A formally verified compiler back-end

This article describes the development and formal verification (proof of semantic preservation) of a compiler back-end from Cminor (a simple imperative intermediate language) to PowerPC assembly code, using the Coq proof assistant both for programming the compiler and for proving its correctness. Such a verified compiler is useful in the context of formal methods applied to the certification of critical software: the verification of the compiler guarantees that the safety properties proved on the source code hold for the executable compiled code as well

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

Data-efficient methods for information extraction

Strukturierte Wissensrepräsentationssysteme wie Wissensdatenbanken oder Wissensgraphen bieten Einblicke in Entitäten und Beziehungen zwischen diesen Entitäten in der realen Welt. Solche Wissensrepräsentationssysteme können in verschiedenen Anwendungen der natürlichen Sprachverarbeitung eingesetzt werden, z. B. bei der semantischen Suche, der Beantwortung von Fragen und der Textzusammenfassung. Es ist nicht praktikabel und ineffizient, diese Wissensrepräsentationssysteme manuell zu befüllen. In dieser Arbeit entwickeln wir Methoden, um automatisch benannte Entitäten und Beziehungen zwischen den Entitäten aus Klartext zu extrahieren. Unsere Methoden können daher verwendet werden, um entweder die bestehenden unvollständigen Wissensrepräsentationssysteme zu vervollständigen oder ein neues strukturiertes Wissensrepräsentationssystem von Grund auf zu erstellen. Im Gegensatz zu den gängigen überwachten Methoden zur Informationsextraktion konzentrieren sich unsere Methoden auf das Szenario mit wenigen Daten und erfordern keine große Menge an kommentierten Daten. Im ersten Teil der Arbeit haben wir uns auf das Problem der Erkennung von benannten Entitäten konzentriert. Wir haben an der gemeinsamen Aufgabe von Bacteria Biotope 2019 teilgenommen. Die gemeinsame Aufgabe besteht darin, biomedizinische Entitätserwähnungen zu erkennen und zu normalisieren. Unser linguistically informed Named-Entity-Recognition-System besteht aus einem Deep-Learning-basierten Modell, das sowohl verschachtelte als auch flache Entitäten extrahieren kann; unser Modell verwendet mehrere linguistische Merkmale und zusätzliche Trainingsziele, um effizientes Lernen in datenarmen Szenarien zu ermöglichen. Unser System zur Entitätsnormalisierung verwendet String-Match, Fuzzy-Suche und semantische Suche, um die extrahierten benannten Entitäten mit den biomedizinischen Datenbanken zu verknüpfen. Unser System zur Erkennung von benannten Entitäten und zur Entitätsnormalisierung erreichte die niedrigste Slot-Fehlerrate von 0,715 und belegte den ersten Platz in der gemeinsamen Aufgabe. Wir haben auch an zwei gemeinsamen Aufgaben teilgenommen: Adverse Drug Effect Span Detection (Englisch) und Profession Span Detection (Spanisch); beide Aufgaben sammeln Daten von der Social Media Plattform Twitter. Wir haben ein Named-Entity-Recognition-Modell entwickelt, das die Eingabedarstellung des Modells durch das Stapeln heterogener Einbettungen aus verschiedenen Domänen verbessern kann; unsere empirischen Ergebnisse zeigen komplementäres Lernen aus diesen heterogenen Einbettungen. Unser Beitrag belegte den 3. Platz in den beiden gemeinsamen Aufgaben. Im zweiten Teil der Arbeit untersuchten wir Strategien zur Erweiterung synthetischer Daten, um ressourcenarme Informationsextraktion in spezialisierten Domänen zu ermöglichen. Insbesondere haben wir backtranslation an die Aufgabe der Erkennung von benannten Entitäten auf Token-Ebene und der Extraktion von Beziehungen auf Satzebene angepasst. Wir zeigen, dass die Rückübersetzung sprachlich vielfältige und grammatikalisch kohärente synthetische Sätze erzeugen kann und als wettbewerbsfähige Erweiterungsstrategie für die Aufgaben der Erkennung von benannten Entitäten und der Extraktion von Beziehungen dient. Bei den meisten realen Aufgaben zur Extraktion von Beziehungen stehen keine kommentierten Daten zur Verfügung, jedoch ist häufig ein großer unkommentierter Textkorpus vorhanden. Bootstrapping-Methoden zur Beziehungsextraktion können mit diesem großen Korpus arbeiten, da sie nur eine Handvoll Startinstanzen benötigen. Bootstrapping-Methoden neigen jedoch dazu, im Laufe der Zeit Rauschen zu akkumulieren (bekannt als semantische Drift), und dieses Phänomen hat einen drastischen negativen Einfluss auf die endgültige Genauigkeit der Extraktionen. Wir entwickeln zwei Methoden zur Einschränkung des Bootstrapping-Prozesses, um die semantische Drift bei der Extraktion von Beziehungen zu minimieren. Unsere Methoden nutzen die Graphentheorie und vortrainierte Sprachmodelle, um verrauschte Extraktionsmuster explizit zu identifizieren und zu entfernen. Wir berichten über die experimentellen Ergebnisse auf dem TACRED-Datensatz für vier Relationen. Im letzten Teil der Arbeit demonstrieren wir die Anwendung der Domänenanpassung auf die anspruchsvolle Aufgabe der mehrsprachigen Akronymextraktion. Unsere Experimente zeigen, dass die Domänenanpassung die Akronymextraktion in wissenschaftlichen und juristischen Bereichen in sechs Sprachen verbessern kann, darunter auch Sprachen mit geringen Ressourcen wie Persisch und Vietnamesisch.The structured knowledge representation systems such as knowledge base or knowledge graph can provide insights regarding entities and relationship(s) among these entities in the real-world, such knowledge representation systems can be employed in various natural language processing applications such as semantic search, question answering and text summarization. It is infeasible and inefficient to manually populate these knowledge representation systems. In this work, we develop methods to automatically extract named entities and relationships among the entities from plain text and hence our methods can be used to either complete the existing incomplete knowledge representation systems to create a new structured knowledge representation system from scratch. Unlike mainstream supervised methods for information extraction, our methods focus on the low-data scenario and do not require a large amount of annotated data. In the first part of the thesis, we focused on the problem of named entity recognition. We participated in the shared task of Bacteria Biotope 2019, the shared task consists of recognizing and normalizing the biomedical entity mentions. Our linguistically informed named entity recognition system consists of a deep learning based model which can extract both nested and flat entities; our model employed several linguistic features and auxiliary training objectives to enable efficient learning in data-scarce scenarios. Our entity normalization system employed string match, fuzzy search and semantic search to link the extracted named entities to the biomedical databases. Our named entity recognition and entity normalization system achieved the lowest slot error rate of 0.715 and ranked first in the shared task. We also participated in two shared tasks of Adverse Drug Effect Span detection (English) and Profession Span Detection (Spanish); both of these tasks collect data from the social media platform Twitter. We developed a named entity recognition model which can improve the input representation of the model by stacking heterogeneous embeddings from a diverse domain(s); our empirical results demonstrate complementary learning from these heterogeneous embeddings. Our submission ranked 3rd in both of the shared tasks. In the second part of the thesis, we explored synthetic data augmentation strategies to address low-resource information extraction in specialized domains. Specifically, we adapted backtranslation to the token-level task of named entity recognition and sentence-level task of relation extraction. We demonstrate that backtranslation can generate linguistically diverse and grammatically coherent synthetic sentences and serve as a competitive augmentation strategy for the task of named entity recognition and relation extraction. In most of the real-world relation extraction tasks, the annotated data is not available, however, quite often a large unannotated text corpus is available. Bootstrapping methods for relation extraction can operate on this large corpus as they only require a handful of seed instances. However, bootstrapping methods tend to accumulate noise over time (known as semantic drift) and this phenomenon has a drastic negative impact on the final precision of the extractions. We develop two methods to constrain the bootstrapping process to minimise semantic drift for relation extraction; our methods leverage graph theory and pre-trained language models to explicitly identify and remove noisy extraction patterns. We report the experimental results on the TACRED dataset for four relations. In the last part of the thesis, we demonstrate the application of domain adaptation to the challenging task of multi-lingual acronym extraction. Our experiments demonstrate that domain adaptation can improve acronym extraction within scientific and legal domains in 6 languages including low-resource languages such as Persian and Vietnamese

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Well-Formed and Scalable Invasive Software Composition

Software components provide essential means to structure and organize software effectively. However, frequently, required component abstractions are not available in a programming language or system, or are not adequately combinable with each other. Invasive software composition (ISC) is a general approach to software composition that unifies component-like abstractions such as templates, aspects and macros. ISC is based on fragment composition, and composes programs and other software artifacts at the level of syntax trees. Therefore, a unifying fragment component model is related to the context-free grammar of a language to identify extension and variation points in syntax trees as well as valid component types. By doing so, fragment components can be composed by transformations at respective extension and variation points so that always valid composition results regarding the underlying context-free grammar are yielded. However, given a language’s context-free grammar, the composition result may still be incorrect. Context-sensitive constraints such as type constraints may be violated so that the program cannot be compiled and/or interpreted correctly. While a compiler can detect such errors after composition, it is difficult to relate them back to the original transformation step in the composition system, especially in the case of complex compositions with several hundreds of such steps. To tackle this problem, this thesis proposes well-formed ISC—an extension to ISC that uses reference attribute grammars (RAGs) to specify fragment component models and fragment contracts to guard compositions with context-sensitive constraints. Additionally, well-formed ISC provides composition strategies as a means to configure composition algorithms and handle interferences between composition steps. Developing ISC systems for complex languages such as programming languages is a complex undertaking. Composition-system developers need to supply or develop adequate language and parser specifications that can be processed by an ISC composition engine. Moreover, the specifications may need to be extended with rules for the intended composition abstractions. Current approaches to ISC require complete grammars to be able to compose fragments in the respective languages. Hence, the specifications need to be developed exhaustively before any component model can be supplied. To tackle this problem, this thesis introduces scalable ISC—a variant of ISC that uses island component models as a means to define component models for partially specified languages while still the whole language is supported. Additionally, a scalable workflow for agile composition-system development is proposed which supports a development of ISC systems in small increments using modular extensions. All theoretical concepts introduced in this thesis are implemented in the Skeletons and Application Templates framework SkAT. It supports “classic”, well-formed and scalable ISC by leveraging RAGs as its main specification and implementation language. Moreover, several composition systems based on SkAT are discussed, e.g., a well-formed composition system for Java and a C preprocessor-like macro language. In turn, those composition systems are used as composers in several example applications such as a library of parallel algorithmic skeletons

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Scientific applications strive for increased memory and computing performance, requiring massive amounts of data and time to produce results. Applications utilize large-scale, parallel computing platforms with advanced architectures to accommodate their needs. However, developing performance-portable applications for modern, heterogeneous platforms requires lots of effort and expertise in both the application and systems domains. This is more relevant for unstructured applications whose workflow is not statically predictable due to their heavily data-dependent nature. One possible solution for this problem is the introduction of an intelligent Domain-Specific Language (iDSL) that transparently helps to maintain correctness, hides the idiosyncrasies of lowlevel hardware, and scales applications. An iDSL includes domain-specific language constructs, a compilation toolchain, and a runtime providing task scheduling, data placement, and workload balancing across and within heterogeneous nodes. In this work, we focus on the runtime framework. We introduce a novel design and extension of a runtime framework, the Parallel Runtime Environment for Multicore Applications. In response to the ever-increasing intra/inter-node concurrency, the runtime system supports efficient task scheduling and workload balancing at both levels while allowing the development of custom policies. Moreover, the new framework provides abstractions supporting the utilization of heterogeneous distributed nodes consisting of CPUs and GPUs and is extensible to other devices. We demonstrate that by utilizing this work, an application (or the iDSL) can scale its performance on heterogeneous exascale-era supercomputers with minimal effort. A future goal for this framework (out of the scope of this thesis) is to be integrated with machine learning to improve its decision-making and performance further. As a bridge to this goal, since the framework is under development, we experiment with data from Nuclear Physics Particle Accelerators and demonstrate the significant improvements achieved by utilizing machine learning in the hit-based track reconstruction process

Procedural Historic Building Information Modelling (HBIM) For Recording and Documenting European Classical Architecture

Procedural Historic Building Information Modelling (HBIM) is a new approach for modelling historic buildings which develops full building information models from remotely sensed data. HBIM consists of a novel library of reusable parametric objects, based on historic architectural data and a system for mapping these library objects to survey data. Using concepts from procedural modelling, a new set of rules and algorithms have been developed to automatically combine HBIM library objects and generate different building arrangements by altering parameters. This is a semi-automatic process where the required building structure and objects are first automatically generated and then refined to match survey data. The encoding of architectural rules and proportions into procedural modelling rules helps to reduce the amount of further manual editing that is required. The ability to transfer survey data such as building footprints or cut-sections directly into a procedural modelling rule also greatly reduces the amount of further editing required. These capabilities of procedural modelling enable a more automated and efficient overall workflow for reconstructing BIM geometry from point cloud data. This document outlines the research carried out to evaluate the suitability of a procedural modelling approach for improving the process of reconstructing building geometry from point clouds. To test this hypothesis, three procedural modelling prototypes were designed and implemented for BIM software. Quantitative accuracy testing and qualitative end-user scenario testing methods were used to evaluate the research hypothesis. The results obtained indicate that procedural modelling has potential for achieving more accurate, automated and easier generation of BIM geometry from point clouds