Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric modeling with primitives

Both man-made or natural objects contain repeated geometric elements that can be interpreted as primitive shapes. Plants, trees, living organisms or even crystals, showcase primitives that repeat themselves. Primitives are also commonly found in man-made environments because architects tend to reuse the same patterns over a building and typically employ simple shapes, such as rectangular windows and doors. During my PhD I studied geometric primitives from three points of view: their composition, simulation and autonomous discovery. In the first part I present a method to reverse-engineer the function by which some primitives are combined. Our system is based on a composition function template that is represented by a parametric surface. The parametric surface is deformed via a non-rigid alignment of a surface that, once converged, represents the desired operator. This enables the interactive modeling of operators via a simple sketch, solving a major usability gap of composition modeling. In the second part I introduce the use of a novel primitive for real-time physics simulations. This primitive is suitable to efficiently model volume-preserving deformations of rods but also of more complex structures such as muscles. One of the core advantages of our approach is that our primitive can serve as a unified representation to do collision detection, simulation, and surface skinning. In the third part I present an unsupervised deep learning framework to learn and detect primitives. In a signal containing a repetition of elements, the method is able to automatically identify the structure of these elements (i.e. primitives) with minimal supervision. In order to train the network that contains a non-differentiable operation, a novel multi-step training process is presented.
How Does Information Technology Really Affect Productivity? Plant-Level Comparisons of Product Innovation, Process Improvement and Worker Skills

This study presents new empirical evidence on the relationship between investments in new computer-based information technology (IT) and productivity by investigating several plant-level mechanisms through which IT could promote productivity growth. We have assembled a data set on plants with a common production technology in a narrowly defined industry - valve manufacturing - to study the effects of new IT on product innovation, production process improvements, employee skills and work practices. The homogeneity of the plants' production processes within this narrowly defined industry together with the estimation of longitudinal models eliminate many sources of unmeasured heterogeneity that could confound productivity comparisons in more aggregate data and in broader samples. The three main results of this study highlight how the adoption of new IT-enhanced machinery involves much more than just the installation of new equipment on the factory floor. We find that adoption of new IT-enhanced equipment (1)alters business strategies, moving valve manufacturers away from commodity production based on long production runs to customized production in smaller batches; (2)improves the efficiency of all stages of the production process with reductions in setup times supporting the change in business strategy and (3)increases the skill requirements of workers while promoting the adoption of new human resource practices.
Dense point cloud acquisition with a low-cost Velodyne VLP-16

Abstract. This study develops a low-cost terrestrial lidar system (TLS) for dense point cloud acquisition. Our system consists of a VLP-16 lidar scanner produced by Velodyne, which we have placed on a motorized rotating platform. This allows us to continuously change the direction and densify the scan. Axis correction is performed in post-processing to obtain accurate scans. The system has been compared indoors with a high-cost system, showing an average absolute difference of ±2.5 cm. Stability tests demonstrated an average distance of ±2 cm between repeated scans with our system. The system has been tested in abandoned mines with promising results. It has a very low price (approximately USD 4000) and opens the door to measuring risky sectors where instrument loss is high but information valuable.
Eavesdropping Hackers: Detecting Software Vulnerability Communication on Social Media Using Text Mining

—Cyber security is striving to ﬁnd new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on Deep/Dark Web and even Surface Web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how feature reduction techniques, such as Document Frequency Reduction, Chi-square and Singular Value Decomposition (SVD) can be used to reduce the number of features of the model without impacting the detection performance. We conclude that: (1) a Support Vector Machine (SVM) algorithm used with traditional Bag of Words achieved highest accuracies (2) The increase of the minority class with Random Oversampling technique improves the detection performance of the model by 5% on average, and (3) The number of features of the model can be reduced by up to 10% without affecting the detection performance. Also, we have provided the labelled dataset used in this work for further research. These ﬁndings can be used to support Cyber Security Threat Intelligence (CTI) with respect to the use of text mining techniques for detecting security-related communication.
An update of Hepatitis C prevalence rates in homeless adults after Hepatitis C treatment paradigm change: A systematic review and meta-analysis

Background: An estimated 100 million people are homeless around the world. Concurrent vulnerabilities, such as psychiatric diseases, addictions with unsafe injection practices increase blood-borne infections risks, including HCV in homeless individuals. A 2012 Lancet Infectious Diseases paper reported HCV prevalence in homeless ranging from 3·9% to 36·2%, but we know very little about HCV treatment in homeless, aside from the fact that treatment is rarely if at all provided or considered. Old treatment regimens from the “interferon era” had many psychiatric side effects, including increased suicide and major depression rates and were contraindicated in patients, who had pre-existing or secondary psychiatric diseases, addictions, and were unstable. Meanwhile, treatment paradigm has changed in HCV management recently. Current HCV treatment options are not contraindicated in people with psychiatric conditions anymore and can help successfully achieve HCV cure. Additionally, new treatment options are shorter in duration, all-oral instead of injections with easier to adhere regimens, and are recommended by current guidelines in unstable individuals as well. This study objective is to update previous study findings, and examine HCV treatment prevalence in homeless adults. Methods: On February 2016, we searched PubMed, EMBASE, and Cumulative Index to Nursing and Allied Health Literature databases for “homeless* and (hepatitis C or HCV)” for studies reporting HCV prevalence in homeless adults published between 31 January 2012 and 15 February 2016. Meta-analysis was conducted following the PRISMA Checklist. Data was tabulated in Comprehensive Meta-Analysis. Findings: Fifteen epidemiological studies yielded. The omnibus prevalence rate for HCV in homeless remains unchanged since 2012, (28%; 95% CI: 23-34; N=15). Only three studies reported HCV treatment investigation, but the data quality could not allow a meta-analysis. Interpretation: Despite a high HCV prevalence among homeless, HCV treatment prevalence information is limited; some studies mention that treatment is not practically provided. This meta-analysis data can help to estimate the frequency of HCV infection, which can help to plan HCV management services for homeless population in a better way. Together with the recent advancements, paradigm changes in HCV treatment the data from this review can also contribute to the global HCV elimination goal.
Permafrost thawing exhibits a greater influence on bacterial richness
and community structure than permafrost age in Arctic permafrost
soils

Abstract. Global warming accelerates permafrost thawing and changes permafrost microbial community structure, but little is known about how microorganisms in permafrost with different ages respond to thawing. Herein, we disentangled the relative importance of permafrost age (young, medium, old, and ancient) spanning from 50 to 5,000 yr and thawing status (active, transitional, and permanently frozen) in shaping bacterial community structure using Hiseq sequencing of the 16S rRNA gene. Our results revealed significant influences of both permafrost thawing and age on bacterial richness. The bacterial richness was significantly higher in the young and thawed permafrost, and the richness increase was mainly observed in Firmicutes, Actinobacteria, Chloroflexi, Deltaproteobacterai, and Alphaproteobacteria. Permafrost thawing led to a gradual change in bacterial community structure and increased the contribution of determinism to shape the bacterial community assembly. Permutational analysis of variance demonstrated that thawing significantly changed bacterial community structure at all soil ages, but the community convergence due to permafrost thawing was not observed. Structural equation modeling revealed that permafrost thawing exhibited a greater influence on both bacterial richness and community structure than permafrost age. Our results indicate that microorganisms in permafrost with different ages respond differently to thawing, which eventually leads to distinct bacterial community compositions and different soil organic carbon degradation processes during permafrost thawing.
Obstacles to using E-Learning in an Advanced Way

— E-Learning has become a common way to teach and learn. The number of technologies for a variety of educational purposes is already quite high and constantly rising. Scientific experiments and studies increasingly confirm the usefulness of various technologies for teaching purposes. Nevertheless, there is still a lack of formal training and support of digital skills within faculty training. Studies that report the potential of E-Learning are matched by those reporting the obstacles. Universities throughout Europe have now established Learning Management Systems (LMS); instead of using these to their full potential, lecturers often just upload their syllabus and some reading material. At the same time, higher education institutions all over Europe are expected to implement innovative technologies and scenarios such as Open Educational Resources (OER) or Massive Open Online Courses (MOOCs), but only a small number of universities actually do so. What are the reasons for not utilizing the pedagogic potentials of E-Learning? What findings does the current research provide on this topic? Which obstacles can be derived from studies and what can be done to avoid them? What are the individual obstacles of one institution or university? This contribution summarizes obstacles that were identified in recent studies and discusses possible solutions to finding connections between obstacles to mitigate the negative effects. It also describes a data collection method (group concept mapping) suitable for identifying the individual obstacles at an institution or university using a study in the European ERASMUS+ project AduLeT (Advanced Use of Technologies in Higher Education) as an example.
Quality of Life among Iranian Postmenopausal Women: A Systematic Review and Meta-Analysis

Menopause can cause mental, physical, vasomotor, and sexual symptoms and problems, which negatively affect the quality of life (QOL). The aim of this study was to systematically evaluate QOL among Iranian postmenopausal women. This systematic review was conducted on cross-sectional studies that were published between 2000 and 2018. An online search to find studies published in English or Persian was conducted in the databases of Web of Science, PubMed, ScienceDirect, Scopus, Google Scholar, Scientific Information Database, Magiran, and IranMedex. Search key terms were "quality of life", "menopause", and "Iran". Fourteen studies were eligible for this study. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was used for quality appraisal. The mean and standard deviation of QOL and its domains were extracted from the selected studies. Study data were analyzed using the Review Manager (v. 5.0) and the STATA (v. 12.0) software. The mean of total QOL among 3413 postmenopausal women was 57.89±12.8 (in the possible range of 0–174). The means of its vasomotor, psychosocial, physical, and sexual domains were 7.86±2.14, 19.43±2.05, 40.58±3.33, and 6.71±1.77, respectively. The QOL among Iranian postmenopausal women is nearly higher than the moderate level. The lowest and the highest levels of QOL are related to the physical and sexual domains, respectively. Health authorities need to develop educational interventions to promote postmenopausal women’s QOL, particularly in the physical domain.
Examination and Comparison of Signatures by Computational Technique

: Many signature verification techniques have been developed in order to distinguish between the genuine signatures and the forgeries. In this paper, an attempt is made to provide an effective method of examination and comparison of signatures by using a computational technique. For various legal problems and in relation to their admissibility in court this computational approach is given which is used as a reflection of the true state of evidence and to strengthen ones observation regarding differences among the two specimens. The computational method of measuring pixel values of signatures based on distance and angle based classification of signatures with better accuracy. The performance of this system is then calculated on the basis of False acceptance range and False rejection range, graphical representations and statistical analysis and provide the better result. Achieved results suggested the adequacy of the selected features. The experimental results is presented with the concluding mark that the presented method showed its accuracy up to 90-95%.
Money Metric Poverty and its Correlates – A Case Study in Tripura

Present study seeks to apply the Money-Metric Approach of poverty measurement, a prevalent method used to quantify poverty based on income or consumption levels which mean that an individual or household is considered as poor if his income or consumption level falls below some bare minimum level (poverty threshold or poverty line) essential to meet his basic needs. The study reported here uses primary data which is collected from a field survey of 120 households from the rural areas of West Tripura District, Tripura. It is reasonably obvious that strategies aimed to reduce the incidence of poverty essentially rely on identification of factors that are strongly linked with poverty. In that direction Binary Logit Regression analysis is used to find out different socio-economic and demographic factors that help in perpetuation of poverty at household level. The study shows that age of the household head, household size, education level of the household head and marital status of the household head are found to be significant predictor of household poverty status.
Evaluation of Sealing Ability of AH Plus And TG Root Canal Sealers in the Presence and Absence of Smear Layer

Background: Smear layer is considered to be the main contributing factor in better sealing and adhesion of a sealer. Objective: The aim of this study is to determine the sealing ability of two root canal sealers (AH Plus and tg). In the presence and absence of smear layer Methodology: Extracted teeth from patients visiting Sardar Begum Dental College 60 in numbers were used in this study. Specimens were divided in to two main groups (group1 and group 2) both groups were instrumented using a step back technique. Irrigation was done during instrumentation with one ml of 5.25% NaOCl solution. In group2 a final flush with 10 ml of 17% EDTA solution followed by 10ml of 5.25% NaOCl was used for smear layer removal. Each group then further divided in to two sub groups (n=15). After obturation specimens were kept in separate containers with wet gauze to maintain 100% humidity. The specimens were coated with nail polish except for apical 2mm. Specimens were then suspended vertically in two % methylene blue dye for 7 days. Longitudinal sectioning was done to determine the extent of dye penetration using a stereo microscope at X30 magnification. Student t test was used for statistical analysis (p≤ 0.05). Results: The results showed that in the presence of smear layer, mean apical dye leakage of AH Plus was2.89 mm and tg was 4.02 mm. While in the absence of smear layer the mean dye leakage of AH Plus was 2.23 mm and tg was 3.28 mm. Student t test was more than p value indicating non significant result for both sealers. Conclusion: Apical sealing ability of AH Plus is better than tg root canal sealer in the presence and absence of smear layer.
Clustering in weighted networks using Binomial stochastic blockmodels

Résumé. Le modèle à blocs stochastiques est un modèle de graphe aléatoire qui vise à partitionner les sommets d’un réseau en groupes appelés blocs, ou plus généralement clusters. Dans la plupart des réseaux du monde réel, les liens entre les noeuds sont affectés par des poids qui représentent la force des relations entre ces noeuds. Il est évidemment très intéressant de pouvoir modéliser et regrouper ces réseaux pondérés en utilisant la structure du réseau et la capacité de leurs liens. Cet article présente le modèle à blocs stochastiques binomial, qui est un modèle probabiliste pour les réseaux ayant les poids sur les arêtes distribués selon une loi binomiale. Un algorithme variationnel d’espérancemaximisation est proposé ici pour effectuer l’inférence. Enfin, nous démontrons l’efficacité de la méthode proposée en considérant un réseau de co-citation dans un contexte de text mining. Mots-clés. Modèle à blocs stochastiques binomial, clustering, classes latentes, fouille de textes, inférence variationnelle, réseaux pondérés.
Complementary and Alternative Medicine Use among Elderly Diabetic Patients attending Assiut University and Health Insurance Hospitals

Background: Diabetes among elderly population is difficult because of complex co-morbid conditions and the generally lower functional status of elderly patients. In recent years, there is a rising concern that many patients use complementary and alternative medicine (CAM). Aim of the study: Determine the percentage of CAM use and its related factors in elderly diabetic patients attending diabetes clinics in Assiut city. Subjects and Methods: crosssectional study was done. The data for this study were collected with 400 elderly diabetic patients attending diabetes outpatient clinics of Assiut University and Health Insurance Hospitals. A semi-structured questionnaire was used for data collection from the first day of September 2013 to the first week of October 2013. Data was analyzed using SPSS version 16. Results: About one third (33.8%) of the respondents used CAM in addition to conventional medicine. Most common type used was herbal medicine. The CAM use was more frequent among male patients, aged 65< 70 years, university educated and urban residents. Conclusion: About one third of the studied elderly diabetic patients used CAM and conventional medicine in managing their illness There is statistical significant difference between CAM use and sex and level of education of our respondents. Recommendations: Evidencebased CAM information should be provided to patients as part of diabetes routine management and counseling.
Screening for Depression in the Primary Care Clinics in Saudi Arabia

Background: Depression has a significant morbidity rate internationally and it is associated with poor health status and reduced quality of life in the community. A lack of identification of depression on primary care practices has been associated with lack of screening tools or specific protocols and lack of adherence to practice-level guidelines and depression screening implementation. Purpose: This project assessed whether primary care medical providers in Saudi Arabia were able to recognize the symptoms of depression in their patients and tested a new protocol and tools to address the issue. This study focused on the population that was visiting a large Healthcare Medical Facility in Dhahran, Saudi Arabia. Results: 150 patients were screened. The results indicated that up to 34.6 % of the patients turned out to be positive with majority being women of reproductive age. It also indicates that majority of the people in need of these services are women. Conclusion: This has confirmed the need for such a screening tool in primary health care facilities in Saudi Arabia. The results further indicate that early diagnosis of depression by such facilities will lead to improved management, increase quality of life, and potentially save resources for the patients who would have been referred, making this project very timely and relevant.
Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system.

Background Bone age can reflect the true growth and development status of a child; thus, it plays a critical role in evaluating growth and endocrine disorders. This study established and validated an optimized Tanner-Whitehouse 3 artificial intelligence (TW3-AI) bone age assessment (BAA) system based on a convolutional neural network (CNN). Methods A data set of 9,059 clinical radiographs of the left hand was obtained from the picture archives and communication systems (PACS) between January 2012 and December 2016. Among these, 8,005/9,059 (88%) samples were treated as the training set for model implementation, 804/9,059 (9%) samples as the validation set for parameters optimization, and the remaining 250/9,059 (3%) samples were used to verify the accuracy and reliability of the model compared to that of 4 experienced endocrinologists and 2 experienced radiologists. The overall variation of TW3-metacarpophalangeal, radius, ulna and short bones (RUS) and TW3-Carpal bone score, as well as each bone (13 RUS + 7 Carpal) between reviewers and the AI, were compared by Bland-Altman (BA) chart and Kappa test, respectively. Furthermore, the time consumption between the model and reviewers was also compared. Results The performance of TW3-AI model was highly consistent with the reviewers' overall estimation, and the root mean square (RMS) was 0.50 years. The accuracy of the BAA of the TW3-AI model was better than the estimate of the reviewers. Further analysis revealed that human interpretations of the male capitate, hamate, the first distal and fifth middle phalanx and female capitate, the trapezoid, and the third and fifth middle phalanx, were most inconsistent. The average image processing time was 1.5±0.2 s in the TW3-AI model, which was significantly shorter than manual interpretation. Conclusions The diagnostic performance of CNN-based TW3 BAA was accurate and timesaving, and possesses better stability compared to diagnostics made by experienced experts.