305 research outputs found
Alignment of multi-cultural knowledge repositories
The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration.Die Fähigkeit mehrere Wissensquellen in einer Anwendung miteinander zu verbinden ist ein wichtiger Bestandteil für verschiedene Anwendungsszenarien wie z.B. dem Auffinden von Dokumenten und der Beantwortung von Fragen. Unabhängig erstellte Datenquellen sind allerdings von Natur aus heterogen, was ihre unterschiedlichen Herkünfte widerspiegelt. Somit besteht ein Bedarf darin, die Konzepte und Entitäten zwischen den Wissensquellen anzugleichen. Frühere Arbeiten sind jedoch auf Datenquellen limitiert, die eine hohe Ähnlichkeit im Sinne von Struktur und Terminologie aufweisen. Das Ziel dieser Dissertation ist, Methoden für Aufbau und Pflege zum Angleich zwischen multikulturellen Wissensquellen zu entwickeln. Der erste Beitrag ist ein System names ACROSS, das auf die Reduzierung der terminologischen Kluft zwischen den Datenquellen abzielt. Der zweite Beitrag sind die Systeme LILIANA und SESAME, welche zum Angleich eben dieser Datenquellen unter Berücksichtigung deren struktureller Unterschiede dienen. Der dritte Beitrag ist ein Verfahren names LAIKA, das den Angleich dynamischer Quellen unterstützt. Unsere Experimente mit einer Reihe von Wissensquellen in Größenordnung des Web zeigen eine hohe Qualität unserer Verfahren. Zudem werden die Vorteile in der Verwendung von LILIANA und SESAME in Anwendungsszenarien für Suche und Exploration dargelegt
Methodology of Algorithm Engineering
Research on algorithms has drastically increased in recent years. Various
sub-disciplines of computer science investigate algorithms according to
different objectives and standards. This plurality of the field has led to
various methodological advances that have not yet been transferred to
neighboring sub-disciplines. The central roadblock for a better knowledge
exchange is the lack of a common methodological framework integrating the
perspectives of these sub-disciplines. It is the objective of this paper to
develop a research framework for algorithm engineering. Our framework builds on
three areas discussed in the philosophy of science: ontology, epistemology and
methodology. In essence, ontology describes algorithm engineering as being
concerned with algorithmic problems, algorithmic tasks, algorithm designs and
algorithm implementations. Epistemology describes the body of knowledge of
algorithm engineering as a collection of prescriptive and descriptive
knowledge, residing in World 3 of Popper's Three Worlds model. Methodology
refers to the steps how we can systematically enhance our knowledge of specific
algorithms. The framework helps us to identify and discuss various validity
concerns relevant to any algorithm engineering contribution. In this way, our
framework has important implications for researching algorithms in various
areas of computer science
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure
Knowledge aggregation in people recommender systems : matching skills to tasks
People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecà rrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessà ria. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implÃcita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explÃcita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecà rrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les à rees d’interès per a caracteritzar les persones es dedueixen dels seus currÃculums i s’expressen en termes d’incertesa evitant que els interessos es demanin explÃcitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implÃcita i atributs indicats explÃcitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les à rees d'aplicació futures i noves perspectives.Postprint (published version
Algorithms for Geometric Optimization and Enrichment in Industrialized Building Construction
The burgeoning use of industrialized building construction, coupled with advances in digital technologies, is unlocking new opportunities to improve the status quo of construction projects being over-budget, delayed and having undesirable quality. Yet there are still several objective barriers that need to be overcome in order to fully realize the full potential of these innovations. Analysis of literature and examples from industry reveal the following notable barriers: (1) geometric optimization methods need to be developed for the stricter dimensional requirements in industrialized construction, (2) methods are needed to preserve model semantics during the process of generating an updated as-built model, (3) semantic enrichment methods are required for the end-of-life stage of industrialized buildings, and (4) there is a need to develop pragmatic approaches for algorithms to ensure they achieve required computational efficiency. The common thread across these examples is the need for developing algorithms to optimize and enrich geometric models. To date, a comprehensive approach paired with pragmatic solutions remains elusive. This research fills this gap by presenting a new approach for algorithm development along with pragmatic implementations for the industrialized building construction sector.
Computational algorithms are effective for driving the design, analysis, and optimization of geometric models. As such, this thesis develops new computational algorithms for design, fabrication and assembly, onsite construction, and end-of-life stages of industrialized buildings. A common theme throughout this work is the development and comparison of varied algorithmic approaches (i.e., exact vs. approximate solutions) to see which is optimal for a given process. This is implemented in the following ways. First, a probabilistic method is used to simulate the accumulation of dimensional tolerances in order to optimize geometric models during design. Second, a series of exact and approximate algorithms are used to optimize the topology of 2D panelized assemblies to minimize material use during fabrication and assembly. Third, a new approach to automatically update geometric models is developed whereby initial model semantics are preserved during the process of generating an as-built model. Finally, a series of algorithms are developed to semantically enrich geometric models to enable industrialized buildings to be disassembled and reused.
The developments made in this research form a rational and pragmatic approach to addressing the existing challenges faced in industrialized building construction. Such developments are shown not only to be effective in improving the status quo in the industry (i.e., improving cost, reducing project duration, and improving quality), but also for facilitating continuous innovation in construction. By way of assessing the potential impact of this work, the proposed algorithms can reduce rework risk during fabrication and assembly (65% rework reduction in the case study for the new tolerance simulation algorithm), reduce waste during manufacturing (11% waste reduction in the case study for the new panel unfolding and nesting algorithms), improve accuracy and automation of as-built model generation (model error reduction from 50.4 mm to 5.7 mm in the case study for the new parametric BIM updating algorithms), reduce lifecycle cost for adapting industrialized buildings (15% reduction in capital costs in the computational building configurator) and reducing lifecycle impacts for reusing structural systems from industrialized buildings (between 54% to 95% reduction in average lifecycle impacts for the approach illustrated in Appendix B). From a computational standpoint, the novelty of the algorithms developed in this research can be described as follows. Complex geometric processes can be codified solely on the innate properties of geometry – that is, by parameterizing geometry and using methods such as combinatorial optimization, topology can be optimized and semantics can be automatically enriched for building assemblies. Employing the use of functional discretization (whereby continuous variable domains are converted into discrete variable domains) is shown to be highly effective for complex geometric optimization approaches. Finally, the algorithms encapsulate and balance the benefits posed by both parametric and non-parametric schemas, resulting in the ability to achieve both high representational accuracy and semantically rich information (which has previously not been achieved or demonstrated).
In summary, this thesis makes several key improvements to industrialized building construction. One of the key findings is that rather than pre-emptively determining the best suited algorithm for a given process or problem, it is often more pragmatic to derive both an exact and approximate solution and then decide which is optimal to use for a given process. Generally, most tasks related to optimizing or enriching geometric models is best solved using approximate methods. To this end, this research presents a series of key techniques that can be followed to improve the temporal performance of algorithms. The new approach for developing computational algorithms and the pragmatic demonstrations for geometric optimization and enrichment are expected to bring the industry forward and solve many of the current barriers it faces
Understanding Applications of Project Planning and Scheduling in Construction Projects
Construction project life-cycle processes must be managed in a more effective and predictable way to meet project stakeholders’ needs. However, there is increasing concern about whether know-how effectively improves understanding of underlying theories of project management processes for construction organizations and their project managers. Project planning and scheduling are considered as key and challenging tools in controlling and monitoring project performance, but many worldwide construction projects appear to give insufficient attention to effective management and definition of project planning, including preplanning stages. Indeed, some planning issues have been completely overlooked, resulting in unsuccessful project performance. There is a lack of knowledge of, and understanding about, the significance of applications of project planning and scheduling theory in construction projects. Thus, improving such knowledge should be incorporated with new management strategies or tools to improve organizational learning and integration in the context of project planning and scheduling. This implies a need to assess project stakeholders’ understanding on the application of project planning and scheduling theories to practice. The main aim was to study and describe project stakeholders’ perspectives regarding a set of identified criteria comprising aspects assumed to be significant in successful project planning and scheduling. The main research question was developed as follows: What level of understanding do project stakeholders have about the application of project planning and scheduling theories in practices of construction projects? This key question is divided into a number of specific questions concerned with various aspects of project planning and scheduling. Three different questionnaire surveys were considered and designed in order to collect and analyse data relevant to the empirical studies presented and discussed under the scope of this thesis. The study context is Oman. The thesis is based on a summary of five appended papers, of which four represent empirical survey studies. The results form the basis of discussions and reflections, and the four key factors identified are: (1) highlighting management tools needed to improve organizational knowledge and understanding of project planning theories and methods; (2) paying particular consideration to the significant factors (enablers and barriers) impacting project planning and scheduling; (3) identifying project management roles and organizational behaviour in planning and scheduling; and (4) increasing project stakeholders’ awareness of front-end planning for a more successful project execution
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Linguistic typology aims to capture structural and semantic variation across
the world's languages. A large-scale typology could provide excellent guidance
for multilingual Natural Language Processing (NLP), particularly for languages
that suffer from the lack of human labeled resources. We present an extensive
literature survey on the use of typological information in the development of
NLP techniques. Our survey demonstrates that to date, the use of information in
existing typological databases has resulted in consistent but modest
improvements in system performance. We show that this is due to both intrinsic
limitations of databases (in terms of coverage and feature granularity) and
under-employment of the typological features included in them. We advocate for
a new approach that adapts the broad and discrete nature of typological
categories to the contextual and continuous nature of machine learning
algorithms used in contemporary NLP. In particular, we suggest that such
approach could be facilitated by recent developments in data-driven induction
of typological knowledge
Adviser\u27s guide to health care: Volume 3, Consulting with Professional Practices
https://egrove.olemiss.edu/aicpa_guides/1802/thumbnail.jp
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