Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Thermal Region of Interest for Human Emotion Estimation

In the recent years, researches in implementing visual-based Automatic Facial Expression Analysis (FEA) and human emotions estimation have been a great interest and had widespread applications. However, vision-based visual systems face challenges such as poor-quality images due to low light conditions, poker-faces, the contrast of expressions and emotions. Because of the advantages of thermal images - unsensitive to ambient light conditions, we focus on developing a human emotion estimating system using thermal-based cues. It overcomes the limits of visible images, however, once a change in emotion occurs, only some areas of the face is affected in terms of temperature. In addition, the loss of thermal information in the glasses areas is a matter of concern. This paper presents our proposed method, thermal region of interest, to fill the gap when using thermal images to estimate seven emotions.
Accelerating Data Loading for Photo-Based Augmented Reality on Web Browser

A photo-based augmented reality (Photo AR) system is an AR system that uses photographic images instead of computer graphics of three-dimensional models. Photo AR can display realistic AR views to users, but it requires large disk space due to its many images. Although all images can be significantly compressed into one video format file, the decoding and memory storage process are time-consuming. In this study, we propose a method to accelerate data loading for Photo AR operating on a web browser in a mobile device. The experimental results demonstrate the effectiveness of our method.
Terrestrial isopods associated with shallow underground of forested scree slopes in the Western Carpathians (Slovakia)

Abstract The shallow underground of forested scree slopes represents a little-studied subterranean biotope. In this paper, species diversity and depth distribution of terrestrial isopod communities studied in the depth profile (5–95 cm from the surface) of eight forested scree slopes in the Western Carpathians (Slovakia) is discussed. The southern edge of the Western Carpathians where the study sites are located represents the northernmost limit of distribution of obligate subterranean fauna in Europe. The sites differ from each other in type of bedrock, forest composition, slope aspect, and altitude. To sample isopods, a set of three subterranean pitfall traps consisting of a plastic cylinder (Ø 110 mm) and ten plastic cups was buried in each studied scree slope. In total, 252 isopods belonging to eleven species were sampled (1–5 species per site). Of the species found, the blind and depigmented Mesoniscusgraniger was the sole species closely associated with deeper parts of the depth profile and was present in most of the sites studied. Another ten species were represented by a small number of individuals and their occurrence deeper in the scree slope profile was rather accidental. A comparison between winter and summer periods indicates apparent differences in seasonal activities of isopods. Ethylene glycol seems to be more appropriate fixative solution for trapping isopods than formaldehyde.
Sensory Restoration of the Facial Region

Abstract Normal sensitivity of the face is very important for preserving its integrity and function as an efferent source of information for the brain. The trigeminal nerve, which is the largest cranial nerve, conducts most of facial sensory function through its 3 branches: the ophthalmic nerve (V1), the maxillary nerve (V2), and the mandibular nerve (V3). The trigeminal nerve may be damaged by a variety of etiologies including inflammatory disorders, brain tumor resection, trauma, iatrogenic injury, or congenital anomalies. Temporary or permanent damage can lead to numbness, lip-biting injury, corneal anesthesia, and, in the worst scenario, even blindness. Different age groups, mechanisms of the injury, and the time between injury and repair can affect the final result of the nerve repair. Unlike the well-understood facial nerve palsy, so far there is no universal approach to restore the facial sensory function. This article serves to thoroughly review the basic anatomy of trigeminal nerve, diagnosis of sensory nerve dysfunction, and attempts to establish a protocol for treatment and rehabilitation of affected patients.
Accuracy Crawler: An Accurate Crawler for Deep Web Data Extraction

With the daily amalgamation in the size of data available on internet, the size of deep web is also continuously growing. The large size of the deep web in comparison with the surface web makes it very difficult to locate various deep web resources. In addition with harvesting the large size of the deep web content, classification of this content accurately is one of the major challenge. We propose a framework, namely Accurate Crawler, for accurately harvesting deep web content. Our crawler provides accurate classification of the deep web content by avoiding visiting a large number of pages. Accurate Crawler ranks sites based on the similarity of the content available, resulting in more accuracy in terms of site classification and extraction of deep web content. Accuracy Crawler has an excavating mechanism and an advanced relevance calculation mechanism to harvest relevant links by link ranking. Our experimental results on a set of representative domains show the accuracy of our proposed crawler framework that is higher than other crawler
Feasibility Evaluation of Neural Network Physical Unclonable Function

In recent years, consumer electronics that utilize artificial intelligence (AI) have attracted attention. As AI is widely used, interest in the security of AI has been heightened. We have proposed a concept of the neural network physical unclonable function (NNPUF) which was suitable for AI edge modules and it utilized neural network propagation delay time as authentication. This study applies the NNPUF to benchmark problems to evaluate its feasibility. Experimental results prove the feasibility.
Versatile SAR-ADC for Biomedical Applications

This paper presents a versatile SAR ADC targeting the acquisition of signals in biomedical applications. The converter is implemented in a CMOS AMS 0. 35µm technology and powered with 3.3V. Its output bit-width can be varied from 4 to 8 bits. The device includes built-in programmable clock generator and voltage reference circuit. The sampling rate can be tuned up to 100kS/s in the 8-bit mode and with a maximal input range of 2.5V. All these parameters can be independently adjusted. This is an unique fully-configurable converter. The simulation results are reported and positioned with respect to the most recent designs.
Involvement of C-terminal truncation mutation of kinesin-5 in resistance to kinesin-5 inhibitor

Cultured cells easily develop resistance to kinesin-5 inhibitors (K5Is) often by overexpressing a related motor protein, kinesin-12/KIF15, or by acquiring mutations in the N-terminal motor domain of kinesin-5/KIF11 itself. We aimed to identify novel mechanisms responsible for resistance to S-trityl L-cysteine (STLC), one of the K5Is, using human osteosarcoma cell lines. Among six lines examined, U-2OS and HOS survived chronic STLC treatment and gave rise to resistant cells with IC50s at least 10-fold higher than those of the respective parental lines. Depletion of KIF15 largely eliminated the acquired K5I resistance in both cases, consistent with the proposed notion that KIF15 is indispensable for it. In contrast to the KIF11-independent property of the cells derived from HOS, those derived from U-2OS still required KIF11 for their growth and, intriguingly, expressed a C-terminal truncated variant of KIF11 resulting from a frame shift mutation (S1017fs). All of the isolated clones harbored the same mutation, suggesting its clonal expansion in the cell population due to the growth advantage during chronic STLC treatment. Transgenic expression of KIF11S1017fs in the parental U-2OS cells, as well as in HeLa cells, conferred a moderate but reproducible STLC resistance, probably owing to STLC-resistant localization of the mutant KIF11 on mitotic spindle. Our observations indicate that both KIF15 and the C-terminal-truncated KIF11 contributes to the STLC resistance of the U-2OS derived cells.
Auditory Brain Computer Interface Using Neural Network

Brain computer interface (BCI) enables motion-free communication between humans and human and machines. In particular, BCIs using visual stimuli (Visual-BCI) have been successfully developed in the speed and accuracy. However, there exists a crucial problem that visually impaired people cannot use the Visual-BCIs. It is thus useful to develop eye-independent BCIs which exploit other modalities than vision. We focus on a BCI using amplitude modulated auditory stimuli which elicit auditory steady-state response (ASSR) as one of the eye-independent BCIs. Conventional auditory BCIs based on ASSR (ASSR- BCI) have had low performance due to nonperiodic fluctuation and small amplitude in ASSR. The present study was aimed at improving the performance of ASSR-BCI using neural network. As a result, the discrimination accuracy of the proposed ASSR-BCI was improved by 11% compared to the conventional ASSR-BCI which used a support vector machine classifier.
Gender differences in restricted and repetitive behaviors and interests in youth with autism

Previous work has found gender differences in restricted and repetitive behaviors and interests (RRBI) for autism spectrum disorder (ASD). Compared to girls, affected boys have increased stereotyped and restricted behaviors; however much less is known about gender differences in other areas of RRBI. This study aims to identify whether specific RRBI (i.e., stereotyped, self‐injurious, compulsive, insistence on sameness, ritualistic, and restricted), as measured by item‐level data on the Repetitive Behavior Scale‐Revised (RBS‐R), can distinguish girls from boys with ASD. Participants included 615 individuals with ASD (507 boys; 82.4%), ages 3–18 years of age (M = 10.26, SD = 4.20), who agreed to share data with the National Database for Autism Research (NDAR). Multivariate analysis of variance and discriminant function analysis (DFA) were used to determine whether item‐level RBS‐R data could correctly classify cases by gender. DFA results suggest that RBS‐R items significantly differentiate gender. Strongly differentiating RBS‐R items had greater success in correctly classifying affected boys (67.90%) than girls (61.00%). Items that best‐discriminated gender were heightened stereotyped behaviors and restricted interests items in boys and compulsive, sameness, restricted, and self‐injurious behavior items in girls. This study is the first to find that girls with ASD may have increased compulsive, sameness, and restricted RRBI compared to boys. Additionally, findings support heightened self‐injurious behaviors in affected girls. Future research should disentangle whether elevated rates of RRBI in girls are central to the presentation of ASD in girls or an epiphenomenon of the high rates of co‐occurring disorders (e.g., anxiety) noted in girls. Autism Res 2019, 12: 274–283 © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
Artificial Intelligence-Based Grading Quality of Bovine Blastocyst Digital Images: Direct Capture with Juxtaposed Lenses of Smartphone Camera and Stereomicroscope Ocular Lens

In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers to the three qualities of the blastocyst grading, contains a description of 24 variables that were extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classify the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40× magnification) and the experimental group (stereomicroscope with maximum of magnification plus 4× zoom from the cell phone camera). The images obtained from the control and experimental groups were uploaded on Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) for automatic quality grade classification by the three ANNs of the Blasto3Q program. Adjustments on the software program through the use of scaling algorithm software were performed to ensure the proper search and segmentation of the embryo in the raw images when they were captured by the smartphone, since this source produced small embryo images compared with those from the inverted microscope. With this new program, 77.8% of the images from smartphones were successfully segmented and from those, 85.7% were evaluated by the Blasto3Q in agreement with the control group.
An Advanced Deep Framework for Recognition of Distracted Driving Behaviors

This paper introduces a trainable deep framework called DedistractedNet for recognizing the distracted driving behaviors from an image. In contrast to other conventional strategies that use physiological sensors or on-board diagnostics, the DedistractedNet directly profiles the features of driving behaviors in the image based on the deep convolutional neural networks. Experiment results manifest that the DedistractedNet achieves superior accuracy than those of other baseline CNN methods.
Mobile Hardware-Information System for Neuro-Electrostimulation

The article describes organizational principles of the mobile hardware-informational system based on the multifactorial neuro-electrostimulation device. The system is implemented with two blocks: the first block forms the spatially distributed field of low-frequency monopolar current pulses between two multielement electrodes in the neck region. Functions of the second block, specialized control interface, are performed by a smartphone. Information is exchanged between two blocks through a telemetric channel. The mobile hardware-informational system allows to remotely change the structure of the current pulses field, to control its biotropic characteristics and to change the targets of the stimulation. Moreover, it provides patient data collection and processing, as well as access to the specialized databases. The basic circuit solutions for the neuro-electrostimulation device, implemented by means of microcontroller and elements of high-level hardware integration, are described. The prospects of artificial intelligence and machine learning application for treatment process management are discussed.
Comparison of balancing scores using the ANCOVA approach for estimating average treatment effect: a simulation study

ABSTRACT In this article, we conducted a simulation study to evaluate the performance of five balancing scores using the Analysis of Covariance (ANCOVA) approach, for adjusting bias in estimating average treatment effects (ATE) in observational studies. The five balancing scores which we used as the covariate(s) in the ANCOVA model were (1) propensity score (P), (2) prognostic score (G), (3) propensity score estimated by prognostic score (PG), (4) prognostic score estimated by propensity score (GP), and (5) both propensity and prognostic scores (P&G). The results of the five balancing scores using the ANCOVA approach were compared to the results of the classic regression approach, which included all observed covariates as the predictors. Simulation results showed that balancing scores P, GP, and (P&G) had the smallest bias and mean squared error (MSE) when the outcome variable and the observed covariates were linearly associated, and PG had the smallest or close to the smallest bias and MSE when the associations were nonlinear, nonadditive and nonlinear & nonadditive.
Parameter Estimation of Software Reliability Growth Models by A Modified Whale Optimization Algorithm

Software reliability growth models (SRGMs) are non-linear in nature, so they are difficult to estimate the proper parameters. An estimation method based on modified Whale Optimization Algorithm (MWOA) in which parameters are estimated is discussed in this paper. The proposed MWOA shows significant advantages in handling variety of modeling problems such as the exponential model (EXPM), power model (POWM) and delayed S-shaped model (DSSM). However, the fitting error of a single model is relatively large. In view of the different growth characteristics at each stage of the model, a three-stage software reliability growth model is proposed in this paper. MWOA is used to estimate the parameters of three-stage software reliability models. Experimental results show that the fitting accuracy of three-stage model is significantly less than that of a single model. MWOA with the three-stage model can provide a better estimate of the software faults.
Importance of maintaining a low omega–6/omega–3 ratio for reducing inflammation

The consumption of seed oils high in the omega-6 polyunsaturated fat (PUFA) linoleic acid (LA) contributes to low-grade inflammation, oxidative stress, endothelial dysfunction and atherosclerosis.1 Moreover, dietary LA significantly increases cyclooxygenase-2 (COX-2) expression in the aorta,2 converting arachidonic acid (AA) to proinflammatory eicosanoids. This may explain why increasing LA intake can actually lower AA levels due to an increased breakdown into harmful proinflammatory metabolites. Additionally, there is an AA-independent pathway of inflammation promoted by the intake of omega-6 seed oils such as increased production of oxidised linoleic acid metabolites (OXLAMs) and proinflammatory LA CYP-eicosanoids.3–5 OXLAMs formed from LA activate NF-kB and increase proinflammatory cytokines, endothelial adhesion molecules, as well as chemokines, all of which are paramount in the formation of atherosclerosis.5–8 LA also induces an inflammatory environment in endothelial cells that may increase the risk of coronary heart disease (CHD). 8 OXLAMs are found at a 50-fold higher concentration in plasma than AA metabolites, suggesting that they are more consequential in CHD and other chronic diseases,2 3 9 and lowering dietary LA reduces OXLAMs in the body.3 By inhibiting COXs and lipoxygenases (LOXs), marine omega-3s can reduce inflammation caused by the metabolism of AA. Indeed, compared with high-oleic sunflower oil (3.5 g/day), fish oil (3.5 g/day) reduces acute phase reactants (haptoglobin precursor, haemopexin, alpha-1-antitrypsin precursor and serum amyloid P component). The authors concluded, ‘The alterations in serum proteins… imply that fish oil activates anti-inflammatory mechanisms believed to impede the early onset of CHD’. 10 In human atherosclerotic lesions, as the atherosclerotic lesion becomes more advanced, the ratio between oxidised LA and unoxidised LA increases.11 Moreover, rats fed LA have a significant increase in tumour necrosis factor (TNF)-alpha (p<0.05) in plasma, and higher levels of vascular cell adhesion molecule-1 (VCAM-1), intracellular adhesion molecule-1 (ICAM-1) …
Energy Trading in Wireless Power Transmission System Considering Nonlinear Rectifier

In this paper, we considered energy trading within a wireless power transmission system. Considering one energy hunger sensor device and battery operator, the battery operator uses electromagnetic wave to transfer power to the sensor device. In the sensor device, a rectifier is used to convert such electromagnetic wave into DC power, and this rectifier is an important part of deciding the actual power transmission efficiency. we considered the nonlinear rectifier model and discussed the problem of optimizing the power to be transmitted. In addition, considering the price per power, we considered the game between sensor device and battery operator.
Biogenic Amines in Plant-Origin Foods: Are they Frequently Underestimated in Low-Histamine Diets?

Low-histamine diets are currently used to reduce symptoms of histamine intolerance, a disorder in histamine homeostasis that increases plasma levels, mainly due to reduced diamine-oxidase (DAO) activity. These diets exclude foods, many of them of plant origin, which patients associate with the onset of the symptomatology. This study aimed to review the existing data on histamine and other biogenic amine contents in nonfermented plant-origin foods, as well as on their origin and evolution during the storage or culinary process. The only plant-origin products with significant levels of histamine were eggplant, spinach, tomato, and avocado, each showing a great variability in content. Putrescine has been found in practically all plant-origin foods, probably due to its physiological origin. The high contents of putrescine in certain products could also be related to the triggering of the symptomatology by enzymatic competition with histamine. Additionally, high spermidine contents found in some foods should also be taken into account in these diets, because it can also be metabolized by DAO, albeit with a lower affinity. It is recommended to consume plant-origin foods that are boiled or are of maximum freshness to reduce biogenic amine intake.
Enhanced Fashion Attribute Learning Framework adapts to Attributes’ inner-group Correlations and Imbalanced Data

So as to know more useful information related to fashion and clothes such as prices or salesrooms, customers can put fashion image into a system which can help to search similar images. Hence, customers not only find out fashion items they want but also receive additional favorable information. Due to the characteristics of fashion data, it is necessary to develop a system which can understand fashion image in fine-grained attribute level. Thus, attribute learning becomes an important part in making an effective searching system. In the traditional way, each attribute will be often trained individually. However, the fashion attributes are always renewed with a large variety in categories and they have inner-group correlations. To improve the performance, we propose a deep local multi-task learning with imbalanced data problem solver framework (LMTL-IDPS framework) on given attribute groups which can exploit advantage of inner-group correlations, give out a solution for imbalanced data problem and flexible system update. To evaluate the effectiveness of this framework, we experimented on DeepFashion Attribute dataset with the pre-trained Nasnet model. The experiments show that our local multi-task learning (local MTL) framework achieves a better performance (14,2higher in recall rate) than single-task learning (STL) in fashion attribute learning. Experiments also show that our model considering imbalanced data achieved better performance (5,14% higher in recall rate for fewer data attributes) than models that do not account for this.
Myelodysplastic syndrome and acute myeloid leukemia after receipt of granulocyte colony‐stimulating factors in older patients with non‐Hodgkin lymphoma

Granulocyte colony‐stimulating factors (G‐CSFs), which are used for the prevention of complications from chemotherapy‐related neutropenia, are linked to the risk of developing second primary myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The objective of this study was to examine the correlation between using a specific G‐CSF agent and the risk of MDS/AML among older patients with non‐Hodgkin lymphoma (NHL).