Miracle-dz/UniFAR-FTUs huggingface dataset:
Designed entrepreneurial legitimacy: the case of a Swedish crowdfunding platform

ABSTRACT Digital entrepreneurs face the liability of newness, like any other entrepreneur. However, this liability of newness is manifested differently: it is mediated by an artefact, in this case a platform. This paper examines how a digital entrepreneur mediated by a platform can build legitimacy, something hitherto thought to be embedded within a social relationship (that is, one that a digital platform may be unable to have). Based on a qualitative research design, we develop the concept of “designed legitimacy”, and we point to how trust may not be enough to overcome the liability of newness. Rather, legitimacy is needed to attract users and resources, and thus for growth and success. We further highlight the means through which a platform may come to be seen as legitimate, namely by designing with legitimacy in mind: by using symbols in design, asymmetric legitimacy building, and sequential two-stage legitimacy building. We end the paper with propositions for further study and the implications of this research for digital entrepreneurship and platforms.
A comparative study of Neural Machine Translation frameworks for the automatic translation of open data resources

Machine Translation (MT) is one of the most active areas in Artificial Intelligence, particularly in Pattern Recognition. MT has recently received a great deal of attention by key technology players such as Google, Facebook, Microsoft, etc. due to the performance boost experienced by MT technology thanks to the incorporation of artificial neural networks that constitute the backbone of deep learning. In this respect, the new era of Neural-based MT (NMT) systems has brought about accurate enough automatic translation to ease human communication in a multilingual society. In this work, conventional theoretical models behind NMT are introduced together with the required background to provide a comprehensive view. In addition, state-of-the-art NMT systems are built on top of two well-known frameworks for machine learning, Tensorflow and MXNet. These systems are trained using the data from two case studies. The first case is the Workshop on Machine Translation for the translation of broadcast news and European Parliament debates from German into English. The second case is poliMedia, UPV’s main repository for educational video lectures, which in this work is used for translation from Spanish into English. The developed systems are evaluated and compared with respect to their quality and efficiency, before being deployed in a real-world production environment such as poliMedia.
Survey on ethnobotanical plants used for wound healing: Nagpur region

Copyright: © Author(s), This is an open access article under the terms of the Creative Commons Attribution-Non-Commercial No Derives License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. The present study was carryout to enhance the knowledge of ethnobotanical plants used by local peoples, tribes of Nagpur region. The 44 plants were listed on the basis of ethno information and also based on literature used for wound healing. The information was compare with flora of Maharashtra, Nagpur, BSI.
Effects of Exercise Intervention on the Improvement of Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disease characterized by hyperandrogenism and insulin resistance. It results in irregular menstruation, obesity and even infertility, which influences badly physical and mental health of the patients. Now, there is no effective drug for treating PCOS and the preferred program is lifestyle modification, including diet control, regular exercise and behavior therapy. Therefore, in the review, we summarize the study progress concerning the effects of lifestyle intervention on the metabolic, reproductive and psychological dysfunctions of PCOS patients and analyze the corresponding mechanisms in these processes. It can radically reduce the factors of PCOS occurrence and development, while providing valu-able information for the prevention and treatment of PCOS and is helpful for further research.
Power Quality Improvement using Improved Approximated Fuzzy Logic Controller for Shunt Active Power Filter

This chapter presents the design approach of an Improved Approximated Simplest Fuzzy Logic Controller (IASFLC). A cascade combination of simplest 4-rule Fuzzy Logic Controller (FLC) and an n th degree polynomial is proposed as an IASFLC to approximate the control characteristics of a 49-rule FLC. The approximation scheme is based on minimizing the sum of square errors between the control outputs of a 49-rule FLC and a simplest 4-rule FLC in the entire range of Universe Of Discourse (UOD). The coefficients of compensating polynomial are evaluated by solving instantaneous square error equations at various test points in the entire UOD. This IASFLC maps the output of a 49-rule FLC with absolute deviation of less than 5%. The proposed IASFLC is used to control the dc link voltage of a three-phase shunt Active Power Filter (APF). A detailed analysis is performed during transient and steady state conditions to check Power Quality (PQ) and dynamic performance indices under randomly varying balanced and unbalanced loading conditions. The performance of proposed IASFLC is compared with a 49-rule FLC and Approximated Simplest Fuzzy Logic Controller (ASFLC) based on minimization of the deviation at central values of Membership Functions (MFs). It is found comparatively better for harmonic and reactive compensation with a comparable dynamic response. The memory requirement and computational time of proposed IASFLC are even lesser than the ASFLC.
Üniversite öğrencilerinde problemli internet kullanımı ve sağlıklı yaşam biçimi davranışları

Amac: Bu calisma, universite ogrencilerinde problemli internet kullanimi ve saglikli yasam bicimi davranislarini belirlemek amaci ile yapilmistir. Gerec ve Yontem: Firat Universitesi’nde okuyan 1723 ogrenci arastirmaya dahil edilmistir. Ogrenciler bolum, sinif ve cinsiyetlerine gore tabakali ornekleme yontemine gore secilmislerdir. Ogrencilere sosyo-demografik ozellikler ile, problemli internet kullanimi ve saglikli yasam bicimi davranislari olcegini iceren anket formu uygulanmistir. Bulgular: Ogrencilerin yas ortalamasi 21.5±2.2’dir. %38.2’si kadin, %67.3’u erkektir. Ogrencilerin %50.9’u haftada en az 6 gun internete baglanmakta, %21.4’u gunde ortalama 2 saat internet baglanmaktadir. Internetin en fazla kullanim amaclari; e-posta adresine bakmak (%87.8), gazete dergi okumak (%63.4), online oyun oynamaktir (%28.4). Uzun sureli internet kullanimina bagli en fazla gorulen problemler ise, gozlerde yanma, boyun kaslarinda agri ve uykusuzluktur. Saglikli yasam bicimi davranislari ile problemli internet kullanimi olcegi puanlari arasinda negatif yonde anlamli duzeyde korelasyon bulunmustur. Sonuc: Ogrencilerde problemli internet kullaniminin, saglikli yasam bicimi davranislarini olumsuz etkiledigi gorulmustur. Problemli internet kullanimini azaltacak, saglikli yasam bicimi davranislarini gelistirecek tedbirler alinmalidir.
A Very Rare Cause of Erythema Nodosum: Rotavirus

Eritema nodozum subkütan yağ dokuyu tutan, eritematöz, ağrılı, hassas nodüllerle seyreden ve en sık görülen pannikülit şeklidir. Tip 4 gecikmiş hipersensitivite reaksiyonudur. Enfeksiyöz (%39.5), nonenfeksiyöz (%29.6) ve idiyopatik (%25) sebepleri vardır. Enfeksiyöz etkenler arasında en sık streptokok enfeksiyonları yer almaktadır, bunu Salmonella, Giardia lamblia ve Yersinia enterocolitica enfeksiyonları takip etmektedir. Eritema nodozum ile ilgili çalışmalara bakıldığında rotavirüs ile ilişkili yalnızca bir olgu bildirilmiştir. Olgumuzda rotavirüs ishali seyrinde eritema nodozum gelişen üç yaşındaki bir kız hasta sunulmuştur.
VR-visualization of High-dimensional Urban Data

The project aims to investigate the possibility of VR in a combination of visualizing high-dimensional urban data. Our study proposes a data-based tool for urban planners, architects, and researchers to 3D visualize and experience an urban quarter. Users have a possibility to choose a specific part of a city according to urban data input like "buildings, streets, and landscapes". This data-based tool is based on an algorithm to translate data from Shapefiles (.sh) in a form of a virtual cube model. The tool can be scaled and hence applied globally. The goal of the study is to improve understanding of the connection and analysis of high-dimensional urban data beyond a two-dimensional static graph or three-dimensional image. Professionals may find an optimized condition between urban data through abstract simulation. By implementing this tool in the early design process, researchers have an opportunity to develop a new vision for extending and optimizing urban materials.
Urgent Control of Rapid Atrial Fibrillation by Landiolol in Patients With Acute Decompensated Heart Failure With Severely Reduced Ejection Fraction

Background: We investigated the clinical usefulness of landiolol for rapid atrial fibrillation (AF) in patients with acute decompensated heart failure (ADHF) and identify the patients eligible for landiolol. Methods and Results: A total of 101 ADHF patients with reduced ejection fraction (HFrEF) with rapid AF were enrolled. Immediately after admission, an initial dose of landiolol was given (1 μg/kg−1/min−1), and then the dose was increased to decrease heart rate (HR) to <110 beats/min and change HR (∆HR) >20% in ≤24 h. Thirty-seven were monitored using right heart catheterization at 3 points (baseline, 1 μg/kg−1/min−1, and maximum dose). We checked the major adverse events (MAE) during initial hospitalization, which included cardiac death, HF prolongation (required i.v. treatment at 30 days), and worsening renal function. The average maximum dose of landiolol was 3.8±2.3 μg/kg−1/min−1. HR (P<0.0001) and pulmonary capillary wedge pressure (P=0.0008) decreased safely. MAE occurred in 39 patients. The patients with left ventricular (LV) end-diastolic volume index <84.0 mL/m2 and mean blood pressure (mean BP) >97 mmHg had less frequent MAE (P<0.0001). Conclusions: Landiolol was effective for safely controlling rapid AF in patients with HFrEF with ADHF, leading to hemodynamic improvement and avoidance of short-term MAE, especially in patients with relatively smaller LV and higher BP.
Sentiment Analysis of Tweets to Classify the Box Office Success of Movies

The movie making industry is one of the top money-making machines one can say based on the revenues being generated every year from movies, a whopping 11 billion dollars were total box-office collections of Hollywood movies for the year 2017. The main aim objective of the project is to able to predict the movies success whether its going to be a "flop","average", "hit" or "superhit" which would be beneficial for the movie distributors and production companies. Tweets related to multiple movies were extracted, sentiments and emotions were extracted for these tweets using R language, and a score is given for each emotion and sentiments, before running the models. All precautions were taken to hide the identifiable information like Tweet Id and Screen name. Machine learning models used in the project were SVM, KNN, Naive Bayes and Random Forrest. The models were run in Python language, they were able to predict the class of the movies whether its a "flop", "average", "hit" or "superhit" with an accuracy of 60.76%, based on the scores of emotions and sentiments, so this would be extremely useful for the entertainment industry for boosting their revenues. After comparison to other models performance, SVM and KNN did a good job in predicting the outcome of the movies. After analysing the results, it was understood that the movies with higher budget need larger revenues for their success because of the additional expenditures these movies have like Print and Advertising.
Evidence-Based Follow-up for Adults With Cancer.

BACKGROUND The objectives of follow-up care for cancer patients include psycho- social assistance and the detection of health problems. The concept of follow-up care rests on the assumption that the early detection of cancer recurrences and disease- or treatment-related complications is beneficial to patients. In this article, we provide an overview of the scientific evidence supporting current recommen- dations for the follow-up care of patients with colorectal cancer, lung cancer, and lymphoma. METHODS This review is based on pertinent publications that were retrieved by a selective search in PubMed, supplemented by the authors' own experience in patient care and guideline creation. RESULTS As recurrences usually arise soon after initial treatment, the recommended follow-up interval is shorter in the first two years (3-6 months) and longer thereafter (6-12 months). The question of which particular follow-up studies should be per- formed has only been systematically analyzed in a few cases. For patients with colorectal cancer, colonoscopy is the most important study. Intensive follow-up care is associated with a statistically non-significant increase in the survival rate compared to minimal follow-up care (77.5% versus 75.8%). Intensive diagnostic follow-up studies have been found to lead to a doubling of the frequency of operations for recurrence with curative intent, yet without any effect on the average survival time. The findings in lung cancer are similar. However, after tumor resection with curative intent, regularly repeated CT scanning leads to a survival advantage. In lymphoma patients, the longer the interval from primary treatment, the greater the likelihood of treatment-related secondary illnesses. It is not yet known how follow-up care should be provided to these patients in order to help them best. CONCLUSION The evidence supporting the efficacy of currently recommended modalities of follow-up care for cancer patients is weak. Until more data from clinical studies become available, the current guidelines should be followed.
Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images

Trained pathologists base colorectal cancer identification on the visual interpretation of microscope images. However, image labeling is not always straightforward and this repetitive task is prone to mistakes due to human distraction. Significant efforts are underway to develop informative tools to assist pathologists and decrease the burden and frequency of errors. The present study proposes a deep learning approach to recognize four different stages of cancerous tissue development, including normal mucosa, early preneoplastic lesion, adenoma and cancer. A dataset of human colon tissue images collected and labeled over a 10-year period by a team of pathologists was partitioned into three sets. These were used to train, validate and test the neural network, comprising several convolutional and a few linear layers. The approach used in the present study is ‘direct’; it labels raw images and bypasses the segmentation step. An overall accuracy of >95% was achieved, with the majority of mislabeling referring to a near category. Tests on an external dataset with a different resolution yielded accuracies >80%. The present study demonstrated that the neural network, when properly trained, can provide fast, accurate and reproducible labeling for colon cancer images, with the potential to significantly improve the quality and speed of medical diagnoses.
A New Strategy for Short-Term Power-Curve Prediction of Wind Turbine Based on PSO-LS-WSVM

Wind turbines are very important and strategic instruments in energy markets. Wind power production is unreliable. Wind power is weather dependent and the extreme wind speed changes make difficult to control of grid voltage and reactive power. Based on these reasons, Wind Power Prediction (WPP) is important for real applications. In this paper, a new short-term WPP method based on Support Vector Machine (SVM) is proposed. In contrast to physical approaches based on very complex differential equations, the proposed method is based on data history. Firstly, data preprocessing and normalization is done. Secondly, formulate the prediction as a regression problem. Thirdly, the prediction model is constructed using the Particle Swarm Optimization (PSO) and Least Square Support Vector Machine (LSSVM). In this paper, instead of using the conventional kernels, such as linear kernel, Polynomial and Radial basis function (RBF), the Wavelet (W) transform is used. The PSO-LS-WSVM accuracy has been tested with industrial wind energy data. This method has been compared with other methods and the experimental results based on practical data illustrate that PSO-LS-WSVM proposed method has better responses than other methods. Statistical results indicate that the predicting error of PSOLS-WSVM is 2.98% for one look-ahead hour.
A Survey on the Placement of Virtual Resources and Virtual Network Functions

Cloud computing and network slicing are essential concepts of forthcoming 5G mobile systems. Network slices are essentially chunks of virtual computing and connectivity resources, configured and provisioned for particular services according to their characteristics and requirements. The success of cloud computing and network slicing hinges on the efficient allocation of virtual resources (e.g., VCPU and VMDISK) and the optimal placement of virtualized network functions (VNFs) composing the network slices. In this context, this paper elaborates issues that may disrupt the placement of VNFs and virtual machines (VMs). This paper classifies the existing solutions for VM placement based on their nature, whether the placement is dynamic or static, their objectives, and their metrics. This paper then proposes a classification of VNF placement approaches, first, regarding the general placement and management issues of VNFs, and second, based on the target VNF type.
Dynamic itemset hiding under multiple support thresholds (Çoklu destek eşiklerinde dinamik sık kümeler gizlemesi)

DYNAMIC ITEMSET HIDING UNDER MULTIPLE SUPPORT THRESOLDS Data sharing is commonly performed between organizations for mutual benefits. However, if confidential knowledge is not hidden before the data is published it may pose threat to security and privacy. The privacy preserving frequent itemset mining is the process of hiding sensitive itemsets from being discovered with any frequent itemset mining algorithm. The privacy constraint of sensitive itemset hiding is sensitive threshold. If support of a given sensitive itemset is under the sensitive threshold, then this sensitive itemset is considered as non-interesting and hidden. One possible way of decreasing support of sensitive itemsets under predefined sensitive threshold is deleting items from a set of transaction. This type of frequent itemset sanitization is called distortion based frequent itemset hiding. The main focus of this thesis is to preserve sensitive itemsets with considering the multiple sensitive thresholds on both static and dynamic environments. Three different distortion based frequent itemset hiding algorithms proposed; Pseodo Graph Based Sanitization (PGBS), Itemset Oriented Pseudo Graph Based Sanitization (IPGBS) and DynamicPGBS are proposed. Both PGBS and IPGBS algorithms are designed for static environment and the DynamicPGBS algorithm is designed for the dynamic environment. The main objective of these three algorithms is to hide all sensitive itemsets with giving minimum distortion on non-sensitive knowledge and data in the resulting sanitized database.
TRANSITIVITY IN WASIAT RENUNGAN MASA PENGALAMAN BARU AND ITS RELEVANCE TO INDONESIAN LANGUAGE LEARNING AT SENIOR HIGH SCHOOL: SFL APPROACH

Abstract This research describes the transitivity realized in Wasiat Renungan Masa Pengalaman Baru and its relevance to Indonesian language learning at SMA (Senior High School) according to Systemic Functional Linguistic approach. The research was designed by employing qualitative descriptive research method. The data was collected with the application of documentation method and note-taking technique. The data was analyzed with the use of identification technique, which covers identification on the words, phrases, and clauses based on the classification of realized processes according to SFL perspective. The result of data analysis was presented using description technique by elaborating the analyzed data. The finding showed that among 112 clauses obtained, the dominant type of process was material process by number realization is 100 (49.29%), mental process is 49 grains (22.68%), rational process is 11 gains (5.11%), behavior process is 10 grains (17.60%), existential process is 8 grains (3.70%). Keywords: Transitivity, Wasiat Renungan Masa Pengalaman Baru, SFL
Measles in Bamako: Epidemiological, Clinical and Therapeutic Features of Patients Hospitalized at University Teaching Hospital of Point "G"

Measles is a highly contagious acute febrile eruptive disease. It can be prevented through vaccination. The aim of this study was to determine the epidemiological, clinical, and therapeutic features of measles cases hospitalized at Point "G" University Teaching Hospital. It was a retrospective study to review the medical files of patients hospitalized for measles between January 2010 and May 2011 at the Infectious Diseases Department of the Point "G" University Teaching Hospital. During the study period, 31 patients (6.4%) were treated for measles, and the majority of cases were seen in April months (20 cases) and May months (5 cases). The most affected age group was 9 - 59 months (58.1%) with a sex ratio of 1.38. The majority of patients (64.5%) consulted at least two health facilities before their hospitalization in Point “G” with an average of 5.3 ± 3.6 days from unset to the hospitalization. Measles immunization was not effective in 16 out of 26 patients and nearly one-third (29.0%) had familial contact measles case. Febrile rash, present in all patients, was associated with cough (96.8%), rhinitis (77.4%) and/or conjunctivitis (77.4%). Pneumonia was the most common complication (83.9%) followed by comorbidities such as gastroenteritis (29%), malnutrition (9.7%) and oral candidiasis (9.7%). Amoxicillin and ceftriaxone were the antibiotics frequently used against complications. Patients were hospitalized for an average of 6.9 ± 4.2 days, and no death was recorded. This study revealed that pneumonia was the main complication leading to hospitalization of patients. For a better control of measles, we need to fully respect the immunization schedule which is a guarantee for vaccine efficacy.
Quantitative Methods in Cognitive Semantics

In line with the increasing use of empirical methods in Cognitive Linguistics, the current volume explores the uses of quantitative, in particular corpus-driven, techniques for the study of meaning. It shows how these techniques contribute to the core theoretical issues of Cognitive Semantics as well as how they inform semantic analysis. The research presented in the volume constitutes an important step towards an Empirical Cognitive Semantics.