Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering Model-Based Adaptive Software Systems

Adaptive software systems are able to cope with changes in the environment by self-adjusting their structure and behavior. Robustness refers to the ability of the systems to deal with uncertainty, i.e. perturbations (e.g., Denial of Service attacks) or not-modeled system dynamics (e.g., independent cloud applications hosted on the same physical machine) that can affect the quality of the adaptation. To build robust adaptive systems we need models that accurately describe the managed system and methods for how to react to different types of change. In this thesis we introduce techniques that will help an engineer design adaptive systems for web applications. We describe methods to accurately model web applications deployed in cloud in such a way that it accounts for cloud variability and to keep the model synchronized with the actual system at runtime. Using the model, we present methods to optimize the deployed architecture at designand run-time, uncover bottlenecks and the workloads that saturate them, maintain the service level objective by changing the quantity of available resources (for regular operating conditions or during a Denial of Service attack). We validate the proposed contributions on experiments performed on Amazon EC2 and simulators. The types of applications that benefit the most from our contributions are webbased information systems deployed in cloud. ii
Big open-source social science: capabilities and methodology for automating social science analytics

Currently, obtaining reliable situational awareness of the social landscape is an arduous, lengthy process involving manual analyses by social scientists. These traditional methods do not scale to the speed and diversity required by DoD operations or the high-speed, international business model in today’s corporate environment. Conversely, “big data” easily scales to meet these challenges but lacks the rigor of social science theory. We present Big Open-Source Social Science (BOSSS), a research and development project that leverages the strengths of social- and computer-science technology to address the operational need for rapid and reliable human-landscape situational-awareness. BOSSS iteratively filters, navigates, and summarizes diverse open-source data to characterize a local population’s social structure, conflicts, cleavages, affinities, and animosities. BOSSS automatically scrapes open-access data from the web and performs natural language processing to populate a knowledge graph with a custom schema. BOSSS then mines the graph to extract key, theory-agnostic socialscience principles of human inter-relations and dynamics: homophily, stratification, sentiment, and conflict. Automated quantitative social-network analysis provides up-to-date indicators of trends or anomalies within the local population’s social landscape. BOSSS’s emerging technology will provide a dramatic reduction in the cognitive workload for the next generation of analysts and will facilitate more rapid situational awareness both for deployed soldiers and private companies conducting operations abroad.
Leadership Self-Efficacy, Change Oriented Behavior and Organizational Citizenship Behavior: The Moderating Effect of Experience

Knowledge is an essential factor in human existence. Education has been found responsible for social, economic, cultural and technological development of human society. Higher education institutions are responsible for producing required skilled human capital needed to enhance sustainable development. For these institutions to meet up with numerous demand of the society and function effectively leadership is an essential factor to be considered. This study examined the effect of leadership self-efficacy, change oriented behavior on staff organizational citizenship behavior in higher education institutions focusing on the moderating effect of experience. A total of 420 staff from 10 different higher education institutions in Lagos State, Nigeria were sampled. Structural Equation Modeling was used to access the moderating effect of experience on these variables. It was found that experience moderates the relationship between leadership self-efficacy, change oriented behavior, change policy and organizational citizenship behavior. Therefore, experience must be considered as a vital factor in higher education management and development.
Adaawk: Thinking and being in our hearts--Sharing the reflections of Otsin, Gitxsan rural indigenous nursing practice

Adaawk is oral history shared through the reflections of the first offering of a Gitxsan rural nursing practice course in follow up to the publication Otsin: Sharing the spirit of an indigenous nursing practice course development. In the spirit of adaawk, the Gitxsan’s reflections of the Gitxsan nursing practice course are themed using the metaphor of a totem pole. Each concept, and partnerships are integral to the development of a totem that reflects the adaawk. The purpose of this article is to demonstrate how the co-creation of relational concepts with the community strengthens preceptor, student and community partnerships for improved preceptor support and student learning outcomes. The author’s reflections demonstrate how the rural Gitxsan nursing practice experience informs the co-creation of cultural safety knowledge. Further, the experience provides a safe forum for students to practice cultural safety and relational practice. It allows students to co-create innovative patient outcomes within rural and community nursing practice context reflecting a two-eyed seeing approach. Students give back to communities through facilitated student placements in leadership/community health nursing for a fourth year nursing practice project with Gitxsan and rural communities in follow up to their third year practice. Authentic community partnerships and co-creation are foundational to enacting relational practice to nursing practice course development, consultation, and cultural safety practice. Learning outcomes of the course are revised to strengthen the parts of the totem and ultimately the essence of adaawk.
Voicing lived-experience and anti-racism: podcasting as a space at the margins for subaltern counterpublics

ABSTRACT Almost 18 years after the podcast medium first emerged, 2018 has witnessed its resurgence. With approximately six million (11%) of the UK population now listening to podcasts in an average week, the podcast renaissance raises new questions about the relationship between cultural production, consumption and representation. This paper explores the significance of the new wave of podcasts, specifically with regards to racial politics in the UK and its potential power as an anti-racist tool. Through a series of interview and focus group discussions with black and Asian podcasters in the UK, it asks what role podcasts play in providing an alternative space for ‘communities of resistance’. These issues are examined against the dual contexts in the UK of an intensifyingly hostile environment for black and minority ethnic groups and a digital and creative sector marked by social and cultural inequalities. The article suggests that in a ‘post fact’ international climate of disinformation that bolsters populist rhetoric around minority cultural groups, podcasts have become a rare space for articulating the lived experiences of these groups, whilst also challenging broader patterns of racialized disenfranchisement, including in the creative industries. Podcasts facilitate new forms of social affiliation and anti-racism; which we analyse through Fraser’s concept of “subaltern counterpublics” to unveil the interruptive potentiality of the medium for marginalised communities seeking to make accessible alternative representations and perspectives on the relationship between race and society.
Scaleable Code Clone Detection

Code clone detection helps connect developers across projects, if we do it on a large scale. The cornerstones that allow clone detection to work on a large scale are: (1) bad hashing (2) lightweight parsing using regular expressions and (3) MapReduce pipelines. Bad hashing means to determine whether or not two artifacts are similar by checking whether their hashes are identical. We show a bad hashing scheme that works well on source code. Lightweight parsing using regular expressions is our technique of obtaining entire parse trees from regular expressions, robustly and efficiently. We detail the algorithm and implementation of one such regular expression engine. MapReduce pipelines are a way of expressing a computation such that it can automatically and simply be parallelized. We detail the design and implementation of one such MapReduce pipeline that is efficient and debuggable. We show a clone detector that combines these cornerstones to detect code clones across all projects, across all versions of each project.
Effect Of Feature Selection, Synthetic Minority Over-sampling (SMOTE) And Under-sampling On Class imbalance Classification

Accurate identification of network intrusions is one of the biggest challenges of Network Intrusion Detection (NID) systems. In recent years Machine learning classification techniques have been used to precisely identify network intrusion. However, the multi class distribution in network intrusion detection system has found to be highly skewed, leading to classification accuracy problem due to class imbalance data set. The work presented in this paper not only explores the role of the attribute selection in improving classification accuracy but also investigates the problem of class imbalance using the Synthetic Minority Over-sampling (SMOTE) and under sampling of major classes. The classification performance is then evaluated over several types of classifiers. The outcome of this work is that for the class imbalance data set the under-sampling technique is more effective than SMOTE in detecting minor classes. It has also found during this research work that the decision tree algorithms (JRIP) and Naive Bayes are more accurate classifiers as compared to the Radial basis neural network and support vector machine. However no single algorithm can be used for the classification of multiclass and it is proposed in this research work that combination of classifier consisting of Naive Bayes and JRIP could be used for the classification of minor classes in an imbalance class data set of intrusion detection system.
The Length and Cost of Banking Crises

This paper reviews how recent studies of banking crises differ with regard to the dating, length, and costs of the crises. Significant discrepancies in these features suggest the absence of analytical consensus. The data allow an examination of the relation between perceived crisis length, as an index of delay in taking actions to resolve a crisis, and crisis costs. Cross-sectional evidence does not show that the length of a crisis is a significant contributor to its resolution cost. A measure of economic cost, the growth shortfall in the crisis period, shows more evidence of a link.
Connectomics: A semiautomatic approach

In connectomics the goal is to generate a full 3D reconstruction of neurons within the brain. This reconstruction will help neuroscientists better understand how the brain functions and also how it fails in the case of dementia and other neurodegenerative diseases. Attempts for this reconstruction are ongoing at varying levels of resolution from the millimeter scale of magnetic resonance imaging (MRI) to the nanometer scale of electron microscopy. In this dissertation, we develop tools that improve the ability of researchers to trace neurons through a volume in vitro at a resolution su cient for the identification of synapses. The first toolset will speed up the process for generating training datasets in newly acquired volumes to be used in supervised learning methods. Current methods of training dataset generation require dense labeling by a trained research scientist over hundreds of hours. This toolset will reduce the time required for training data generation by using sparse sampling and reduce the cost of that time by using a priori knowledge of the structure to allow for less specialized researchers to assist in the process. The second toolset is targeted at speeding up the correction of errors introduced in automatic neuron segmentation methods. Often referred to as proofreading, current methods require significant user input and fail to fully incorporate the information generated by the automatic segmentation method. I will use novel 2D and 3D visualization techniques to take advantage of the information generated during automatic segmentation into the proofreading process. This toolset will consist of two di↵erent applications, with one process targeting 2D proofreading with 3D linking and the other process targeting direct 3D proofreading.
Difficulties to teach mathematics and beliefs on mathematical modelling by prospective teachers

From a research program on the teaching of modelling we present results related to French prospective teachers. These students have answered an online questionnaire about the difficulties to teach mathematics taking in account biographical variables and beliefs on mathematics, mathematics teaching and mathematical. Heterogeneous beliefs are revealed and the analysis constructs three clusters from difficult to easy relation to different topics on mathematics teaching. The next steps of this ongoing research are mentioned, particularly looking for partners.
The Broader Autism Phenotype in the Parents of Children With Autism Spectrum Conditions

Progress in our understanding of the aetiology of Autism Spectrum Conditions (ASC) can be informed by research into the expression of the Broader Autism Phenotype (BAP) in the unaffected genetic relatives of people with ASC. This thesis commences with a comprehensive literature review of the BAP (chapter one), followed by an online study into the BAP in people with ASC, their first-degree relatives and controls focusing on empathy and basic facial emotion recognition (chapter two). Results provide support for the BAP in male first-degree relatives (fathers) who self-reported significantly lower empathy than controls. After setting out the general methods (chapter three), three further empirical studies are described (chapters four to six) that assess whether there are quantitative differences in the expression of autistic traits and related phenotypes in the unaffected parents of simplex (single incidence) and multiplex (multiple incidence) autism families. Multiplex parents were significantly less accurate than simplex parents at attributing mental states to others after controlling for verbal intelligence and performed significantly worse than either simplex parents or controls at identifying specific negative basic emotions from facial expressions. These significant differences in the social domain provide support for the hypothesis that differential genetic mechanisms operate in multiplex and simplex autism. There was also significantly greater aggregation of ADHD traits in multiplex families compared to simplex families, which supports the hypothesis of genetic overlap between ASC and ADHD and bolsters future investigations of crosssyndrome endopheno types for these conditions. The final empirical study of this thesis (chapter seven) explores the hypothesis that autistic characteristics are ‘fractionable’ in ASC parents in ways that are consistent with the DSM-5 defined dyad of behavioural impairments characterizing clinical ASC. The thesis concludes with a summary of findings and implications for future autism research and clinical practice, together with suggestions for future directions in the area of BAP research (chapter eight).
Do ETFs Increase Liquidity?

This paper investigates the impact of exchange-traded funds (ETFs) on the liquidity of their underlying stockholdings. Using a difference-in-differences methodology for large changes in the index weights of stocks in the S&P 500 and NASDAQ 100 indexes, we find that increases in ETF ownership are associated with increases in commonly used measures of liquidity. Stocks with high ETF ownership have higher price resilience and lower adverse selection costs. However, ETFs are linked to higher liquidation costs during the 2011 U.S. debt-ceiling crisis, suggesting that stocks with high ETF ownership may experience impaired liquidity during major market stress events.
IPv6 Segment Routing Security Considerations

Segment Routing (SR) allows a node to steer a packet through a controlled set of instructions, called segments, by prepending a SR header to the packet. A segment can represent any instruction, topological or service-based. SR allows to enforce a flow through any path (topological, or application/service based) while maintaining per-flow state only at the ingress node to the SR domain. Segment Routing can be applied to the IPv6 data plane with the addition of a new type of Routing Extension Header. This document analyzes the security aspects of the Segment Routing Extension Header (SRH) and how it is used by SR capable nodes to deliver a secure service.
Cross Domain Recommendation Using Vector Modeling and Genre Correlations

Recommender systems are basically information retrieval systems that offer guidance to users in making individual decisions related to choosing items based on personal interests. On Internet, there are infinite numbers of results for a particular query like movies, music, books, clothes etc. Sorting through every result is very tedious and time-consuming. Recommender system is very important application of data science and machine learning. They make the job of recommendation and prediction of preferences of users very simple. There are many limitations in classical recommender system because they provide recommendations in single domain only. With proliferating e-commerce sites and limitations in collaborative and content based recommender systems, cross domain recommender system are now widely in use. They can address the data sparsity and cold start problem by utilizing data from other related domains. In this paper, we propose recommendations across different domains by combining the benefit of plot keywords extracted from storyline and genre details from the two entertainment domains. We illustrate the working of our proposed CDR scheme using the movie as source domain and book as target domain.
Robust Semi-Parametric Inference in Semi-Supervised Settings

In this dissertation, we consider semi-parametric estimation problems under semisupervised (SS) settings, wherein the available data consists of a small or moderate sized labeled data (L), and a much larger unlabeled data (U). Such data arises naturally from settings where the outcome, unlike the covariates, is expensive to obtain, a frequent scenario in modern studies involving large electronic databases. It is often of interest in SS settings to investigate if and when U can be exploited to improve estimation efficiency, compared to supervised estimators based on L only. In Chapter 1, we propose a class of Efficient and Adaptive Semi-Supervised Estimators (EASE) for linear regression. These are semi-non-parametric imputation based two-step estimators adaptive to model mis-specification, leading to improved efficiency under model mis-specification, and equal (optimal) efficiency when the linear model holds. This adaptive property is crucial for advocating safe use of U. We provide asymptotic results establishing our claims, followed by simulations and application to real data. In Chapter 2, we provide a unified framework for SS M-estimation problems based on general estimating equations, and propose a family of EASE estimators that are always as efficient as the supervised estimator and more efficient whenever U is actually informative for the parameter of interest. For a subclass of problems, we also provide a flexible seminon-parametric imputation strategy for constructing EASE. We provide asymptotic results establishing our claims, followed by simulations and application to real data. In Chapter 3, we consider regressing a binary outcome (Y) on some covariates (X) based
Stable Plasmonic Coloration of Versatile Surfaces via Colloidal Monolayer Transfer Printing

Preparing nanoscale patterns with bright and high resolution structural colors for object decoration remains a challenge. Herein, the authors propose a strategy for fabricating surface‐universal and large‐area photonic crystal monolayer membrane with stable plasmonic coloration based on polydopamine (PDA) modification, self‐assembly, transfer printing, and e‐beam evaporation. In practice, the membrane is achieved with different diameters of silica and polystyrene (PS) colloidal nanoparticles through layer‐by‐layer (LBL) self‐assembled method. The photonic crystals are prepared on the PDA‐coated substrates and experimental spectra of different periodic plasma substrates with 20 nm‐gold‐coating are obtained and analyzed. Based on this, different plasma display patterns are prepared and solvent‐response plasma pattern is fabricated. Compared with traditional coloration method, this method is low‐cost, stable, and surface‐universal and can be used to proceed large‐area plasmonic coloration, which will be promising in the fields of plasma display, optical sensor and anti‐counterfeiting.
White Student Comfort and Conflict in Experiential Racial Dialogues

Background: White students are both curious and sometimes apprehensive about engaging in dialogues about race. Purpose: We investigate white university students’ experience of comfort and conflict in racial interaction in inter- and intragroup dialogues. Methodology/Approach: We analyzed student papers written at the beginning and end of the dialogue as well as their post-semester interviews, for their hopes/fears, classroom racial experiences, and learnings. Findings/Conclusions: White students in two types of semester-long dialogue courses reported issues of relative comfort and conflict as they explored their own and others’ racial histories and outlooks. They reported feeling safer in the white-only (intragroup) dialogues, as they learned about white culture and privilege; they also lamented not having racialized “others” to learn from. White students in the interracial (intergroup) dialogues often reported more discomfort, risk, and tension, as they learned about the impact of white culture and privilege on students of color, as well as about racism as a white problem. Implications: We draw implications for educational practice that include dissonance and conflict as stimuli for student learning and the use of experienced-based pedagogical techniques that encourage student sharing, critical reflection on narratives and encounters, and mutual participation.
Competitive Strategies and New Venture Performance

This paper presents the results of a longitudinal survey of 100 owner-managed new and small ventures. The survey was designed to explore the strategic orientations of new ventures and their performance implications. Results of the analytical study based on the application of factor analysis and cluster analysis indicate the existence of multiple strategies adopted by new ventures in order to remain competitive. The multiple regression models and cluster-wise performance analysis further indicate that a venture's competitive strategy significantly influences its performance. On the other hand, a complete lack of strategic orientation in a new venture may lead to negative performance and subsequently may even threaten its very survival.