Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovative Texture Database Collecting Approach and Feature Extraction Method based on Combination of Gray Tone Difference Matrixes, Local Binary Patterns,and K-means Clustering

Texture analysis and classification are some of the problems which have been paid much attention by image processing scientists since late 80s. If texture analysis is done accurately, it can be used in many cases such as object tracking, visual pattern recognition, and face recognition.Since now, so many methods are offered to solve this problem. Against their technical differences, all of them used same popular databases to evaluate their performance such asBrodatz or Outex, which may be made their performance biased on these databases. In this paper, an approach is proposed to collect more efficient databases of texture images. The proposed approach is included two stages. The first one is developing feature representation based on gray tone difference matrixes and local binary patterns features and the next one is consisted an innovative algorithm which is based on K-means clustering to collect images based on evaluated features. In order to evaluate the performance of the proposed approach, a texture database is collected and fisher rate is computed for collected one and well known databases. Also, texture classification is evaluated based on offered feature extraction and the accuracy is compared by some state of the art texture classification methods.
Dynamic automated synovial imaging (DASI) for differential diagnosis of rheumatoid arthritis

Inflammatory rheumatic diseases are leading causes of disability and constitute a frequent medical disorder, leading to inability to work, high comorbidity and increased mortality. The gold-standard for diagnosing and differentiating arthritis is based on patient conditions and radiographic findings, as joint erosions or decalcification. However, early signs of arthritis are joint effusion, hypervascularization and synovial hypertrophy. In particular, vascularization has been shown to correlate with arthritis’ destructive behavior, more than clinical assessment. Contrast Enhanced Ultrasound (CEUS) examination of the small joints is emerging as a sensitive tool for assessing vascularization and disease activity. The evaluation of perfusion pattern rely on subjective semiquantitative scales, that are able to capture the macroscopic degree of vascularization, but are unable to detect the subtler differences in kinetics perfusion parameters that might lead to a deeper understanding of disease progression and a better management of patients. We show that after a kinetic analysis of contrast agent appearance, providing the quantitative features characterizing the perfusion pattern of the joint, it is possible to accurately discriminate RA from PSA by building a random forest classifier on the computed features. We compare its accuracy with the assessment performed by expert radiologist blinded of the diagnosis.
A Personalized Hybrid Tourism Recommender System

This paper focuses on building personalized recommender system in the tourism field. The application recommends to a tourist the best attractions in a particular place according to his preferences, his profile and his appreciation to previous visited places. This paper proposes a hybrid recommender system that combines the three most known recommender methods which are: the collaborative filtering (CF), the content-based filtering (CB) and the demographic filtering (DF). In order to implement these recommender methods, we have applied different machine learning algorithms which are the K-nearest neighbors (K-NN) for both CB and CF and the decision tree for the DF. The hybridization is a good choice to make the best of their advantages and to overcome the cold start problem. To enhance the recommendation accuracy, we use two hybridization techniques: switching and weighted. For the weighted approach, a novel linear programming model is applied to obtain the optimal weights' values. An extensive experimental study is conducted based on different evaluation metrics using extracted data from TripAdvisor. Our results show that the hybrid method is more accurate than the other recommender approaches used separately.
Adaptive Video Transmission Control System Based on Reinforcement Learning Approach Over Heterogeneous Networks

Video may pass through various types of heterogeneous networks during the process of transmission, which has adverse impacts on the real-time video quality. Traditional methods focus on how to compress videos based on the video flow without considering the real-time network information. This paper presents an adaptive method that combines video encoding and the video transmission control system over heterogeneous networks. This method includes the following steps: first, to collect and standardize the real-time information describing the network and the video, then to assess the video quality and calculate the video coding rate based on the standardized information, and then to process the encoded compression of the video according to the calculated coding rate and transfer the compressed video. The experiments show that there is a significant improvement for the quality of real-time videos transmission without changing the existing network, particularly the core equipment. Our solution is easy to deploy and implement quickly and may help to extensively ensure video quality for normal users.
Internet of Things data analytics for user authentication and activity recognition

In recent years Internet of Things has been among the hot topics of research in the field of computing and information technology. It has enabled thousands of connected devices including sensors, cell phones and daily home appliances to store data for various useful purposes. The amount of data generated by IoT devices is huge, and there is a need to analyze IoT data for prospective uses. Heterogeneity and the structure of IoT data make it a challenging task. This paper presents a frame work for IoT data analytics for creating models for user identification, user authentication and activity recognition. Focus of this study is on the data set of accelerometer sensor from internet of things perspectives to add an extra layer of security. The novelty of our approach lies in the customization of the data set, and the experiments performed for the construction of models for each individual activity and user. The dataset is collected from 19 different subjects of real world conditions performing basic activities, i.e. walking, sitting and standing. This data set is used for the authentication process without requiring any additional information. In existing studies, the results are obtained using more than one accelerometer sensor reading or a combination of gyroscope sensor and accelerometer sensor. Whereas we have used single sensing tri-axial sensor reading for activity recognition and user authentication models. These models are later verified by real time data sets which were not used in the training process. The results of the experiments show accuracy up to 93%. The results obtained by the experiments are also helpful for future research directions in the field of IoT data analytics, activity recognition and user authentication. By enhancing the accuracy and adding context aware aspects in the authentication models can lead to the significant advances in the biometric authentication process using IoT data.
Gender Differences in Myogenic Regulation along the Vascular Tree of the Gerbil Cochlea

Regulation of cochlear blood flow is critical for hearing due to its exquisite sensitivity to ischemia and oxidative stress. Many forms of hearing loss such as sensorineural hearing loss and presbyacusis may involve or be aggravated by blood flow disorders. Animal experiments and clinical outcomes further suggest that there is a gender preference in hearing loss, with males being more susceptible. Autoregulation of cochlear blood flow has been demonstrated in some animal models in vivo, suggesting that similar to the brain, blood vessels supplying the cochlea have the ability to control flow within normal limits, despite variations in systemic blood pressure. Here, we investigated myogenic regulation in the cochlear blood supply of the Mongolian gerbil, a widely used animal model in hearing research. The cochlear blood supply originates at the basilar artery, followed by the anterior inferior cerebellar artery, and inside the inner ear, by the spiral modiolar artery and the radiating arterioles that supply the capillary beds of the spiral ligament and stria vascularis. Arteries from male and female gerbils were isolated and pressurized using a concentric pipette system. Diameter changes in response to increasing luminal pressures were recorded by laser scanning microscopy. Our results show that cochlear vessels from male and female gerbils exhibit myogenic regulation but with important differences. Whereas in male gerbils, both spiral modiolar arteries and radiating arterioles exhibited pressure-dependent tone, in females, only radiating arterioles had this property. Male spiral modiolar arteries responded more to L-NNA than female spiral modiolar arteries, suggesting that NO-dependent mechanisms play a bigger role in the myogenic regulation of male than female gerbil cochlear vessels.
Structured beamforming designs for spectral efficiency and energy efficiency in a three-node amplify-and-forward relay network

The amplify-and-forward (AF) relay system with consideration of the weak direct link is studied. As different data streams are transmitted by the source, there is interference between the different data links. In order to decode the data reliably, the source beamforming for the direct link is designed to make the interference of the source–destination link as low as possible. In the first stage, in order to avoid the interference being amplified by the relay, the source beamforming for the relay link is designed to cancel the inter-link interference. In the second stage, beamforming of the relay is designed to maximise the spectral efficiency (SE). Two types of source beamformings are proposed by combining the null space vectors of useful signal and by combining the orthogonal subspace vectors of the interference signal, respectively. The obtained beamforming structures are not only used for the SE optimisation problem, but also the energy efficiency (EE) optimisation problem. Simulation results show that the proposed beamforming design algorithms can obtain higher SE or higher EE compared with existing methods.
Towards a Multi-array Architecture for Accelerating Large-scale Matrix Multiplication on FPGAs

Large-scale floating-point matrix multiplication is a fundamental kernel in many scientific and engineering applications. Most existing work only focus on accelerating matrix multiplication on FPGA by adopting a linear systolic array. This paper towards the extension of this architecture by proposing a scalable and highly configurable multi-array architecture. In addition, we propose a work-stealing scheme to ensure the equality in the workload partition among multiple linear arrays. Furthermore, an analytical model is developed to determine the optimal design parameters. Experiments on a real-life convolutional neural network (CNN) show that we can obtain the optimal extension of the linear array architecture.
Synthesis and design of a fully integrated multi-topology switched capacitor DC-DC converter with gearbox control

This paper discusses a methodology of minimizing the amount of switches in a multi-topology fully integrated switched capacitor dc-dc converter powered by a super capacitor for energy harvesting purposes. The design of a simple controlling circuit for the multi-topology power stage using a gearbox approach is presented with all the required circuits. The converter is able to generate a output voltage of 1.2 V from a 470 mF capacitor charged to 3 V down to 1.4 V. The output voltage is regulated with a ripple voltage below 7 mV. The controlling circuit including buffers with ideal comparators has a power consumption of 129 μW, the average efficiency is 67% and the peak efficiency of the converter is 81%.
Reproductive Rates in Australian Rodents Are Related to Phylogeny

Background The native rodents of Australia are commonly divided into two groups based on the time of their colonization of the Sahulian continent, which encompasses Australia, New Guinea, and the adjacent islands. The first group, the “old endemics,” is a diverse assemblage of 34 genera that are descended from a single colonization of the continent during the Pliocene. A second group, the “new endemics,” is composed of several native Rattus species that are descended from a single colonization during the Pleistocene. Finally, a third group is composed of three non-native species of Rattus and Mus introduced into Australia by humans over the last 200 years. Previous studies have claimed that the three groups differ in their reproductive rates and that this variation in rates is associated with the unique environmental conditions across Australia. We examined these hypotheses using phylogenetically controlled methods. Methodology and Results We examined the relationship between the reproductive rates of the Australian rodents and the environmental variations across the continent, as well as the epoch of their colonization of the continent. Our results revealed no significant correlation with environmental variables but a significant association between colonization age and all the reproductive parameters examined. Discussion Based on a larger phylogeny of the subfamily Murinae, we showed that significant differences in reproductive rates among colonization groups are shared with their closest relatives outside Sahul. Therefore, the lower reproductive rates in the old endemics are more likely to be the result of phylogenetic history and conservation of traits than an adaptation to the Australian environment. In the new endemics, we found a trend of increasing reproductive rates with diversification. We suggest that the differences in reproductive rates of the old endemic rodents and the native Rattus represent alternative adaptive strategies that have allowed them to utilize similar ecological niches across Australia.
Avenues to molecular imaging of dying cells: Focus on cancer

Successful treatment of cancer patients requires balancing of the dose, timing, and type of therapeutic regimen. Detection of increased cell death may serve as a predictor of the eventual therapeutic success. Imaging of cell death may thus lead to early identification of treatment responders and nonresponders, and to “patient‐tailored therapy. ” Cell death in organs and tissues of the human body can be visualized, using positron emission tomography or single‐photon emission computed tomography, although unsolved problems remain concerning target selection, tracer pharmacokinetics, target‐to‐nontarget ratio, and spatial and temporal resolution of the scans. Phosphatidylserine exposure by dying cells has been the most extensively studied imaging target. However, visualization of this process with radiolabeled Annexin A5 has not become routine in the clinical setting. Classification of death modes is no longer based only on cell morphology but also on biochemistry, and apoptosis is no longer found to be the preponderant mechanism of cell death after antitumor therapy, as was earlier believed. These conceptual changes have affected radiochemical efforts. Novel probes targeting changes in membrane permeability, cytoplasmic pH, mitochondrial membrane potential, or caspase activation have recently been explored. In this review, we discuss molecular changes in tumors which can be targeted to visualize cell death and we propose promising biomarkers for future exploration.
Estimation and prediction of HAZ softening in thermomechanically controlled-rolled and accelerated-cooled steel

In order to evaluate the degree of HAZ softening in fine-grained ferritic-pearlitic steel manufactured by TMCP, variations of HAZ microhardness and tensile strength with different heat inputs were investigated. The degree of softening was also predicted using an established microstructural evolutional model and a rule of mixtures. All specimens welded with higher than 1 kJ/mm heat input showed a softened zone that had a lower hardness than that of the base metal. Even though softened zone width increased continuously to 10 kJ/mm, the minimum hardness in a softened zone decreased slightly after a rapid decrease of up to 6 kJ/mm. Due to the softening effect, welded-joint tensile specimens were broken at the HAZ instead of the base metal. The reduction in tensile strength was similar to that for hardness and showed a maximum of 20% at 6 kJ/mm heat input. The degree of HAZ softening calculated with a modified microstructural evolution model and a rule of mixtures showed reasonably good agreement with a measured one.
(Invited) Current and Future Applications of ALD in Micro-Electronics

This paper describes the status of current and future applications of Atomic Layer Deposition (ALD) and Plasma Enhanced ALD (PEALD) in the field of Micro-electronics. Substantial expansion of the ALD market is expected in the coming decade, both in IC manufacturing, but also in adjacent non-IC applications. Several techniques will be described that work around the relatively slow deposition rate of ALD.
Static analysis of JavaScript libraries in a scalable and precise way using loop sensitivity

Statically analyzing JavaScript applications often requires an analysis of JavaScript libraries because many JavaScript applications use libraries. However, static analysis techniques for JavaScript are not yet ready for analyzing libraries in a scalable and precise manner. Simply loading JavaScript libraries uses various dynamic features of JavaScript, which cause static analyzers to suffer from mutually intermingled problems of scalability and imprecision. In this paper, we present a loop‐sensitive analysis (LSA) technique, which can improve the analysis scalability when analyzing JavaScript libraries by enhancing the analysis precision of loops. The LSA technique distinguishes loop iterations when loop conditions can be determined to be either true or false precisely. We formalize LSA in the abstract interpretation framework in the presence of tricky language features such as exceptions and prove its soundness and precision theorems using Coq. We evaluate our LSA implementation with the analysis results of programs that use 5 JavaScript libraries and show that LSA significantly improves the analysis scalability and precision of an existing JavaScript static analyzer when analyzing JavaScript libraries. In addition, using the configurability of LSA, we experimentally show the correlation between scalability and precision in the analysis of JavaScript libraries. We found that even the analysis of simple programs that just load jQuery, which is the most popular JavaScript library, in a scalable way requires distinguishing not only the last 4 functions being called but also 40 iterations in each loop with 2‐level nested loops at least. Both the mechanization and implementation of LSA are publicly available.
Microbicides Alter the Expression and Function of RND-Type Efflux Pump AdeABC in Biofilm-Associated Cells of Acinetobacter baumannii Clinical Isolates

ABSTRACT Acinetobacter baumannii is a Gram-negative bacterium that causes nosocomial infections worldwide. This microbe's propensity to form biofilms allows it to persist and to survive on clinical abiotic surfaces for long periods. In fact, A. baumannii biofilm formation and its multidrug-resistant nature severely compromise our capacity to care for patients in hospital environments. In contrast, microbicides such as cetrimide (CT) and chlorhexidine (CHX) play important roles in the prevention and treatment of infections. We assessed the efficacy of CT and CHX, either alone or in combination, in eradicating A. baumannii biofilms formed by clinical isolates, by using stainless steel washers to mimic hard abiotic surfaces found in hospital settings. We demonstrated that increasing amounts of each microbicide, alone or in combination, were able to damage and to reduce the viability of A. baumannii biofilms efficaciously. Interestingly, the adeB gene of the resistance-nodulation-cell division (RND) family is predominantly associated with acquired resistance to antimicrobials in A. baumannii. We showed that CT and CHX adversely modified the expression and function of the RND-type efflux pump AdeABC in biofilm-associated A. baumannii cells. Furthermore, we established that these microbicides decreased the negative charges on A. baumannii cell membranes, causing dysregulation of the efflux pump and leading to cell death. Our findings suggest that CT and CHX, alone or in combination, can be used efficaciously for eradication of A. baumannii from hospital surfaces, in order to reduce infections caused by this nosocomial agent.
PHYSICAL AGING IN Bi5Se95 CHALCOGENIDE GLASS: EFFECT OF ANNEALING TIME AND TEMPERATURE

Chalcogenide glasses are the target of extensive studies in recent years as they are promising materials for many applications. Due to their remarkable structural, thermal and optical properties they are widely used in infrared optics, xerography, photonics, and optical recording. Kinematical studies give important conclusions for the suitable usage of a chalcogenide glass in the proper application field [1-5]. However, because it is a characteristic feature of nonequilibrium glassy state, many physical properties in glasses change with time. Extensive research was devoted to this physical aging phenomenon because of its profound importance on commercial applications of glassy materials [6]. The kinetics of structural relaxation associated with physical aging in glassy materials is characterized by nonlinear and nonexponential behavior [7-10]. The nonexponential behavior is a consequence of the distribution of relaxation times. The most widely used equation for structural relaxation in glasses is the Kohlrausch-Williams-Watts (KWW) response function:
An EEG-based brain—computer interface with real-time artifact removal using independent component analysis

This paper presents a steady-state visually evoked potential (SSEVP) EEG-based brain-computer interface (BCI) with real-time artifact removal using independent component analysis (ICA). This system comprises an SSEVP-based stimulator, a four-channel electroencephalogram (EEG) front-end module, an ICA-based artifact removal module, and a canonical correlation analysis (CCA) detection module. The proposed SSEVP-based BCI system with artifact removal module is implemented and verified using ARM-based system on chip platform. The test patterns of 9Hz and 10Hz SSEVP raw data with a 2-second window, advancing with a 1-sec interval, are applied to this system, and the improvements of the hit rate are achieved approximately 53% and 48%, respectively.