6,359 research outputs found
A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling
It has become progressively more evident that a single data source is unable to comprehensively capture the
variability of a multi-faceted concept, such as product design, driving behaviour or human trust, which has
diverse semantic orientations. Therefore, multi-faceted conceptual modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is frequently applied to cope with the high
dimensionality and data heterogeneity. The consideration of intra-facets relationships is also indispensable. In
this context, a knowledge graph (KG), which can aggregate the relationships of multiple aspects by semantic
associations, was exploited to facilitate the multi-faceted conceptual modelling based on heterogeneous and
semantic-rich data. Firstly, rules of fault mechanism are extracted from the existing domain knowledge repository, and node attributes are extracted from multi-sourced data. Through abstraction and tokenisation of
existing knowledge repository and concept-centric data, rules of fault mechanism were symbolised and integrated with the node attributes, which served as the entities for the concept-centric knowledge graph (CKG).
Subsequently, the transformation of process data to a stack of temporal graphs was conducted under the CKG
backbone. Lastly, the graph convolutional network (GCN) model was applied to extract temporal and attribute
correlation features from the graphs, and a temporal convolution network (TCN) was built for conceptual
modelling using these features. The effectiveness of the proposed approach and the close synergy between the
KG-supported approach and multi-faceted conceptual modelling is demonstrated and substantiated in a case
study using real-world data
Social Prescribing for Autistic Adults
Background
Autistic adults are affected by health and social disparities that impact life expectancy and quality of life, frequently resulting in escalating wellbeing concerns requiring costly acute care. Evidence suggests barriers to healthcare and a lack of post-diagnostic support may contribute to these inequalities. Social prescribing, a low-intensity personalised care model receiving increasing attention from policymakers and commissioners, offers opportunities to address isolation, build skills and promote health through collaborations between services and communities. However, social prescribing research and provision has overlooked wellbeing and access needs of autistic adults. This PhD project aimed to investigate factors affecting accessibility of social prescribing pathways, which can comprise a variety of models and mechanisms, and their suitability for autistic adults from initial referral through to prescribed activities.
Methods
The research delivered a systematic mapping review and mixed-methods study. The review synthesised previous reviews of literature on outcomes, settings and service pathways within community-based services for autistic adults. An online survey of 128 autistic adults explored barriers to primary healthcare, the point of access to social prescribing, across changing contexts using regression analysis. Semi-structured interviews with 23 autistic participants investigated perspectives on wellbeing, attitudes towards social prescribing as a response to wellbeing barriers, and provision of wider support in the community. Qualitative data were analysed using reflexive thematic analysis, incorporating critical realism and the candidacy framework, to examine individual, relational and systemic factors.
Results
Findings suggest that access to social prescribing for autistic adults via referral from health and social care services involves patient and provider evaluations, socioeconomic factors and wider contexts. Self-determination was found to link themes relating to meanings of wellbeing for autistic adults. Social prescribing may promote self-determination through its tailored approach. However, pathways require adaptions to maximise engagement, including offering alternative referral routes, novel prescriptions and additional support at key transition points. Providers should work with the autistic community to improve access and acceptability, and bring mutual benefits for individuals and services.
Conclusions
Service commissioners and policymakers should consider supporting a social prescribing pathway embedded in autism diagnostic services or upskilling existing social prescribing pathways to adapt their practice for autistic adults. The research also adds to understandings of peer support and self-determination as important mechanisms in wellbeing for autistic adults
Information actors beyond modernity and coloniality in times of climate change:A comparative design ethnography on the making of monitors for sustainable futures in Curaçao and Amsterdam, between 2019-2022
In his dissertation, Mr. Goilo developed a cutting-edge theoretical framework for an Anthropology of Information. This study compares information in the context of modernity in Amsterdam and coloniality in Curaçao through the making process of monitors and develops five ways to understand how information can act towards sustainable futures. The research also discusses how the two contexts, that is modernity and coloniality, have been in informational symbiosis for centuries which is producing negative informational side effects within the age of the Anthropocene. By exploring the modernity-coloniality symbiosis of information, the author explains how scholars, policymakers, and data-analysts can act through historical and structural roots of contemporary global inequities related to the production and distribution of information. Ultimately, the five theses propose conditions towards the collective production of knowledge towards a more sustainable planet
Pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inženjerstvu vođenom modelima
In this thesis, we present an approach to the production process specification and generation based on the model-driven paradigm, with the goal to increase the flexibility of factories and respond to the challenges that emerged in the era of Industry 4.0 more efficiently. To formally specify production processes and their variations in the Industry 4.0 environment, we created a novel domain-specific modeling language, whose models are machine-readable. The created language can be used to model production processes that can be independent of any production system, enabling process models to be used in different production systems, and process models used for the specific production system. To automatically transform production process models dependent on the specific production system into instructions that are to be executed by production system resources, we created an instruction generator. Also, we created generators for different manufacturing documentation, which automatically transform production process models into manufacturing documents of different types. The proposed approach, domain-specific modeling language, and software solution contribute to introducing factories into the digital transformation process. As factories must rapidly adapt to new products and their variations in the era of Industry 4.0, production must be dynamically led and instructions must be automatically sent to factory resources, depending on products that are to be created on the shop floor. The proposed approach contributes to the creation of such a dynamic environment in contemporary factories, as it allows to automatically generate instructions from process models and send them to resources for execution. Additionally, as there are numerous different products and their variations, keeping the required manufacturing documentation up to date becomes challenging, which can be done automatically by using the proposed approach and thus significantly lower process designers' time.У овој дисертацији представљен је приступ спецификацији и генерисању производних процеса заснован на инжењерству вођеном моделима, у циљу повећања флексибилности постројења у фабрикама и ефикаснијег разрешавања изазова који се појављују у ери Индустрије 4.0. За потребе формалне спецификације производних процеса и њихових варијација у амбијенту Индустрије 4.0, креиран је нови наменски језик, чије моделе рачунар може да обради на аутоматизован начин. Креирани језик има могућност моделовања производних процеса који могу бити независни од производних система и тиме употребљени у различитим постројењима или фабрикама, али и производних процеса који су специфични за одређени систем. Како би моделе производних процеса зависних од конкретног производног система било могуће на аутоматизован начин трансформисати у инструкције које ресурси производног система извршавају, креиран је генератор инструкција. Такође су креирани и генератори техничке документације, који на аутоматизован начин трансформишу моделе производних процеса у документе различитих типова. Употребом предложеног приступа, наменског језика и софтверског решења доприноси се увођењу фабрика у процес дигиталне трансформације. Како фабрике у ери Индустрије 4.0 морају брзо да се прилагоде новим производима и њиховим варијацијама, неопходно је динамички водити производњу и на аутоматизован начин слати инструкције ресурсима у фабрици, у зависности од производа који се креирају у конкретном постројењу. Тиме што је у предложеном приступу могуће из модела процеса аутоматизовано генерисати инструкције и послати их ресурсима, доприноси се креирању једног динамичког окружења у савременим фабрикама. Додатно, услед великог броја различитих производа и њихових варијација, постаје изазовно одржавати неопходну техничку документацију, што је у предложеном приступу могуће урадити на аутоматизован начин и тиме значајно уштедети време пројектаната процеса.U ovoj disertaciji predstavljen je pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inženjerstvu vođenom modelima, u cilju povećanja fleksibilnosti postrojenja u fabrikama i efikasnijeg razrešavanja izazova koji se pojavljuju u eri Industrije 4.0. Za potrebe formalne specifikacije proizvodnih procesa i njihovih varijacija u ambijentu Industrije 4.0, kreiran je novi namenski jezik, čije modele računar može da obradi na automatizovan način. Kreirani jezik ima mogućnost modelovanja proizvodnih procesa koji mogu biti nezavisni od proizvodnih sistema i time upotrebljeni u različitim postrojenjima ili fabrikama, ali i proizvodnih procesa koji su specifični za određeni sistem. Kako bi modele proizvodnih procesa zavisnih od konkretnog proizvodnog sistema bilo moguće na automatizovan način transformisati u instrukcije koje resursi proizvodnog sistema izvršavaju, kreiran je generator instrukcija. Takođe su kreirani i generatori tehničke dokumentacije, koji na automatizovan način transformišu modele proizvodnih procesa u dokumente različitih tipova. Upotrebom predloženog pristupa, namenskog jezika i softverskog rešenja doprinosi se uvođenju fabrika u proces digitalne transformacije. Kako fabrike u eri Industrije 4.0 moraju brzo da se prilagode novim proizvodima i njihovim varijacijama, neophodno je dinamički voditi proizvodnju i na automatizovan način slati instrukcije resursima u fabrici, u zavisnosti od proizvoda koji se kreiraju u konkretnom postrojenju. Time što je u predloženom pristupu moguće iz modela procesa automatizovano generisati instrukcije i poslati ih resursima, doprinosi se kreiranju jednog dinamičkog okruženja u savremenim fabrikama. Dodatno, usled velikog broja različitih proizvoda i njihovih varijacija, postaje izazovno održavati neophodnu tehničku dokumentaciju, što je u predloženom pristupu moguće uraditi na automatizovan način i time značajno uštedeti vreme projektanata procesa
Climate Change and Critical Agrarian Studies
Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial
GitTables: A Large-Scale Corpus of Relational Tables
The success of deep learning has sparked interest in improving relational
table tasks, like data preparation and search, with table representation models
trained on large table corpora. Existing table corpora primarily contain tables
extracted from HTML pages, limiting the capability to represent offline
database tables. To train and evaluate high-capacity models for applications
beyond the Web, we need resources with tables that resemble relational database
tables. Here we introduce GitTables, a corpus of 1M relational tables extracted
from GitHub. Our continuing curation aims at growing the corpus to at least 10M
tables. Analyses of GitTables show that its structure, content, and topical
coverage differ significantly from existing table corpora. We annotate table
columns in GitTables with semantic types, hierarchical relations and
descriptions from Schema.org and DBpedia. The evaluation of our annotation
pipeline on the T2Dv2 benchmark illustrates that our approach provides results
on par with human annotations. We present three applications of GitTables,
demonstrating its value for learned semantic type detection models, schema
completion methods, and benchmarks for table-to-KG matching, data search, and
preparation. We make the corpus and code available at
https://gittables.github.io
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
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