Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Combinations of Character Embeddings and Coattention

In this project, I attempt to build a model for the Stanford Question Answering Dataset (SQuAD) v. 2.0 [1]. I consider 3 different models, the baseline model, or Bi-directional Attention Flow (BiDAF) without character level embedding [2], BiDAF with character level embedding, and a Dynamic Co-attention Network [3] with character level embedding. Some conclusions drawn from my experiment was that implementing character level embedding in the BiDAF model signiﬁcantly improved EM and F1 scores over the baseline. However, even though the Dynamic Co-Attention Network with character level embedding was an improvement over the baseline, it scored lower on both F1 and EM scores than BiDAF with character level embedding. On the development set, the BiDAF with character embedding has an F1 score of 63.030 and EM score of 59.839. The Dynamic Co-attention Network with character embedding has an F1 score of 61.54 and an EM of 57.81. My best result on the SQuAD testing set was the BiDAF with character embeddings, achieving an F1 score of 62.266 and an EM score of 58.952.
Exploring First Order Gradient Approximation Meta Learning for Robust QA Systems

Finetuning pretrained language models such as BERT have shown good performance on new target domains. However, one limitation of finetuning has been the requirement of sufficient downstream task data for good generalization. This project explores meta learning for fast adaption on a pretrained language model DistilBERT with limited training examples. This project explores a meta learning approach called REPTILE as a first order approximation of the gradient based Model Agnostitic Meta Learning (MAML) approach. Current findings suggest Reptile may not improve test accuracy without sufficient hyper-parameter tuning. A new method introduced RandEPTILE adds random start points for Reptile which shows improvement on the validation accuracy
A NOVEL METHOD FOR MUTUAL AUTHENTICATION IN CLOUD COMPUTING

: Cloud computing is the fastest emergent internet centered technology for sharing computer resources on an as-needed base. In the cloud, security is really important. We propose a mutual authentication mechanism for cloud users and the cloud. In a cloud context, the framework also manages session keys. The mutual authentication process in this approach is split into three phases: server initialization, registration and authentication. The framework's security has been thoroughly examined in order to confirm its effectiveness. The proposed system is also immune to a variety of cloud computing assaults.
Multi-Quantum Well (MQW) GaAs/AlGaAs Solar Cell

Solar photovoltaic technologies have garnered an increasing amount of attention due to their relevance to developing sustainable energy systems in the future which do not harm the environment. Multiquantum well (MQW) solar cells offer a potential device structure which can improve the current generation and efficiency of conventional p-i-n junction solar cells. Typically, only photons with energies matching or exceeding that of the bandgap of a solar cell can be absorbed to convert the irradiated light into an electrical current. Photons with less energy than the bandgap are not able to excite free charge carriers, which in effect reduces the efficiency of a solar cell. Layered quantum well structures in place of the intrinsic region of a solar cell could potentially harness these lower energy photons to generate current from charge carriers confined within those quantum wells. In this study, a single-junction MQW GaAs solar cell structure using 13 GaAs/Al0.35Ga0. 65As quantum wells is proposed and simulated using Crossslight APSYS (TCAD software). The structure of the quantum well region is optimized by investigating the effects of the Al0.35Ga0. 65As barrier thickness and the GaAs quantum well thickness on the device performance. The optimized cell efficiency is found to be 7.636% when 50 nm quantum wells and 1 nm barriers are used. This performance provides insights into how tunneling can reduce the performance of a MQW from its maximum theoretical efficiency and the trade-off between the thickness of the barriers and wells to design an optimal solar cell configuration.
Investigation of effective variables on predicting farmers' safety behavior, based on an integrated model of planned behavior and health belief theory

Iran Occupational Health http://ioh.iums. ac.ir Iran Occupational Health. 2020 (4 Apr);17:1. Original Article Lighting and color temperature assessment in the office workplaces and relationship to visual fatigue Rostam Golmohammadi, (*Corresponding author), Professor, Department of Occupational Health, School of Health and Center for Health Sciences Research, Hamedan University of Medical Sciences, Hamedan, Iran. golmohamadi@umsha.ac.ir Zahra Pirmoradi, MSc, Department of Occupational Hygiene, School of Health, Hamadan University of Medical Sciences, Hamadan, Iran Majid Motamedzadeh Torghabeh, Professor, Department of Ergonomics, School of Health, Hamadan University of Medical Sciences, Hamadan, Iran Javad Fardmal, Professor, Associate Professor, Department of Biostatistics, School of Health, Hamadan University of Medical Sciences, Hamadan, Iran
Opinions and Practice on Research among Nurses in Sri Lanka

Nursing is a professional practice. Evidence-based practice is important in improving the quality of nursing care. Lack of research knowledge and less involvement in research are the main barriers to evidence-based practice in improving nursing clinical practice. The study aimed to assess the opinions and practice on research among nurses in the selected hospital in Batticaloa district, Sri Lanka. A sample of 270 nurses was selected using a systematic sampling technique. A self-administered questionnaire assessed the socio-demographic characters, nurses’ opinions towards research, and practices on research. The opinions questions were answered using “Yes”, “No” and “Do not know”. A descriptive statistical test was used and results were present as a percentage. Out of 270 participants, the majority (73.0%) of them were female, Tamil (76.3%), diploma holders (78.9%), had more than ten years of experience in nursing (45.6%) and did not participate in any research seminars (81.5%). A few numbers of participants (10.7%) carried out the research. The majority of the participants mentioned that the research is conducted to improve the nurse's professional development (90.0%), difficult to change the practice based on the research findings (71.5%), and insufficient equipment to transfer research findings into practice (53.0%). The findings of this study showed that involvement in research was poor among participants even though the majority of them identified research will help to improve their professional careers. Therefore, the educational program that consists of research methodology, and dissemination of research findings could be initiated among this target population to enhance their involvement in research and improve their clinical practice.
Do Falsifiers Leave Traces? Finding Recognizable Response Patterns in Interviewer Falsifications

Fraud by interviewers is a ubiquitous threat to data quality in survey practice, whenever face-to-face surveys are conducted. Particularly if interviewers use stereotypes about respondents to fill in questionnaires, falsifications can limit the variety of possible answers, lead erroneously to significant correlations and distort survey results. In addition to external control mechanisms to detect fraud (such as postcards or time stamps) more recent research has started to also consider internal indicators (such as the number of missing values or open answers) as a monitoring strategy. This latter approach relies on ex-post statistical analyses and implicitly assumes that falsifiers apply rational behavioral strategies which result in detectable response patterns. This study examines to what extent fieldwork monitoring can benefit from such approaches, by empirically assessing how effective different indicators are at detecting known cases of fabrication. In contrast to most previous research, which often relies on laboratory fabrications, this study uses authentic cases of detected interviewer fraud from a survey on the fairness of earnings conducted in Germany. The main goal of this study is to examine to what extent the falsifiers’ attempts to produce unsuspicious data led to recognizable response patterns. For this purpose, we test a wide range of indicators that could potentially identify falsifications: avoidance of extreme categories and open text-based answers, low rates of item-nonresponse, strategic use of filter questions to shorten the questionnaire and non-compliance of responses to numeric questions with Benford‘s Law. Furthermore, we compare authentic and fabricated interviews according to their values on a social desirability scale and report results from an innovative trick question that was especially designed to detect falsifiers.
Association between Gut Microbiome and Parkinson’s Disease Revealed by Sparse Learning

Background: Many studies indicate that the human gut microbiota is likely to have connections with Parkinson disease (PD). Based on these indications, this thesis explores the association between PD and human gut microbiota, from a statistical machine learning perspective. With the purpose of identifying the association between PD and gut microbiota, we assess the predictivity of microbial operational taxonomy units (OTUs) that are extracted from participants’ gut. Methods: We use linear support vector machine (SVM) and logistic regression combined with L1 penalty and elastic-net penalty, to identify informative OTUs for PD. L1 penalty is able to do shrinkage for features, which effectively implements feature selection by setting the coefficients of non-significant variables to be zero. Conversely, coefficients with larger absolute values indicate that the OTUs are more closely related to PD. Elastic-net penalty is capable of grouping correlated variables. Under these two penalties, SVM and logistic regression can achieve good predictive results as well as feature selection. In order to make full use of dataset and to avoid overfitting, we run models with Leave-one-out cross-validation (LOOCV). There are tuning parameters, λ for each regularization. After running models with LOOCV, we choose the optimal λ for each model, using test error rate as the criterion. Results and Conclusions: We analyze the performance of each optimal model , by calculating and understanding evaluation metrics of these models. Then, we find that for our dataset, logistic regression with L1 penalty has the best performance. R 2 ER, R 2 AMLP , AUC and AUPR of logistic regression with L1 are 43.9%, 25.7%, 0.8259 and 0.8788. We focus on the selected OTUs based on coefficients generated by models, and to the ranking of OTUs, according to their level of relevance to PD. Then, we find that some OTUs selected by logistic regression with L1 have been identified in previous studies of micro-organisms, including Lactobacillus, Roseburia, Bluatia, Akkermansia and Bifidobacterium. We also explore predictive performances of logistic regression with elastic-net and regularized SVM, and then focus on OTUs selected by these models. The OTUs selected by these models also overlap with those identified by previous researchers.
ORAL HUMAN PAPILLOMAVIRUS: ABOUT A CASE OF SQUAMOUS PAPILLOMA

Ikrame Boumendil(corresponding author), Hajar Ait Taleb, Ihssane Allouch, Sophia Nitassi, Razika Bencheikh, MohamedAnas Benbouzid, Abdelilah Oujilal, Leila Essakalli 1 Resident physician at the department of otorhinolaryngology and head and neck surgery, Ibn Sina university hospital 2 Professor at the department of otorhinolaryngology and head and neck surgery, Ibn Sina university hospital Mohammed V University, Rabat, Morocco.
The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.
Digital Transformation Framework

Technological pressures forced organizations to undertake digital transformation initiatives. Due to the increasing demand by business leaders, several consulting ﬁrms and researchers have developed maturity assessment models for digital transformation. However, these models, among others, do not explain the research process underlying their design and lack scientiﬁc validation. Furthermore, the process angle of the research is often overlooked in the context of digital transformation. To address this gap, we propose a staged digital transformation capability maturity model framework that enables organizations to assess their present digital capability based on processes and establish a plan of improvements to guide them towards higher digital capability level. This framework was developed using a design science research approach, building on the ISO/IEC 330xx family of standards to provide structure to a set of digital transformation processes identiﬁed using a systematic literature review. At a time when companies look for orientation to navigate their digital transformation, the contribution of this thesis is a framework, from a process perspective, rooted in solid scientiﬁc concepts, to guide practitioners on how to assess digital transformation initiatives.
Digital transformation in a car-rental company: A case study

Competition is grim. Means of differentiation from the competition is imperative and innovation is key. Many resort to digital transformation investments, yet few truly succeed to reap most of its beneﬁts, misusing a substantial part of the investment. This research aims to examine a successful digital transformation solution in the car-rental business, Key’n Go using a case study as the research method, in order to understand what were the steps taken to achieve its huge success. This research used an acknowledged often used for IS/IT projects, approach named beneﬁts management, since it provides a wider view of the project, which supports a rational decision on its investments and alignment with the company’s objectives.
Repossession and Recognition System: Transliteration of Antique Tamil Brahmi Typescript

Tamil is among the ancient languages in the world with a rich literature. Recognition of antique Tamil scripts is difficult and different from the present form of the language. The character recognition of Brahmi script poses a big challenge even today. In this paper, a new technique for extracting the features is proposed, and converting the ancient Tamil script into the present form. Initially, the system is implemented by performing the pre-processing steps. Then the characters are individually separated using the segmentation process. The processed image undergoes a new feature extraction technique, where the system applies a chi-square test to check whether all the zoning feature values of the image are independent or dependent. The characters are recognized from the extracted features using neural networks. NNTool is employed to train the featured image and the data are compared with the database to recognize the Brahmi characters. The feature extraction technique along with the neural network achieved recognition rate accuracy of 91.3% and error rate of 8.7% using the confusion matrix. Our experiment has been simulated using MATLAB.
Face Recognition in Infrared Imaging using Deep Learning

- Face recognition is a rapidly growing research area due to increasing demands for security in commercial and military applications. Systems based on visible spectrum are sensitive to illumination changes, pose and expression changes leading to reduction in accuracy. Thus, there is a need of Infrared(IR) imaging as they are invariant to changes in illumination and can capture even in low light conditions. This paper presents a deep learning approach based on convolution neural networks for face recognition in infrared imaging.
Socio-Economic Determinants of Risky Sexual Behavior in sub-Saharan Africa

Socio-Economic Determinants of Risky Sexual Behavior in sub-Saharan Africa by Frida Tende MPH/MPA, University of Alabama at Birmingham, 2013 BS, University of Alabama at Birmingham, 2010 Doctoral Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Public Health Walden University August 2020
Livestock resources and their conservation facing climate change

Objective: To analyze the importance of local livestock resources facing climate change. Methodology: A review of studies referenced in scientific databases disclosed in the livestock sector and animal genetic resources was made within the context of climate change. Results: Livestock breeding is an economic activity that contributes to the food security of the country; in view of its importance, technologies and necessary changes to perform this according to the accelerated changes that occur in the environment, brought by human activity, should be implemented. Implications: Using highly productive races that depend on external inputs and are not adapted to face the effects of climate change, make it a priority to appraise the use of local races that contribute to production under adverse conditions that prevail in warm weathers in the inter-tropical zone. Conclusions: Adapted local race breeders should preserve local animal genetic resources so that they perform as a climate change adaptation alternative that will have repercussions on livestock production systems.
iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability

Causality knowledge is vital to building robust AI systems. Deep learning models often perform poorly on tasks that require causal reasoning, which is often derived using some form of commonsense knowledge not immediately available in the input but implicitly inferred by humans. Prior work has unraveled spurious observational biases that models fall prey to in the absence of causality. While language representation models preserve contextual knowledge within learned embeddings, they do not factor in causal relationships during training. By blending causal relationships with the input features to an existing model that performs visual cognition tasks (such as scene understanding, video captioning, video question-answering, etc.), better performance can be achieved owing to the insight causal relationships bring about. Recently, several models have been proposed that have tackled the task of mining causal data from either the visual or textual modality. However, there does not exist widespread research that mines causal relationships by juxtaposing the visual and language modalities. While images offer a rich and easy-to-process resource for us to mine causality knowledge from, videos are denser and consist of naturally time-ordered events. Also, textual information offers details that could be implicit in videos. We propose iReason, a framework that infers visual-semantic commonsense knowledge using both videos and natural language captions. Furthermore, iReason's architecture integrates a causal rationalization module to aid the process of interpretability, error analysis and bias detection. We demonstrate the effectiveness of iReason using a two-pronged comparative analysis with language representation learning models (BERT, GPT-2) as well as current state-of-the-art multimodal causality models.
Diyabetik Sıçan Karaciğerinde Pazı Ekstresinin Glikoprotein Bileşikleri ve İleri Oksidasyon Protein Ürün Seviyeleri Üzerindeki Koruyucu Etkisi

Amac: Diyabet, yasam kalitesini dusuren ve hatta olume neden olan, tum dunyada bircok insani etkileyen metabolik bir hastalik-tir. Bu yuzden diyabet uzerine yapilan arastirmalar populerligini korumakta ve gelisimini devam ettirmektedir. Pazi (Beta vulgarisL. var. cicla) Turkiye'de seker hastalarinin alternatif hipoglisemik ajan olarak kullandigi bir bitkidir. Bu calismada pazi ekstraktinin, diyabetik sicanlarin karaciger dokusunda bulunan glikoprotein-ler (heksoz, heksozamin, fukoz ve siyalik asit) ve ileri oksidasyon protein urun seviyeleri uzerindeki koruyucu etkisinin arastirilmasi amaclanmistir. Gerec ve Yontem: Deneyde kullanilan erkek Sprague-Dawley si-canlari 3 gruba ayrildi. Kontrol sicanlari; Streptozotosin (STZ) ile di-yabetik yapilan sicanlar; pazi ekstresi verilen STZ diyabetik sicanlar; Sicanlarin diyabet olmasindan on dort gun sonra, sicanlara pazi ekstresi (2 g/kg/gun, gavaj ile) belirtilen dozda 45 gun uygulandi. 60. gunde sican karacigerleri cikarildi ve glikoproteinler ve ileri ok-sidasyon protein urunlerinin analizi icin %10 (w/v) karaciger homo-jenizatlari hazirlandi. Bulgular: Yapilan deneyler sonucunda diyabetik sicanlarin gli-koprotein parametreleri olan heksoz, heksozamin, fukoz, siyalik asit ve ileri oksidasyon protein urun seviyelerinin arttigi bulunmus-tur. Pazi ekstresinin uygulamasi, incelenen biyokimyasal parametre degerlerinin tersine cevirdigi belirlenmistir. Sonuc: Elde edilen sonuclardan pazi ektresinin diyabetik sicanlar uzerinde koruyucu bir etkiye sahip oldugu ileri surulebilir.
Prediction of Potential Drug Targets for Cutaneous Leishmaniasis By Leishmania major and Leishmania tropica: A Quantitative Proteomics and Bioinformatics Approach

Nasrin Amiri-Dashatan, Marzieh Ashrafmansouri, Mostafa Rezaei-Tavirani, Mehdi Koushki and Nayebali Ahmadi* Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Diagnostic Laboratory Sciences and Technology Research Center, Faculty of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran Department of Clinical Biochemistry, Zanjan University of Medical Sciences, Zanjan, Iran
Seasonality Modeling through LSTM Network in Inflation-Indexed Swaps Looking for a bridge between the traditional standard pricing approach and the new FinTech techniques

—An Inflation-Indexed Swap (IIS) is a derivative in which, at every payment date, the counterparties swap an inflation rate with a fixed rate. For the calculation of the Inflation Leg cash flows, it is necessary to build a mathematical model suitable for the Consumer Price Index (CPI) projection. For this purpose, quants typically start by using market quotes for the Zero-Coupon swaps in order to derive the future trend of the inflation index, together with a seasonality model for capturing the typical periodical effects. In this study, I propose a forecasting model for inflation seasonality based on a Long Short-Term Memory (LSTM) network: a deep learning methodology particularly useful for forecasting purposes. Thanks to its architecture, able to capture highly nonlinear relationships, and to the design of a careful training, able to satisfy both statistical and econometric features, the proposed methodology can be considered more accurate rather than the traditional one. As a result, the study shows how the CPI predictions, conducted using a FinTech paradigm, can be integrated in the respect of the traditional quantitative finance theory developed in this research field.