Miracle-dz/UniFAR-FTUs huggingface dataset:
An experimental study on retrofitted fiber-reinforced concrete beams using acoustic emission

Fiber Reinforced Polymer (FRP) sheets are becoming increasingly important as a structural repair method when the load carrying capacity of deteriorated concrete beams is judged to be inadequate. In this field, the repair process must successfully integrate new materials with the old ones, forming a composite system capable of enduring exposure to service loads, environment and time. In this work, a set of Fiber Reinforced Concrete (FRC) beams has been retrofitted with fiber-reinforced polymer (FRP) sheets in order to improve their loading carrying capacity. These beams have been tested up to failure under three-point bending. During the loading tests, the acoustic emission AE technique has been applied to evaluate the damage evolution. By identifying the complete shape of the signals, the three-dimensional locations of damage sources are determined from the AE sensor records. In this connection, the authors have tuned an original procedure using advanced techniques that are usually applied for the analysis of seismic events.
A Recent Overview: Rare Association Rule Mining

association rules are mine useful information form large dataset. Traditional association mining methods generate frequent rules based on frequent itemsets with reference of minimum support and minimum confidence threshold which specified by user. It called as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. A rule that contains rare items can consider as rare association rule. Rare Association Rules Represent unpredictable or unknown association, so it is more interesting than frequent association rule. Rare association rule mining provides relationship between items which occurs uncommonly. This paper presents brief survey in the area of rare association rule mining. Keywordspattern, support, confidence, Rare Items
From Requirements Engineering to UML using Natural Language Processing – Survey Study

In the paper process of moving from software requirements to UML diagrams has been studied. It shows the importance of this process and discusses many comparative studies in the field. A questionnaire related to the study was distributed worldwide to many research groups, academia, and industry to know the current status of using requirement management tools, knowledge of using UML in software development, frequently used UML diagrams, and the methodology used to generate UML diagrams from requirements. The paper emphasises that there is a need to do some important research in the area of requirements NLP to obtain UML diagrams, and generalize process of using automatic or semi-automatic methodology to generate UML diagrams from requirements.
Alpha-1-antitrypsin types and chronic obstructive lung disease in an industrial community in Northern Sweden.

In a community in Vasterbotten county in northern Sweden occupationally dominated by a sulphite pulp factory all individuals (n = 3,466) received a questionnaire concerning respiratory symptoms, 94.0% answered and 8.1% claimed to have symptoms indicative of chronic obstructive lung disease (COLD). Of these individuals 93% were examined clinically, and the relationships between respiratory symptoms, verified COLD, smoking, occupation and quantitative and qualitative variations in the α1-antitrypsin (α1-AT) system (Pi) were examined. The frequency of all rare Pi types together was increased among individuals with COLD compared to a sample of individuals without respiratory symptoms and to the population. In individuals with COLD the frequencies of the MS, MZ and MF types taken separately were significantly increased when compared to the population. There were no significant differences in Pi frequencies between patients with chronic bronchitis and bronchial asthma, nor between sulphite pulp workers with COLD and other patients with COLD. Among individuals with respiratory symptoms chronic bronchitis was found to be correlated with occupation at the sulphite pulp factory, smoking and rare Pi types, the correlation coefficients being 0.42, 0.34 and 0.14, respectively. The M subtypes showed no association with COLD. Quantitative α1-AT differences between individuals with COLD and individuals without respiratory symptoms were found only in the M1M2 type. Low α1-AT levels (below (–)2 SD units) among M type individuals as a whole were not associated with COLD.
BIAXIAL TENSILE TESTS OF COATED FABRICS

Abstract : This report discusses the design, operation, and purpose of a new biaxial tensile test instrument to measure the stress-strain behavior of fabrics. The report also presents the stress-strain test results on three air- supported tent fabrics performed with the new test instrument. A correlation and comparison is made between these data and previous work performed with other tensile test equipment.
Identification of a cross-reactive allergen (presumably tropomyosin) in shrimp, mite and insects.

A monoclonal antibody to Dermatophagoides pteronyssinus is described that cross-reacts with an IgE-binding antigen present in insects, Crustacea (e.g. shrimp) and other invertebrates. By means of sodium dodecyl sulfate-polyacrylamide gel electrophoresis, gel filtration and immunofluorescence it was shown that this monoclonal antibody presumably recognizes tropomyosin. Tropomyosin was shown to be involved in cross-reactivity between mite, shrimp and insects in shrimp-allergic patients.
Creativity Tests versus Cognitive Computing: How Automated Personality Mining Tools Can Enhance Team Composition

Optimal composition of teams is an issue most enterprises face. Research conducted on this topic has identified personality as one of the key factors influencing team performance. The Big Five model, a framework for assessing personality, has standardized five personality traits, of which openness is reported to have a positive relationship with creativity. Creativity is regarded as one of the most relevant qualities for innovation. However, creativity as an ability manifested by performance on creativity tests is associated with difficulties. We therefore present cognitive systems as an alternative way, to not only find creative potential but also as a strategy to enhance team composition. Within our pilot study, we attempted to find a linkage between variables of creativity tests and the Big Five personality traits. Although our findings showed no salient correlations between these variables, we believe that automated personality mining tools would outperform creativity tests in the long run.
Local patch descriptor using deep convolutional generative adversarial network for loop closure detection in SLAM

Recently, the augmented reality and virtual reality fields have been actively researched and a lot of major companies have been aggressively investing the fields. On the core of the research field, the simultaneous localization and mapping (SLAM) algorithm which estimates the camera's position in a global coordinate and simultaneously constructs a 3D environment map firmly settled. Among typical components of modern SLAM framework, we are focusing on a loop closure detection for determining whether the current position of a robot agent was visited previously. The conventional algorithms for the loop closure detection relied on clustering hand-crafted features like SIFT, SURF, and ORB which appear a weakness to handle variations in the image such as a viewpoint change, illumination change, deformation, and occlusion. In this paper, we propose a local patch descriptor using a deep convolutional generative adversarial network to deal with the variations. The experiment result displays the proposed method well clusters the image patches with similar appearances better than the hand-craft features.
Finding Street Gang Member Profiles on Twitter

Balasuriya, Lakshika. M.S., Department of Computer Science and Engineering, Wright State University, 2017. Finding Street Gang Member Profiles on Twitter The crime and violence street gangs introduce into neighborhoods is a growing epidemic in cities around the world. Today, over 1.4 million people, belonging to more than 33,000 gangs, are active in the United States, of which 88% identify themselves as being members of a street gang. With the recent popularity of social media, street gang members have established online presences coinciding with their physical occupation of neighborhoods. Recent studies report that approximately 45% of gang members participate in online offending activities such as threatening, harassing individuals, posting violent videos or attacking someone on the street for something they said online in social media platforms. Thus, their social media posts may be useful to social workers and law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur in a community. Finding these posts, however, requires a method to discover gang member social media profiles. This is a challenging task since gang members represent a very small population compared to the active social media user base. This thesis studies the problem of automatically identifying street gang member profiles on Twitter, which is a popular social media platform that is commonly used by street gang members to promote their online gang-related activities. It outlines a process to curate one of the largest sets of verifiable gang member Twitter profiles iii that have ever been studied. A review of these profiles establishes differences in the language, profile and cover images, YouTube links, and emoji shared on Twitter by gang members compared to the rest of the Twitter population. Beyond the earlier efforts in Twitter profile identification that utilize features derived from the profile and tweet text, this thesis uses additional heterogeneous sets of features from the emoji usage, profile images, and links to YouTube videos reflecting gang-related music culture towards solving the gang member profile identification problem. Features from this review are used to train a series of supervised machine learning classifiers and they are further improved upon by using word embeddings learned over a large corpus of tweets. Experimental results demonstrate that heterogeneous features enabled our classifiers to achieve low false positive rates and promising F1-scores.
A Flexible and Integrated System for the Remote Acquisition of Neuropsychological Data in Stroke Research.

BACKGROUND Neuropsychological testing is a central aspect of stroke research because it provides critical information about the cognitive-behavioral status of stroke survivors, as well as the diagnosis and treatment of stroke-related disorders. Standard neuropsychological methods rely upon face-to-face interactions between a patient and researcher, which creates geographic and logistical barriers that impede research progress and treatment advances. INTRODUCTION To overcome these barriers, we created a flexible and integrated system for the remote acquisition of neuropsychological data (RAND). The system we developed has a secure architecture that permits collaborative videoconferencing. The system supports shared audiovisual feeds that can provide continuous virtual interaction between a participant and researcher throughout a testing session. Shared presentation and computing controls can be used to deliver auditory and visual test items adapted from standard face-to-face materials or execute computer-based assessments. Spoken and manual responses can be acquired, and the components of the session can be recorded for offline data analysis. MATERIALS AND METHODS To evaluate its feasibility, our RAND system was used to administer a speech-language test battery to 16 stroke survivors with a variety of communication, sensory, and motor impairments. The sessions were initiated virtually without prior face-to-face instruction in the RAND technology or test battery. RESULTS Neuropsychological data were successfully acquired from all participants, including those with limited technology experience, and those with a communication, sensory, or motor impairment. Furthermore, participants indicated a high level of satisfaction with the RAND system and the remote assessment that it permits. CONCLUSIONS The results indicate the feasibility of using the RAND system for virtual home-based neuropsychological assessment without prior face-to-face contact between a participant and researcher. Because our RAND system architecture uses off-the-shelf technology and software, it can be duplicated without specialized expertise or equipment. In sum, our RAND system offers a readily available and promising alternative to face-to-face neuropsychological assessment in stroke research.
DENSELY CONNECTED RECURRENT NEURAL NET-

Deep neural networks based sequence-to-sequence learning has achieved remarkable progress in applications like machine translation and text summarization. However, sequence-to-sequence models suffer from severe inefficiency in training process, requiring huge amount of training time as well as memory usage. In this work, inspired by densely connected layers in modern convolutional neural network, we introduce densely connected sequence-to-sequence learning mechanism to tackle this challenge. In this mechanism, multiple layers of representations from stacked recurrent neural networks are concatenated to enhance feature reuse. Furthermore, a densely connected attention model is elaborately leveraged to improve information flow with more efficient parameter usage via multi-branch structure and local sparsity. We show that such a densely connected mechanism significantly reduces training time and memory usage for sequence-to-sequence learning. In particular, in WMT-14 English-French translation task with a subset of 12M training data, it takes half of training time and model parameters to achieve similar BLEU as typical stacked LSTM models.
Fontan Physiology Revisited

The Fontan operation places the systemic and pulmonary circulations in series, driven by a single ventricular chamber. It has become the treatment strategy of choice for palliating single-ventricle congenital heart disease. This anatomy engenders profound changes in physiology, affecting the cardiovascular and respiratory systems with direct implications for anesthetic and intensive care. The physical basis of these changes and their sequelae are reviewed.
A dynamic programming variant of non-negative matrix deconvolution for the transcription of struck string instruments

Given a musical audio recording, the goal of music transcription is to determine a score-like representation of the piece underlying the recording. Most current transcription methods employ variants of non-negative matrix factorization (NMF), which often fails to robustly model instruments producing non-stationary sounds. Using entire time-frequency patterns to represent sounds, non-negative matrix deconvolution (NMD) can capture certain types of non-stationary behavior but is only applicable if all sounds have the same length. In this paper, we present a novel method that combines the non-stationarity modeling capabilities available with NMD with the variable note lengths possible with NMF. Identifying frames in NMD patterns with states in a dynamical system, our method iteratively generates sound-object candidates separately for each pitch, which are then combined in a global optimization. We demonstrate the transcription capabilities of our method using piano pieces assuming the availability of single note recordings as training data.
Comparative investigation on wear behavior and self-sharpening phenomenon of polycrystalline cubic boron nitride and monocrystalline cubic boron nitride grains in high-speed grinding

High-speed grinding experiments were conducted on a nickel-based superalloy Inconel718 with the monolayer brazed wheel containing monocrystalline cubic boron nitride grains and polycrystalline cubic boron nitride grains. Comparative investigation on the wear behavior and self-sharpening phenomenon of polycrystalline cubic boron nitride and monocrystalline cubic boron nitride grains was carried out based on the fractal analysis. The results obtained indicate that the wear process of the monocrystalline cubic boron nitride grain cutting edges is, in order, attritious wear → large fracture → micro fracture → large fracture → attritious wear, while that of the polycrystalline cubic boron nitride grain cutting edges is micro fracture → attritious wear → micro fracture. Micro fracture of polycrystalline cubic boron nitride grain occurs easily in the particular zone where large impact load is formed due to the first contact between the grain cutting edges and the workpiece material. The fractal dimension of monocrystalline cubic boron nitride wheel is 2.040–2.047, while that of the polycrystalline cubic boron nitride wheel is 2.049–2.054, which indicates that the polycrystalline cubic boron nitride grain cutting edges are finer than that of the monocrystalline cubic boron nitride counterparts. Compared to monocrystalline cubic boron nitride grains, better performance, that is, smaller radial wheel wear, lower grinding force and forces ratio, is obtained for polycrystalline cubic boron nitride grains due to micro fracture behavior and self-sharpening phenomenon in high-speed grinding.
Adaptive control using neural networks and approximate models

The NARMA model is an exact representation of the input-output behavior of dynamical systems. However, it is not convenient for purposes of control. In this paper, the authors introduce two classes of models which are approximations to the NARMA model, and at the same time substantially simplifies the control problem.
Neurocognition in Post-Traumatic Stress Disorder

The negative behavioural and emotional symptoms of Post-Traumatic Stress Disorder (PTSD) have been extensively reported in the literature. However, much less is known about the neuropsychological and neurobiological characteristics of the disorder. This thesis consists of two papers, the first being a review which highlights the emerging picture of literature in the field of neuropsychology in PTSD, with particular reference to findings in those cognitive domains of general intellectual functioning, memory, attention and executive function. Given that the findings associated within these domains are mixed, the second paper reports the outcome from a neuropsychological study of cognitive differences that was conducted to contribute to current knowledge in the area of neurocognition and visual memory in PTSD in particular. Trauma exposure, current PTSD, depressive and anxiety symptoms and performance on a range of neuropsychological tests were examined in tertiary care outpatients with PTSD (n=26), individuals who had been exposed to severe trauma but without current PTSD (n=26), and healthy controls (n=26). In addition to previously reported deficits in verbal learning and fluency in PTSD, deficits in visual spatial memory were also found. These observable deficits in visual memory may reflect characteristic features of PTSD, such as reported difficulties in remembering certain aspects of traumatic events and the presence of visual flashbacks. It is uncertain whether these deficits represent a risk factor for PTSD, or a consequence of trauma, as suggested by research in animal models.
The Use of Mosquito Nets and the Prevalence of Plasmodium falciparum Infection in Rural South Central Somalia

Background There have been resurgent efforts in Africa to estimate the public health impact of malaria control interventions such as insecticide treated nets (ITNs) following substantial investments in scaling-up coverage in the last five years. Little is known, however, on the effectiveness of ITN in areas of Africa that support low transmission. This hinders the accurate estimation of impact of ITN use on disease burden and its cost-effectiveness in low transmission settings. Methods and Principal Findings Using a stratified two-stage cluster sample design, four cross-sectional studies were undertaken between March-June 2007 across three livelihood groups in an area of low intensity malaria transmission in South Central Somalia. Information on bed net use; age; and sex of all participants were recorded. A finger prick blood sample was taken from participants to examine for parasitaemia. Mantel-Haenzel methods were used to measure the effect of net use on parasitaemia adjusting for livelihood; age; and sex. A total of 10,587 individuals of all ages were seen of which 10,359 provided full information. Overall net use and parasite prevalence were 12.4% and 15.7% respectively. Age-specific protective effectiveness (PE) of bed net ranged from 39% among <5 years to 72% among 5–14 years old. Overall PE of bed nets was 54% (95% confidence interval 44%–63%) after adjusting for livelihood; sex; and age. Conclusions and Significance Bed nets confer high protection against parasite infection in South Central Somalia. In such areas where baseline transmission is low, however, the absolute reductions in parasitaemia due to wide-scale net use will be relatively small raising questions on the cost-effectiveness of covering millions of people living in such settings in Africa with nets. Further understanding of the progress of disease upon infection against the cost of averting its consequent burden in low transmission areas of Africa is therefore required.
Manifold Learning of Overcomplete Feature Spaces in a Multimodal Biometric Recognition System of Iris and Palmprint

This paper presents a bimodal biometric recognition system based on iris and palmprint. Different wavelet-based filters including log Gabor, Discrete Cosine Transform (DCT), Walsh and Haar are used to extract features from images. Then we fuse iris and palmprint at the feature level by concatenating the feature vectors from two modalities. Since wavelet transforms generate huge number of features, a dimensionality reduction step is necessary to make the classification and matching steps tractable and computationally feasible. In this paper, two well-known dimensionality reduction algorithms including Laplacian eigenmaps and Singular Value Decomposition (SVD) are used to reduce the size of feature space. Applying these dimensionality reduction methods not only decreases the computational cost of matching remarkably but also it improves the accuracy of recognition by reducing the unnecessary model complexity. Eventually multiple classification techniques are used in the transformed feature spaces for the final matching and recognition. CASIA datasets for iris and palmprint are used in this study. The experiments show the effectiveness of our feature level fusion method and also the dimensionality reduction methods we used. Based on our experiments, our multimodal biometric system always outperforms the unimodal recognition systems with higher accuracy. Moreover, an appropriate dimensionality reduction algorithm always helps to improve the accuracy of classifier. Finally, the log Gabor filter extracts the most discriminative features from images compared to other wavelet transforms.