Efficient Late Binding of Dynamic Function Compositions

Adaptive software becomes more and more important as computing is increasingly context-dependent. Runtime adaptability can be achieved by dynamically selecting and applying context-specific code. Role-oriented programming has been proposed as a paradigm to enable runtime adaptive software by design. Roles change the objects’ behavior at runtime and thus allow adapting the software to a given context. However, this increased variability and expressiveness has a direct impact on performance and memory consumption. We found a high overhead in the steady-state performance of executing compositions of adaptations. This paper presents a new approach to use run-time information to construct a dispatch plan that can be executed efficiently by the JVM. The concept of late binding is extended to dynamic function compositions. We evaluated the implementation with a benchmark for role-oriented programming languages leveraging context-dependent role semantics achieving a mean speedup of 2.79× over the regular implementation

Adaptive and Reactive Rich Internet Applications

In this thesis we present the client-side approach of Adaptive and Reactive Rich Internet Applications as the main result of our research into how to bring in time adaptivity to Rich Internet Applications. Our approach leverages previous work on adaptive hypermedia, event processing and other research disciplines. We present a holistic framework covering the design-time as well as the runtime aspects of Adaptive and Reactive Rich Internet Applications focusing especially on the run-time aspects

Web-oriented Event Processing

How can the Web be made situation-aware? Event processing is a suitable technology for gaining the necessary real-time results. The Web, however, has many users and many application domains. Thus, we developed multi-schema friendly data models allowing the re-use and mix from diverse users and application domains. Furthermore, our methods describe protocols to exchange events on the Web, algorithms to execute the language and to calculate access rights

Model-based Quality Assurance of Cyber-Physical Systems with Variability in Space, over Time and at Runtime

Cyber-physical systems (CPS) are frequently characterized by three essential properties: CPS perform complex computations, CPS conduct control tasks involving continuous data- and signal-processing, and CPS are (parts of) distributed, and even mobile, communication systems. In addition, modern software systems like CPS have to cope with ever-growing extents of variability, namely variability in space by means of predefined configuration options (e.g., software product lines), variability at runtime by means of preplanned reconfigurations (e.g., runtime-adaptive systems), and variability over time by means of initially unforeseen updates to new versions (e.g., software evolution). Finally, depending on the particular application domain, CPS often constitute safety- and mission-critical parts of socio-technical systems. Thus, novel quality-assurance methodologies are required to systematically cope with the interplay between the different CPS characteristics on the one hand, and the different dimensions of variability on the other hand. This thesis gives an overview on recent research and open challenges in model-based specification and quality-assurance of CPS in the presence of variability. The main focus of this thesis is laid on computation and communication aspects of CPS, utilizing evolving dynamic software product lines as engineering methodology and model-based testing as quality-assurance technique. The research is illustrated and evaluated by means of case studies from different application domains

Context Dependence and Procedural Meaning: The Semantics of Definites

This thesis argues that there is a theoretically interesting connection between members of the intuitive category of context-dependent expressions, including "we", "tall", "local", "every man", "the woman", "it", "those donkeys" and so on. A treatment of the linguistic meaning of these expressions will be proposed based on the idea that their use raises issues for the audience about the proper understanding of the utterances in which they occur. The proposal will be developed in terms of a semantics for questions, which draws on the idea that to know the meaning of a question is to know what would count as an answer. It can be summarised along similar lines: to know the meaning of a context-dependent expression is to know what properties or relations (of the appropriate type) it could be used to express. The framework in which this idea will be developed can account for why the expressions that are given this issue-based treatment can also be given dependent, bound readings. The class of definite expressions, including descriptions and pronouns, is analysed in detail. A quantificational approach, where the determiner is existential, is assumed for all forms of definiteness. In all cases, the restrictor is interpreted by an atomic definite concept. The audience's grasp of the properties which definite concepts express is the result of inferential processes which take the linguistic meaning of a definite expression as input. These processes are constrained by pragmatic principles. The analysis of context-dependent expressions is extended to account for dependent interpretations. A treatment of donkey sentences that accounts for their variable quantificational force is shown to follow naturally from the analysis. A pragmatic account of infelicitous uses of definites is provided and shown to compare favourably with that provided by dynamic semantic theories. Also, a novel treatment of plural definites is provided which accounts for their variable quantificational force

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in natural language processing (NLP), information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading

Classifying complex topics using spatial-semantic document visualization: An evaluation of an interaction model to support open-ended search tasks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualization of this retrieved document set. Our thesis is that unsupervised spatial-semantic visualization can automatically classify retrieved documents into a two-level hierarchy of relevance. In particular we hypothesise that the locality of any given aspect exemplar will tend to comprise a sufficient proportion of same-aspect documents to support a visually guided strategy for focused, same-aspect searching that we term the aspect cluster growing strategy. We examine spatial-semantic classification and potential aspect cluster growing performance across three scenarios derived from topics and relevance judgements from the TREC test collection. Our analyses show that the expected classification can be represented in spatial-semantic structures created from document similarities computed by a simple vector space text analysis procedure. We compare two diametrically opposed approaches to layout optimisation: a global approach that focuses on preserving the all similarities and a local approach that focuses only on the strongest similarities. We find that the local approach, based on a minimum spanning tree of similarities, produces a better classification and, as observed from strategy simulation, more efficient aspect cluster growing performance in most situations, compared to the global approach of multidimensional scaling. We show that a small but significant proportion of aspect clustering growing cases can be problematic, regardless of the layout algorithm used. We identify the characteristics of these cases and, on this basis, demonstrate a set of novel interactive tools that provide additional semantic cues to aid the user in locating same-aspect documents