Survey of Template-Based Code Generation

L'automatisation de la génération des artefacts textuels à partir des modèles est une étape critique dans l'Ingénierie Dirigée par les Modèles (IDM). C'est une transformation de modèles utile pour générer le code source, sérialiser les modèles dans de stockages persistents, générer les rapports ou encore la documentation. Parmi les différents paradigmes de transformation de modèle-au-texte, la génération de code basée sur les templates (TBCG) est la plus utilisée en IDM. La TBCG est une technique de génération qui produit du code à partir des spécifications de haut niveau appelées templates. Compte tenu de la diversité des outils et des approches, il est nécessaire de classifier et de comparer les techniques de TBCG existantes afin d'apporter un soutien approprié aux développeurs. L'objectif de ce mémoire est de mieux comprendre les caractéristiques des techniques de TBCG, identifier les tendances dans la recherche, et éxaminer l'importance du rôle de l'IDM par rapport à cette approche. J'évalue également l'expressivité, la performance et la mise à l'échelle des outils associés selon une série de modèles. Je propose une étude systématique de cartographie de la littérature qui décrit une intéressante vue d'ensemble de la TBCG et une étude comparitive des outils de la TBCG pour mieux guider les dévloppeurs dans leur choix. Cette étude montre que les outils basés sur les modèles offrent plus d'expressivité tandis que les outils basés sur le code sont les plus performants. Enfin, Xtend2 offre le meilleur compromis entre l'expressivité et la performance.A critical step in model-driven engineering (MDE) is the automatic synthesis of a textual artifact from models. This is a very useful model transformation to generate application code, to serialize the model in persistent storage, generate documentation or reports. Among the various model-to-text transformation paradigms, Template-Based Code Generation (TBCG) is the most popular in MDE. TBCG is a synthesis technique that produces code from high-level specifications, called templates. It is a popular technique in MDE given that they both emphasize abstraction and automation. Given the diversity of tools and approaches, it is necessary to classify and compare existing TBCG techniques to provide appropriate support to developers. The goal of this thesis is to better understand the characteristics of TBCG techniques, identify research trends, and assess the importance of the role of MDE in this code synthesis approach. We also evaluate the expressiveness, performance and scalability of the associated tools based on a range of models that implement critical patterns. To this end, we conduct a systematic mapping study of the literature that paints an interesting overview of TBCG and a comparative study on TBCG tools to better guide developers in their choices. This study shows that model-based tools offer more expressiveness whereas code-based tools performed much faster. Xtend2 offers the best compromise between the expressiveness and the performance

Macro-micro approach for mining public sociopolitical opinion from social media

During the past decade, we have witnessed the emergence of social media, which has prominence as a means for the general public to exchange opinions towards a broad range of topics. Furthermore, its social and temporal dimensions make it a rich resource for policy makers and organisations to understand public opinion. In this thesis, we present our research in understanding public opinion on Twitter along three dimensions: sentiment, topics and summary. In the first line of our work, we study how to classify public sentiment on Twitter. We focus on the task of multi-target-specific sentiment recognition on Twitter, and propose an approach which utilises the syntactic information from parse-tree in conjunction with the left-right context of the target. We show the state-of-the-art performance on two datasets including a multi-target Twitter corpus on UK elections which we make public available for the research community. Additionally we also conduct two preliminary studies including cross-domain emotion classification on discourse around arts and cultural experiences, and social spam detection to improve the signal-to-noise ratio of our sentiment corpus. Our second line of work focuses on automatic topical clustering of tweets. Our aim is to group tweets into a number of clusters, with each cluster representing a meaningful topic, story, event or a reason behind a particular choice of sentiment. We explore various ways of tackling this challenge and propose a two-stage hierarchical topic modelling system that is efficient and effective in achieving our goal. Lastly, for our third line of work, we study the task of summarising tweets on common topics, with the goal to provide informative summaries for real-world events/stories or explanation underlying the sentiment expressed towards an issue/entity. As most existing tweet summarisation approaches rely on extractive methods, we propose to apply state-of-the-art neural abstractive summarisation model for tweets. We also tackle the challenge of cross-medium supervised summarisation with no target-medium training resources. To the best of our knowledge, there is no existing work on studying neural abstractive summarisation on tweets. In addition, we present a system for providing interactive visualisation of topic-entity sentiments and the corresponding summaries in chronological order. Throughout our work presented in this thesis, we conduct experiments to evaluate and verify the effectiveness of our proposed models, comparing to relevant baseline methods. Most of our evaluations are quantitative, however, we do perform qualitative analyses where it is appropriate. This thesis provides insights and findings that can be used for better understanding public opinion in social media

Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels

2006 Eighteenth Annual IMSA Presentation Day

We believe that our goal of creating decidedly-different learners is already being met and will make a profound impact on the future of humanity.https://digitalcommons.imsa.edu/archives_sir/1020/thumbnail.jp

Identifying immunological biomarkers of sepsis using cytometry bioinformatics and machine learning

Sepsis is a leading cause of mortality and significantly strains healthcare systems worldwide. Improving sepsis care and outcomes depends on appropriate risk stratification and timely identification of the causative pathogen to guide patient management and treatment. Enormous efforts have been made to identify diagnostic and prognostic biomarkers to aid decision making, but to date, they have failed to identify candidates with acceptable accuracy and precision to have an impact in the clinic. Past studies have often focused on individual biomarkers without considering the potential benefit of multi-marker panels incorporating deep immunological phenotyping. This work addressed this issue with a cross-disciplinary approach that integrated sepsis biomarker discovery, cytometry bioinformatics, and supervised machine learning. Firstly, a novel framework for cytometry data analysis was developed, along with a new ensemble clustering algorithm that reduced the risk of biasing exploratory analyses with the application of a single clustering technique. Secondly, the analysis framework was applied to a study cohort of severe sepsis patients, and their early immunological profile consisting of cellular and humoral parameters (within 36 hours of diagnosis) was determined. The captured immunological parameters were then combined with routine clinical data and lipid plasma concentrations to generate interpretable machine learning models for predicting mortality and the underlying cause of infection. The generated models discriminated between survivors and non-survivors, and between Gram-negative and Gram-positive infections, and identified potential combinations of biomarkers with predictive value