Miracle-dz/UniFAR-FTUs huggingface dataset:
Chess Results Analysis Using Elo Measure with Machine Learning

An Elo score is a known measure of past performance in chess and other games. This paper investigates the impact of Elo ratings on chess game results and whether this measure can be used to predict future performance in matches. To achieve the aim, various machine learning classification techniques have been evaluated using chess data sourced from an online chess server. We examine how much influence the Elo score has on the prediction power of these techniques based on classifiers they derive. The prime objective of this experiment is to accurately predict the winner of a chess game from attributes that are available before the game starts. We are particularly interested in how large an impact the Elo score has on the prediction when compared with other features. Empirical results reported that classifiers derived by artificial neural network (Multilayer Perceptron), Decision Tree (J48/C4.5), Rule Induction (JRip/RIPPER) and Probabilistic (Naïve Bayes) showed how useful the Elo is at predicting chess results, at least on the dataset considered, improving classifiers’ performance with respect to accuracy, precision, recall and area under curve, among others.
Predictive Adaptive Responses to Maternal High-Fat Diet Prevent Endothelial Dysfunction but Not Hypertension in Adult Rat Offspring

Background— Population-based studies suggest that fetal adaptive responses to maternal dietary imbalance confer survival benefit when the postnatal diet remains suboptimal but increase susceptibility to cardiovascular disease when postnatal nutrition is improved. We have investigated “predictive adaptive” responses in a rodent model in which adult offspring of fat-fed dams develop characteristics of the metabolic syndrome. Methods and Results— Sprague-Dawley rats were fed a fat-rich diet or normal chow throughout pregnancy and weaning. Vascular endothelial function and blood pressure were determined in 180-day-old offspring of fat-fed dams raised on standard chow (FC) or on the fat-rich diet (FF) and in offspring of chow-fed dams raised on chow (CON). Small mesenteric artery endothelium-dependent dilation to acetylcholine was impaired in male and female FC (by ANOVA, P<0.001 versus CON) but similar to CON in FF (P=NS). Blood glucose was lower in FF versus FC. Heart rate was reduced in male FC versus CON (.
Total cardiovascular risk among native population of Kyrgyzstan with diabetes mellitus type 2 and impaired glucose tolerance in high- and low-altitude regions

Objective: In diabetes mellitus (DM) type 2, there is a very high risk of developing cardiovascular (CV) events. We aimed to study the total CV risk of the indigenous people of Kyrgyzstan with impaired carbohydrate metabolism (DM type 2 and impaired glucose tolerance, IGT), depending on the region of residence (low and high altitudes), and to develop targeted preventive measures. Methods: Overall, 248 families (992 people) in the Naryn (high-altitude) (study group) and 260 families (1041 people) in the Chui region (low-altitude) (control group) were surveyed and 363 people with DM type 2 and IGT were identified, of which: 138 people in the study group and 225 people in the control group. Results: In patients with DM type 2 and IGT, moderate CV risk was found in the low-altitude and high-altitude in men and women aged 46–65 years, the leading risk factor in men is BMI&qt;25 kg/m2; in women, hypertension (HT) and hypercholesterolemia. A high risk of CV complications was detected only in the low-altitude in men aged 46–65 years, the leading risk factor is HT. High and very high risk of CV complications are rarely found both in the low-altitude and in the high-altitude. Conclusion: During stratification of total CV risk, 363 people with carbohydrate metabolism disorders (DM type 2 and IGT) of 2033 native population living in 2 regions of Kyrgyzstan revealed features of the total CV risk: BMI&qt;25kg/m2 for men of high- altitude and low-altitude, HT and hypercholesterolemia for women of high-altitude, and for men, low-altitude only HT. Both in the high-altitude and in the low-altitude, low and moderate CV risk are predominantly expressed, and high and very high CV risk are rare.
Survey of pedestrian action recognition techniques for autonomous driving

The development of autonomous driving has brought with it requirements for intelligence, safety, and stability. One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic, complex, and uncertain environments. Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies. Specifically, vehicles need to detect pedestrians, recognize their limb movements, and understand the meaning of their actions before making appropriate decisions in response. In this survey, we present a detailed description of the architecture for pedestrian action recognition in autonomous driving, and compare the existing mainstream pedestrian action recognition techniques. We also introduce several commonly used datasets used in pedestrian motion recognition. Finally, we present several suggestions for future research directions.
Towards realtime robot reactions – patterns for modular device driver interfaces

Getting new robot hardware to work requires the implementation device drivers. When this is seen as tedious work, initial versions of drivers often tend to lack clean software architecture or component design. This leads to drivers that work acceptably in the given context, but exhibit little modularity or reusability for other contexts. Especially for use cases that contain real-time robot reactions (reflexes) to sensor events, these implementations are often not usable. This paper describes different device driver implementation patterns found in existing robot software, and analyses them towards advantages and disadvantages, aiming to provide advice which pattern to use in which context.
Korean Dependency Parser using Stack-Pointer Network and Information of Word Units

구문 분석은 문장의 구조를 이해하며 의미의 중의성을 해결하는 것이다. 일반적으로 한국어는 어순 배열 의 자유도가 높고 문장 성분의 생략이 빈번한 특성이 있기 때문에 의존 구문 분석이 주된 연구 대상이 되 어 왔다. 스택-포인터 네트워크 모델은 의존 구문 파서에 맞게 포인터 네트워크 모델을 확장한 것이다. 스택-포인터 네트워크는 각 단어에서 의존소를 찾는 하향식 방식의 모델로 기존 모델의 장점을 유지하면 서 각 단계에서 파생된 트리 정보도 사용한다. 본 연구에서는 스택-포인터 네트워크 모델을 한국어에 적 용해보고 이와 함께 어절 정보를 반영하는 방법을 제안한다. 모델의 실험 결과는 세종 구문 구조를 중심 어 후위(head-final)를 엄격히 준수하여 의존 구문 구조로 변환한 것을 기준으로 UAS 92.65%의 정확도를 얻었다.
Ontofitting: Specialization of Word Vectors for Semantic Representation

우리는 단어 임베딩에 외부지식을 내재할 수 있는 Ontofitting 방법을 제안한다. 이 방법은 retrofitting의 한 방법으로 유의어, 반의어, 상위어, 하위어 정보를 단어 임베딩에 내재할 수 있다. 유의어와 반의어 정 보를 내재하기 위해서 벡터의 각 유사도를 사용하였고 상하위어 정보를 내재하기 위해서 벡터의 길이 정 보를 사용하였다. 유의어 사이에는 작은 각도를 가지고 반의어 사이에는 큰 각도를 가지게 된다. 하위어는 상위어보다 상대적으로 작은 길이를 가지게 된다. SimLex와 HyperLex로 실험하여 효과와 안정성을 검증 하였다. 의미정보를 내재한 임베딩을 사용할 수 있다면 QA, 대화 등 응용에서 보다 좋은 성능을 보일 수 있을 것이다. 주제어: 단어 임베딩, Representation Learning, Retrofitting, 기계학습
A Study on Data Collection and Object Detection using Faster R-CNN for Application to Construction Site Safety

Among the industrial accidents occurring in Korea, the construction sector accounts for 28.55% of theaccidents associated with this high-risk sector. Although active progress is being made to reduce such industrial accidents, studies concerning the applications to construction site safety are currently insufficient. In this study, research is conducted on the development of a Korean data learning model using the latest in artificial intelligence algorithms, R-CNN, for the identification of safety protection equipmentworn by workers in the field. As a result of this study, the discriminant data learning model concerning the donning of a new Korean safety hard hat provided excellent results with a mean average precision of 0.82. This advancement is expected to reduce industrial accidents in the field of construction and improve safety.
Increasing Advance Care Planning in the Primary Care Setting

Evidence shows the many benefits of advance care planning (ACP) discussions, including; giving patients peace of mind, decreasing healthcare costs, increasing quality of care, and decreasing distress at the end of life. Due to the many proven benefits of ACP, the United States Department of Health and Human Services as well as many leading national health organizations are promoting ACP, even tying it to reimbursement. The best setting and time to initiate ACP is in the primary care setting as early as possible, before patients have any cognitive decline. In this context, patients have an established relationship with their provider, and the discussion can be ongoing and involve family members. However, despite the national push and abundant evidence showing the importance of ACP, the rate of ACP discussions and advance directive completion in primary care remains low. This evidence-based practice pilot project is guided by the Iowa Model of Evidence-Based Practice to Promote Quality Care-Revised (Iowa Model Collaborative, 2017). Incorporating the concepts of the Transtheoretical Model of Health Behavior Change (Prochaska & Velicer, 1997), the project sought to increase ACP discussions and the rate of completion of advance directives in a primary care office through educating clinicians. The pilot project was successful in increasing ACP documentation, with a pre-intervention prevalence of ACP conversations of 0% and a post-intervention prevalence of ACP conversations of 23.33%, which is consistent with the current evidence in the literature.
Understanding Patient Perspectives of Anti-VEGF Treatment Experience and Effect in Age-Related Macular Degeneration

Purpose: Understand patients’ perspectives of intravitreal anti-VEGF therapy efficacy for exudative age-related macular degeneration (eAMD). Methods: 107 patients receiving intravitreal anti-VEGF injections in an academic retina practice completed a 28-question survey during their clinic visit. IRB approval and patient consent was obtained. Results: 61 patients (85 eyes) with eAMD were analyzed. Vision improved 3 lines in Snellen acuity in 19% (16/85), remained stable within 3 lines in 66% (56/85), and worsened by 3 lines in 15% (13/85). 38% of patients (23/61) perceived improvement in their vision, 21% (13/61) were uncertain, and 41% (25/61) felt no improvement. There was a statistically significant correlation between the objective effect of treatment on vision and the patients’ perception of the effect in bilaterally treated patients (p-value: 0.000) and borderline significance in unilaterally treated patients (p-value:
Investigation into the molecular mechanisms of congenital insensitivity to pain

4 List of Figures . 11 List of Tables . 13 Abbreviations . 15 1 Chapter One . 18 1.1 Overview of sensory perception . 19 1.2 Development of the nociceptor . 23 1.3 Activation of the nociceptor . 28 1.3. 1 Heat . 28 1.3. 2 Cold . 29 1.3. 3 Mechanical . 29 1.3. 4 Chemical . 30 1.4 Conduction of pain signals . 30 1.5 Central modulation of pain signalling . 31 1.6 Adaptive pain states . 32 1.7 Maladaptive pain states . 33 1.8 Mechanism of action of analgesics . 34 1.8. 1 Non-steroid anti-inflammatory drugs (NSAIDs). 34 1.8. 2 Paracetamol . 34 1.8. 3 Opioids . 35 1.8. 4 Local anaesthetics . 35 1.9 Disorders of pain sensation . 35 1.10 Dysfunctional disorders of nociceptors . 36 1.10. 1 SCN9A . 36 1.10. 2 SCN11A . 38 1.11 Developmental disorders . 38 1.11.1 CLTCL1 . 38 1.11.2 PRDM12 . 38 1.11.3 NTRK1 . 39 1.11.4 NGF . 41 1.12 Steps required to assign pathogenicity to genetic variants . 42 1.13 Aims and scope of the dissertation . 46
A Review of Gamification Applied to Phishing

Phishing is a set of devastating techniques which lure target users to provide critical resources. They are successful because they rely on human weaknesses. Gamification which is a recent and non-traditional learning method with purpose to motivate and engage user to carry out activities, is more and more applied to prevent such cyber threats. This paper provides the first survey of gamified solutions dedicated to educate against phishing from 2007 to 2019. The investigation is conducted on eight proposals in terms of core concepts, game mechanics and learning process. We provide three taxonomies of dimensions to systematically characterize researches on gamified solutions, discuss lacks of surveyed works and opens further orientations to enhance this research area. Some key results are: solutions do not consider elementary level of knowledge and do no offer basic notions; solutions are not adapted to general audience and therefore not reliably applicable in different contexts; platforms partially educate about phishing; learners are evaluated predictably and within a short period. This study constitutes a cornerstone to understand and enhance research on phishing education.
Measuring Countries’ Human Rights Positions in UN Universal Periodic Review

We use country reviews in United Nations’s Universal Periodic Review (UPR) to obtain multi-dimensional measures of similarity between countries. UPR is a mechanism that involves a review of all UN member states. The process is designed to treat all states equally and be cognizant of the level of development and specificities of countries under review. One of the unique aspects of the UPR is the peer review, where any state can offer recommendations for the state under review. The first review cycle was conducted from 2008-2012 and the second cycle has finished in November 2016. We obtain measures of similarity based on the similarity of the recommendations that states gave other states and also based on the similarity of the recommendations that states received from other states. In order to obtain these measures of semantic similarity, we use a method that exploits continuous vector representation of words known as word vectors, which is considered the state-of-the-art for measuring textual similarities in natural language processing. We use word-vectors pre-trained on the Google News dataset to ensure a reasonable coverage of political terminology. This method provides many improvements over the existing methods. Most importantly, there are fewer subjective decisions to be made (for example, one does not need to choose the number of latent topics). The proposed method does not need to rely on a huge corpus and can be used on small and large data sets alike. Furthermore, the method is computationally very efficient and does not need to solve a non-convex optimization problem. Our early results indicate that comments received by countries in the UPR process indeed captures real human rights records of countries, especially when one averages over all the comments received from different states.
Transparent Artificial Intelligence – A Conceptual Framework for Evaluating AI-based Clinical Decision Support Systems

With Artificial Intelligence (AI) technology advancing at a breathtaking speed – nearing application stage in many fields of medicine – the need for regulation ensuring quality, utility and security of the emerging AI-based clinical decision support systems becomes increasingly pressing. Here, we suggest a conceptual framework from which to derive requirements for building, validating, deploying, and managing AI-based systems in daily clinical practice.
The Characteristics of Blood Pressure Variability in stage 3 chronic kidney disease patients with diabetic and (not or) non-diabetic nephropathy

background Hypertension (HTN) and metabolic changes of DM accelerate progression of ischemic heart disease, heart and renal failure, neurologic dysfunction, peripheral vascular diseases, which in turn causes increased risk of complications, disability and premature death. In this study we analyzed 24-hour blood pressure monitoring in patients with diabetic (DN) and nondiabetic nephropathy (NDN) to clarify features of BP profiles in these groups and identify factors that may influence on blood pressure variability in patients with diabetic nephropathy. Aim The aim of this study was to understand the relationship between circadian rhythm of blood pressure and renal function characteristics in subjects with chronic kidney disease (CKD) stage III of diabetic and non-diabetic etiology. Methods Thirty chronic kidney disease (CKD)-hypertensive patients without diabetes (NDN group) and thirty type 2 diabetic patients with overt DN (DN group) were enrolled in this study. The values of shortterm BP variability were obtained from 24 h ambulatory BP monitoring (ABPM). Results Variance analysis or nonparametric analysis revealed that 24-h systolic BP variability was significantly higher and nighttime systolic BP variability of the DN group were significantly lower than those of the NDN group [(12,23±3,66) vs. (13,67±2,99) mmHg, P<0.05; (10,74±5,26) vs. (16,35±3,69) mmHg, P<0.05]. The patients with overt DN had larger 24-h diastolic pressure [(124,50±33,78) vs. (111,50±11,5) mmHg, P<0.05;], higher load pressure [(12,23±3,66) vs. (13,67±2,99) mmHg, P<0.05; (10,74±5,26) vs. (16,35±3,69) mmHg, P<0.05] and the frequency and severity of violations of the circadian rhythm of blood pressure in a lower variability and a higher magnitude and speed of morning rise in blood pressure, compared with NDN group. In the DN group, at equal rates GFR, albuminuria level and disturbances of lipid profile were significantly higher than in the group with NDN patients. Conclusion Taken together, larger short-term BP variability was detected in hypertensive type 2 diabetic patients with overt nephropathy and renal insufficiency. It may imply that the optimal BP variability level could benefit from a better glycaemic control.
Optimal UAV Deployment for Data Collection in Deadline-based IoT Applications

The deployment of UAVs is one of the key challenges in UAV-based communications while using UAVs for IoT applications. In this article, a new scheme for energy efficient data collection with a deadline time for the Internet of things (IoT) using the Unmanned Aerial Vehicles (UAV) is presented. We provided a new data collection method, which was set to collect IoT node data by providing an efficient deployment and mobility of multiple UAV, used to collect data from ground internet of things devices in a given deadline time. In the proposed method, data collection was done with minimum energy consumption of IoTs as well as UAVs. In order to find an optimal solution to this problem, we will first provide a mixed integer linear programming model (MILP) and then we used a heuristic to solve the time complexity problem. The results obtained in the simulation results indicate the optimal performance of the proposed scheme in terms of energy consumption and the number of used UAVs.
Autonomous Mobile Ground Control Point Improves Accuracy of Agricultural Remote Sensing through Collaboration with UAV

Ground control points (GCPs) are critical for agricultural remote sensing that require georeferencing and calibration of images collected from an unmanned aerial vehicles (UAV) at different times. However, the conventional stationary GCPs are time-consuming and labor-intensive to measure, distribute, and collect their information in a large field setup. An autonomous mobile GCP and a collaboration strategy to communicate with the UAV were developed to improve the efficiency and accuracy of the UAV-based data collection process. Prior to actual field testing, preliminary tests were conducted using the system to show the capability of automatic path tracking by reducing the root mean square error (RMSE) for lateral deviation from 34.3 cm to 15.6 cm based on the proposed look-ahead tracking method. The tests also indicated the feasibility of moving reflectance reference panels successively along all the waypoints without having detrimental effects on pixel values in the mosaicked images, with the percentage errors in digital number values ranging from −1.1% to 0.1%. In the actual field testing, the autonomous mobile GCP was able to successfully cooperate with the UAV in real-time without any interruption, showing superior performances for georeferencing, radiometric calibration, height calibration, and temperature calibration, compared to the conventional calibration method that has stationary GCPs.
DUAL LOX/COX INHIBITION: A NOVEL STRATEGY TO PREVENT NEUROVASCULAR LEAKAGE IN EPILEPSY

OF THESIS DUAL LOX/COX INHIBITION: A NOVEL STRATEGY TO PREVENT NEUROVASCULAR LEAKAGE IN EPILEPSY Epilepsy affects 3.4 million patients in the USA and is characterized by recurring seizures. The blood-brain barrier is leaky in epilepsy and may contribute to seizure progression but the mechanisms which cause this leakage are not fully understood. We hypothesized that seizures trigger LOXand COX-mediated blood-brain barrier leakage and that dual LOX/COX inhibition prevents barrier leakage in vivo. To test this hypothesis, we administered either the dual LOX/COX inhibitor licofelone or a combination of the 5-LOX inhibitor zileuton and the COX-2 inhibitor celecoxib to rats that experienced status epilepticus (SE). Serum and brain capillaries were isolated 48 hours after SE and serum S100β levels were measured and Texas Red leakage rates were determined. Dual inhibition of 5-LOX and COX prevented serum S100β elevations observed in SE rats in a dose-dependent manner with licofelone. Inhibition of 5-LOX and COX-2 with zileuton and celecoxib completely prevented serum S100β elevation. Texas Red leakage rates for SE rats were also reduced in a dose-depended manner with licofelone and reduced to control rates with zileuton and celecoxib. These data support our hypothesis that seizure-induced blood-brain barrier leakage is mediated by LOX and COX, and inhibition of these enzymes prevents barrier leakage.
Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Summary Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA). Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of . 967/. 980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.