MegSDF Mega-system development framework

A framework for developing large, complex software systems, called Mega-Systems, is specified. The framework incorporates engineering, managerial, and technological aspects of development, concentrating on an engineering process. MegSDF proposes developing Mega-Systems as open distributed systems, pre-planned to be integrated with other systems, and designed for change. At the management level, MegSDF divides the development of a Mega-System into multiple coordinated projects, distinguishing between a meta-management for the whole development effort, responsible for long-term, global objectives, and local managements for the smaller projects, responsible for local, temporary objectives. At the engineering level, MegSDF defines a process model which specifies the tasks required for developing Mega-Systems, including their deliverables and interrelationships. The engineering process emphasizes the coordination required to develop the constituent systems. The process is active for the life time of the Mega-System and compatible with different approaches for performing its tasks. The engineering process consists of System, Mega-System, Mega-System Synthesis, and Meta-Management tasks. System tasks develop constituent systems. Mega-Systems tasks provide a means for engineering coordination, including Domain Analysis, Mega-System Architecture Design. and Infrastructure Acquisition tasks. Mega-System Synthesis tasks assemble Mega-Systems from the constituent systems. The Meta-Management task plans and controls the entire process. The domain analysis task provides a general, comprehensive, non-constructive domain model, which is used as a common basis for understanding the domain. MegSDF builds the domain model by integrating multiple significant perceptions of the domain. It recommends using a domain modeling schema to facilitate modeling and integrating the multiple perceptions. The Mega-System architecture design task specifies a conceptual architecture and an application architecture. The conceptual architecture specifies common design and implementation concepts and is defined using multiple views. The application architecture maps the domain model into an implementation and defines the overall structure of the Mega-System, its boundaries, components, and interfaces. The infrastructure acquisition task addresses the technological aspects of development. It is responsible for choosing, developing or purchasing, validating, and supporting an infrastructure. The infrastructure integrates the enabling technologies into a unified platform which is used as a common solution for handling technologies. The infrastructure facilitates portability of systems and incorporation of new technologies. It is implemented as a set of services, divided into separate service groups which correspond to the views identified in the conceptual architecture

Preimplantation genetic screening during in vitro fertilization, clinical applications and insight into embryological development

Aneuploidy (extra or missing individual chromosomes) is the leading cause of miscarriage, embryo wastage and in-vitro fertilization (IVF) failure. Aneuploidy increases with maternal age and is widespread in human preimplantation embryos. Thus, aneuploidy screening before implantation during an IVF cycle (preimplantation genetic screening or PGS), to increase pregnancy rates and decreasing miscarriage rates, is also widespread. Despite this, PGS faces challenges in terms of both biological and technical limitations that may impede its full potential. Biologically, the phenomenon of chromosomal mosaicism (the presence of two or more cell lines - typically, one aneuploid and one euploid) may lead to false positives or false negatives, and the discard or transfer of euploid or aneuploid embryos, respectively. Technically, it is uncertain whether diagnosis on the biopsied piece is representative of the remaining embryo. Because these dilemmas it is unknown if PGS will only benefit a few selected groups of patients or potentially the entire IVF patient population. In a series of published works, this thesis demonstrates a significant contribution to field of preimplantation genetics, provides insight into technical and biological limitations of PGS, and into the etiology of aneuploidy and mosaicism. Specifically, I introduce a novel technique to "map" chromosomal mosaicism, by reconstructing a virtual image of the blastocyst with the approximate location of individual cells and their corresponding chromosomal makeup. I also demonstrate the ability of PGS to be performed on blastocysts that were previously frozen; thus, blastocysts have to be thawed/warmed, biopsied, vitrified and rewarmed prior to use. From a clinical standpoint, I present evidence of the differences in PGS outcomes between day 5 and day 6 blastocysts: The data suggests that day 6 blastocysts are less likely to be euploid than day 5 blastocysts. Furthermore, day 6 euploid blastocysts exhibit similar pregnancy and implantation rates when compared to their day 5 counterparts. I also published on a study examining differences in PGS outcomes in those patients that are defined as "presumed fertile" as opposed to those that are "infertile". Another study examined pregnancy and implantation rates between two competing platforms, quantitative polymerase chain reaction (qPCR) and array comparative genomic hybridization (aCGH). I also examined the pregnancy rates of poor quality embryos on day 6 that would have been discarded. From a biological standpoint, I examined the mechanisms through which embryos diagnosed as aneuploid on day 3 could develop to a euploid blastocyst, demonstrating that euploid blastocysts can develop from aneuploid cleavage stage embryos. I also demonstrated differences in aneuploidy rates between polar, mural, and a piece defined as "mid" trophectoderm, and blastocysts diagnosed as aneuploid may not reflect the chromosomal constitution of the whole embryo proper. This work herein presented provides a deeper understanding of the technical limitations of PGS and into the etiology of the chromosomal basis of early human development

Meeting decision detection: multimodal information fusion for multi-party dialogue understanding

Modern advances in multimedia and storage technologies have led to huge archives of human conversations in widely ranging areas. These archives offer a wealth of information in the organization contexts. However, retrieving and managing information in these archives is a time-consuming and labor-intensive task. Previous research applied keyword and computer vision-based methods to do this. However, spontaneous conversations, complex in the use of multimodal cues and intricate in the interactions between multiple speakers, have posed new challenges to these methods. We need new techniques that can leverage the information hidden in multiple communication modalities – including not just “what” the speakers say but also “how” they express themselves and interact with others. In responding to this need, the thesis inquires into the multimodal nature of meeting dialogues and computational means to retrieve and manage the recorded meeting information. In particular, this thesis develops the Meeting Decision Detector (MDD) to detect and track decisions, one of the most important outcomes of the meetings. The MDD involves not only the generation of extractive summaries pertaining to the decisions (“decision detection”), but also the organization of a continuous stream of meeting speech into locally coherent segments (“discourse segmentation”). This inquiry starts with a corpus analysis which constitutes a comprehensive empirical study of the decision-indicative and segment-signalling cues in the meeting corpora. These cues are uncovered from a variety of communication modalities, including the words spoken, gesture and head movements, pitch and energy level, rate of speech, pauses, and use of subjective terms. While some of the cues match the previous findings of speech segmentation, some others have not been studied before. The analysis also provides empirical grounding for computing features and integrating them into a computational model. To handle the high-dimensional multimodal feature space in the meeting domain, this thesis compares empirically feature discriminability and feature pattern finding criteria. As the different knowledge sources are expected to capture different types of features, the thesis also experiments with methods that can harness synergy between the multiple knowledge sources. The problem formalization and the modeling algorithm so far correspond to an optimal setting: an off-line, post-meeting analysis scenario. However, ultimately the MDD is expected to be operated online – right after a meeting, or when a meeting is still in progress. Thus this thesis also explores techniques that help relax the optimal setting, especially those using only features that can be generated with a higher degree of automation. Empirically motivated experiments are designed to handle the corresponding performance degradation. Finally, with the users in mind, this thesis evaluates the use of query-focused summaries in a decision debriefing task, which is common in the organization context. The decision-focused extracts (which represent compressions of 1%) is compared against the general-purpose extractive summaries (which represent compressions of 10-40%). To examine the effect of model automation on the debriefing task, this evaluation experiments with three versions of decision-focused extracts, each relaxing one manual annotation constraint. Task performance is measured in actual task effectiveness, usergenerated report quality, and user-perceived success. The users’ clicking behaviors are also recorded and analyzed to understand how the users leverage the different versions of extractive summaries to produce abstractive summaries. The analysis framework and computational means developed in this work is expected to be useful for the creation of other dialogue understanding applications, especially those that require to uncover the implicit semantics of meeting dialogues

Knowledge-based and data-driven approaches for geographical information access

Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present in queries and documents. This PhD thesis presents, describes and evaluates several heterogeneous approaches for the following three GeoIA tasks: Geographical Information Retrieval (GIR), Geographical Question Answering (GeoQA), and Textual Georeferencing (TG). The GIR task deals with user queries that search over documents (e.g. ¿vineyards in California?) and the GeoQA task treats questions that retrieve answers (e.g. ¿What is the capital of France?). On the other hand, TG is the task of associate one or more georeferences (such as polygons or coordinates in a geodetic reference system) to electronic documents. Current state-of-the-art AI algorithms are not yet fully understanding the semantic meaning and the geographical constraints and terms present in queries and document collections. This thesis attempts to improve the effectiveness results of GeoIA tasks by: 1) improving the detection, understanding, and use of a part of the geographical and the thematic content of queries and documents with Toponym Recognition, Toponym Disambiguation and Natural Language Processing (NLP) techniques, and 2) combining Geographical Knowledge-Based Heuristics based on common sense with Data-Driven IR algorithms. The main contributions of this thesis to the state-of-the-art of GeoIA tasks are: 1) The presentation of 10 novel approaches for GeoIA tasks: 3 approaches for GIR, 3 for GeoQA, and 4 for Textual Georeferencing (TG). 2) The evaluation of these novel approaches in these contexts: within official evaluation benchmarks, after evaluation benchmarks with the test collections, and with other specific datasets. Most of these algorithms have been evaluated in international evaluations and some of them achieved top-ranked state-of-the-art results, including top-performing results in GIR (GeoCLEF 2007) and TG (MediaEval 2014) benchmarks. 3) The experiments reported in this PhD thesis show that the approaches can combine effectively Geographical Knowledge and NLP with Data-Driven techniques to improve the efectiveness measures of the three Geographical Information Access tasks investigated. 4) TALPGeoIR: a novel GIR approach that combines Geographical Knowledge ReRanking (GeoKR), NLP and Relevance Feedback (RF) that achieved state-of-the-art results in official GeoCLEF benchmarks (Ferrés and Rodríguez, 2008; Mandl et al., 2008) and posterior experiments (Ferrés and Rodríguez, 2015a). This approach has been evaluated with the full GeoCLEF corpus (100 topics) and showed that GeoKR, NLP, and RF techniques evaluated separately or in combination improve the results in MAP and R-Precision effectiveness measures of the state-of-the-art IR algorithms TF-IDF, BM25 and InL2 and show statistical significance in most of the experiments. 5) GeoTALP-QA: a scope-based GeoQA approach for Spanish and English and its evaluation with a set of questions of the Spanish geography (Ferrés and Rodríguez, 2006). 6) Four state-of-the-art Textual Georeferencing approaches for informal and formal documents that achieved state-of-the-art results in evaluation benchmarks (Ferrés and Rodríguez, 2014) and posterior experiments (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).L'Accés a la Informació Geogràfica (GeoAI) pot ser definit com una forma de recuperar informació de col·lecions textuals que inclou l'anàlisi automàtic i la interpretació dels termes i restriccions geogràfiques que apareixen en consultes i documents. Aquesta tesi doctoral presenta, descriu i avalua varies aproximacions heterogènies a les seguents tasques de GeoAI: Recuperació de la Informació Geogràfica (RIG), Cerca de la Resposta Geogràfica (GeoCR), i Georeferenciament Textual (GT). La tasca de RIG tracta amb consultes d'usuari que cerquen documents (e.g. ¿vinyes a California?) i la tasca GeoCR tracta de recuperar respostes concretes a preguntes (e.g. ¿Quina és la capital de França?). D'altra banda, GT es la tasca de relacionar una o més referències geogràfiques (com polígons o coordenades en un sistema de referència geodètic) a documents electrònics. Els algoritmes de l'estat de l'art actual en Intel·ligència Artificial encara no comprenen completament el significat semàntic i els termes i les restriccions geogràfiques presents en consultes i col·leccions de documents. Aquesta tesi intenta millorar els resultats en efectivitat de les tasques de GeoAI de la seguent manera: 1) millorant la detecció, comprensió, i la utilització d'una part del contingut geogràfic i temàtic de les consultes i documents amb tècniques de reconeixement de topònims, desambiguació de topònims, i Processament del Llenguatge Natural (PLN), i 2) combinant heurístics basats en Coneixement Geogràfic i en el sentit comú humà amb algoritmes de Recuperació de la Informació basats en dades. Les principals contribucions d'aquesta tesi a l'estat de l'art de les tasques de GeoAI són: 1) La presentació de 10 noves aproximacions a les tasques de GeoAI: 3 aproximacions per RIG, 3 per GeoCR, i 4 per Georeferenciament Textual (GT). 2) L'avaluació d'aquestes noves aproximacions en aquests contexts: en el marc d'avaluacions comparatives internacionals, posteriorment a avaluacions comparatives internacionals amb les col·lections de test, i amb altres conjunts de dades específics. La majoria d'aquests algoritmes han estat avaluats en avaluacions comparatives internacionals i alguns d'ells aconseguiren alguns dels millors resultats en l'estat de l'art, com per exemple els resultats en comparatives de RIG (GeoCLEF 2007) i GT (MediaEval 2014). 3) Els experiments descrits en aquesta tesi mostren que les aproximacions poden combinar coneixement geogràfic i PLN amb tècniques basades en dades per millorar les mesures d'efectivitat en les tres tasques de l'Accés a la Informació Geogràfica investigades. 4) TALPGeoIR: una nova aproximació a la RIG que combina Re-Ranking amb Coneixement Geogràfic (GeoKR), PLN i Retroalimentació de Rellevancia (RR) que aconseguí resultats en l'estat de l'art en comparatives oficials GeoCLEF (Ferrés and Rodríguez, 2008; Mandl et al., 2008) i en experiments posteriors (Ferrés and Rodríguez, 2015a). Aquesta aproximació ha estat avaluada amb el conjunt complert del corpus GeoCLEF (100 topics) i ha mostrat que les tècniques GeoKR, PLN i RR avaluades separadament o en combinació milloren els resultats en les mesures efectivitat MAP i R-Precision dels algoritmes de l'estat de l'art en Recuperació de la Infomació TF-IDF, BM25 i InL2 i a més mostren significació estadística en la majoria dels experiments. 5) GeoTALP-QA: una aproximació basada en l'àmbit geogràfic per espanyol i anglès i la seva avaluació amb un conjunt de preguntes de la geografía espanyola (Ferrés and Rodríguez, 2006). 6) Quatre aproximacions per al georeferenciament de documents formals i informals que obtingueren resultats en l'estat de l'art en avaluacions comparatives (Ferrés and Rodríguez, 2014) i en experiments posteriors (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b)

Knowledge-based and data-driven approaches for geographical information access

Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present in queries and documents. This PhD thesis presents, describes and evaluates several heterogeneous approaches for the following three GeoIA tasks: Geographical Information Retrieval (GIR), Geographical Question Answering (GeoQA), and Textual Georeferencing (TG). The GIR task deals with user queries that search over documents (e.g. ¿vineyards in California?) and the GeoQA task treats questions that retrieve answers (e.g. ¿What is the capital of France?). On the other hand, TG is the task of associate one or more georeferences (such as polygons or coordinates in a geodetic reference system) to electronic documents. Current state-of-the-art AI algorithms are not yet fully understanding the semantic meaning and the geographical constraints and terms present in queries and document collections. This thesis attempts to improve the effectiveness results of GeoIA tasks by: 1) improving the detection, understanding, and use of a part of the geographical and the thematic content of queries and documents with Toponym Recognition, Toponym Disambiguation and Natural Language Processing (NLP) techniques, and 2) combining Geographical Knowledge-Based Heuristics based on common sense with Data-Driven IR algorithms. The main contributions of this thesis to the state-of-the-art of GeoIA tasks are: 1) The presentation of 10 novel approaches for GeoIA tasks: 3 approaches for GIR, 3 for GeoQA, and 4 for Textual Georeferencing (TG). 2) The evaluation of these novel approaches in these contexts: within official evaluation benchmarks, after evaluation benchmarks with the test collections, and with other specific datasets. Most of these algorithms have been evaluated in international evaluations and some of them achieved top-ranked state-of-the-art results, including top-performing results in GIR (GeoCLEF 2007) and TG (MediaEval 2014) benchmarks. 3) The experiments reported in this PhD thesis show that the approaches can combine effectively Geographical Knowledge and NLP with Data-Driven techniques to improve the efectiveness measures of the three Geographical Information Access tasks investigated. 4) TALPGeoIR: a novel GIR approach that combines Geographical Knowledge ReRanking (GeoKR), NLP and Relevance Feedback (RF) that achieved state-of-the-art results in official GeoCLEF benchmarks (Ferrés and Rodríguez, 2008; Mandl et al., 2008) and posterior experiments (Ferrés and Rodríguez, 2015a). This approach has been evaluated with the full GeoCLEF corpus (100 topics) and showed that GeoKR, NLP, and RF techniques evaluated separately or in combination improve the results in MAP and R-Precision effectiveness measures of the state-of-the-art IR algorithms TF-IDF, BM25 and InL2 and show statistical significance in most of the experiments. 5) GeoTALP-QA: a scope-based GeoQA approach for Spanish and English and its evaluation with a set of questions of the Spanish geography (Ferrés and Rodríguez, 2006). 6) Four state-of-the-art Textual Georeferencing approaches for informal and formal documents that achieved state-of-the-art results in evaluation benchmarks (Ferrés and Rodríguez, 2014) and posterior experiments (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).L'Accés a la Informació Geogràfica (GeoAI) pot ser definit com una forma de recuperar informació de col·lecions textuals que inclou l'anàlisi automàtic i la interpretació dels termes i restriccions geogràfiques que apareixen en consultes i documents. Aquesta tesi doctoral presenta, descriu i avalua varies aproximacions heterogènies a les seguents tasques de GeoAI: Recuperació de la Informació Geogràfica (RIG), Cerca de la Resposta Geogràfica (GeoCR), i Georeferenciament Textual (GT). La tasca de RIG tracta amb consultes d'usuari que cerquen documents (e.g. ¿vinyes a California?) i la tasca GeoCR tracta de recuperar respostes concretes a preguntes (e.g. ¿Quina és la capital de França?). D'altra banda, GT es la tasca de relacionar una o més referències geogràfiques (com polígons o coordenades en un sistema de referència geodètic) a documents electrònics. Els algoritmes de l'estat de l'art actual en Intel·ligència Artificial encara no comprenen completament el significat semàntic i els termes i les restriccions geogràfiques presents en consultes i col·leccions de documents. Aquesta tesi intenta millorar els resultats en efectivitat de les tasques de GeoAI de la seguent manera: 1) millorant la detecció, comprensió, i la utilització d'una part del contingut geogràfic i temàtic de les consultes i documents amb tècniques de reconeixement de topònims, desambiguació de topònims, i Processament del Llenguatge Natural (PLN), i 2) combinant heurístics basats en Coneixement Geogràfic i en el sentit comú humà amb algoritmes de Recuperació de la Informació basats en dades. Les principals contribucions d'aquesta tesi a l'estat de l'art de les tasques de GeoAI són: 1) La presentació de 10 noves aproximacions a les tasques de GeoAI: 3 aproximacions per RIG, 3 per GeoCR, i 4 per Georeferenciament Textual (GT). 2) L'avaluació d'aquestes noves aproximacions en aquests contexts: en el marc d'avaluacions comparatives internacionals, posteriorment a avaluacions comparatives internacionals amb les col·lections de test, i amb altres conjunts de dades específics. La majoria d'aquests algoritmes han estat avaluats en avaluacions comparatives internacionals i alguns d'ells aconseguiren alguns dels millors resultats en l'estat de l'art, com per exemple els resultats en comparatives de RIG (GeoCLEF 2007) i GT (MediaEval 2014). 3) Els experiments descrits en aquesta tesi mostren que les aproximacions poden combinar coneixement geogràfic i PLN amb tècniques basades en dades per millorar les mesures d'efectivitat en les tres tasques de l'Accés a la Informació Geogràfica investigades. 4) TALPGeoIR: una nova aproximació a la RIG que combina Re-Ranking amb Coneixement Geogràfic (GeoKR), PLN i Retroalimentació de Rellevancia (RR) que aconseguí resultats en l'estat de l'art en comparatives oficials GeoCLEF (Ferrés and Rodríguez, 2008; Mandl et al., 2008) i en experiments posteriors (Ferrés and Rodríguez, 2015a). Aquesta aproximació ha estat avaluada amb el conjunt complert del corpus GeoCLEF (100 topics) i ha mostrat que les tècniques GeoKR, PLN i RR avaluades separadament o en combinació milloren els resultats en les mesures efectivitat MAP i R-Precision dels algoritmes de l'estat de l'art en Recuperació de la Infomació TF-IDF, BM25 i InL2 i a més mostren significació estadística en la majoria dels experiments. 5) GeoTALP-QA: una aproximació basada en l'àmbit geogràfic per espanyol i anglès i la seva avaluació amb un conjunt de preguntes de la geografía espanyola (Ferrés and Rodríguez, 2006). 6) Quatre aproximacions per al georeferenciament de documents formals i informals que obtingueren resultats en l'estat de l'art en avaluacions comparatives (Ferrés and Rodríguez, 2014) i en experiments posteriors (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).Postprint (published version

Word meaning in context : a probabilistic model and its application to question answering

The need for assessing similarity in meaning is central to most language technology applications. Distributional methods are robust, unsupervised methods which achieve high performance on this task. These methods measure similarity of word types solely based on patterns of word occurrences in large corpora, following the intuition that similar words occur in similar contexts. As most Natural Language Processing (NLP) applications deal with disambiguated words, words occurring in context, rather than word types, the question of adapting distributional methods to compute sense-specific or context-sensitive similarities has gained increasing attention in recent work. This thesis focuses on the development and applications of distributional methods for context-sensitive similarity. The contribution made is twofold: the main part of the thesis proposes and tests a new framework for computing similarity in context, while the second part investigates the application of distributional paraphrasing to the task of question answering.Die Notwendigkeit der Beurteilung von Bedeutungsähnlichkeit spielt für die meisten sprachtechnologische Anwendungen eine wesentliche Rolle. Distributionelle Verfahren sind solide, unbeaufsichtigte Verfahren, die für diese Aufgabe sehr effektiv sind. Diese Verfahren messen die Ähnlichkeit von Wortarten lediglich auf Basis von Mustern, nach denen die Wörter in großen Korpora vorkommen, indem sie der Erkenntnis folgen, dass ähnliche Wörter in ähnlichen Kontexten auftreten. Da die meisten Anwendungen im Natural Language Processing (NLP) mit eindeutigen Wörtern arbeiten, also eher Wörtern, die im Kontext vorkommen, als Wortarten, hat die Frage, ob distributionelle Verfahren angepasst werden sollten, um bedeutungsspezifische oder kontextabhängige Ähnlichkeiten zu berechnen, in neueren Arbeiten zunehmend an Bedeutung gewonnen. Diese Dissertation konzentriert sich auf die Entwicklung und Anwendungen von distributionellen Verfahren für kontextabhängige Ähnlichkeit und liefert einen doppelten Beitrag: Den Hauptteil der Arbeit bildet die Präsentation und Erprobung eines neuen framework für die Berechnung von Ähnlichkeit im Kontext. Im zweiten Teil der Arbeit wird die Anwendung des distributional paraphrasing auf die Aufgabe der Fragenbeantwortung untersucht

Designing Service-Oriented Chatbot Systems Using a Construction Grammar-Driven Natural Language Generation System

Service oriented chatbot systems are used to inform users in a conversational manner about a particular service or product on a website. Our research shows that current systems are time consuming to build and not very accurate or satisfying to users. We find that natural language understanding and natural language generation methods are central to creating an e�fficient and useful system. In this thesis we investigate current and past methods in this research area and place particular emphasis on Construction Grammar and its computational implementation. Our research shows that users have strong emotive reactions to how these systems behave, so we also investigate the human computer interaction component. We present three systems (KIA, John and KIA2), and carry out extensive user tests on all of them, as well as comparative tests. KIA is built using existing methods, John is built with the user in mind and KIA2 is built using the construction grammar method. We found that the construction grammar approach performs well in service oriented chatbots systems, and that users preferred it over other systems

Supporting distributed computation over wide area gigabit networks

The advent of high bandwidth fibre optic links that may be used over very large distances has lead to much research and development in the field of wide area gigabit networking. One problem that needs to be addressed is how loosely coupled distributed systems may be built over these links, allowing many computers worldwide to take part in complex calculations in order to solve "Grand Challenge" problems. The research conducted as part of this PhD has looked at the practicality of implementing a communication mechanism proposed by Craig Partridge called Late-binding Remote Procedure Calls (LbRPC). LbRPC is intended to export both code and data over the network to remote machines for evaluation, as opposed to traditional RPC mechanisms that only send parameters to pre-existing remote procedures. The ability to send code as well as data means that LbRPC requests can overcome one of the biggest problems in Wide Area Distributed Computer Systems (WADCS): the fixed latency due to the speed of light. As machines get faster, the fixed multi-millisecond round trip delay equates to ever increasing numbers of CPU cycles. For a WADCS to be efficient, programs should minimise the number of network transits they incur. By allowing the application programmer to export arbitrary code to the remote machine, this may be achieved. This research has looked at the feasibility of supporting secure exportation of arbitrary code and data in heterogeneous, loosely coupled, distributed computing environments. It has investigated techniques for making placement decisions for the code in cases where there are a large number of widely dispersed remote servers that could be used. The latter has resulted in the development of a novel prototype LbRPC using multicast IP for implicit placement and a sequenced, multi-packet saturation multicast transport protocol. These prototypes show that it is possible to export code and data to multiple remote hosts, thereby removing the need to perform complex and error prone explicit process placement decisions