Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted Cox Regression Using the R Package coxphw

Cox's regression model for the analysis of survival data relies on the proportional hazards assumption. However, this assumption is often violated in practice and as a consequence the average relative risk may be under- or overestimated. Weighted estimation of Cox regression is a parsimonious alternative which supplies well interpretable average effects also in case of non-proportional hazards. We provide the R package coxphw implementing weighted Cox regression. By means of two biomedical examples appropriate analyses in the presence of non-proportional hazards are exemplified and advantages of weighted Cox regression are discussed. Moreover, using package coxphw, time-dependent effects can be conveniently estimated by including interactions of covariates with arbitrary functions of time.
Wide-area Software-defined Storage

The proliferation of commodity cloud services helps developers build wide-area “system-of-systems” applications by harnessing cloud storage, CDNs, and public datasets as reusable building blocks. But to do so, developers must contend with two long-term challenges. First, whenever developers change storage providers, they must work to preserve the application’s expected storage semantics, i.e. the rules governing how the application expects the storage provider to handle its reads and writes. Today, changing storage providers is costly, because developers need to patch the application to make it compatible with the new provider’s data consistency model, access controls, replica placement strategies, and so on. At the same time, users have certain expectations about how their data will be used, which the application must meet. For example, depending on the application, users may expect that their data will be kept private from other users, that their data will be exportable to other applications, that accesses to their data will be logged in an auditable way, and so on. In the limit, each user’s expectations represent an implicit policy constraining how their data can be stored. Honoring these policies is difficult for developers who rely on third-party storage providers because the storage provider is often unaware of them. This thesis addresses these challenges with a new wide-area storage paradigm, called “software-defined storage” (SDS), that runs in-between applications and cloud services. SDS-enabled applications do not host data, but instead let users bring their preferred cloud services to the application. By taking a user-centric approach to hosting data, users are empowered to programmatically specify their policies independent of their applications and select services that will honor them. To support this approach and to tolerate service provider changes, SDS empowers developers to programmatically specify their application’s storage semantics independent of storage providers. iii This thesis presents the design principles for SDS, and validates their real-world applicability with two SDS implementations and several non-trivial applications built on top of them. Most of these applications are used in production today. This thesis presents microbenchmarks of the SDS implementations and uses real-world experiences to show how to make the most of SDS.
The Role of Seed Classes in Improving Purification and Productivity of Rice

The field experimentes were conducted during 2016 and 2017 rice growing seasons. The investigation aimed to study the effect of seed classes i.e. breeder, foundation, registered and farmer and the studied cultivars i.e. Sakha 101, Giza 177, and Giza 178 on the productivity of rice (Oriza sativa). Split plot design with three replications was used, the main plots were randomely occupied by four classes of seeds, while the cultivars Sakha 101, Giza 177, and Giza 178 were assigned to the sub plots. Results showed that, breeder seeds gave the highest grain yield (10.97 and 11.34 t ha-1) in 2016 and 2017 seasons, respectively. The highest grain yield (10.51 and 10.83 t/ha) was obtained from variety Sakha 101 in both seasons, respectively. The interaction between cultivars and seed classes was highly significant for germination (%), shoot length (cm), root length (cm), days to heading, number of tillers/hill, panicle weight (g), panicle length (cm), seed set (%), 1000-grain weight and grain yield, while not significant was found for plant height (cm) during 2016 and 2017 seasons. The results indicated that both of rice varieties and seed classes were important for increasing grain yield.
Finding the Adequate Location Scenario After the Merger of Fire Brigades Thanks to Multiple Criteria Decision Analysis Methods

Abstract This paper addresses the issue of selecting a suitable location for a fire station in canton of Fribourg, as a result of a fire brigades’ merger, by applying Multiple Criteria Decision Analysis (MCDA) methods. Solving the problem of determining fire station locations through various methods has been analyzed in-depth by researchers. However, a different approach, based on application of ELECTRE I and ELECTRE II methods is advanced in this paper. The selection of the most suitable fire station site is obtained by applying the designated methods to five distinctive alternatives (called scenarios), taking into consideration the relatively limited information and specifics, and the extensive number of relevant criteria that summed up to sixty-one. Taking the merger of the three local fire departments as an example, the proposed methods for selecting a suitable location for the fire station demonstrate and justify the reason behind this choice. Research shows that the applied methods have been proven to be useful and powerful tools that exhibited acceptable levels of consistency when selecting the best project. The main finding is that one scenario in particular proved to be preferred over the others and most suitable in determining the fire station location.
A Practical and Conceptual Framework for Learning in Control

We propose a fully Bayesian approach for efficient reinforcement learning (RL) in Markov decision processes with continuous-valued state and action spaces when no expert knowledge is available. Our framework is based on well-established ideas from statistics and machine learning and learns fast since it carefully models, quantifies, and incorporates available knowledge when making decisions. The key ingredient of our framework is a probabilistic model, which is implemented using a Gaussian process (GP), a distribution over functions. In the context of dynamic systems, the GP models the transition function. By considering all plausible transition functions simultaneously, we reduce model bias, a problem that frequently occurs when deterministic models are used. Due to its generality and efficiency, our RL framework can be considered a conceptual and practical approach to learning models and controllers when expert knowledge is difficult to obtain or simply not available, which makes system identification hard.
Smart Garage Implementation and Design Using Whatsapp Communication Media

WhatsApp is a social networking app that serves as a communication medium. According to the Online Audience Measurement Standards named comScore, in 2017, WhatsApp application users in Indonesia amounted to 35.8 million people. As the most popular mobile application with the most users in the country, in this research the authors chose Whatsapp as a communication medium that will be integrated into one application of Internet of Things (IoT), that is Smart Garage. Smart Garage is a combination of information technology and computing technology that is applied to a house by relying on efficiency and device automation. The results of this research shows that it is better to use mobile data networks than using the wireless networks. The maximum delay when using mobile data is 7.5 s and 7.7 s when using wireless networks. The research using WhatsApp application still rare especially in the field of IoT.
Machine Learning based Spam E-Mail Detection

Spam email is one of the biggest issues in the world of internet. Spam emails not only influence the organisations financially but also exasperate the individual email user. This paper aims to propose a machine learning based hybrid bagging approach by implementing the two machine learning algorithms: Naïve Bayes and J48 (decision tree) for the spam email detection. In this process, dataset is divided into different sets and given as input to each algorithm. Total three experiments are performed and the results obtained are compared in terms of precision, recall, accuracy, f-measure, true negative rate, false positive rate and false negative rate. The two experiments are performed using individual Naïve Bayes & J48 algorithms. Third experiment is the proposed SMD system implemented using hybrid bagged approach. The overall accuracy of 87.5% achieved by the hybrid bagged approach based SMD system.
Computational super-resolution phase retrieval from multiple phase-coded diffraction patterns: simulation study and experiments

In this paper, we consider computational super-resolution inverse diffraction phase retrieval. The optical setup is lensless, with a spatial light modulator for aperture phase coding. The paper is focused on experimental tests of the super-resolution sparse phase amplitude retrieval algorithm. We start from simulations and proceed to physical experiments. Both simulation tests and experiments demonstrate good-quality imaging for super-resolution with a factor of 4 and a serious advantage over diffraction-limited resolution as defined by Abbe’s criterion.
Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy

Feature selection has been widely applied in many areas such as classification of spam emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray analysis. Classification involves building predictive models to predict the target variable based on several input variables (features). This study compares filter and wrapper feature selection methods to maximise the classifier accuracy. The logistic regression was used as a classifier while the performance of the feature selection methods was based on the classification accuracy, Akaike information criteria (AIC), Bayesian information criteria (BIC), Area Under Receiver operator curve (AUC), as well as sensitivity and specificity of the classifier. The simulation study involves generating data for continuous features and one binary dependent variable for different sample sizes. The filter methods used are correlation based feature selection and information gain, while the wrapper methods are sequential forward and sequential backward elimination. The simulation was carried out using R, an open-source programming language. Simulation results showed that the wrapper method (sequential forward selection and sequential backward elimination) methods were better than the filter method in selecting the correct features.
Computer-aided analysis of fetal cardiac ultrasound videos

This thesis addresses the task of developing automatic algorithms for analysing the two-dimensional ultrasound video footage obtained from fetal heart screening scans. These scans are typically performed in the second trimester of pregnancy to check for congenital heart anomalies and require significant training and anatomical knowledge to perform. The aim is to develop a tool that runs at high frame rates with no user initialisation and infers the visibility, position, orientation, view classification, and cardiac phase of the heart, and additionally the locations of cardiac structures of interest (such as valves and vessels) in a manner that is robust to the various sources of variation that occur in real-world ultrasound scanning. This is the first work to attempt such a detailed automated analysis of these videos. The problem is posed as a Bayesian filtering problem, which provides a principled framework for aggregating uncertain measurements across a number of frames whilst exploiting the constraints imposed by anatomical feasibility. The resulting inference problem is solved approximately with a particle filter, whose state space is partitioned to reduce the problems associated with filtering in high-dimensional spaces. Rotation-invariant features are captured from the videos in an efficient way in order to tackle the problem of unknown orientation. These are used within random forest learning models, including a novel formulation to predict circular-valued variables. The algorithm is validated on an annotated clinical dataset, and the results are compared to estimates of inter- and intra-observer variation, which are significant in both cases due to the inherent ambiguity in the imagery. The results suggest that the algorithm’s output approaches these benchmarks in several respects, and fall slightly behind in others. The work presented here is an important first step towards developing automated clinical tools for the detection of congenital heart disease.
CONTROL OF SOME MICROBIAL SKIN DISEASES BY SOME MARINE ALGAL EXTRACT

In this study, thirty two bacterial isolates were randomly isolated from skin, wound and hair of patients. Antibiotic susceptibility of the collected bacterial isolates to seventeen antibiotics was studied using the disc diffusion method. Results revealed that bacterial isolates number (9) and (17) were proved to be the most multi-drug resistant isolates recording 100% resistance. Bacterial isolate number (9) was subjected to molecular identification by using 16S rRNA gene sequence, the tested bacterial isolate revealed a similarity of 98% with Staphylococcus aureus subsp. aureus JH1. The antibacterial activity of Saragassum latifolium, Ulva lactuca and Jania rubens were tested against the most multidrug resistant bacterial isolate. Saragassum Latifolium extract appeared the highest antibacterial activity. Ultraviolet spectrum of some compounds is recorded in conjunction with other spectral data such as infrared and GC-MS analysis as an attempt to deduce the compounds which caused inhibition to the growth of bacterial isolate. The suggested chemical structure of purified antagonistic material isolated from Saragassum latifolium.
Reconstruction of CT Secondary Waveform Using ANN and Exponential Smoothing

- Instrumentation transformers act as eyes and ears of a power system. Many measurement and protection related activities depend on current transformers (CTs) as primary sensing unit. Hence, it is of utmost important that the output of a CT should be absolutely trust-worthy. However, CTs show a tendency of getting saturated. This leads to an erroneous secondary waveform, which can lead to malfunctioning of systems which are dependent on CT. This paper proposes a technique to enhance ANN based reconstruction of erroneous secondary current waveform. The proposed technique uses artificial neural network to forecast ideal waveform. The network uses two inputs: 1. Erroneous secondary waveform. 2. Exponentially smoothed secondary waveform, which acts as an assisting input. The smoothing factor is determined using genetic algorithm. Extensive simulations indicate that the proposed technique efficiently generates reconstructed CT secondary waveform.
Estimation of age groups using facial recognition features

Facial recognition has been used to detect emotions, gender, expressions and identity. These topics have been extensively studied. But, automatic Age Estimation is a topic that has not been researched much. The basis of our topic is that there are features on the human face that change as our age increases and in our research we utilize these features for age group classification. We classify the images into 6 age groups- (0-6, 8-12, 15-20, 25-32, 38-53, 60-100). The process involves 3 stages: -Pre-processing, Feature Extraction, Classification. Preprocessing includes commotion decreasing, standardization and change of the crude information into structure that is suitable for the pattern recognition. A small set of good features is selected from the available features, in order to provide best matching information. This process discovers important features to obtain an effective and improved answer for a given problem which is the next step. At last the classification or clustering stage is performed. Classification comprises of allotting a class mark to a given pattern while clustering discoveries homogeneous small groups in information. We also deal with comparison study of various feature extraction techniques (Haar feature extraction, HOG feature extraction) and classification techniques (Naive Bayes, SVM, KNN, Neural Network-Back propagation algorithm) and finally try to determine the best possible combination for age group classification. Convolutional Neural Networks is also applied to estimate the age groups.
Improving generalized inverted index lock wait times

Concurrent operations on tree like data structures is a cornerstone of any database system. Concurrent operations intended for improving read\write performance and usually implemented via some way of locking. Deadlock-free methods of concurrency control are known as tree locking protocols. These protocols provide basic operations(verbs) and algorithm (ways of operation invocations) for applying it to any tree-like data structure. These algorithms operate on data, managed by storage engine which are very different among RDBMS implementations. In this paper, we discuss tree locking protocol implementation for General inverted index (Gin) applied to multiversion concurrency control (MVCC) storage engine inside PostgreSQL RDBMS. After that we introduce improvements to locking protocol and provide usage statistics about evaluation of our improvement in very high load environment in one of the world’s largest IT company.
Development of a user-friendly app for exploring and analyzing research topics in psychology

Background Keeping track of the developments in a scientific field can be challenging. Regarding increasing numbers of publications, summarizing the contents of hundreds of thousands of scientific publications on specific topics is necessary to gain insights into the processes of a scientific field. Many databases offer classifications, i.e., broad subject headings for categorizing the publications’ contents. Past research has treated these categories as research topics (e.g., Krampen, 2016), but regarding the level of detail, topicality, and flexibility this approach has been criticized (e.g., Bittermann & Fischer, 2018). Techniques for automated content analysis represent a promising approach for getting insight into large text corpora. Topic modeling (e.g., Blei, Ng, & Jordan, 2003), in particular, is gaining in popularity in scientometrics. In their well-known paper, Griffiths and Steyvers (2004) demonstrated how to find scientific topics by applying topic models to a corpus of scientific abstracts. A topic-guided and user-friendly interface for databases of scientific literature can open publication trends to a broader audience with various user scenarios: exploring the current “hot topics,” investigating the ups and downs of topic popularity over time, or comparing publication trends concerning societal processes (e.g., the increasing trend of a topic referring to refugees and emotional trauma in psychological publications from the German-speaking countries after 2015).
Route choice analysis using a gps-based data collection

This paper aims to provide greater insight into the factors underlying route choice behaviour mechanisms, examining actual user route choice decisions. The information has been drawn from a database containing information collected using a smartphone with built-in GPS tracking device, for recording both the spatial-temporal characteristics of the routes chosen and the activities carried out by the users during the survey. We monitored a sample comprising 109 volunteers, who travelled 626 routes during morning peak hours. The main objective was to compare the observed choices with the least cost paths in terms of time and distance using both GIS and a specialised software for simulating transport systems (CUBE, Citilabs). The results show that for systematic habitual travel individuals tend to place more importance on time and distance attributes and thus to choose routes similar to those simulated using the model (least cost paths). In addition, for repetitive trips between the same origin and destination, users travel the same route 70% of the time, the remaining 30% involving two or more routes.
Hashtag Sentiment Analysis using Tweets for the Ternary Classification

To analyze the utility of the linguistic features for detecting the sentiment of the given Twitter messages. Evaluating the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in micro blogging. In addition to,apply for a supervised approach to the problem, but to make use of the existing hashtags in the Twitter data for building training data.
Research on Load Balancing in Task Allocation Process in Heterogeneous Hadoop Cluster

The original task scheduling algorithm of Hadoop cannot meet the performance requirements of heterogeneous clusters. The existing Hadoop task scheduling algorithm assumes that the performance of each node is consistent. This algorithm can perform well in homogeneous clusters. However, in the heterogeneous Hadoop cluster, due to different performance between CPU, disks and memory, the load imbalance will occur in the cluster. In the view of the unbalanced load of Hadoop cluster in heterogeneous environment, this paper proposes a new algorithm named Load balancing algorithm based on heterogeneous environment (LBAHE), which takes into account the performance differences of each node. And when measuring the performance of nodes, the number of slots is no longer the only criterion. We also add the CPU, disk memory and other factors. Experiments show the efficiency of the new algorithm and it can perform tasks faster in heterogeneous clusters than original algorithm.
xMWAS: an R package for data-driven integration and differential network analysis

Summary Integrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Further-more, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, an R package for data integration, network visualization, clustering, differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms. Availability https://sourceforge.net/projects/xmwas/ Contact kuppal2@emory.edu