Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-supervised method for sensitivity based documents’ classification for online service providers

Article history: Received 4 November 2019 Received in revised form 5 February 2020 Accepted 7 February 2020 In today’s digital era, many services providing companies exist on the web whereas service is the logical product of a company, which can be utilized through the Internet. Different service providers provide these services i.e., Online counselling service, online doctor consultation, cloud service provider, web hosting service, etc. to their customers. When customers face some problems, they may text to their providers. One solution is that providers can solve these issues based on the First-Come-First-Serve formula. But there should be an option to detect sensitive issue which may need to be solved first. How can this sensitivity be determined? Already there is a lot of researched work based on text to determine the polarity as positive and negative. Besides this classification, there are also some other classification methods investigated, such as aspect, not aspect, subjective, objective, spam, not spam, etc. regarding text sensitivity, whether it is sensitive or not? This classification is not yet considered for service providers. This paper presents a strategy for sensitivity based classification using Latent Semantic Indexing (LSI). The purpose of LSI is to rank documents concerning a given query. However, in this study, a mechanism was provided to generate query automatically based on sensitive general words with the words from all documents. This is a semi-supervised approach because 4782 sensitive words have been labeled from various sources and used based on an unsupervised approach to detect the sensitivity of the document. The sorted lists of documents based on the LSI scores generated by the sensitive-query were checked manually and were proved to be highly satisfactory. The topmost document in this list was the most sensitive, and the last document in the list was least sensitive.
A Worst-Case-Aware Design Methodology for Noise-Tolerant Oscillator-Based True Random Number Generator With Stochastic Behavior Modeling

This paper presents a worst-case-aware design methodology for an oscillator-based true random number generator (TRNG) that produces highly random bit streams even under deterministic noise. We propose a stochastic behavior model to efficiently determine design parameters, and identify a class of deterministic noise under which the randomness gets the worst. They can be used to directly estimate the worst χ value of a poker test under deterministic noise without generating bit streams, which enables efficient exploration of design space and guarantees sufficient randomness in a hostile environment. The proposed model is validated by measuring prototype TRNGs fabricated with a 65-nm CMOS process.
Genus Bifidobacterium: taxonomy studies and gene expression analysis on folate pathway

Folates (vitamin B9) are essential water soluble vitamins, whose deficiency in humans may contribute to the onset of several diseases, such as anaemia, cancer, cardiovascular diseases, neurological problems as well as defects in embryonic development. Human and other mammals are unable to synthesize ex novo folate obtaining it from exogenous sources, via intestinal absorption. Recently the gut microbiota has been identified as an important source of folates and the selection and use of folate producing microorganisms represents an innovative strategy to increase human folate levels. The aim of this thesis was to gain a fundamental understanding of folate metabolism in Bifidobacterium adolescentis. The work was subdivided in three main phases, also aimed to solve different problems encountered working with Bifidobacterium strains. First, a new identification method (based on PCR-RFLP of hsp60 gene) was specifically developed to identify Bifidobacterium strains. Secondly, Bifidobacterium adolescentis biodiversity was explored in order to recognize representing strains of this species to be screened for their folate production ability. Results showed that this species is characterized by a wide variability and support the idea that a possible new taxonomic re-organization would be required. Finally B. adolescentis folate metabolism was studied using a double approach. A quantitative analysis of folate content was complemented by the examination of expression levels of genes involved in folate related pathways. For the normalization process, required to increase the robustness of the qRT-PCR analysis, an appropriate set of reference genes was tested using two different algorithms. Results demonstrate that B.adolescentis strains may represent an endogenous source of natural folate and they could be used to fortify fermented dairy products. This bio-fortification strategy presents many advantages for the consumer, providing native folate forms more bio-available, and not implicated in the discussed controversy concerning the safety of high intake of synthetic folic acid.
Crowd Teaching with Imperfect Labels

The need for annotated labels to train machine learning models led to a surge in crowdsourcing - collecting labels from non-experts. Instead of annotating from scratch, given an imperfect labeled set, how can we leverage the label information obtained from amateur crowd workers to improve the data quality? Furthermore, is there a way to teach the amateur crowd workers using this imperfect labeled set in order to improve their labeling performance? In this paper, we aim to answer both questions via a novel interactive teaching framework, which uses visual explanations to simultaneously teach and gauge the confidence level of the crowd workers. Motivated by the huge demand for fine-grained label information in real-world applications, we start from the realistic and yet challenging assumption that neither the teacher nor the crowd workers are perfect. Then, we propose an adaptive scheme that could improve both of them through a sequence of interactions: the teacher teaches the workers using labeled data, and in return, the workers provide labels and the associated confidence level based on their own expertise. In particular, the teacher performs teaching using an empirical risk minimizer learned from an imperfect labeled set; the workers are assumed to have a forgetting behavior during learning and their learning rate depends on the interpretation difficulty of the teaching item. Furthermore, depending on the level of confidence when the workers perform labeling, we also show that the empirical risk minimizer used by the teacher is a reliable and realistic substitute of the unknown target concept by utilizing the unbiased surrogate loss. Finally, the performance of the proposed framework is demonstrated through experiments on multiple real-world image and text data sets.
Offline security for corporate mobile application

1 Белорусский государственный университет Минск, Беларусь e-mail: tan2tan@gmail.com, krasnoproshin@bsu.by 2 Федеральный университет Рио-Гранде-ду-Сул, UFRGS INF CP: 15064, 91501-970, Порто-Алегре, Бразилия, e-mail: edisonpf@gmail.com 3 Университет Бразилиа, UnB FT Эне CP: 4386, 70910-900, Бразилиа, Бразилия e-mail: robson.pesquisador@gmail.com, desousa1961@gmail.com 4 Byelex Multimedia Products BV Argon 1, 4751 XC Oud Gastel, Нидерланды e-mail: azalesky@by.byelex.com, h.vissia@byelex.com
A Framework to Leverage and Mature Learning Ecosystems

With the average shelf life of an employee’s skills at less than five years, it is im-perative that organizations support their employees in staying current in new and emerging skills and in learning how to learn. Learning management systems, once seen as a one-size-fits all learning solution, have not effectively kept pace with wider technology development, and the needs and expectations of workplace learning. Moreover, organizations tend to have too narrow a view when consider-ing the elements that affect learning at their organization. An ecological and holis-tic approach is needed to improve learning environments and to future-proof these environments for new developments in education and technology. This pa-per explores the existing literature and frameworks for learning ecosystems and proposes a new learning ecosystem framework that consists of seven key ele-ments: (1) technology and data architecture, (2) governance, (3) analytics, (4) se-mantic ePortfolios, (5) intrinsic and extrinsic motivators, (6) social learning and engagement, and (7) personalization.
Characterisation of Arabidopsis thaliana heat-responsive RNA-binding protein (HRR) gene: molecular roles in plant thermotolerance and development

Plants face a multiplicity of biotic and abiotic stresses, of which the most typical are extreme temperatures. High temperatures cause considerable morphological, physiological and molecular alterations that adversely affect plant growth and productivity. The acquisition of thermotolerance is controlled by activation and regulation of specific stress-related genes that lead to adjustments on plant transcriptome and proteome. From a previous transcriptomic analysis of heat-stressed Arabidopsis seedlings, the HRR gene was singled out by presenting a specific high heat-stress response. The principal aim of this work is the functional characterisation of this uncharacterized gene. At the end, this work is expected to contribute for the general understanding of RNA-binding proteins involvement in heat stress responses. These proteins are expected to be associated to the transcriptome organisation responsible for adaptation to heat stress, as well during plant development. The in silico analysis revealed that HRR codes for a putative RNA-binding protein, containing a RRM domain and a PABP-1234 functional domain. HRR was predicted to be highly expressed under heat stress conditions. However, HRR seems to present a basal expression throughout the plant life cycle, being prevised the highest levels during flower development, seed maturation and germination. The predicted co-interaction with other Arabidopsis RRM-containing proteins (UBP1 and RBP45 proteins) and phylogenetic relationship with metazoan orthologues suggests that HRR could play functions in the stability of HS-induced transcripts. The phenotypic analysis of hrr loss-in-function and HRR over-expression mutant lines showed that HRR seems to be strongly involved in plant thermotolerance responses, at least during seed germination. In opposition to bioinformatic data, HRR appears to be also involved in responses to salt stress imposition. Furthermore, HRR was suggested to be a positive regulator in the metabolism and signalling of phytohormone ABA. When seedlings were subjected to heat stress, the HRR expression analysis revealed that HRR transcripts are subjected to alternative splicing process, originating the canonical HRR.1 and intron-retained HRR.2 transcripts. A mRNA decay analysis suggested that HRR.2 transcripts could
Yawn Based Driver Fatigue Level Prediction

: The fatigue-related accident is increasing due to long work hours, medical reasons, and age that decrease response time in a moment of hazard. One of drowsiness and fatigue visual indicators is excessive yawning. In this paper, a non-optical sensor presented as a car dashcam that is used to record driving scenarios and imitates real-life driving situations such as being distracted or talking to a passenger next to the driver. We built a deep CNN model as the classifier to classify each frame as a yawning or non-yawning driver. We can classify the drivers' fatigue into three levels, alert, early fatigue and fatigue based on the judgement of the number of yawns. Alert level means when the driver is not yawning, while, early fatigue is when the driver yawns once in a minute. Fatigued is when the driver yawns more than once in a minute. An overall decision is made by analyzing the source score and the condition of the driver's fatigue state. The robustness of the proposed method was tested under various illumination contexts and a variety of head motion modes. Experiments are conducted using YAWDD dataset that contains 322 subjects to show that our model presents a promising framework to accurately detect drowsiness level in a less complex way.
Evolution of a Web-Scale Near Duplicate Image Detection System

Detecting near duplicate images is fundamental to the content ecosystem of photo sharing web applications. However, such a task is challenging when involving a web-scale image corpus containing billions of images. In this paper, we present an efficient system for detecting near duplicate images across 8 billion images. Our system consists of three stages: candidate generation, candidate selection, and clustering. We also demonstrate that this system can be used to greatly improve the quality of recommendations and search results across a number of real-world applications. In addition, we include the evolution of the system over the course of six years, bringing out experiences and lessons on how new systems are designed to accommodate organic content growth as well as the latest technology. Finally, we are releasing a human-labeled dataset of ~53,000 pairs of images introduced in this paper.
Software evolution visualization

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Application and Development of Virtual Reality Technology in Artificial Intelligence Deep Learning

Virtual reality technology is a key technology that can support multiple dimensional spaces. Virtual reality technology is often supported by computer graphics, digital image processing, multimedia technology, and sensor technology. Virtual reality technology is often not a single technology or discipline. With the rise of deep learning theory, virtual reality technology has also been vigorously developed with the help of computer graphics and other related theories. Virtual reality technology has been widely used in the commercial field, and products such as virtual VR glasses have become more and more popular. This article will give a more comprehensive introduction to the development of virtual reality technology, and this paper will introduce the relevant theory of deep learning to further introduce virtual reality technology.
A Multi-Feature Fusion Based Vehicle Logo Recognition Approach for Traffic Checkpoint

In this paper, we propose a vehicle logo recognition algorithm based on multifeature fusion using a hierarchical classification approach, which can be applied at traffic checkpoints. First, a typical database of vehicle logos is set up based on surveillance images recorded at traffic checkpoints. Next, three features, HOG, Curvature histograms, and GIST, are extracted and three corresponding first level classifiers are trained using the support vector machine (SVM) algorithm. The probability that a certain test sample belongs to a certain kind can be obtained by predicting the sample with each level-1 classifier. All these probabilities are then concatenated and used as features for training a second-level SVM classifier. The resultant new classifier is used for classifying the vehicle logos of the test set. The experimental results show that the proposed approach to hierarchically integrate multiple features provides excellent accuracy for the vehicle logo recognition task.
Control of Morphogenesis and Neoplasia by the Oncogenic Translation Factor eEF1A2

ii Acknowledgements . iv Table of Contents . vi List of abbreviations. ix List of Figures . xi List of Tables.xiii 1: General Introduction and literature review . 1 1.1 Breast and Ovarian Cancer. 1 1.2 Protein Translation Factors and Cancer . 2 1.3 Protein Translation . 3 1.4 Eukaryotic elongation factor 1A in protein translation . 5 1.5 Two mammalian forms of eEF1A . 8 1.6 eEF1A2 . 13 1.7 eEF1A2 and Ovarian Cancer . 14 1.8 eEF1A2 and Breast Cancer . 15 1.9 eEF1A2 and binding partners.16 1.10 Phosphatidylinositol-4 Kinase . 18 1.11 PI4KIIIβ . 26
Semi-supervised learned sinogram restoration network for low-dose CT image reconstruction

With the development of deep learning (DL), many deep learning (DL) based algorithms have been widely used in the low-dose CT imaging and achieved promising reconstruction performance. However, most DL-based algorithms need to pre-collect a large set of image pairs (low-dose/high-dose image pairs) and trains networks in a supervised end-to-end manner. Actually, it is not feasible in clinical to obtain such a large amount of paired training data, especially for high-dose ones. Therefore, in this work, we present a semi-supervised learned sinogram restoration network (SLSR-Net) for low-dose CT image reconstruction. The presented SLSR-Net consists of supervised sub-network and unsupervised sub-network. Specifically, different from the traditional supervised DL networks which only use low-dose/high-dose sinogram pairs, the presented SLSR-Net method is capable of feeding only a few supervised sinogram pairs and massive unsupervised low-dose sinograms into the network training procedure. The supervised pairs are used to capture critical features (i.e., noise distribution, and tissue characteristics) latent in a supervised way and the unsupervised sub-network efficiently learns these features using a conventional weighted least-squares model with a regularization term. Moreover, another contribution of the presented SLSR-Net method is to adaptively transfer learned feature distribution from supervised subnetwork with the paired sinograms to unsupervised sub-network with unlabeled low-dose sinograms to obtain high-fidelity sinogram with a Kullback-Leibler divergence. Finally, the filtered backprojection algorithm is used to reconstruct CT images from the obtained sinograms. Real patient datasets are used to evaluate the performance of the presented SLSR-Net method and the corresponding experimental results show that compared with the traditional supervised learning method, the presented SLSR-Net method achieves competitive performance in terms of noise reduction and structure preservation in low-dose CT imaging.
An update of the Guangzhou Twin Eye Study

The Guangzhou Twin Eye Study (GTES) is a population-based study of young twins residing in Guangzhou City. The major aim of GTES is to explore the impact of genes, environmental factors and gene-environment interactions on common eye diseases. From 2006, for more than 1,300 twin pairs, age 7–26 years old, progressive ocular phenotypes, such as refraction, ocular biometrics, weight, and height were collected annually, while non-progressive phenotypes such as parental refraction, corneal thickness, retinal fundus, intraocular pressure and DNA only collected at baseline. In the current study, we summarize the major findings on the etiology of myopia in recent decades.
Spatiotemporal modes of climatic variability: building blocks of complex networks?

The theory of complex networks offers a rich set of tools aimed at understanding various aspects of high-dimensional spatiotemporal systems [1]. Recently, methods based on complex network theory have been applied to quantities characterizing climatic variability (e.g. [9]). However, complex network methods are typically applied directly to reanalysis data, which is available on a high-resolution planetary grid. However, reanalysis data products results from data assimilation of sparse measurement by means of a global Climate Data Assimilation System [5] and are thus spatially interpolated. Strong local correlations bias computations of complex network statistics, such as the clustering coefficient [8]. Additionally, care must be taken in constructing the network structure from climatic data, which manifest spatial inhomogeneity biasing the estimates of the statistical dependence between regions [7]. Our approach is to reduce the full complexity of the dataset using a suitable method to a much smaller number of spatiotemporal components which preserve allmost all of the variance. Network characteristics are then computed and analyzed on this reduced dataset.
Characterization and efficacy of bacterial strains for biological control of soil-borne diseases caused by Phytophthora cactorum and Meloidogyne javanica on Rosaceous plants

S'avaluaren 58 soques de Pseudomonas fluorescens i Pantoea agglomerans per la seva eficacia en el biocontrol de la malaltia causada per l'oomicet Phytophthora cactorum en maduixera i pel nematode formador de gal·les Meloidogyne javanica en el portaempelt GF-677. Es desenvolupa un metode ex vivo d'inoculacio de fulla amb l'objectiu de seleccionar soques bacterianes com a agents de control biologic de P. cactorum en maduixera. Tres soques de P. fluorescens es seleccionaren com a soques eficaces en el biocontrol del patogen en fulles i en la reduccio de la malaltia en plantes de maduixera. La combinacio de soques sembla millorar la consistencia del biocontrol en comparacio amb les soques aplicades individualment. Tres soques de P. fluorescens es seleccionaren per la seva eficacia en la reduccio de la infeccio de M. javanica en portaempelts GF-677. La combinacio d'aquestes soques no incrementa l'eficacia del biocontrol, pero sembla reduir la seva variabilitat.
The next 700 data description languages

In the spirit of Landin, we present a calculus of dependent types to serve as the semantic foundation for a family of languages called data description languages. Such languages, which include pads, datascript, and packettypes, are designed to facilitate programming with ad hoc data, that is, data not in well-behaved relational or xml formats. In the calculus, each type describes the physical layout and semantic properties of a data source. In the semantics, we interpret types simultaneously as the in-memory representation of the data described and as parsers for the data source. The parsing functions are robust, automatically detecting and recording errors in the data stream without halting parsing. We show the parsers are type-correct, returning data whose type matches the simple-type interpretation of the specification. We also prove the parsers are “error-correct,” accurately reporting the number of physical and semantic errors that occur in the returned data. We use the calculus to describe the features of various data description languages, and we discuss how we have used the calculus to improve pads.
Smaller, Faster & Lighter KNN Graph Constructions

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard’s index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and show that GoldFinger can drastically accelerate a large range of existing KNN algorithms with little to no overhead. As a side effect, we also show that the compact representation of the data protects users’ privacy for free by providing k-anonymity and l-diversity. Our extensive evaluation of the resulting approach on several realistic datasets shows that our approach delivers speedups of up to 78.9% compared to the use of raw data while only incurring a negligible to moderate loss in terms of KNN quality. To convey the practical value of such a scheme, we apply it to item recommendation and show that the loss in recommendation quality is negligible.