Miracle-dz/UniFAR-FTUs huggingface dataset:
A Critical Reflection on Ontologies and their Applications in Business

In recent history, the term ontology has been used as if conveyed a great deal of weight and importance when, in many cases, the term has been used incorrectly. This diffusion of meaning is often the path by which a perfectly acceptable and well-defined word becomes a buzzword, reduced in meaning and a warning to readers that poor science is ahead. Frequently, a hyped buzzword will lead the reader to form expectations that are never fulfilled. This research work provides a critical reflection on ontologies, their frequent misuse in research and business applications, and concerns aspects why ontologies have not been successful in large-scale business applications until now. When a definition that changes over time, as is the case for ontologies, this may be indicative of a lack of understanding in the field, or an inability to effectively communicate and share a common understanding. Whatever the reason is for this case, introducing numerous definitions for one concept, especially a complex concept, leads to confusion and, consequently, people from various research communities can (and do) use the term ontology with different, partly incompatible meanings in mind. The general misuse of this semantic technology can be explained by (i) the many existing, sometimes conflicting, definitions; (ii) too imprecise a specification of semantic technologies; and (iii) the existence of complex modeling processes that are too abstract or too complex. Because the term is used with a variety of meanings, only some of which are accurate, it has become increasingly difficult to discover who was or was not truly using ontologies. However, the relatively rare use in business applications can be attributed (i) to unknown (or unavailable knowledge about handling of) ontology modeling processes; (ii) to the lacking support of the modeling process because of missing state-of-the-art modeling tools; and (iii) to a lack of mature experience, developed over time. If we are to clearly demonstrate the benefits of an ontological approach, we must first clearly define what ontologies are and exactly when they are in use. Therefore, the causes for the general misuse and rare use of ontologies must be identified. Furthermore, it has to be clearly defined what an ontology is and guidelines are necessary to answer the question as to when ontologies should be used, how they can be used and when they should not be used. Finally, adequate representation languages should be applied to the problem and the ontology design process should be easy to understand and couched in terms that technical and non-technical users alike can understand. The main focus of this research work is to provide clear decisions to select a correct model, methodology and toolset to meet user requirements with the most efficient use of resources.
Determination of facilitators and barriers in post stroke life, in Kerman city

Background and Aim: Stroke is the third leading cause of death, second cause of long-term disability and one of the very important challenging in life. The aim of this research was to explain the barriers and facilitating factors affected on stroke patients’ life, and to identify relationship among them with demographic factors and severity of disease. Materials and methods: The descriptive-analytic technique was used. All patients who suffered from stroke were recruited in Kerman city. Finally 83 patients were selected, and data were gathered by demographic qualification questionnaire, proven questionnaire by researcher for studying barriers and facilitating factors (after testing validity and reliability), and NIHSS tools . Then, data were analyzed by using descriptive and referential statistics ANOVA test, T-Test, and Pearson coefficient correlation by SPSS software (ver. 16). Results: The findings showed that patients had more problems in the domains of politics, environmental factors, physical, and economic status. Also, they introduced social support from friends, family and prayer as facilitating factors in their lives. There was a significant positive relationship between age and physical and economical problems. Women have used social support and religious coping more than men. Married patients received more social support and their physical and economic problems raised less. Conclusion: Patients with higher severity of disease had more psychological problems such as depression and worry about future and Patients with lower socio-economic status more faced with barriers.
Gray Matter, White Matter and Cerebrospinal Volume in Schizophrenia and Subgroups: Mr Segmentation and Volumetric Study

Objective: Magnetic Resonance Imaging (MRI) has very high soft tissue resolution that’s why we are able to discriminate gray matter and white matter y well voxel based volume analysis can reliably be made. In this study we aimed to investigate the volume differences of the total brain, gray matter, white matter and cerebrospinal fluid (CSF) between healthy controls and the subgroups of schizophrenia which are deficit syndrome (DS) and non-deficit syndrome (NDS).
An Easy-to-Use Pipeline for an RGBD Camera and an AR Headset

The contribution of this article lies in providing the code for a working system running with off-theshelf hardware, but not in advancing theory in computer vision or graphics. The current work presents a system which uses one RGBD camera (Microsoft Kinect v2) to capture people in places, and an AR headset (Microsoft HoloLens) to display the scene. While the fidelity of the system is relatively low compared to others which utilize multiple cameras (i.e., Orts-Escolano et al., 2016), it displays with a high frame rate, has low latency, and is mobile in that it does not require a render computer.
A Preliminary Impact Study of Wind on Assimilation and Forecast Systems into the One-Dimensional Fog Forecasting Model COBEL-ISBA over Morocco

The assimilation impact of wind data from aircraft measurements (AMDAR), surface synoptic observations (SYNOP) and 3D numerical weather prediction (NWP) mesoscale model, on short-range numerical weather forecasting (up to 12 h) and on the assimilation system, using the one-dimensional fog forecasting model COBEL-ISBA (Code de Brouillard à l’Échelle Locale-Interactions Soil Biosphere Atmosphere), is studied in the present work. The wind data are extracted at Nouasseur airport, Casablanca, Morocco, over a winter period from the national meteorological database. It is the first time that wind profiles (up to 1300 m) are assimilated in the framework of a single-column model. The impact is assessed by performing NWP experiments with data denial tests, configured to be close to the operational settings. The assimilation system estimates the flow-dependent background covariances for each run of the model and takes the cross-correlations between temperature, humidity and wind components into account. When assimilated into COBEL-ISBA with an hourly update cycle, the wind field has a positive impact on temperature and specific humidity analysis and forecasts accuracy. Thus, a superior fit of the analysis background fields to observations is found when assimilating AMDAR without NWP wind data. The latter has shown a detrimental impact in all experiments. Besides, wind assimilation gave a clear improvement to short-range forecasts of near-surface thermodynamical parameters. Although, assimilation of SYNOP and AMDAR wind measurements slightly improves the probability of detection of fog but also increases the false alarms ratio by a lower magnitude.
COMPARATIVE ANALYSIS ON THE ANTIOXIDANT ACTIVITIES OF THREE OPUNTIA SPP . FROM KOREA – IN HIGHLIGHT OF O . HUMIFUSA F . JEOLLAENSIS

To compare the potential antioxidant activities of three Opuntia spp. , O. humifusa f. jeollaensis (Wang-gasi Chunnyuncho; OHJ), O. humifusa (Chunnyuncho; OH) and O. ficus-indica (Baiknyuncho; OFI), five antioxidant assays were performed using 70% methanol extraction on several plant parts. Significant differences were observed in five tested methods. Although there are soml differences in the tested material volume among the plant parts, the total polyphenol contents of OHJ, OH, and OFI extracts were measured to be 38.87±3.01, 13.32±2.47, and 22.22±2.53 mg GAE/100g, respectively. Those of the total flavonoid contents were measured to be 98.11±0.58, 78.67±0.61, and 2.61±0.13 mg QE/100g, respectively. The average IC50 values of OHJ, OH, and OFI alcoholic extracts against on DPPH radical scavenging activities, in testing 0.125, 0.25, 0.5, and 1 μg/ml concentrations in order, were 0.85, 0.90 and 1.18 μg/ml, respectively (ascorbic acid, 10.37 μg/ml). Those of the nitric oxide scavenging activity were 0.17, 0.12, and 0.10 μg/ml, respectively (ascorbic acid, 7.75 μg/ml). Finally, those of the reducing power were 0.49, 0.72 and 1.16 μg/ml, respectively (ascorbic acid, 4.62 μg/ml). We have found that a new form of Opuntia named O. humifusa f. jeollaensis contains the most amounts of polyphenols and flavonoids in comparing to OH and OFI well known as potential source of natural antioxidants so far. Opuntia humifusa f. jeollaensis also showed more potent antioxidant activities including on DPPH radical scavenging activities and the reducing power.
Annotation-free Quality Estimation of Food Grains using Deep Neural Network

We propose a fast and accurate system for automatically estimating the quality of food grains on resource constrained portable devices using computer vision. We are motivated by an urgent need in India for grain quality estimation to ensure transparency in the agricultural supply chain and empower poor farmers to get the correct price for their crops. The system uses instance segmentation of touching grains, followed by classiﬁ-cation of each grain according to E-NAM 1 parameters. To the best of our knowledge, this is the ﬁrst attempt to use Deep Learning to estimate quality of cluttered sample of grains using only mobile phone. Samples are collected from various Agricultural Pro-duce Market Committee (APMC) yards, which are used to generate synthetic data to simulate realistic clutter of grains for training our instance-segmentation network. Novel augmentation techniques while training make the system robust to illumination changes. Our system obtains the state-of-the-art performance and has been tested in various locations in India. At a mAP score of 0.74 and classiﬁcation accuracy 92%, our system takes less than 100s as compared to 15 minutes of manual quality estimation.
Which model is the best predictor of learning achievement: raw score, relative growth or knowledge retention score?

Objective: To verify the student learning performance using the raw score, relative growth score and knowledge retention score. Methods: Thirty-two nurse anesthetist students in academic years 2011-12, Faculty of Medicine, Siriraj Hospital, Mahidol University, volunteered to participate. After pretest, they studied the designated subject via a website. After 3 weeks, the system was locked and students underwent the post-test. The final1 and final2 test were held in a classroom without prior notice. The post and final1 test as well as the final1 and final2 test took place exactly 4 weeks apart. The difference scores between pre and post-test, pre and final1 test, as well as pre and final2 test were calculated for relative growth score G1, G2 and G3 respectively. Thus the differences between G1 and G2 as well as G1 and G3 were determined as knowledge retention score R1 and R2 respectively. Results: The post, final1 and final2 test scores were significantly higher than the pretest one significantly. However, the post, final1 and final2 test scores showed no statistical difference. Though G2 and G3 appeared to decrease as compared to G1, they were not significant. The R2 showed higher than R1 without significant difference; however, they showed a strong correlation to each other (r = .69) Conclusion: The knowledge retention score was the best prediction on academic gains.
The Role of Perspective Taking on Attention: A Review of the Special Issue on the Reflexive Attentional Shift Phenomenon

Attention is a process that alters how cognitive resources are allocated, and it allows individuals to efficiently process information at the attended location. The presence of visual or auditory cues in the environment can direct the focus of attention toward certain stimuli even if the cued stimuli are not the individual’s primary target. Samson et al. demonstrated that seeing another person in the scene (i.e., a person-like cue) caused a delay in responding to target stimuli not visible to that person: “alter-centric intrusion. ” This phenomenon, they argue, is dependent upon the fact that the cue used resembled a person as opposed to a more generic directional indicator. The characteristics of the cue are the core of the debate of this special issue. Some maintain that the perceptual-directional characteristics of the cue are sufficient to generate the bias while others argue that the cuing is stronger when the cue has social characteristics (relates to what another individual can perceive). The research contained in this issue confirms that human attention is biased by the presence of a directional cue. We discuss and compare the different studies. The pattern that emerges seems to suggest that the social relevance of the cue is necessary in some contexts but not in others, depending on the cognitive demand of the experimental task. One possibility is that the social mechanisms are involved in perspective taking when the task is cognitively demanding, while they may not play a role in automatic attention allocation.
Breast cancer and ovulation induction Meme kanseri ve ovulasyon indüksiyonu

Corresponding author / Sorumlu yazar: Pervin Karlı Address / Adres: Amasya Üniversitesi Kadın Hastalıkları ve Doğum Anabilim Dalı, Amasya, Türkiye e-Mail: parpi2300@hotmail.com ⸺ Conflict of Interest: No conflict of interest was declared by the authors. Çıkar Çatışması: Yazarlar çıkar çatışması bildirmemişlerdir. ⸺ Financial Disclosure: The authors declared that this study has received no financial support. Finansal Destek: Yazarlar bu çalışma için finansal destek almadıklarını beyan etmişlerdir. ⸺ Published: 8/29/2019 Yayın Tarihi: 29.08. 2019
Enhanced the Performance of VANET Network Using Clustering Based Thresholding Technique for Reliable Communication

The current decade of technology gives the concept of intelligent transportation systems. The intelligent transportation systems need the reliable communication. For the reliability of communication in VANET network used various security and communication model. In this paper proposed the cluster based threshold function for the reliable communication. The proposed algorithm used distributed clustering technique for the region selection of mobile vehicles. The speed and path divergence set the value of threshold for the selection of new path and avoid the malicious packet from the other nodes. The malicious packets increase the traffic load and decrease the performance of VANET network. The proposed algorithm simulated in MATLAB and used urban traffic scenario for the evaluation of parameters.
Machine Reading Comprehension on SQuAD 2 . 0

In this paper, we present an end-to-end neural network model for question answering on the SQuAD 2.0. Our model features ideas from the Hierarchical Attention Fusion Networks to combine representations from multi-level granularity. It achieves 57.9 F1 and 54.4 EM score on test set on the Non-PCE leaderboard; 66.6 F1 and 63.7 EM on development set.
DeepIntent: Deep Icon-Behavior Learning for Detecting Intention-Behavior Discrepancy in Mobile Apps

Mobile apps have been an indispensable part in our daily life. However, there exist many potentially harmful apps that may exploit users' privacy data, e.g., collecting the user's information or sending messages in the background. Keeping these undesired apps away from the market is an ongoing challenge. While existing work provides techniques to determine what apps do, e.g., leaking information, little work has been done to answer, are the apps' behaviors compatible with the intentions reflected by the app's UI? In this work, we explore the synergistic cooperation of deep learning and program analysis as the first step to address this challenge. Specifically, we focus on the UI widgets that respond to user interactions and examine whether the intentions reflected by their UIs justify their permission uses. We present DeepIntent, a framework that uses novel deep icon-behavior learning to learn an icon-behavior model from a large number of popular apps and detect intention-behavior discrepancies. In particular, DeepIntent provides program analysis techniques to associate the intentions (i.e., icons and contextual texts) with UI widgets' program behaviors, and infer the labels (i.e., permission uses) for the UI widgets based on the program behaviors, enabling the construction of a large-scale high-quality training dataset. Based on the results of the static analysis, DeepIntent uses deep learning techniques that jointly model icons and their contextual texts to learn an icon-behavior model, and detects intention-behavior discrepancies by computing the outlier scores based on the learned model. We evaluate DeepIntent on a large-scale dataset (9,891 benign apps and 16,262 malicious apps). With 80% of the benign apps for training and the remaining for evaluation, DeepIntent detects discrepancies with AUC scores 0.8656 and 0.8839 on benign apps and malicious apps, achieving 39.9% and 26.1% relative improvements over the state-of-the-art approaches.
The Influences of Capital Structure and Corporate Performance ( Case Study of Selected Companies in the Nigeria Stock Exchange )

This study investigated whether financing decisions affect corporate performances. It answers the question whether optimum capital structure actually exists; and if so, whether such optimum mix of financing has relationship with corporate performance. Cross sectional data that covers thirty firms in three years (2012-2014) were used. The estimation procedures employed in this empirical study is based on Multiple Regression Analysis of Ordinary Least Square Method. The appropriate transformation of data being through the use-of the natural logarithm. The study reveals that the three sources of finance explain 48.9% of changes in corporate earnings in 2012, 39.34% in 2013 and 18.90% in 2014. The trend obviously shows that the firms were far from operating at equilibrium as regards capital structure. There is the need therefore for corporate entities to place emphasis on ensuring optimum capital structure decisions so as to utilize capital in ways that will maximise corporate earnings.
RISK STRATIFICATION IN CARDIAC SURGERY Algorithms and Applications

The aims of this research was to compare different risk score algorithms with regard to their validity to predict 30-day and one-year mortality after open-heart surgery, to evaluate if the preoperative risk stratification model EuroSCORE predicts the different components of resource utilization in cardiac surgery, and to systematically evaluate the accuracy and performance of artificial neural networks (ANNs) to select and rank the most important risk factors for operative mortality in open-heart surgery. Preoperative evaluation of the surgical risk is an important component in cardiac surgery. Risk stratification can give patients and their relatives insight into the existent risk of complications and mortality, and aid in the selection of cases for surgery versus alternative, non-surgical therapies. It may also predict the need for hospital care resources and improve the quality of care. A few comparative studies of different risk algorithms exist, but the relative performance of the risk scoring systems currently used is unclear, and it still remains difficult to risk-stratify individual patients. The present work identified four cardiac surgical risk models with a superior performance, with the EuroSCORE algorithm performing best. Though the algorithms were originally designed to predict early mortality, the one-year mortality prediction was also reasonably accurate. The additive EuroSCORE algorithm was also shown to be useful to predict intensive care unit (ICU) cost and an ICU stay more than two days after open-heart surgery. In an attempt to improve the mortality prediction further, a machine-learning technique, ANNs, was used. This identified mortality risk factors in a ranked order and defined a minimal set of risk variables resulting in a superior mortality prediction, compared with previously developed algorithms.
Structured light plethysmography as an assessment tool for dysfunctional breathing in children

Background Dysfunctional breathing (DB) is a respiratory disorder characterised by abnormal breathing patterns. Currently there is no comprehensive method available for objectively measuring these patterns. Aim The aim of this study was to investigate whether structured light plethysmography (SLP) can be used for the objective measurement of breathing patterns in children with DB. Methods Tidal breathing was recorded, using the Thora3Di TM SLP device, in 41 children (20 with DB and 21 healthy controls). Measured parameters included overall phase, principle angle, breath phase, inspiratory time and expiratory time. Results No significant difference was found in the measured parameters between healthy children and those with DB, when comparing periods of settled tidal breathing (p>0.19). However, when comparing all tidal breaths recorded for each child, a significant difference (p=0.03) in breath phase was found between the 2 groups. Breath phase is the difference in phase angle between the thorax and abdomen signals and was closer to zero in the DB group, indicating a more synchronous pattern than the control group. Conclusion SLP can be used to objectively measure breathing patterns in children with DB, but longer captures may be required to achieve more reliable data.
Lexical Diversity and Colour Hues in Russian Poetry : A Corpus-Based Study of Adjectives

This paper describes the distribution of colour adjectives in Russian poetry of the Silver Age and defines individual preferences with regard to poetic tradition, syllable structure, and metrical restrictions. The research method combines a lexico-semantic approach, formal literary analysis, and quantitative metrics obtained via the frequency database of the Russian Poetry Corpus (over 10 M words, incl. 1 M adjectives). The database allows the user to compare subcorpora and create graphs of timeline distribution, which demonstrate that the lexical diversity and relative frequencies of colour adjectives start to grow rapidly in the 1890s, as modernists employ colour adjectives to upgrade the poetic inventory. The adjectives referring to non-banal hues (e.g. fioletovyj ‘violet’, lazorevyj ‘azur’) belong to the middle part of the ranked wordlist. Correspondence analysis of the data reveals individual colour preferences and stylistic similarities among the most prominent poets of the Silver Age; for example, Anna Akhmatova and Alexander Blok are similar regarding their use of the white hues. The distribution of the selected colour hue adjectives across metrical types highlights the strong association of multi-syllabic adjectives with certain meters, although some words have a more complex distribution. 132 Quantitative Approaches to Versification