871 research outputs found
Automatic Generation of Personalized Recommendations in eCoaching
Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging
In the modern world, we are permanently using, leveraging, interacting with,
and relying upon systems of ever higher sophistication, ranging from our cars,
recommender systems in e-commerce, and networks when we go online, to
integrated circuits when using our PCs and smartphones, the power grid to
ensure our energy supply, security-critical software when accessing our bank
accounts, and spreadsheets for financial planning and decision making. The
complexity of these systems coupled with our high dependency on them implies
both a non-negligible likelihood of system failures, and a high potential that
such failures have significant negative effects on our everyday life. For that
reason, it is a vital requirement to keep the harm of emerging failures to a
minimum, which means minimizing the system downtime as well as the cost of
system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can
be generally applied to troubleshoot systems of a wide variety of types,
including all the ones mentioned above, and many more. It exploits and
orchestrates i.a. techniques for knowledge representation, automated reasoning,
heuristic problem solving, intelligent search, optimization, stochastics,
statistics, decision making under uncertainty, machine learning, as well as
calculus, combinatorics and set theory to detect, localize, and fix faults in
abnormally behaving systems.
In this thesis, we will give an introduction to the topic of model-based
diagnosis, point out the major challenges in the field, and discuss a selection
of approaches from our research addressing these issues.Comment: Habilitation Thesi
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Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives
Design of an E-learning system using semantic information and cloud computing technologies
Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process.
We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers.
In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy.
Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí
Natural Language Reasoning on ALC knowledge bases using Large Language Models
Τα προεκπαιδευμένα γλωσσικά μοντέλα έχουν κυριαρχήσει στην επεξεργασία φυσικής
γλώσσας, αποτελώντας πρόκληση για τη χρήση γλωσσών αναπαράστασης γνώσης για
την περιγραφή του κόσμου. Ενώ οι γλώσσες αυτές δεν είναι αρκετά εκφραστικές για να
καλύψουν πλήρως τη φυσική γλώσσα, τα γλωσσικά μοντέλα έχουν ήδη δείξει σπουδαία
αποτελέσματα όσον αφορά την κατανόηση και την ανάκτηση πληροφοριών απευθείας σε
δεδομένα φυσικής γλώσσας. Διερευνούμε τις επιδόσεις των γλωσσικών μοντέλων για
συλλογιστική φυσικής γλώσσας στη περιγραφική λογική ALC. Δημιουργούμε ένα σύνολο
δεδομένων από τυχαίες βάσεις γνώσης ALC, μεταφρασμένες σε φυσική γλώσσα, ώστε
να αξιολογήσουμε την ικανότητα των γλωσσικών μοντέλων να λειτουργούν ως συστήματα
απάντησης ερωτήσεων πάνω σε βάσεις γνώσης φυσικής γλώσσας.Pretrained language models have dominated natural language processing, challenging
the use of knowledge representation languages to describe the world. While these lan-
guages are not expressive enough to fully cover natural language, language models have
already shown great results in terms of understanding and information retrieval directly
on natural language data. We explore language models’ performance at the downstream
task of natural language reasoning in the description logic ALC. We generate a dataset
of random ALC knowledge bases, translated in natural language, in order to assess the
language models’ ability to function as question-answering systems over natural language
knowledge bases
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Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project ‘Sonic Palimpsest’1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include women’s voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Large language models (LLMs) have significantly advanced the field of natural
language processing (NLP), providing a highly useful, task-agnostic foundation
for a wide range of applications. However, directly applying LLMs to solve
sophisticated problems in specific domains meets many hurdles, caused by the
heterogeneity of domain data, the sophistication of domain knowledge, the
uniqueness of domain objectives, and the diversity of the constraints (e.g.,
various social norms, cultural conformity, religious beliefs, and ethical
standards in the domain applications). Domain specification techniques are key
to make large language models disruptive in many applications. Specifically, to
solve these hurdles, there has been a notable increase in research and
practices conducted in recent years on the domain specialization of LLMs. This
emerging field of study, with its substantial potential for impact,
necessitates a comprehensive and systematic review to better summarize and
guide ongoing work in this area. In this article, we present a comprehensive
survey on domain specification techniques for large language models, an
emerging direction critical for large language model applications. First, we
propose a systematic taxonomy that categorizes the LLM domain-specialization
techniques based on the accessibility to LLMs and summarizes the framework for
all the subcategories as well as their relations and differences to each other.
Second, we present an extensive taxonomy of critical application domains that
can benefit dramatically from specialized LLMs, discussing their practical
significance and open challenges. Last, we offer our insights into the current
research status and future trends in this area
Large Language Models for Software Engineering: A Systematic Literature Review
Large Language Models (LLMs) have significantly impacted numerous domains,
notably including Software Engineering (SE). Nevertheless, a well-rounded
understanding of the application, effects, and possible limitations of LLMs
within SE is still in its early stages. To bridge this gap, our systematic
literature review takes a deep dive into the intersection of LLMs and SE, with
a particular focus on understanding how LLMs can be exploited in SE to optimize
processes and outcomes. Through a comprehensive review approach, we collect and
analyze a total of 229 research papers from 2017 to 2023 to answer four key
research questions (RQs). In RQ1, we categorize and provide a comparative
analysis of different LLMs that have been employed in SE tasks, laying out
their distinctive features and uses. For RQ2, we detail the methods involved in
data collection, preprocessing, and application in this realm, shedding light
on the critical role of robust, well-curated datasets for successful LLM
implementation. RQ3 allows us to examine the specific SE tasks where LLMs have
shown remarkable success, illuminating their practical contributions to the
field. Finally, RQ4 investigates the strategies employed to optimize and
evaluate the performance of LLMs in SE, as well as the common techniques
related to prompt optimization. Armed with insights drawn from addressing the
aforementioned RQs, we sketch a picture of the current state-of-the-art,
pinpointing trends, identifying gaps in existing research, and flagging
promising areas for future study
Neurosymbolic AI for Reasoning on Graph Structures: A Survey
Neurosymbolic AI is an increasingly active area of research which aims to
combine symbolic reasoning methods with deep learning to generate models with
both high predictive performance and some degree of human-level
comprehensibility. As knowledge graphs are becoming a popular way to represent
heterogeneous and multi-relational data, methods for reasoning on graph
structures have attempted to follow this neurosymbolic paradigm. Traditionally,
such approaches have utilized either rule-based inference or generated
representative numerical embeddings from which patterns could be extracted.
However, several recent studies have attempted to bridge this dichotomy in ways
that facilitate interpretability, maintain performance, and integrate expert
knowledge. Within this article, we survey a breadth of methods that perform
neurosymbolic reasoning tasks on graph structures. To better compare the
various methods, we propose a novel taxonomy by which we can classify them.
Specifically, we propose three major categories: (1) logically-informed
embedding approaches, (2) embedding approaches with logical constraints, and
(3) rule-learning approaches. Alongside the taxonomy, we provide a tabular
overview of the approaches and links to their source code, if available, for
more direct comparison. Finally, we discuss the applications on which these
methods were primarily used and propose several prospective directions toward
which this new field of research could evolve.Comment: 21 pages, 8 figures, 1 table, currently under review. Corresponding
GitHub page here: https://github.com/NeSymGraph
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