings show that the use of AutoLog application has a positive perception in terms of attitude, ease of use, user satisfaction, and intention to use. Besides, the findings also prove that the proposed solution helped the drivers in improving risky driving behavior.
A Portable Microwave Interferometry Sensor for Permittivity Detection Based on CCMRC

A portable microwave complex permittivity sensor based on the interferometry configuration is proposed. A complementary compact microstrip resonant cell (CCMRC) is applied as the sensitive element, which converts the dielectric information of the material under test (MUT) into the phase variations of its transmission coefficient. A miniaturized interferometry platform based on a down-converting mixer further translates the phase change into DC output voltage variation, which can be readily recorded with a direct readout circuit. In this context, expensive and bulky vector network analyzer is no longer needed, thereby leading to a low hardware cost. With comprehensive theoretical analysis, the material permittivity is simply extracted using a specific extrapolation algorithm. As a proof of concept, several different solid material samples with known permittivity values are used to verify the devised detection system.
Lenient Regret for Multi-Armed Bandits

We consider the Multi-Armed Bandit (MAB) problem, where an agent sequentially chooses actions and observes rewards for the actions it took. While the majority of algorithms try to minimize the regret, i.e., the cumulative difference between the reward of the best action and the agent's action, this criterion might lead to undesirable results. For example, in large problems, or when the interaction with the environment is brief, finding an optimal arm is infeasible, and regret-minimizing algorithms tend to over-explore. To overcome this issue, algorithms for such settings should instead focus on playing near-optimal arms. To this end, we suggest a new, more lenient, regret criterion that ignores suboptimality gaps smaller than some ε. We then present a variant of the Thompson Sampling (TS) algorithm, called ε-TS, and prove its asymptotic optimality in terms of the lenient regret. Importantly, we show that when the mean of the optimal arm is high enough, the lenient regret of ε-TS is bounded by a constant. Finally, we show that ε-TS can be applied to improve the performance when the agent knows a lower bound of the suboptimality gaps.
Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

The use of Distributed Ledger Technologies such as Blockchain for certifying Demand Response services allows for the creation of a distributed system in which customers can communicate with the system operator to provide their flexibility, in a secure, transparent and traceable way. Blockchain technology also supports incentive mechanisms for users taking part in the service through the generation of utility tokens to recognize the user’s contribution. This paper presents the experimental test of a novel methodology for Demand Response programs implementation by using the Blockchain technology. The latter is employed for defining a distributed Demand Response service and a new system for its tracing and certification. For this work, a Smart Contract has been conceived and written to execute Demand Response events, calculate users’ baseline, compute the support provided by each user towards the fulfilment of the requested load curve modification and remunerate each user with utility tokens proportionally to their contribution. To test the methodology, a Hyperledger Fabric network and a Smart Contract were deployed on four nodes of the Microgrid Laboratory of the Department of Energy Technology at Aalborg University (DK). Subsequently, a realistic scenario comprising two consumer nodes was developed using power electronic converters for generating the household profiles and Smart Meters for the measurement of the consumption profiles. Theoretical and experimental results show the feasibility of Distributed Ledger Technologies in smart grids management with a minimum investment in new hardware while enabling the active participation of customers in Demand Response more transparently and fairly.
Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

The instantaneous search and retrieval of the most relevant images to a specific query image is a desirable application for all digital libraries. The automatic extraction and classification according to the most distinguishable features, is a crucial step to detect the similarities among images successfully. This study introduces a novel approach that utilizes a fusion model for classifying and retrieving historical Arabic manuscripts’ images. To accomplish our goal, the images are first classified according to their extracted deep learning visual features utilizing a pre-trained convolutional neural network. Then, the texts written in the manuscripts’ images are extracted and pre-processed to classify the images according to their textual features using an optimized bidirectional LSTM deep learning model with attention and batch normalization layers. Finally, both the visual and textual deep learning models are fused at three different fusion-levels named: decision-level, features-level, and score-level. The score-level fusion model resulted in a considerable improvement of each model used individually. Extensive experimentation and evaluation of the proposed fusion method on the collected ancient Arabic manuscripts dataset proved its robustness against other state-of-the-art methods recording 99% classification accuracy and 98% mean accuracy on the top-10 image retrieval.
LQR Feedback Linearization Method to Control the Motions of a Spherical Serial Mechanism

In the present paper, we propose a linear quadratic regulator feedback linearization (LQR-FL) method to control the motion of a spherical serial mechanism which has two degrees of freedom (DOF). The spherical serial mechanism is a part of a 5 DOF hybrid manipulator robot (parallel-serial). The dynamic model of the serial mechanism has been developed to demonstrate the system state model. Then it has been validated using SimMechanics after exporting the model designed by SolidWorks. Here, the objective is to control the system so that the end-effector of the robot (magnetic actuator) to reaches the desired position around the patient’s head. Using the developed dynamic model, the control problem was formulated to the serial spherical mechanism. To illustrate the efficiency of the proposed approach of control, some experimental results are given using LabView software.
Intermittent Information-Driven Multi-Agent Area-Restricted Search

The problem is a two-dimensional area-restricted search for a target using a coordinated team of autonomous mobile sensing platforms (agents). Sensing is characterised by a range-dependent probability of detection, with a non-zero probability of false alarms. The context is underwater surveillance using a swarm of amphibious drones equipped with active sonars. The paper develops an intermittent information-driven search strategy, which alternates between two phases: the fast and non-receptive displacement phase (called the ballistic phase) with a slow displacement and sensing phase (called the diffusive phase). The proposed multi-agent search strategy is carried out in a decentralised manner, which means that all computations (estimation and motion control) are done locally. Coordination of agents is achieved by exchanging the data with the neighbours only, in a manner which does not require global knowledge of the communication network topology.
Genotypic response of chickpea (Cicer arietinum L.) for resistance against gram pod borer (Helicoverpa armigera)

Background: Chickpea is an important pulse crop of Pakistan. The pod borer, Helicoverpa armigera (Hubner), is the major pest in most of the chickpea growing areas of the country. A field trial was carried out at Entomological Research Area, Ayub Agriculture Research Institute (AARI), Faisalabad, during growing season of 2012-13 to evaluate the resistance of chickpea genotypes against gram pod borer ( Helicoverpa armigera ). Methods: Experiments were conducted in randomized complete block design (RCBD) with four replications having plot size of 3 ft × 20 ft. Morphological characters of chickpea like pod trichome, pod wall thickness, pod length, pod breadth, pod area and number of pods per plant were measured. The pod borer larval population/pod infestation was recorded from fifteen randomly selected plants per plot after ten days interval. Results: It was found that the genotypes which had higher trichomes length and density and pod wall thickness were more resistant against Helicoverpa infestation. Significant genetic variation for resistance against Helicoverpa armigera attack was found in chickpea and variety K-70005 showed resistant behavior followed by K-08004 and K-60062. Conclusions: It was concluded from prescribed study that the most susceptible genotypes were K-70005, K-08004 and K-70008 for the attack of Helicoverpa armigera to reduce grain yield.
The Rare Richardsitas Betsch (Collembola, Symphypleona, Sminthuridae): A New Species from Australia with Comments on the Genus and on the Sminthurinae

Richardsitas Betsch is a small genus of Sminthurinae with only two species described so far, both from Madagascar. It resembles other Sminthurinae with long antennae, especially Temeritas Richards. Here we provide the first record of Richardsitas from Australia, Richardsitas subferoleum sp. nov., which is similar to R. najtae Betsch and R. griveaudi Betsch in males’ large abdomen chaetotaxy and presence of tenent-hairs on tibiotarsi II–III, but lacks mucronal chaeta and has 28 segments on the fourth antennal segment plus a unique pair of sensilla on the second. We also provide an updated genus diagnosis to Richardsitas, a key to its species, a discussion of the affinities of Temeritas and Richardsitas to other Sminthurinae, and an updated key to this subfamily.
Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications †

Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.
Optical coherence tomography and color fundus photography in the screening of age-related macular degeneration: A comparative, population-based study

Purpose To analyze the individual value and the contribution of color fundus photography (CFP) and optical coherence tomography (OCT) in the screening of age-related macular degeneration (AMD) of an unselected population. Methods CFP and OCT images of 15957 eyes of 8069 subjects older than 55 years, obtained during a population-based screening for AMD using a single diagnostic non-mydriatic imaging device, were analyzed by a blinded examiner. The two techniques were preliminary evaluated considering the dichotomous parameter "gradable/ungradable", then gradable images were classified. CFP were graded according to the standardized classification of AMD lesions. OCT images were also categorized considering the presence of signs of early/intermediate AMD, late AMD, or other retinal diseases. Another blinded operator re-graded 1978 randomly selected images (for both CFP and OCT), to assess test reproducibility. Results Of the 15957 eyes, 8356 CFP (52.4%) and 15594 (97.7%) OCT scans were gradable. Moreover, most of the eyes with ungradable CFP (7339, 96.6%) were gradable at OCT. AMD signs were revealed in 7.4% of gradable CFP and in 10.4% of gradable OCT images. Moreover, at OCT, AMD signs were found in 1110 (6.9%) eyes whose CFP were ungradable or without AMD (847 and 263 eyes, respectively). The inter-operator agreement was good for the gradable versus ungradable parameter, and optimal for the AMD grading parameter of CFP. The agreement was optimal for all OCT parameters. Conclusions OCT provided gradable images in almost all examined eyes, compared to limited CFP efficiency. Moreover, OCT images allowed to detect more AMD eyes compared to gradable photos. OCT imaging appears to significantly improve the power of AMD screening in a general, unselected population, compared to CFP alone.
Color and translucency stability of contemporary resin-based restorative materials.

OBJECTIVE To determine color and translucency stability of present-day resin-based restorative materials. MATERIALS AND METHODS Disk-shaped (1.0 × 10 mm) resin composites (Filtek Universal, 3M ESPE; Spectra ST, Dentsply Sirona; Gradia Direct X, GC America; Estilite Quick, Tokuyama; Harmonize, Kerr; ACTIVA Bioactive-Restorative, Pulpdent) were prepared and divided into four staining groups (n = 5); coffee (55°C), grape juice (5°C), deionized water (25°C) and thermocycling (20 000 cycles, 5°C to 55°C with a 30-second dwell time). Spectrophotometer measurements were taken at baseline and 14 days for each group and the color stability (CIEDE2000) and changes in the translucency parameter (RTPCIEDE2000 ) and luminous transmittance of each composite were determined. A 2-way analysis of variance (ANOVA) using maximum likelihood estimations was used for analysis for these changes, with subsequent pairwise comparisons using Bonferroni-corrected Student's t tests and an experiment-wise α = 0.05. RESULTS A significant interaction (P < .0001) between material and treatment was found for each change in the esthetic property tested over 14 days. Multiple significant differences between materials for each treatment and between treatments for each material were identified. CONCLUSIONS The severity of color change and translucency of resin-based materials are dependent on their type, mostly affected by the coffee staining medium. Nano-filled resin composite is most resistant to color change and with stable translucency after artificial aging. CLINICAL RELEVANCE Nano-filled resin composite may be the most esthetically durable resin- based restorative material. Micro-hybrid resin composite and resin-modified glass ionomer restorative material are susceptible to color change and losing their translucency. Hot coffee solution may shorten the esthetic lifespan of a resin-based material.
Factors associated with HIV testing and intention to test for HIV among the general population of Nonthaburi Province, Thailand

Background Research on HIV testing in Thailand has largely focused on at-risk population groups, with limited information about the prevalence of correlates of HIV testing among the Thai general population. This study addresses this gap in research by using a population-based probability sample to examine correlates of HIV testing experience and intention to test. Methods We conducted a cross-sectional survey in Nonthaburi, Thailand during October-December 2012 using tablet computers to collect self-administered questionnaires from 2138 men and women (aged 15–59 years) identified through three-stage stratified cluster sampling. Findings Almost half of the respondents, 962 (45%), reported having been tested for HIV while an almost equal proportion, 1032 (48.3%), indicated their intention to test for HIV. Being sexually experienced, having a history of sexually transmitted infection, personally knowing someone infected with HIV, and youth were associated with both history of HIV testing and intention to test for HIV. High perceived risk of HIV, knowledge of an HIV testing location, and having been married were associated with having been tested for HIV. Having been tested for HIV and HIV/AIDS education were associated with intention to test for HIV. The most common reasons for testing were routine medical checkup and antenatal care. The major reasons for not testing were perception of having no or low risk. Conclusion A substantially low proportion of the respondents reported a history of HIV testing and intention to test for HIV. Culturally appropriate programs that address HIV risk perception and provide accurate information related to HIV infection and HIV testing may be beneficial in increasing uptake of HIV testing among the general population in Thailand.
Major Adverse Cardiovascular Events in Antidepressant Users Within Patients With Ischemic Heart Diseases: A Nationwide Cohort Study.

BACKGROUND To evaluate the effect of antidepressants on the development of major adverse cardiovascular events (MACEs) in patients with ischemic heart disease (IHD), we analyzed the association between antidepressant use and the risk of MACE development. METHODS Patients with depression and a history of IHD between 2008 and 2012 were identified by using the National Health Insurance Service database. A cohort was followed up for the development of MACE until the end of 2015. Hazard ratios (HRs) for myocardial infarction, stroke, and cardiovascular death were estimated by Cox proportional hazard regression in a propensity score-matched cohort. RESULTS Over a median 4.2 years of follow-up, 2943 MACE occurred in 18,981 antidepressant users. Use of antidepressants was not associated with the incidence of MACE when compared with antidepressant nonusers (adjusted HR, 1.00; 95% confidence interval, 0.95-1.05). Among the analyses according to different classes of antidepressants, the increased risk of stroke was only observed in the subgroup of selective serotonin reuptake inhibitor (SSRI) users. Dose-response findings reported greater risk in those with higher doses of use (SSRIs with ≥1.0 defined daily dose; adjusted HR, 1.14; 95% confidence interval, 1.06-1.23), whereas duration response does not support association. CONCLUSIONS Compared with antidepressant nonusers, use of antidepressants was not associated with occurrence of MACEs in patients with depression and IHD. Although the overall effect of antidepressants on the risk of MACE was neutral, careful monitoring of MACE development is recommended in high-dose SSRI users.
Antioxidant Activities and Volatile Flavor Components of Selected Single-Origin and Blend Chocolates

The biological activity of chocolates gains more and more attention of consumers. Its antioxidant properties depend, among other factors, mainly on the origin of cocoa and the characteristics that this origin gives to the final product. Therefore, the aim of the study was to measure and compare the total content of polyphenols, antioxidant activity, and key odorants of commercial chocolates made from blend cocoa with single-origin ones. The highest content of polyphenols was found in 90% blend cocoa chocolate and single-origin samples, while the lowest content was exhibited by 100% chocolate from blend cocoa mass. The highest antioxidant activity measured by 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) and ferric reducing antioxidant power (FRAP) assays was observed in the sample of chocolate with 90% cocoa solids from blend mass, followed by single-origin chocolates. A high positive correlation between ABTS assay and the total polyphenol and phenolic acids’ content, as well as among the total content of polyphenols, flavonoids, and phenolic acids was found. Mineral composition analysis showed that dark chocolate is a valuable source of some elements, especially Mg, Fe, and Zn. Potentially toxic elements were not detected or below permitted limits. Moreover, it was noticed that the main volatile compound in all tested samples was acetic acid, but pyrazines were considered the most important group of chocolate odorants.
In vitro, in vivo and in silico rationale for the muscle loss due to therapeutic drugs used in the treatment of Mycobacterium tuberculosis infection

Abstract Tuberculosis globally affects millions of people every year and is responsible for high rates of mortality and morbidity in tropical countries like India. The treatment of tuberculosis involves using the first line of drugs especially Isoniazid, Pyrazinamide, Streptomycin, Ethambutol and Rifampicin for treatment under the DOTS (Directly Observed Treatment Shots) regime which can last up to minimum of six months. These drugs although widely used against Mycobacterium tuberculosis has given rise to multi drug resistant (MDR) tuberculosis strain. It has been observed widely that prolonged drug treatment for tuberculosis patient has rendered several side effects that include increasing muscle wasting and malnutrition. In our study, we have investigated the role of these major tuberculosis drugs namely Rifampicin, Streptomycin, Isoniazid, Pyrazinamide, and Ethambutol on actin polymerization which are famously known to be a central player in the sarcomere region of the muscle in human body. For in vitro studies, we have used biophysical approaches such as 90° scattering assay (RLS), size exclusion chromatography (SEC), Dynamic light scattering (DLS), Circular dichroism spectroscopy (CD), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), kinetic analysis to understand the time taken to break down effect of above mentioned drugs on actin disruption. In vivo analysis was carried out on yeast Δend3 mutants which are rich in F-actin filaments in order to understand the effect of the aforementioned drugs in rendering the muscle wasting phenomenon in tuberculosis. Furthermore, we also carried out in silico analysis to understand the probable modes of binding of these drugs on actin filaments. Communicated by Ramaswamy H. Sarma