928 research outputs found
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GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS
Graphs are ubiquitous data structures, present in many machine-learning tasks, such as link prediction of products and node classification of scientific papers. As gradient descent drives the training of most modern machine learning architectures, the ability to encode graph-structured data using a differentiable representation is essential to make use of this data. Most approaches encode graph structure in Euclidean space, however, it is non-trivial to model directed edges. The naive solution is to represent each node using a separate source and target vector, however, this can decouple the representation, making it harder for the model to capture information within longer paths in the graph.
In this dissertation, we propose to model graphs by representing each node as a \textit{box} (a Cartesian product of intervals) where directed edges are captured by the relative containment of one box in another. Theoretical proof shows that our proposed box embeddings have the expressiveness to represent any \emph{directed acyclic graph}. We also perform rigorous empirical evaluations of vector, hyperbolic, and region-based geometric representations on several families of synthetic and real-world directed graphs. Extensive experimental results suggest that the box containment can allow for transitive relationships to be modeled easily. We further propose t-Box, a variant of box embeddings that learns the temperature together during training. t-Box uses a learned smoothing parameter to achieve better representational capacity than vector models in low dimensions, while also avoiding performance saturation common to other geometric models in high dimensions.
Though promising, modeling directed graphs that both contain cycles and some element of transitivity, two properties common in real-world settings, is challenging. Box embeddings, which can be thought of as representing the graph as an intersection over some learned super-graphs, have a natural inductive bias toward modeling transitivity, but (as we prove) cannot model cycles. To address this issue, we propose binary code box embeddings, where a learned binary code selects a subset of graphs for intersection. We explore several variants, including global binary codes (amounting to a union over intersections) and per-vertex binary codes (allowing greater flexibility) as well as methods of regularization. Theoretical and empirical results show that the proposed models not only preserve a useful inductive bias of transitivity but also have sufficient representational capacity to model arbitrary graphs, including graphs with cycles.
Lastly, we discuss the use case where box embeddings are not free parameters but are produced by functions. In particular, we explore whether neural networks can map node features into the box space. This is critical in many real-world scenarios. On the one hand, graphs are sparse and the majority of vertices only have few connections or are completely isolated. On the other hand, there may exist rich node features such as attributes and descriptions, that could be useful for prediction tasks. The experimental analysis points out both the effectiveness and insufficiency of multi-layer perceptron-based encoders under different circumstances
Soundscape in Urban Forests
This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests
Hybrid human-AI driven open personalized education
Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer.
In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer).
All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result
Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology
Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/
Throwing therapy at the problem mental health and humanitarian intervention in Palabek refugee settlement, northern Uganda
This thesis examines the social, moral, and political lives of humanitarian mental health interventions in a refugee settlement in Uganda. It is written at the junction of two increasingly significant trends: the search for durable solutions for mass displacement, and the establishment of the field of global mental health as a key actor in the management of psychological suffering across the Global South. Through a scalar structure, it interrogates the intersections of psychological programmes with socio-economic conditions of chronic poverty and food insecurity, from policy discourses to refugeesâ phenomenological experiences of suffering. In so doing, it critically examines the political significance and therapeutic potential of mental health interventions in extremely resource-poor contexts. Global mental health scholars and practitioners have developed approaches to refugee mental health based on three assumptions: that refugeesâ emotional distress should be tackled by purely psychological interventions; that these programmes are clinically significant and politically neutral; and that the âcontextualâ factors that should be considered in their implementation mostly concern âlocalâ interpretations of mental health and illness which diverge from Western biomedical frameworks. By ethnographically exploring experiences of psychological suffering among South Sudanese â and particularly Acholi â refugees in the settlement of Palabek, northern Uganda, this thesis disputes these contentions. Based on fourteen months of in-depth ethnographic fieldwork, this thesis puts forward a critique of global mental health and humanitarian interventions that takes seriously the role of poverty and power in shaping refugeesâ afflictions. This thesis shows that forms of suffering experienced by refugees in Uganda are closely linked to the structural constraints of life in displacement. It shows how these interventions intersect with refugeesâ phenomenological experiences of temporality and moral personhood. In so doing it argues that, when divorced from direct engagement with forms of structural injustice, current global mental health approaches actively âdo harmâ by contributing to refugeesâ psychological afflictions. Finally, this thesis proposes new directions for refugee and global mental health; it argues for a âtemporal turnâ in refugee mental health which foregrounds refugeesâ moral agency, and for the central role of livelihood interventions in generating therapeutic outcomes
Data ethics : building trust : how digital technologies can serve humanity
Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century:
For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car,
from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad,
for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world?
How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations
in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication.
The authors and institutions come from all continents.
The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust!
The book is a continuation of the volume âCyber Ethics 4.0â published in 2018 by the same editors
Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21â22 September 2023
Heaths, Commons, and Wastes:An investigation into the character, management, and perceptions of heathland landscapes in the medieval and post-medieval periods, with particular reference to the counties of Norfolk, Suffolk, Essex, and Hertfordshire
Lowland heathland is a priority habitat for conservation in the United Kingdom but is also valued as a historical cultural landscape.1 Many rare or endangered species of both flora and fauna, unable to thrive in modern agricultural or urban landscapes, inhabit heathland environments. These have long been recognised as the products of past management systems which have been in decline since at least the 18th century, and have now been largely discontinued. For the purposes of conservation, the practices which created and sustained them, based on historical examples, must be maintained or reintroduced in order to perpetuate conditions favourable to those species.
This research details both the landscape character of historic heathland within the study area, and the various management practices which influenced and changed that character. As well as making an original contribution to a subject of historical importance, and modern interest, this research will inform the future management of heathland landscapes by showing, clearly and with evidence, how they were managed in the past.
Where management practices were referred to directly in historical documents, or recorded in full, this work presents them in detail and each technique is analysed in terms of its probable environmental impacts. Where heaths appear in the documentary record, but direct references to management were not found, landscape character was reconstructed using place-name and other linguistic evidence, and by examining what flora and fauna were mentioned in association with them â many of which lived only in certain kinds of habitats.
The results of this work detail a highly variable landscape. Heaths were sometimes open and characterised by low shrubs, but also sometimes wooded â either sparsely or densely â or even largely devoid of flora in some parts. The fauna present on heaths also varied widely between regions and periods; including sheep, pigs, cattle, horses, deer, goats, rabbits, geese, and the Brown Bear. Heaths historically were found on a broad range of soil types, not all of them sandy in nature, and contained a variety of both wet and dry habitats. As the term âheathâ was applied to all of these landscapes historically, cultural perceptions of what constituted heathland were also highly variable
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