Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Adaptive Backstepping Control of a Robotic Manipulator With Prescribed Performance Constraint

This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional–integral-derivative and TBC methods.
Thorax x‐ray and CT interventional dataset for nonrigid 2D/3D image registration evaluation

PURPOSE The aim of this paper is to provide a novel, publicly available standard image dataset with a useful evaluation framework for assessing nonrigid two-/three-dimensional (2D/3D) registration algorithms. ACQUISITION AND VALIDATION METHODS A pig lung model was used to obtain the image dataset. Inflated with different amounts of oxygen, a sequence of 3D volume data was acquired with computed tomography (CT), which ideally simulated different respiratory phases. With the model inflated and kept in certain states, 3D CT, 2D CT scout image and 2D x-ray were acquired for the same respiratory phases, making them suitable to establish the evaluation dataset for 2D/3D registration algorithms. A total of 120 well-distributed landmarks in every 3D volume were manually annotated and checked by several radiologists using semi-automatic software to generate the dataset. DATA FORMAT AND USAGE NOTES All 3D image data were stored in both DICOM and ITK Meta format, and 2D image data were stored in DICOM format. A total of 120 landmarks were manually annotated for each 3D image. Among these landmarks, eight landmarks located on large branch of the bronchial tree were also annotated in 2D images. The landmark coordinates were stored in a text file. The detailed usage including a standard evaluation framework for the proposed dataset is also provided. The dataset can be downloaded from the Zenodo repository (https://doi.org/10.5281/zenodo.997887). POTENTIAL APPLICATIONS Our standard dataset was acquired with advanced clinical imaging devices and is quite suitable for quantitatively evaluating state-of-art, nonrigid 2D/3D registration algorithms.
Deep and Multi-Algorithmic Gender Classification of Single Fingerprint Minutiae

Accurate fingerprint gender estimation can positively affect several applications, since fingerprints are one of the most widely deployed biometrics. For example, gender classification in criminal investigations may significantly minimize the list of potential subjects. Previous work mainly offered solutions for the task of gender classification based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications, including forensics and the fast growing field of consumer electronics. Moreover, partial fingerprints are not well-defined. Therefore, this work improves the gender decision performance on a well-defined partition of the fingerprint. It enhances gender estimation on the level of a single minutia. Working on this level, we propose three main contributions that were evaluated on a publicly available database. First, a convolutional neural network model is offered that outperformed baseline solutions based on hand crafted features. Second, several multi-algorithmic fusion approaches were tested by combining the outputs of different gender estimators that help further increase the classification accuracy. Third, we propose including minutia detection reliability in the fusion process, which leads to enhancing the total gender decision performance. The achieved gender classification performance of a single minutia is comparable to the accuracy that previous work reported on a quarter of aligned fingerprints including more than 25 minutiae.
Chemsex among men - a questionnaire study.

BAKGRUNN Formålet med undersøkelsen var å kartlegge forekomsten av rusmiddelbruk under sex (chemsex) blant et utvalg av mannlige pasienter ved Olafiaklinikken i Oslo, en poliklinikk for seksuelt overførbare infeksjoner. Vi ønsket også å se hvilke variabler innen psykisk helse, seksuelt overførbare infeksjoner og seksualatferd som var assosiert med chemsex for menn som har sex med menn og menn som har sex med kvinner. MATERIALE OG METODE Studien var anonym og spørreskjemabasert blant mannlige pasienter ved poliklinikken i perioden 1.7. 2016-20.10. 2016. RESULTATER Svarprosenten var 96 (1 050 fikk utdelt skjema, 1 013 ble inkludert). Av disse rapporterte 144 (14 %) bruk av chemsex i løpet av det siste året - 87 (17 %) menn som har sex med menn og 57 (12 %) menn som har sex med kvinner. Av de som hadde hatt chemsex, oppga flere menn som har sex med menn hivinfeksjon, at de hadde hatt syfilis, over ti sexpartnere og hadde deltatt på sexfest det siste året. Flere menn som har sex med kvinner oppga psykiske plager. FORTOLKNING Det bør utredes nærmere hvordan helsevesenet best kan møte chemsexbrukernes behov. Spesielt er det viktig med informasjon om skadereduksjonstiltak og støtte til de som ønsker å slutte eller redusere bruken av chemsex.
Attitude Control of Pitch-Decoupled VTOL Fixed Wing Tiltrotor

Attitude control of a VTOL Tilt Rotor Aircraft is presented such that the tiltrotor pitch is independently controlled from the remainder of the aircraft. This is made possible using the recently introduced pitch decoupled system in which the rotor pitch is passively coupled to the aircraft. This passive coupling facilitates differential thrust control to re-orient the self-same rotors while eliminating complex transition algorithms typically required for tilt rotor systems. This feature results in continuously variable transition and stable operation throughout the entire range of rotor pitch angles.
PTSD From a Suicide Attempt: Phenomenological and Diagnostic Considerations

Objective: A suicide attempt is at least somewhat life-threatening by definition and is, for some, traumatic. Thus, it is possible that some individuals may develop posttraumatic stress disorder (PTSD) from a suicide attempt. Method: In this article, we consider whether one’s suicide attempt could fulfill Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for a PTSD Criterion A event and contribute to the development of attendant PTSD symptoms (e.g., flashbacks, avoidance, shame/guilt, nightmares); discuss theoretical models of PTSD as they relate to suicide attempts; reflect on factors that might influence rates of suicide attempt–related PTSD; highlight methodological limitations that have hampered our understanding of suicide attempt–related PTSD; and posit areas for future scientific and clinical inquiry. Results: Strikingly, the degree to which a suicide attempt leads to PTSD is unknown. Conclusions: We conclude with a call for research to systematically assess for suicide attempts alongside other potentially traumatic experiences (e.g., combat exposure, rape) that are included in standardized PTSD assessments.
Backward Compatible Layered Video Coding for 360° Video Broadcast

Recently, coding of 360° video contents has been investigated in the context of over-the-top streaming services. To be delivered using terrestrial broadcast, it is required to provide backward compatibility of such content to legacy receivers. In this paper, a novel layered coding scheme is proposed to address the delivery of 360° video content over terrestrial broadcast networks. One or several views are extracted from the 360° video and coded as base layers using standard HEVC encoding. Inter-layer reference pictures are built based on projected base-layers and are used in the enhancement layer to encode the 360° video. Experimental results show that the proposed approach provides substantial coding gains of 14.99% compared to simulcast coding and enables limited coding overhead of 5.15% compared to 360° single-layer coding.
Discriminative Deep Feature Learning for Semantic-Based Image Retrieval

Semantic-based image retrieval plays an important role in many practical applications, which aims to look for images with similar contents. Extracting discriminative representations of images is the real crux of this task. Directly utilizing the results of fully connected layers of convolutional neural networks (CNNs) is one of the best feature extraction methods. However, the fully connected layer is only supervised by the softmax loss, which only aims to maximize the accuracy of object classification and hardly pays attention to the spatial distribution of features, especially the intra-class and inter-class feature distances which are of great importance in semantic-based image retrieval. To compensate the performance degradation due to this reason, we try to address the spatial distribution of the features by two different loss functions. The first loss function jointly combines the softmax loss and the center loss in the training of the CNNs, which simultaneously ensures that the features of images with different contents are separable and the features of images in the same class are close. The second loss function is defined as an improved center loss which not only penalizes the distance between the obtained deep feature and the feature center of its own class, but also encourages a far distance between the feature and the feature centers of any other classes. We have conducted experiments on both ILSVRC data set and Caltech256 data set, demonstrated that the deep features got by our approaches can achieve better performance than other methods in semantic-based image retrieval, and the improved center loss can further outperform the joint supervision of the softmax loss and the center loss.
Unsupervised Domain Adaptation by Mapped Correlation Alignment

The goal of unsupervised domain adaptation aims to utilize labeled data from source domain to annotate the target-domain data, which has none of the labels. Existing work uses Siamese network-based models to minimize the domain discrepancy to learn a domain-invariant feature. Alignment of the second-order statistics (covariances) of source and target distributions has been proven an effective method. Previous papers use Euclidean methods or geodesic methods (log-Euclidean) to measure the distance. However, covariances lay on a Riemannian manifold, and both methods cannot accurately calculate the Riemannian distance, so they cannot align the distribution well. To tackle the distribution alignment problem, this paper proposes mapped correlation alignment (MCA), a novel technique for end-to-end domain adaptation with deep neural networks. This method maps covariances from Riemannian manifold to reproducing kernel Hilbert space and uses Gaussian radial basis function-based positive definite kernels on manifolds to calculate the inner product on reproducing kernel Hilbert space, and then uses Euclidean metric accurate measuring the distance to align the distribution better. This paper builds an end-to-end model to minimize both the classification loss and the MCA loss. The model can be trained efficiently using back-propagation. Experiments show that the MCA method yields the state-of-the-art results on standard domain adaptation data sets.
Relative importance of informational items in participant information leaflets for trials: a Q-methodology approach

Objectives To identify which information items potential participants and research nurses rank as the most important, and the reasons for this, when considering participation in a randomised controlled trial. Design Q-methodology approach alongside a think-aloud process. Using a vignette outlining a hypothetical trial, participants were asked to rank statements about informational items usually included in a participant information leaflet (PIL) on a Q-grid, while undertaking a real-time think-aloud process to elicit the underpinning decision processes. Analysis of quantitative data was conducted using descriptive statistics and qualitative data was coded using content analysis. Participants 20 participants (10 potential trial participants and 10 research nurses). Setting UK-based participants. Results Ten research nurses and 10 potential trial participants provided data for the study. Both stakeholder groups ranked similar statements in their top three most important statements, with ‘What are the possible disadvantages and risks of taking part? ’ featuring in both. However, considerable variability existed between the groups with regard to their ranking of statements of least importance. Participants identified that sufficient information to make a decision was secured using around 14 items. Participants also identified other items of importance not routinely included in PILs. Conclusions This study has provided a unique insight into how and why different trial stakeholder groups rank informational items currently contained within PILs. These results have implications for those developing future PILs and those who develop guidance on their content; PILs should focus most on the information items that potential trial participants want and need to make an informed choice about trial participation.
The functions of the lumbar spine during stepping in the cat

To examine the functional roles played by the lumbar spine during overground stepping, seven adult cats were run in electromyographic (EMG) experiments. Recordings were made bilaterally from mm. iliocostalis, longissimus dorsi and multifidus at a single vertebral level (L3) and from m. rectus abdominis. Stepping movements were monitored synchronously either by videotape or by high speed cinematography. During alternate use of the hindlimbs (walking and trotting), both epaxial and abdominal muscles were active bilaterally and biphasically. During in‐phase use of the hindlimbs (galloping and half‐bounding), single bursts of activity were observed. Phasic bursts of activity in rectus abdominus were reciprocal to those of epaxial muscles. Second bursts of activity in either group were noted infrequently. Recordings from the same back muscle at several vertebral levels indicated little difference from these patterns. Movements of the lumbar spine during galloping and half‐bounding steps, both angular and linear, are easily correlated with muscle activity patterns. Movements of the lumbar spine during walking and trotting show no particular pattern. Only small angular and linear movements are found. It is concluded that the lumbar spine contributes substantially to step length and limb speed during galloping and half‐bounding steps and the epaxial and abdominal musculature may also act as elastic bodies. During walking and trotting steps, the epaxial muscles are proposed to act to stabilize the pelvic girdle to provide a firm base for limb muscles which arise on the pelvis and are synchronously active.
Uncertain Pattern Classification Based on Evidence Fusion in Different Domains

It is a challenging problem for pattern classification with few labeled instances. Transfer learning provides an efficient solution to improve the classification accuracy using some training knowledge in the related domain (called source domain). Nevertheless, the single transformation in one direction may be uncertain in some cases, and this is harmful for classification. So we propose a new classification method based on the fusion of data transformations in different directions between source domain and target domain. At first, the mapping of target in the source domain is estimated by K-nearest neighbor technique using some one-to-one instance pairs, and the estimated mapping instance (pattern) can be classified in the source domain according to the available training data. Then, the credibility of classification result is evaluated. If the credibility achieves the expected threshold, the classification result is directly output. Otherwise, it indicates that the transformation may be not very reliable, and the labeled instances in source domain will be transferred to target domain for the classification of target. The two versions of classification results will be fused with different weights based on evidential reasoning, and the weighting factors are optimized using the available training instances. By doing this, we can efficiently reduce the uncertainty of transformation and improve the classification accuracy. Some real data sets from UCI have been employed to validate the effectiveness of the proposed by comparing with other related methods.
TARAS: Trust-Aware Role-Based Access Control System in Public Internet-of-Things

Due to the proliferation of Internet-of-Things (IoT) environments, humans working with heterogeneous, smart objects in public IoT environments become more popular than ever before. This situation often requires to establish trust relationships between a user and a smart object for their secure interactions, but without the presence of prior interactions. In this work, we are interested in how a smart object can grant an access right to a human user in the absence of any prior knowledge in which some users may be malicious aiming to breach security goals of the IoT system. To solve this problem, we propose a trust-aware, role-based access control system, namely TARAS, which provides adaptive authorization to users based on dynamic trust estimation. In TARAS, for the initial trust establishment, we take a multidisciplinary approach by adopting the concept of I-sharing from psychology. The I-sharing follows the rationale that people with similar roles and traits are more likely to respond in a similar way. This theory provides a powerful tool to quickly establish trust between a smart object and a new user with no prior interactions. In addition, TARAS can adaptively filter malicious users out by revoking their access rights based on adaptive, dynamic trust estimation. Our experimental results show that the proposed TARAS mechanism can maximize system integrity in terms of correctly detecting malicious or benign users while maximizing service availability to users particularly when the system is fine-tuned based on the identified optimal setting in terms of an optimal trust threshold.
Volume of ropivacaine 0.2% and common peroneal nerve block duration: a randomised, double‐blind cohort trial in healthy volunteers

The volume‐duration relationship using low concentrations of ropivacaine for peripheral nerve blocks is unknown, even though low concentrations of ropivacaine are increasingly used clinically. We investigated the effect of ropivacaine 0.2% on common peroneal nerve block duration. With ethical committee approval, 60 consenting, healthy volunteers were randomly allocated to receive one of five volumes of ropivacaine 0.2% (2.5, 5.0, 10, 15 or 20 ml) administered by ultrasound‐guided, catheter‐based injection (at 10 ml.min−1) near the common peroneal nerve. Our primary outcome was duration of sensory block, defined by insensitivity to a cold stimulus. Our secondary outcome was duration of motor block. Outcomes were assessed every hour from onset of block to complete remission. Intergroup differences were tested using one‐way ANOVA followed by regression analyses using the 20 ml intervention group as reference. Block durations varied significantly (p < 0.0001) between groups. Mean (SD) sensory block durations were 9.2 (3.3), 12.5 (3.0), 15.5 (4.4), 17.3 (3.5) and 17.3 (4.6) h. Mean (SD) motor block durations were 3.3 (2.1), 7.2 (2.5), 9.2 (2.2), 12.7 (2.5) and 12.5 (2.5) h. Regression analysis showed that the effect of volume on block duration was progressively smaller with increasing volume, reaching a threshold volume above which there was no effect on nerve block duration (10 ml for sensory block and 15 ml for motor block). We conclude that there is a ceiling effect of increasing volume of ropivacaine 0.2% on both sensory and motor block duration of the common peroneal nerve.
Automatic Blood Vessels Segmentation & Extraction in Fundus Images for Identification

Personal identification and authentication is a vital problem in any biometric application. The advancements in the field of biometric research enable us to find more complex methodologies which are accurate for authentication. Biometrics technology identifies the persons identity based upon his physiological or behavioural traits. The human retina provides one of the most reliable biometric technologies which is more accurate and secure. The retina is an internal organ of the eye, yet visible externally which is well protected from the environment and remains stable over time. The retinal blood vessel is extracted, which is unique for one person even in the case of twins and it is used for identification and authentication. Gray scale conversion from colored retinal image, resizing the retinal image and local contrast enhancement is performed for pre-processing the retinal image. Fractal dimension is calculated using box counting algorithm. Branch and crossover points are used as features for Morphological Segmentation and Branching point method. Euclidean distance measure is used for retinal identification process for fractal dimension and bifurcation methods. Vascular skeleton is segmented using morphological method called watershed transformation. The performance of all the three methods is evaluated.
A Compact Belief Rule-Based Classifier with Interval-Constrained Clustering

In this paper, a rule learning method based on interval-constrained clustering is proposed to efficiently design a compact belief rule-based classifier. The main idea of this method is to learn a compact belief rule base based on a set of prototypes generated from the original training set. First, an interval-constrained clustering algorithm is used to divide the training data for each class into several clusters, with which the number of data belonging to each cluster can be constrained within a given interval. Then, we define a belief rule based on the centroid of each cluster. Finally, a two-objective optimization procedure is designed to get a compact belief rule base with a better trade-off between accuracy and interpretability. Two experiments based on synthetic and benchmark data sets have been carried out to evaluate the performance of the proposed classifier.
Improving the Performance of Low-Resource SMT Using Neural-Inspired Sentence Generator

Machine translation (MT) is an important aspect of natural language processing (NLP) which uses bilingual data set and other language assets to translate text from a source language to text in a target language. The widely used statistical machine translation (SMT) system mainly relies on translation memories and glossaries to learn language pattern and translation rules. However the usage of these SMT systems under low-resource conditions remain a challenge. These SMT systems have a higher output quality when trained using domain specific training data since the texts belonging to same domain follow same pattern or usage of words. The current paper aims to develop and improve the performance of an SMT system under low-resource conditions for translation of health domain specific text from Tamil-to-English and English-to-Tamil. The translation quality of the machine translation system is improved using tuning based on minimum error rate training (MERT) and a novel neural-inspired sentence generator as a post-processor. The quality of translation and its performance analysis is evaluated in terms of the bilingual language understudy (BLEU) score and translation edit rate (TER).