Miracle-dz/UniFAR-FTUs huggingface dataset:
Hemostatic alterations in patients with obstructive sleep apnea: an observational study

Background: Obstructive sleep apnoea (OSA) is characterized by repetitive partial or complete collapse of the upper airway during sleep, which results in disruptions of normal sleep architecture. It is associated with cardiopulmonary consequences like hypertension, myocardial infarction and stroke. Although the pathogenesis of this association remains unclear, an alteration in coagulability is suspected as a linkage. Hence, the present study aims at the reliability of Bleeding time, platelet count, PT, aPTT and INR to assess the effect on OSA patients’ cardiovascular system.Methods: This is an observational study done on 32 individuals diagnosed with OSA after level I polysomnography from time period of January 01, 2018 to December 31, 2018. The blood coagulation parameters studied for each individual were platelet count, bleeding time (BT), activated partial thromboplastin time (aPTT) and prothrombin time (PT/INR). Results: Out of a total 32 subjects, 17 (53.13%) were male and 15 (46.87%) were female. There is a significant difference in mean prothrombin time (p=0.022). Kruskal-Wallis test showed a significant difference in the median of the PT/INR (p=0.01) and AHI (p<0.001) for different categories of OSA. Prothrombin time is the only factor which is affecting the OSA.Conclusions: Patients with severe OSA may have elevated coagulability levels, particularly in the length of prothrombin time. The potential for anticoagulant and antiplatelet medications to reduce mortality in patients with OSA merits exploration, particularly for patients who are unwilling or unable to achieve full control of OSA with currently available treatment options.
F-Crowds: An Anonymity Scheme for P2P File-Sharing

The traditional encryption methods pay more attention to the confidentiality of the message content, but ignore the protection of identity information to the communicating parties. By encrypting, the contents of the communication acquired by attackers became relatively difficult, but they can still found the identity information of the two sides to communicate easily because in the TCP / IP protocol, the sender and receiver of the message are exposed. The anonymity communication technology, is used to solve this problem. The first anonymity communication technology can date back to 1981, Chaum proposed MIX [1] which is a very important technology for reference by a number of researchers of anonymity communication. The rapid development of the Internet has made things convenience to people, but also led to more personal information exposed to the public. As a result, a growing number of areas that required anonymity communication technology to protect the user's privacy. P2P file-sharing is not exception. P2P is undoubtedly the most popular way for file-sharing, but popular P2P file-sharing protocols, such as BitTorrent, can’t provide users anonymity communication service. Many people choose to build upon existing anonymous communication tools, such as Tor, to achieve anonymous P2P file transfer, but Tor primarily provides anonymous service for web browsing, instant message or other low latency applications. A large number of P2P file traffic will seriously threaten the service of Tor. Therefore, the anonymity communication service specifically for P2P file sharing system is very important. In this paper, we presents an anonymity communication scheme specifically for P2P file-sharing networks called F-Crowds which based on Crowds [2] to provide an adjustable anonymity service for P2P file-sharing.
New Techniques in Deep Representation Learning

New Techniques in Deep Representation Learning Galen Andrew Chair of the Supervisory Committee: Associate Professor Emanuel Todorov CSE, joint with AMATH The choice of feature representation can have a large impact on the success of a machine learning algorithm at solving a given problem. Although human engineers employing taskspecific domain knowledge still play a key role in feature engineering, automated domainindependent algorithms, in particular methods from the area of deep learning, are proving more and more useful on a variety of difficult tasks, including speech recognition, image analysis, natural language processing, and game playing. This document describes three new techniques for automated domain-independent deep representation learning: • Sequential deep neural networks (SDNN) learn representations of data that is continuously extended in time such as audio. Unlike “sliding window” neural networks applied to such data or convolutional neural networks, SDNNs are capable of capturing temporal patterns of arbitrary span, and can encode that discovered features should exhibit greater or lesser degrees of continuity through time. • Deep canonical correlation analysis (DCCA) is a method to learn parametric nonlinear transformations of multiview data that capture latent shared aspects of the views so that the learned representation of each view is maximally predictive of (and predicted by) the other. DCCA may be able to learn to represent abstract properties when the two views are not superficially related. • The orthant-wise limited-memory quasi-Newton algorithm (OWL-QN) can be employed to train any parametric representation mapping to produce parameters that are sparse (mostly zero), resulting in more interpretable and more compact models. If the prior assumption that parameters should be sparse is reasonable for the data source, training with OWL-QN should also improve generalization. Experiments on many different tasks demonstrate that these new methods are computationally efficient relative to existing comparable methods, and often produce representations that yield improved performance on machine learning tasks.
Inter-Cell Interference Mitigation in LTE-Advanced Heterogeneous Networks by Dynamic Subframes Assignment Guaranteeing Fairness

Dense network deployment is an important approach to meet the requirements of the future mobile communications. LTE-Advanced Heterogeneous Networks (HetNets) has been introduced by the 3rd Generation Partnership Project (3GPP) to increase cell throughput, offload data from macro-cell and improve indoor coverage. However, it also brings serious inter-cell interference with the co-channel deployment. Although the Almost Blank Subframes (ABS) scheme, one solution of Enhanced Inter-Cell Interference Coordination (eICIC), mitigates the interference, it results in undesirable fairness, huge difference of users’ throughput in the system and even the incapability of communication. To address this problem, we introduce Dynamic Subframes Assignment Guaranteeing Fairness (DSAGF), in which the ABS pattern is set to mitigate inter-cell interference while guaranteeing fairness. As demonstrated in the simulation results, DSAGF significant decline in outage ratios of user equipments, narrowing difference of users’ throughput, and improvement for fairness with inter-cell interference mitigation.
A Language to Describe Context-Aware Roles

As wireless networks become more prevalent, a widening array of computational resources becomes available to the mobile user. Since not all users should have unrestricted access to these resources, a method of access control must be devised. In a context-aware environment, context information can be used to supplement more conventional password-based access control systems. We believe the best way to achieve this is through the use of Context-Aware Role-Based Access Control, a model in which permissions are assigned to entities called roles, each principal is a member of one or more roles, and a role’s membership is determined using context information. We designed and implemented RDL (RoleDefinition Language), a simple, expressive and somewhat extensible programming language to facilitate the description of roles in terms of context information.
A Brief Review about the Effect of Honey on Diabetic Foot Ulcer; from In Vitro Studies to Clinical Trials

Diabetes mellitus has many complications; one of the most important ones is diabetic foot ulcer (DFU). Due to the increasing prevalence of diabetes in the world and evidences of the role of honey in treatment of DFU, we decided to review the effect of honey on DFU. We searched the keywords of “honey”, “diabetic foot ulcer”, “treatment”, “diabetes mellitus”, “wound dressing” and “wound healing” in indexing systems of PubMed, Scopus and ISI Web of Science between 2007 to 1 Jun 2017, in English and Persian languages. Out of 120 articles published, 20 articles met the inclusion criteria, including one in vivo study, four in vitro studies and 15 human studies. The results showed the efficacy of honey as a wound healing agent in some types of wounds. Mechanisms of healing methods included hyper osmosis, stimulating cytokine production, producing transforming growth factor-β1 and etc. In some articles, honey was used in combination with other medications like Commiphora molmol, Nigella sativa and povidone iodine. Honey showed minor side effects, in some studies, like mild burning or minor sensation. Despite these complications, the treatment was not stopped and in some cases the disorder was resolved by changing the combination ingredients. Although, the reviewed studies showed healing effect for honey in DFU, more studies are needed to confirm the efficacy.
Presence of cyniclomyces guttulatus in a faecal dog sample with chronic diarrhea.
 Case report

Cyniclomyces guttulatus is a commensal fungus of the digestive tract of rabbits and rodents, but it has also been diagnosed in dogs, being responsible for diarrhea, and it may also be present in healthy dogs. A faecal sample of dogs with chronic diarrhea and vomiting was received at the Laboratory of Parasitic Diseases of the Federal University of Pelotas, for coproparasitological analysis. The sample was analyzed through the techniques of Willis-Mollay (1921), Faust (1938) and Hoffman, Pons and Janer (1934), with no genus of gastrointestinal parasite. However, through the Faust method, structures compatible with C. guttulatus were evidenced. From the diagnosis, treatment with fluconazole 5mg / kg was instituted. After treatment, the tutor reported that there was resolution of the condition.
A Comparative Analysis Between Context-based Reasoning (cxbr) And Contextual Graphs (cxgs).

Context-based Reasoning (CxBR) and Contextual Graphs (CxGs) involve the modeling of human behavior in autonomous and decision-support situations in which optimal human decision-making is of utmost importance. Both formalisms use the notion of contexts to allow the implementation of intelligent agents equipped with a contextsensitive knowledge base. However, CxBR uses a set of discrete contexts, implying that models created using CxBR operate within one context at a given time interval. CxGs use a continuous context-based representation for a given problem-solving scenario for decision-support processes. Both formalisms use contexts dynamically by continuously changing between necessary contexts as needed in appropriate instances. This thesis identifies a synergy between these two formalisms by looking into their similarities and differences. It became clear during the research that each paradigm was designed with a very specific family of problems in mind. Thus, CXBR best implements models of autonomous agents in environment, while CxGs is best implemented in a decisionsupport setting that requires the development of decision-making procedures. Cross applications were implemented on each and the results are discussed.
Vestibular assessment and management in adults: South African audiologists’ expressed level of confidence and knowledge explored

Abstract Background: Vestibular assessment and management is within the scope of practice of audiology, and therefore audiologists are expected and required to assess and manage patients with vestibular disorders. Due to the developing nature of South Africa and the developing nature of audiology as a profession in the country, paucity of evidence-base regarding audiologists’ training and practice confidence in this field exists. Objective: To explore South African audiologists’ level of confidence and training in performing vestibular assessment and management. Methods: A survey research methodology with a cross-sectional research design was adopted, where a 29 item web-based survey was used to survey 32 audiologists from across the country. All audiologists surveyed were qualified and registered with the Health Professions Council of South Africa (HPCSA) independent practitioners. Data were analyzed using descriptive statistics. Results: Just over half of the participants (53.6%) conducting vestibular assessments in their practices. More than half (65.6%) of the audiologists received formal education from the university; while only 40.6% received clinical training. 59.4% of the participants were not confident in performing vestibular assessment; while 65.5% of participants were not confident in performing vestibular management. Over 80% of audiologists who were not confident in performing vestibular assessment and management work in public hospitals where over 70% of the South African population accesses health care. Conclusion: More audiologists are not confident in performing vestibular assessment and management, particularly those who are working in public hospitals. The findings raise implications for training and resource planning and allocation.
Robust GOB intra refresh scheme for H.264/AVC video over UMTS

3G mobile systems are designed to provide multimedia services. To fulfill QoS requirements for video applications, a robust video codec should be employed. In this paper, a robust and effective algorithm for group of blocks (GOB) intra refresh in H.264/AVC is presented. The objective is to mitigate the error propagation and improve the perceptual video quality over the mobile networks. Experimental results show that the proposed algorithm can efficiently improve the video quality when errors occur.
Assessment of beta-catenin expression by immunohistochemistry in colorectal neoplasms and its role as an additional prognostic marker in colorectal adenocarcinoma

Background Cancer is one of the world’s biggest health care challenges, with colorectal cancer (CRC) being one of the three most frequently encountered malignancy worldwide. The main cause of mortality associated with CRC is tumour invasion and metastasis. Pathogenesis of CRC is a multistep process, during which different molecular pathways come into play. The cardinal genomic alteration that has been found universally present in CRC is a mutation in the adenomatous polyposis coli gene (APC). APC mutation causes unrestricted action of the Wnt signaling pathway which subsequently enhances the intracellular accumulation of a protein called beta-catenin, responsible for cell proliferation, differentiation and enhanced survival of colorectal epithelial cells. Aim This study was conducted to analyze beta–catenin expression in various colorectal neoplasms, and its change with respect to different grades and stage of colorectal adenocarcinoma. Study design This was a cross-sectional observational study. Methods A total of 66 cases were enrolled in this study. Census method of sampling was used. Data was collected using a pre-designed, pretested semi-structured schedule on dependent variables like beta–catenin expression and independent variables like clinico-pathological profile including dietary history, macroscopic findings, histological type, histological grade, stage and other relevant parameters. An institution based cross sectional observational study was performed between February 2016 and July 2017. Representative sections taken from the specimens included in the study were subjected to histopathological examination followed by immunohistochemistry [IHC] for beta-catenin expression; the data obtained were analyzed by mean ± SD, Student t test, Chi-square/ Fisher Exact test using statistical software SPSS 18.0. Results A statistically significant correlation (P = 0.004), of beta–catenin localization and IHC score was noted between the benign, premalignant and malignant neoplasms following a gradual transition from a membranous to a nuclear positivity; also, a significant (P<0.001) correlation between beta–catenin nuclear score and the corresponding American Joint Committee on Cancer (AJCC) stage of colorectal adenocarcinoma was also found in this study. Conclusion The purpose of this study was to determine the change in beta-catenin expression which demonstrates a gradual shift from a membranous to subsequent cytoplasmic and nuclear positivity from normal colorectal tissue to benign, premalignant and malignant neoplasms respectively. This property of beta-catenin can determine the malignant potential of various premalignant neoplasms of the large intestine, thus aiding in an early initiation of prophylactic treatment, which can prevent the development of an invasive disease. The membranous, cytoplasmic and nuclear scores show a linear progression with the advancing stages of colorectal carcinoma, making beta–catenin a prognostic marker in malignant colorectal neoplasms.
Parallel Evolutionary Computation in R

Evolutionary Computation (EC) is a branch of Artificial Intelligence which encompasses heuristic optimization methods loosely based on biological evolutionary processes. These methods are efficient in finding optimal or near-optimal solutions in large, complex non-linear search spaces. While evolutionary algorithms (EAs) are comparatively slow in comparison to deterministic or sampling approaches, they are also inherently parallelizable. As technology shifts towards multicore and cloud computing, this overhead becomes less relevant, provided a parallel framework is used. In this chapter the authors discuss how to implement and run parallel evolutionary algorithms in the popular statistical programming language R. R has become the de facto language for statistical programming and it is widely used in biostatistics and bioinformatics due to the availability of thousands of packages to manipulate and analyze data. It is also extremely easy to parallelize routines within R, which makes it a perfect environment for evolutionary algorithms. EC is a large field of research, and many different algorithms have been proposed. While there is no single silver bullet that can handle all classes of problems, an algorithm that is extremely simple, efficient, and with good generalization properties is Differential Evolution (DE). Herein the authors discuss step-by-step how to implement DE in R and how to parallelize it. They then illustrate with a toy genome-wide association study (GWAS) how to identify candidate regions associated with a quantitative trait of interest.
Distributed sensor registration based on random finite set representations

In the investigation of multi-sensor fusion problems, it is commonly assumed that all the parameters necessary to transform the information from the sensors to a common frame are known. Imperfect knowledge of these registration parameters induce systematic biases which would inhibit the benefits of multisensor fusion. For example, they can result in deterioration of the target localisation accuracy. We consider the problem of sensor registration in a distributed setting in which the sensor platforms propagate random finite set distributions that describe the multiobject scene using Probability Hypothesis Density (PHD) filters, and, transmit these posteriors to their neighbours. To find the respective registration error between two platforms, we propose a recursive method whereby a posterior distribution on the registration error is updated upon receiving the incoming multiobject distribution. The update is performed in a Bayesian fashion using a likelihood that captures the similarity of the multiobject scene as determined by the two sensors. In particular, we utilise the Bhattacharyya distance of multi-object distributions as a measure of similarity. We demonstrate the proposed method through a simulation example. (5 pages)
Projects to encourage female students in STEM areas

Today, the number of female students that enrol in degrees related to Science, Technology, Engineering and Mathematics (STEM) areas is quite low. So, numerous initiatives have arisen to promote these degrees and encourage female students in these areas. In this context, the EuLES Network (u-Learning Environments in Higher Education), an interdisciplinary network created in 2010 at the University of Zaragoza (Spain) to foster research, interaction, cooperation and transfer of knowledge and technologies related to learning and open education, has developed two projects oriented to High School Students: “WikinformáticA! en Aragon” and “Women in STEM by EuLES”. WikinformáticA! en Aragón is a competition for student groups in which they develop a wiki on prominent women in the history of Information and Communication Technology (ICT). The objective is the visibility of women involved in technology. The purpose of the Women in STEM project is to offer testimonies of women in Science, Technology, Engineering and Mathematics to encourage scientific vocations, especially in young people and girls. The project consists of conducting video interviews of women who work or study in these disciplines. All the videos, along with a short biography, are posted on the web.
Synopses for Massive Data

Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches describes basic principles and recent developments in building approximate synopses (i.e., lossy, compressed representations) of massive data. Such synopses enable approximate query processing, in which the user's query is executed against the synopsis instead of the original data. The monograph focuses on the four main families of synopses: random samples, histograms, wavelets, and sketches. A random sample comprises a "representative" subset of the data values of interest, obtained via a stochastic mechanism. Samples can be quick to obtain, and can be used to approximately answer a wide range of queries. A histogram summarizes a data set by grouping the data values into subsets, or "buckets," and then, for each bucket, computing a small set of summary statistics that can be used to approximately reconstruct the data in the bucket. Histograms have been extensively studied and have been incorporated into the query optimizers of virtually all commercial relational DBMSs. Wavelet-based synopses were originally developed in the context of image and signal processing. The data set is viewed as a set of M elements in a vector - i.e., as a function defined on the set {0, 1, 2, . , M-1} - and the wavelet transform of this function is found as a weighted sum of wavelet "basis functions. " The weights, or coefficients, can then be "thresholded", for example, by eliminating coefficients that are close to zero in magnitude. The remaining small set of coefficients serves as the synopsis. Wavelets are good at capturing features of the data set at various scales. Sketch summaries are particularly well suited to streaming data. Linear sketches, for example, view a numerical data set as a vector or matrix, and multiply the data by a fixed matrix. Such sketches are massively parallelizable. They can accommodate streams of transactions in which data is both inserted and removed. Sketches have also been used successfully to estimate the answer to COUNT DISTINCT queries, a notoriously hard problem. Synopses for Massive Data describes and compares the different synopsis methods. It also discusses the use of AQP within research systems, and discusses challenges and future directions. It is essential reading for anyone working with, or doing research on massive data.