Miracle-dz/UniFAR-FTUs huggingface dataset:
Face recognition based on multi features extractors

Face recognition is the core application in the biometrie technology area. It is widely used in the advanced application of robotics and computer vision. Raising commercial of face recognition and low enforcement makes a request of it increases in the last decade. In this paper, we present state of the art of in face recognition technologies by focusing on some traditional issues and some techniques to treats these problems. The advantage of suggested algorithm is to solve the popular issues in face recognition such as light conditions and environmental factors that lead to the low-performance. In proposed algorithm, groups of edge detection filters (Sobel, Prewitt, and Roberts) were used to extract edges of the faces in images. First derivative edge detection filters were performed to get best features of data set. moreover, using edge detection process that used by first order filter is to reduce data as much as possible by removing image background. The new method used as feature extractor addition to traditional PCA. Gathering features by using slope method and PCA is to find the optimal faces vectors as the inputs to the classifier (NNMLP neural network). Results have revealed acceptable correct classification. As data test set we used BIO-ID data base in the proposed system.
Ontology research and development. Part 1 - a review of ontology generation

Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next wave of web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This survey is presented in two parts. The first part reviews the state-of-the-art techniques and work done on semi-automatic and automatic ontology generation, as well as the problems facing such research. The second complementary survey is dedicated to ontology mapping and ontology ‘evolving’. Through this survey, we have identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data. However, relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impediment to ontology learning and applicability. Further research is encouraged to find appropriate and efficient ways to detect or identify relations through semi-automatic and automatic means.
Graph signals classification using total variation and graph energy informations

In this work, we consider the problem of graph signals classification. We investigate the relevance of two attributes, namely the total variation (TV) and the graph energy (GE) for graph signals classification. The TV is a compact and informative attribute for efficient graph discrimination. The GE information is used to quantify the complexity of the graph structure which is a pertinent information. Based on these two attributes, three similarity measures are introduced. Key of these measures is their low complexity. The effectiveness of these similarity measures are illustrated on five data sets and the results compared to those of five kernel-based methods of the literature. We report results on computation runtime and classification accuracy on graph benchmark data sets. The obtained results confirm the effectiveness of the proposed methods in terms of CPU runtime and of classification accuracy. These findings also show the potential of TV and GE informations for graph signals classification.
3D technologies for analog/RF applications

In this work, we will review possible technology options for next generation wireless communication. Next to the introduction of specific device architectures and materials, dissimilar from standard Si CMOS, the challenge will lie in the co-integration of these non-Si technologies with CMOS to enable power efficient systems with high performance, in this case high speed and output power, and reduced form factor. Next to monolithic integration, sequential 3D, currently been investigated for LOGIC density scaling, can be one of the enablers, allowing to combine technologies with very different needs at a finer grain and thus higher density than traditional 3D-SOC and 3D-IC technologies.
Random Walker Framework for Sensor-Based Echocardiography Fusion

Image fusion techniques in 3-D echocardiography attempt to improve the field-of-view by combining multiple 3-D ultrasound (3-DUS) volumes. Echocardiography fusion techniques are mostly based on either image registration or sensor tracking. Compared to registration techniques, sensor tracking approaches are image independent and do not need any spatial overlap between the images. Once the images are spatially aligned the pixel intensities in the overlapping regions are determined using fusion algorithms such as average fusion (AVG) and maximum fusion (MAX). However, averaging generally results in reduced contrast while maximizing results in amplification of noise artifacts in the fused image. Wavelet fusion (WAV) overcomes these issues by selectively enhancing the low-frequency components in the image, but this could result in pixelation artifacts. We propose a new method for image fusion based on a generalized random walker framework (GRW) using ultrasound confidence maps. The maps are based on: 1) focal properties of the transducer and 2) second order image features. The fusion technique was validated on image pairs sampled from 3-DUS volumes acquired from six healthy volunteers. All the images were spatially aligned using optical tracking, and the fusion algorithm was used to determine the pixel intensity in the overlapping region. Comparisons based on quantitative measures showed statistically significant improvements for GRW (p < 0:01) when compared to AVG, MAX, and WAV for contrast-to-noise ratio: 0.85 ± 0.03, signal-to-noise ratio: 7.42 ± 1.98, Wang–Bovik metric (Q0): 0.80 ± 0.15. The Piella metric (Q1): 0.82 ± 0.01 also gave higher values for GRW, but the difference was not statistically significant. Upon visual inspection, the GRW fusion had the lowest amount of stitching and pixelation artifacts. The fusion technique proposed could help in improving the diagnostic accuracy and clinical acceptance of 3-D echocardiography.
NLOS and LOS of the 28 GHz bands millimeter-wave in 5G cellular networks

5G cellular systems tend to operate at the centimeter-wave frequency band (3–30 GHz) or millimeter-wave frequency band (30 to 100 GHz). Line of Sight (LOS) is a direct line between the radio transmitter and receiver and many types of radio transmissions depend on it. Unfortunately, for arbitrary transmission networks, obstacles are commonly caused by non-line-of-sight (NLOS) conditions. This paper presents a comparison study of LOS and NLOS performance in the 28 GHz band for millimeter-Wave wireless networks, where a vast amount of underutilized bandwidth exists world-wide. This paper discusses the radio propagation mechanisms that impacts the performance of the network in the form of time delays, received power, azimut AoD, Evevation AoD, Azimut AoA, Elevation AoA, path-lost and RMS delay in LOS and NLOS environments.
Phylogeny in Aid of the Present and Novel Microbial Lineages: Diversity in Bacillus

Bacillus represents microbes of high economic, medical and biodefense importance. Bacillus strain identification based on 16S rRNA sequence analyses is invariably limited to species level. Secondly, certain discrepancies exist in the segregation of Bacillus subtilis strains. In the RDP/NCBI databases, out of a total of 2611 individual 16S rDNA sequences belonging to the 175 different species of the genus Bacillus, only 1586 have been identified up to species level. 16S rRNA sequences of Bacillus anthracis (153 strains), B. cereus (211 strains), B. thuringiensis (108 strains), B. subtilis (271 strains), B. licheniformis (131 strains), B. pumilus (83 strains), B. megaterium (47 strains), B. sphaericus (42 strains), B. clausii (39 strains) and B. halodurans (36 strains) were considered for generating species-specific framework and probes as tools for their rapid identification. Phylogenetic segregation of 1121, 16S rDNA sequences of 10 different Bacillus species in to 89 clusters enabled us to develop a phylogenetic frame work of 34 representative sequences. Using this phylogenetic framework, 305 out of 1025, 16S rDNA sequences presently classified as Bacillus sp. could be identified up to species level. This identification was supported by 20 to 30 nucleotides long signature sequences and in silico restriction enzyme analysis specific to the 10 Bacillus species. This integrated approach resulted in identifying around 30% of Bacillus sp. up to species level and revealed that B. subtilis strains can be segregated into two phylogenetically distinct groups, such that one of them may be renamed.
Genetic Variation at Nuclear Loci Fails to Distinguish Two Morphologically Distinct Species of Aquilegia

Aquilegia formosa and pubescens are two closely related species belonging to the columbine genus. Despite their morphological and ecological differences, previous studies have revealed a large degree of intercompatibility, as well as little sequence divergence between these two taxa [1], [2]. We compared the inter- and intraspecific patterns of variation for 9 nuclear loci, and found that the two species were practically indistinguishable at the level of DNA sequence polymorphism, indicating either very recent speciation or continued gene flow. As a comparison, we also analyzed variation at two loci across 30 other Aquilegia taxa; this revealed slightly more differentiation among taxa, which seemed best explained by geographic distance. By contrast, we found no evidence for isolation by distance on a more local geographic scale. We conclude that the extremely low levels of genetic differentiation between A. formosa and A.pubescens at neutral loci will facilitate future genome-wide scans for speciation genes.
Whole Execution Traces and Their Use in Debugging

Profiling techniques have greatly advanced in recent years. Extensive amounts of dynamic information can be collected (e.g., control flow, address and data values, data, and control dependences), and sophisticated dynamic analysis techniques can be employed to assist in improving the performance and reliability of software. In this chapter we describe a novel representation called whole execution traces that can hold a vast amount of dynamic information in a form that provides easy access to this information during dynamic analysis. We demonstrate the use of this representation in locating faulty code in programs through dynamic-slicing- and dynamic-matching-based analysis of dynamic information generated by failing runs of faulty programs.
Membranes, energetics, and evolution across the prokaryote-eukaryote divide

The evolution of the eukaryotic cell marked a profound moment in Earth’s history, with most of the visible biota coming to rely on intracellular membrane-bound organelles. It has been suggested that this evolutionary transition was critically dependent on the movement of ATP synthesis from the cell surface to mitochondrial membranes and the resultant boost to the energetic capacity of eukaryotic cells. However, contrary to this hypothesis, numerous lines of evidence suggest that eukaryotes are no more bioenergetically efficient than prokaryotes. Thus, although the origin of the mitochondrion was a key event in evolutionary history, there is no reason to think membrane bioenergetics played a direct, causal role in the transition from prokaryotes to eukaryotes and the subsequent explosive diversification of cellular and organismal complexity.
Transcriptional profiling of articular cartilage in a porcine model of early post‐traumatic osteoarthritis

To identify the molecular pathophysiology present in early post‐traumatic osteoarthritis (PTOA), the transcriptional profile of articular cartilage and its response to surgical PTOA induction were determined. Thirty six Yucatan minipigs underwent anterior cruciate ligament (ACL) transection and were randomly assigned in equal numbers to no further treatment, reconstruction or ligament repair. Cartilage was harvested at 1 and 4 weeks post‐operatively and histology and RNA‐sequencing were performed and compared to controls. Microscopic cartilage scores significantly worsened at 1 (p = 0.028) and 4 weeks (p = 0.001) post‐surgery relative to controls, but did not differ between untreated, reconstruction or repair groups. Gene expression after ACL reconstruction and ACL transection were similar, with only 0.03% (including SERPINB7 and CR2) and 0.2% of transcripts (including INHBA) differentially expressed at 1 and 4 weeks respectively. COL2A1, COMP, SPARC, CHAD, and EF1ALPHA were the most highly expressed non ribosomal, non mitochondrial genes in the controls and remained abundant after surgery. A total of 1,275 genes were differentially expressed between 1 and 4 weeks post‐surgery. With the treatment groups pooled, 682 genes were differentially expressed at both time‐points, with the most significant changes observed in MMP1, COCH, POSTN, CYTL1, and PTGFR. This study confirmed the development of a microscopic PTOA stage after ACL surgery in the porcine model. Upregulation of multiple proteases (including MMP1 and ADAMTS4) were found; however, the level of expression remained orders of magnitude below that of extracellular matrix protein‐coding genes (including COL2A1 and ACAN). In summary, genes with established roles in PTOA as well as novel targets for specific intervention were identified. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:318–329, 2018.
A Software Framework for Remote Patient Monitoring by Using Multi-Agent Systems Support

Background Although there have been significant advances in network, hardware, and software technologies, the health care environment has not taken advantage of these developments to solve many of its inherent problems. Research activities in these 3 areas make it possible to apply advanced technologies to address many of these issues such as real-time monitoring of a large number of patients, particularly where a timely response is critical. Objective The objective of this research was to design and develop innovative technological solutions to offer a more proactive and reliable medical care environment. The short-term and primary goal was to construct IoT4Health, a flexible software framework to generate a range of Internet of things (IoT) applications, containing components such as multi-agent systems that are designed to perform Remote Patient Monitoring (RPM) activities autonomously. An investigation into its full potential to conduct such patient monitoring activities in a more proactive way is an expected future step. Methods A framework methodology was selected to evaluate whether the RPM domain had the potential to generate customized applications that could achieve the stated goal of being responsive and flexible within the RPM domain. As a proof of concept of the software framework’s flexibility, 3 applications were developed with different implementations for each framework hot spot to demonstrate potential. Agents4Health was selected to illustrate the instantiation process and IoT4Health’s operation. To develop more concrete indicators of the responsiveness of the simulated care environment, an experiment was conducted while Agents4Health was operating, to measure the number of delays incurred in monitoring the tasks performed by agents. Results IoT4Health’s construction can be highlighted as our contribution to the development of eHealth solutions. As a software framework, IoT4Health offers extensibility points for the generation of applications. Applications can extend the framework in the following ways: identification, collection, storage, recovery, visualization, monitoring, anomalies detection, resource notification, and dynamic reconfiguration. Based on other outcomes involving observation of the resulting applications, it was noted that its design contributed toward more proactive patient monitoring. Through these experimental systems, anomalies were detected in real time, with agents sending notifications instantly to the health providers. Conclusions We conclude that the cost-benefit of the construction of a more generic and complex system instead of a custom-made software system demonstrated the worth of the approach, making it possible to generate applications in this domain in a more timely fashion.
Adverse weather simulation for training neural networks

Convolutional neural networks generally require considerable amount of data for training to perform adequately well in all real-world scenarios. Many times, the data for all scenarios is hard to collect and ground truth annotation is also a challenge. Similar problem exists in training networks for the autonomous vehicles given the diverse weather conditions where these cars are expected to be driven. Thus, a synthetic data generation model is imperative and we go about building a weather simulation framework. This framework is intended to generate weather conditions over different driving scenarios. To start with, we go about implementing a completely configurable rain/fog/windshield simulation model. The scope of this framework, however is much more than these three models. Apart from refining these models further as and when need, we intend to build in mechanisms to simulate more diverse weather conditions within this framework. There are multiple challenges in the implementation of these models. To begin with, we need a mechanism to simulate diverse weather conditions in a driving environment. One method could be to simulate the entire 3D environment, with the roads, automobiles, and an artificial world, but this approach would be extremely challenging both in terms of the realism that can be achieved, and in terms of the time it would take for the implementation. Another method is to overlay the rain/fog effect on top of pre-rendered videos. This 2D overlaying technique is a practical solution, since there exist many driving videos at our disposal. In this paper, we outline methods to implement this effectively, and showcase the results obtained in training a Neural Network with this approach.
Mobile Health in Oncology: A Patient Survey About App-Assisted Cancer Care

Background In the last decade, the health care sector has been enriched by numerous innovations such as apps and connected devices that assist users in weight reduction and diabetes management. However, only a few native apps in the oncological context exist, which support patients during treatment and aftercare. Objective The objective of this study was to analyze patients’ acceptance regarding app use and to investigate the functions of an oncological app that are most required, and the primary reasons for patients to refuse app-assisted cancer care. Methods We designed and conducted a survey with 23 questions, inquiring patients about their technical knowledge and equipment, as well as the possible advantages and disadvantages, data transfer, and general functionality of an app. Results A total of 375 patients participated; the participation rate was 60.7% (375/618). Gender distribution was about 3:4 (female:male) with a median age of 59 years (range 18-92 years). Whereas 69.6% (261/375) of patients used mobile devices, 16.3% (61/375) did not own one, and 9.1% (34/375) only used a personal computer (PC). About half of the patients rated their usability skills as very good and good (18.9% 71/375; 35.2% 132/375), 23.5% (88/375) described their skills as intermediate, and 14.4% (54/375) as bad. Of all patients, 182 (48.5%, 182/375) were willing to send data to their treating clinic via an app, that is, to a server (61.0% 111/182) or as email (33.5%, 61/182). About two-thirds (68.7%, 125/182) believed that additional and regularly sent data would be an ideal complement to the standard follow-up procedure. Additionally, 86.8% (158/182) wished to be contacted by a physician when entered data showed irregularities. Because of lack of skills (34.4%, 56/163), concerns about the use of data (35.0%, 57/163), lack of capable devices (25.8%, 42/163), and the wish for personal contact with the treating physician (47.2%, 77/163), a total of 163 (43.5%, 163/375) patients refused to use an app. Pearson correlation showed a significant but mild relationship between age and app use (P=.03, r=−.12), favoring younger age; male gender correlated as well (P=.04; r=−.11). Conclusions The results show that the introduction of mobile apps needs to follow different strategies depending on the patients’ attitude. Age and gender seem to be the strongest predictive factors. For oncology patients, our survey showed that about half of the patients were willing to send data via an app supporting their treatment. In the future, clinical data such as quality of life and treatment satisfaction recorded by mobile health (mHealth) devices could be used to evaluate and improve therapy workflow. Furthermore, apps could support classical visits, document adverse effects, and remind patients of treatment dates or drug intake.
Exploring the Measurement Properties of the eHealth Literacy Scale (eHEALS) Among Baby Boomers: A Multinational Test of Measurement Invariance

Background The eHealth Literacy Scale (eHEALS) is one of only a few available measurement scales to assess eHealth literacy. Perhaps due to the relative paucity of such measures and the rising importance of eHealth literacy, the eHEALS is increasingly a choice for inclusion in a range of studies across different groups, cultures, and nations. However, despite its growing popularity, questions have been raised over its theoretical foundations, and the factorial validity and multigroup measurement properties of the scale are yet to be investigated fully. Objective The objective of our study was to examine the factorial validity and measurement invariance of the eHEALS among baby boomers (born between 1946 and 1964) in the United States, United Kingdom, and New Zealand who had used the Internet to search for health information in the last 6 months. Methods Online questionnaires collected data from a random sample of baby boomers from the 3 countries of interest. The theoretical underpinning to eHEALS comprises social cognitive theory and self-efficacy theory. Close scrutiny of eHEALS with analysis of these theories suggests a 3-factor structure to be worth investigating, which has never before been explored. Structural equation modeling tested a 3-factor structure based on the theoretical underpinning to eHEALS and investigated multinational measurement invariance of the eHEALS. Results We collected responses (N=996) to the questionnaires using random samples from the 3 countries. Results suggest that the eHEALS comprises a 3-factor structure with a measurement model that falls within all relevant fit indices (root mean square error of approximation, RMSEA=.041, comparative fit index, CFI=.986). Additionally, the scale demonstrates metric invariance (RMSEA=.040, CFI=.984, ΔCFI=.002) and even scalar invariance (RMSEA=.042, CFI=.978, ΔCFI=.008). Conclusions To our knowledge, this is the first study to demonstrate multigroup factorial equivalence of the eHEALS, and did so based on data from 3 diverse nations and random samples drawn from an increasingly important cohort. The results give increased confidence to researchers using the scale in a range of eHealth assessment applications from primary care to health promotions.