Miracle-dz/UniFAR-FTUs huggingface dataset:
Surgical pelvic anatomy in gynecologic oncology

Laparoscopy, with its many advantages, has revolutionized the field of pelvic surgery and enabled surgeons to gain a far greater understanding of pelvic anatomy. As technology evolves, our understanding is continuously improving. This article is a tribute to the anatomy of the pelvis, which itself has resisted evolutionary changes and remained the same, whereas our understanding of how to view and approach various organ structures has evolved.
Continuous and Discrete State Estimation in Linear Switched Systems by Sliding Mode Observers with Residuals' Projection

This paper addresses the problem of simultaneous discrete and continuous state reconstruction in the framework of linear autonomous switched systems. Main ingredient of the approach is a stack of sliding mode observers producing suitable residual signals. These residuals are used for reconstructing the discrete state and, at the same time, for identifying the unique observer of the stack that provides an asymptotically converging estimate of the continuous state. The main result and most significant novelty of this work is a projection procedure for deriving residuals characterized by finite-time convergence. These finite-time converging projected residuals allow a faster reconstruction of the system's discrete state after the switching instants, as compared to their “unprojected” counterparts. The approach is constructively developed and theoretically substantiated along the paper, and simulation results confirm its effectiveness.
Self-reported smoking status and urinary cotinine levels in patients with asthma

ABSTRACT Objective: To determine the frequency of active smoking among patients with asthma and individuals without asthma by self-report and urinary cotinine measurement. Methods: This was a cross-sectional study conducted in the city of Salvador, Brazil, and involving 1,341 individuals: 498 patients with severe asthma, 417 patients with mild-to-moderate asthma, and 426 individuals without asthma. Smoking status was determined by self-report (with the use of standardized questionnaires) and urinary cotinine measurement. The study variables were compared with the chi-square test and the Kruskal-Wallis test. Results: Of the sample as a whole, 55 (4.1%) reported being current smokers. Of those, 5 had severe asthma, 17 had mild-to-moderate asthma, and 33 had no asthma diagnosis. Of the 55 smokers, 32 (58.2%) were daily smokers and 23 (41.8%) were occasional smokers. Urinary cotinine levels were found to be high in self-reported nonsmokers and former smokers, especially among severe asthma patients, a finding that suggests patient nondisclosure of smoking status. Among smokers, a longer smoking history was found in patients with severe asthma when compared with those with mild-to-moderate asthma. In addition, the proportion of former smokers was higher among patients with severe asthma than among those with mild-to-moderate asthma. Conclusions: Former smoking is associated with severe asthma. Current smoking is observed in patients with severe asthma, and patient nondisclosure of smoking status occurs in some cases. Patients with severe asthma should be thoroughly screened for smoking, and findings should be complemented by objective testing.
Alternative framework for the iterated unscented Kalman filter

The iterated version of a family of non-linear Kalman filters, named the unscented transform (UT) based unscented Kalman filters (UKF), are revisited. Two existing frameworks of the iterated UKF are analysed and some shortcomings of them are pointed out. A new framework is proposed based on the statistical linear regression (SLR) perspective of the UT and the framework of the iterated extended Kalman filter (IEKF). The virtue of the proposed framework is twofold: first, the observation equation is linearised strictly following the SLR perspective implying that the regression error is also considered; second, it strictly follows the framework of the IEKF implying that in each iteration, the linearised equation is used to correct the a priori estimate rather than the latest estimate. A simple but illustrative benchmark example is simulated to check the feasibility of the proposed framework, and the results demonstrate the efficacy of the proposed framework.
Analisis Kinerja Smoothing pada Naive Bayes untuk Pengkategorian Soal Ujian

Cognitive domain taxonomy bloom is a reference to classify examination based on difficulty levels on education world, the result of those categories will be used to compile a variety of exam questions. In this research, Naive bayes classifier will be used to categorize the text about biology exam for high school. Chi-square is feature selection method that will be used to remove an unuse features on examination, and increase speed of process text categorization. In addition, naive bayes classifier is a method that causing missclassification result of categorization text, this case will be happen if testing data is not found in training data, so we need another method to minimize it, that method called by smoothing method. In this research we will test perfomance and impact of smothing method for naive bayes classifier and chi-square as feature selection method. The smoothing methods to be compared on this research are : Laplace, Dirichlet and Two Stage smoothing. DOI: https://doi.org/10.26905/jtmi.v4i2. 2080
Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks

Process parameter estimation, to a large extent, determines the industrial production quality. However, limited sensors can be deployed in a traditional wired manner, which results in poor process parameter estimation in hostile environments. Industrial wireless sensor networks (IWSNs) are techniques that enrich sampling points by flexible sensor deployment and then purify the target by collaborative signal denoising. In this paper, the process industry scenario is concerned, where the workpiece is transferred on the belt and the parameter estimate is required before entering into the next process stage. To this end, a consensus-based sequential estimation (CSE) framework is proposed which utilizes the co-design of IWSN and parameter state estimation. First, a group-based network deployment strategy, together with a TDMA (Time division multiple access)-based scheduling scheme is provided to track and sample the moving workpiece. Then, by matching to the tailored IWSN, the sequential estimation algorithm, which is based on the consensus-based Kalman estimation, is developed, and the optimal estimator that minimizes the mean-square error (MSE) is derived under the uncertain wireless communications. Finally, a case study on temperature estimation during the hot milling process is provided. The results show that the estimation error can be reduced to less than 3∘C within a limited time period, although the measurement error can be more than 100∘C in existing systems with a single-point temperature sensor.
State Machine Replication Is More Expensive Than Consensus

Consensus and State Machine Replication (SMR) are generally considered to be equivalent problems. In certain system models, indeed, the two problems are computationally equivalent: any solution to the former problem leads to a solution to the latter, and vice versa. In this paper, we study the relation between consensus and SMR from a complexity perspective. We find that, surprisingly, completing an SMR command can be more expensive than solving a consensus instance. Specifically, given a synchronous system model where every instance of consensus always terminates in constant time, completing an SMR command does not necessarily terminate in constant time. This result naturally extends to partially synchronous models. Besides theoretical interest, our result also corresponds to practical phenomena we identify empirically. We experiment with two well-known SMR implementations (Multi-Paxos and Raft) and show that, indeed, SMR is more expensive than consensus in practice. One important implication of our result is that—even under synchrony conditions—no SMR algorithm can ensure bounded response times. 2012 ACM Subject Classification Computing methodologies → Distributed algorithms
Compiler assisted vulnerability assessment

With computer software pervading every aspect of our lives, vulnerabilities pose an active threat. Moreover, with shorter software development cycles and a security-asan-afterthought mindset, vulnerabilities in shipped code are inevitable. Therefore, recognizing and fixing vulnerabilities has gained in importance. At the same time, there is a demand for methods to diagnose vulnerabilities within the software development process. This dissertation introduces compiler assisted vulnerability assessment, a novel approach that brings together techniques from program analysis and software testing for recognizing vulnerabilities as software is written. The main idea behind our approach is to leverage compile-time analysis to abstract program information that is relevant for vulnerability assessment from source code. Our analysis is targeted at an in-memory code representation that exposes program syntax and semantics, making it both fast and precise. We show that our approach not only enables identification of well-studied vulnerability classes such as buffer overflows but also assists software testing and regression analysis. To this end, we develop three different methods for software vulnerability assessment, namely, a method to identify vulnerabilities spread across multiple source files, a method to infer the input format of parsing applications to test them more effectively, and a method to perform vulnerability template matching from fuzzer crashes. These methods address distinct tasks encountered by security practitioners on a regular basis. In doing so, they show how program-centric analyses and input-centric testing can complement each other towards vulnerability assessment. We evaluate our methods on popular open-source software (OSS), quantifying their benefit in terms of number of new vulnerabilities exposed, time to expose vulnerabilities, and test coverage improvement. Our tools have found tens of zero-day vulnerabilities in production software such as Open vSwitch and tcpdump, and improved test coverage by 10–15%. In controlled settings, they have speeded up the time to vulnerability exposure by an order of magnitude. Our work shows that compiler assisted analysis has the potential to reduce the likelihood of vulnerabilities propagating to production software. More importantly, it shows that static program analysis holds promise for advancing the state-of-the-art in vulnerability assessment.
Stochastic Learning Under Random Reshuffling With Constant Step-Sizes

In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data uniformly. Recent works have pursued justifications for this behavior by examining the convergence rate of the learning process under diminishing step sizes. This work focuses on the constant step-size case and strongly convex loss functions. In this case, convergence is guaranteed to a small neighborhood of the optimizer albeit at a linear rate. The analysis establishes analytically that random reshuffling outperforms uniform sampling by showing explicitly that iterates approach a smaller neighborhood of size <inline-formula><tex-math notation="LaTeX">$O(\mu ^2)$</tex-math></inline-formula> around the minimizer rather than <inline-formula><tex-math notation="LaTeX">$O(\mu)$</tex-math></inline-formula>. Furthermore, we derive an analytical expression for the steady-state mean-square-error performance of the algorithm, which helps clarify in greater detail, the differences between sampling with and without replacement. We also explain the periodic behavior that is observed in random reshuffling implementations.
HIV-genetic diversity and drug resistance transmission clusters in Gondar, Northern Ethiopia, 2003-2013

Background The HIV-1 epidemic in Ethiopia has been shown to be dominated by two phylogenetically distinct subtype C clades, the Ethiopian (C’-ET) and East African (C-EA) clades, however, little is known about the temporal dynamics of the HIV epidemic with respect to subtypes and distinct clades. Moreover, there is only limited information concerning transmission of HIV-1 drug resistance (TDR) in the country. Methods A cross-sectional survey was conducted among young antiretroviral therapy (ART)-naïve individuals recently diagnosed with HIV infection, in Gondar, Ethiopia, 2011–2013 using the WHO recommended threshold survey. A total of 84 study participants with a median age of 22 years were enrolled. HIV-1 genotyping was performed and investigated for drug resistance in 67 individuals. Phylogenetic analyses were performed on all available HIV sequences obtained from Gondar (n = 301) which were used to define subtype C clades, temporal trends and local transmission clusters. Dating of transmission clusters was performed using BEAST. Result Four of 67 individuals (6.0%) carried a HIV drug resistance mutation strain, all associated with non-nucleoside reverse transcriptase inhibitors (NNRTI). Strains of the C-EA clade were most prevalent as we found no evidence of temporal changes during this time period. However, strains of the C-SA clade, prevalent in Southern Africa, have been introduced in Ethiopia, and became more abundant during the study period. The oldest Gondar transmission clusters dated back to 1980 (C-EA), 1983 (C-SA) and 1990 (C’-ET) indicating the presence of strains of different subtype C clades at about the same time point in Gondar. Moreover, some of the larger clusters dated back to the 1980s but transmissions within clusters have been ongoing up till end of the study period. Besides being associated with more sequences and larger clusters, the C-EA clade sequences were also associated with clustering of HIVDR sequences. One cluster was associated with the G190A mutation and showed onward transmissions at high rate. Conclusion TDR was detected in 6.0% of the sequenced samples and confirmed pervious reports that the two subtype C clades, C-EA and C’-ET, are common in Ethiopia. Moreover, the findings indicated an increased diversity in the epidemic as well as differences in transmission clusters sizes of the different clades and association with resistance mutations. These findings provide epidemiological insights not directly available using standard surveillance and may inform the adjustment of public health strategies in HIV prevention in Ethiopia.
Accuracy of flash glucose monitoring in insulin‐treated patients with type 2 diabetes

The present study evaluated the accuracy of interstitial glucose measurements by flash glucose monitoring (FGM) and continuous glucose monitoring (CGM). Five diabetes patients simultaneously underwent FGM (FreeStyle Libre Pro) and CGM (iPro™2), and their glucose levels were compared with venous blood and capillary blood glucose levels. The range of daily venous blood glucose levels (30 measurements) was 70–245 mg/dL, with a median of 138 mg/dL. There were good correlations of glucose levels measured by FGM (r2 = 0.90, mean absolute relative difference 8.2 ± 5.6%), CGM (r2 = 0.86, mean absolute relative difference 9.2 ± 9.1%) and capillary blood (r2 = 0.87, mean absolute relative difference 7.2 ± 7.2%) with venous blood glucose levels. The accuracy of FGM measurements was also shown against CGM, with 99.9% of the FGM values (1,279 measurements) being within the Parkes error grid zones A and B. The results suggest that the accuracy of FGM is similar to that of CGM, and that FGM is a useful tool for determining daily glucose profile.
Learning deep features to recognise speech emotion using merged deep CNN

This study aims at learning deep features from different data to recognise speech emotion. The authors designed a merged convolutional neural network (CNN), which had two branches, one being one-dimensional (1D) CNN branch and another 2D CNN branch, to learn the high-level features from raw audio clips and log-mel spectrograms. The building of the merged deep CNN consists of two steps. First, one 1D CNN and one 2D CNN architectures were designed and evaluated; then, after the deletion of the second dense layers, the two CNN architectures were merged together. To speed up the training of the merged CNN, transfer learning was introduced in the training. The 1D CNN and 2D CNN were trained first. Then, the learned features of the 1D CNN and 2D CNN were repurposed and transferred to the merged CNN. Finally, the merged deep CNN initialised with transferred features was fine-tuned. Two hyperparameters of the designed architectures were chosen through Bayesian optimisation in the training. The experiments conducted on two benchmark datasets show that the merged deep CNN can improve emotion classification performance significantly.
Information behavior in everyday life: Research on street-level sex workers, new immigrants, and hair stylists. Sponsored by SIG USE

Little research has focused on the information behavior of the general public (Julien & Duggan, 2000). In this session we report findings on how information is sought and used as part of everyday life by three distinct groups: (1) street-level sex workers, (2) new immigrants, and (3) hairdressers.
Inferring User Gender from User Generated Visual Content on a Deep Semantic Space

In this paper we address the task of gender classification on picture sharing social media networks such as Instagram and Flickr. We aim to infer the gender of an user given only a small set of the images shared in its profile. We make the assumption that user's images contain a collection of visual elements that implicitly encode discriminative patterns that allow inferring its gender, in a language independent way. This information can then be used in personalisation and recommendation. Our main hypothesis is that semantic visual features are more adequate for discriminating high-level classes. The gender detection task is formalised as: given an user's profile, represented as a bag of images, we want to infer the gender of the user. Social media profiles can be noisy and contain confounding factors, therefore we classify bags of user-profile‘s images to provide a more robust prediction. Experiments using a dataset from the picture sharing social network Instagram show that the use of multiple images is key to improve detection performance. Moreover, we verify that deep semantic features are more suited for gender detection than low-level image representations. The methods proposed can infer the gender with precision scores higher than 0.825, and the best performing method achieving 0.911 precision.
European Journal of Education Studies-ISSN 2501-1111

The study investigated the effect of cumulative practice approach on SS one students’ achievement in mathematical geometrical construction in Ankpa Local Government Area of Kogi State, Nigeria. The study employed a pre-test and post-test quasiexperimental control group design. Four intact classes, one each from each of the four schools setting were used. The sample for the study consisted of 204 SSS1 students. The instrument used for this study was the Mathematics Achievement Test (MAT), with reliability coefficient of 0.77. Mean and standard deviation were used to answer the research question, while Analysis of Covariance (ANCOVA) was used to test the hypotheses at 0.05 level of significance. The study revealed that students who were taught using cumulative practice approach achieved higher than those that were taught using conventional practice approach. The study recommended among others, workshops/seminars for teachers to enable students acquire the basic skills of the cumulative practice approach. Curriculum planners and publishers should make efforts of providing text books or computer assisted materials in mathematics that conform with cumulative practice approach.
Bidirectional wireless information and power transfer for decode-and-forward relay systems

In this study, the authors investigate the bidirectional wireless information and power transfer (BWIPT) in decode-and-forward (DF) relay systems, where the bidirectional relay can decode and forward information from the user to the access point (AP), and assist the wireless power transfer from the AP to the user. The relay employs the power splitting (PS) protocol to coordinate the received signal energy for information transmission and energy harvesting. By converting the multi-relay system information rate maximisation problem into a convex optimisation problem, the distributed power allocation scheme is obtained to maximise the information rate. Particularly, for single-relay systems, the authors derive the closed-form expression of the optimal PS factor, which can maximise the information rate. Simulation results show that the BWIPT for DF relay systems outperforms the BWIPT for amplify-and-forward relay systems.
Modulating stability of functionalized fullerene cations [R‐C60]+ with the nature of R‐group

In this study, the first comprehensive theoretical investigation of the stability of functionalized fullerene‐based cations [R‐C60]+ and its relationship with the nature of the attached R‐group was performed. C60‐Fullerene core was functionalized with an alkyl group of different length (R = (CH2)nCH3, where n = 0–9). This set was further complemented by bulky isopropyl and tert‐butyl and conjugated phenyl groups. A detailed study of the relative stability of target cationic species was accompanied by in‐depth investigation of their electronic structure and aromaticity using a large set of descriptors of different nature. The stability of target species was considered with respect to two alternative and competing mechanisms of bond breaking, namely, heterolytic ([R‐C60]+ → R+ + C60) and homolytic ([R‐C60]+ → R• + C60+•) ones. The transformation of strained sp2‐carbon atom in unperturbed C60‐fullerene to nonconstrained tetrahedral sp3‐type in functionalized derivatives was found to be the driving force for the formation of its functionalized cations. In spite of the fact that all systems under consideration were found to be corresponding to local minima on corresponding potential energy surfaces, the functionalization of C60‐core with the smallest and simplest methyl group resulted in most stable compound, as evaluated by bonding energy between R+ and fullerene fragment (in the light of both mechanisms). Subsequent elongation of the alkyl chain or introducing bulky groups led to notable decrease of the bonding energy and, as consequence, of the stability within the framework of heterolytic bond cleavage, whereas homolytic pathway assumes opposite—slight increase of stability along with lengthening of the R‐group. The orbital interaction (ΔEorb) was identified as the main driving force for these trends. In general, the homolytic path was found to be dominating for small‐length R‐groups such as those with n = 0 and 1. At n = 2, heterolytic and homolytic pathways are equally probable (the difference in corresponding bonding energies is about 1 kcal/mol). However, when the alkyl chain becomes longer (n = 3–9), the cationic bond cleavage appears as the most energetically favorable. © 2018 Wiley Periodicals, Inc.