735 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases
In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
Geometric Learning on Graph Structured Data
Graphs provide a ubiquitous and universal data structure that can be applied in many domains such as social networks, biology, chemistry, physics, and computer science. In this thesis we focus on two fundamental paradigms in graph learning: representation learning and similarity learning over graph-structured data. Graph representation learning aims to learn embeddings for nodes by integrating topological and feature information of a graph. Graph similarity learning brings into play with similarity functions that allow to compute similarity between pairs of graphs in a vector space. We address several challenging issues in these two paradigms, designing powerful, yet efficient and theoretical guaranteed machine learning models that can leverage rich topological structural properties of real-world graphs.
This thesis is structured into two parts. In the first part of the thesis, we will present how to develop powerful Graph Neural Networks (GNNs) for graph representation learning from three different perspectives: (1) spatial GNNs, (2) spectral GNNs, and (3) diffusion GNNs. We will discuss the model architecture, representational power, and convergence properties of these GNN models. Specifically, we first study how to develop expressive, yet efficient and simple message-passing aggregation schemes that can go beyond the Weisfeiler-Leman test (1-WL). We propose a generalized message-passing framework by incorporating graph structural properties into an aggregation scheme. Then, we introduce a new local isomorphism hierarchy on neighborhood subgraphs. We further develop a novel neural model, namely GraphSNN, and theoretically prove that this model is more expressive than the 1-WL test. After that, we study how to build an effective and efficient graph convolution model with spectral graph filters. In this study, we propose a spectral GNN model, called DFNets, which incorporates a novel spectral graph filter, namely feedback-looped filters. As a result, this model can provide better localization on neighborhood while achieving fast convergence and linear memory requirements. Finally, we study how to capture the rich topological information of a graph using graph diffusion. We propose a novel GNN architecture with dynamic PageRank, based on a learnable transition matrix. We explore two variants of this GNN architecture: forward-euler solution and invariable feature solution, and theoretically prove that our forward-euler GNN architecture is guaranteed with the convergence to a stationary distribution.
In the second part of this thesis, we will introduce a new optimal transport distance metric on graphs in a regularized learning framework for graph kernels. This optimal transport distance metric can preserve both local and global structures between graphs during the transport, in addition to preserving features and their local variations. Furthermore, we propose two strongly convex regularization terms to theoretically guarantee the convergence and numerical stability in finding an optimal assignment between graphs. One regularization term is used to regularize a Wasserstein distance between graphs in the same ground space. This helps to preserve the local clustering structure on graphs by relaxing the optimal transport problem to be a cluster-to-cluster assignment between locally connected vertices. The other regularization term is used to regularize a Gromov-Wasserstein distance between graphs across different ground spaces based on degree-entropy KL divergence. This helps to improve the matching robustness of an optimal alignment to preserve the global connectivity structure of graphs. We have evaluated our optimal transport-based graph kernel using different benchmark tasks. The experimental results show that our models considerably outperform all the state-of-the-art methods in all benchmark tasks
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!
Resource efficient action recognition in videos
This thesis traces an innovative journey in the domain of real-world action recognition, in particular focusing on memory and data efficient systems. It begins by introducing a novel approach for smart frame selection, which significantly reduces computational costs in video classification. It further optimizes the action recognition process by addressing the challenges of training time and memory consumption in video transformers, laying a strong foundation for memory efficient action recognition.
The thesis then delves into zero-shot learning, focusing on the flaws of the currently existing protocol and establishing a new split for true zero-shot action recognition, ensuring zero overlap between unseen test classes and training or pre-training classes. Building on this, a unique cluster-based representation, optimized using reinforcement learning, is proposed for zero-shot action recognition. Crucially, we show that a joint
visual-semantic representation learning is essential for improved performance. We also experiment with feature generation approaches for zero-shot action recognition by introducing a synthetic sample selection methodology extending the utility of zero-shot learning to both images and videos and selecting high-quality samples for synthetic data augmentation. This form of data valuation is then incorporated for our novel video data augmentation approach where we generate video composites using foreground and background mixing of videos. The data valuation helps us choose good composites at a reduced overall cost. Finally, we propose the creation of a meaningful semantic space for action labels. We create a textual description dataset for each action class and propose a novel feature generating approach to maximise the benefits of this semantic space. The research contributes significantly to the field, potentially paving the way for more efficient, resource-friendly, and robust video processing and understanding techniques
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Integrality and cutting planes in semidefinite programming approaches for combinatorial optimization
Many real-life decision problems are discrete in nature. To solve such problems as mathematical optimization problems, integrality constraints are commonly incorporated in the model to reflect the choice of finitely many alternatives. At the same time, it is known that semidefinite programming is very suitable for obtaining strong relaxations of combinatorial optimization problems. In this dissertation, we study the interplay between semidefinite programming and integrality, where a special focus is put on the use of cutting-plane methods. Although the notions of integrality and cutting planes are well-studied in linear programming, integer semidefinite programs (ISDPs) are considered only recently. We show that manycombinatorial optimization problems can be modeled as ISDPs. Several theoretical concepts, such as the Chvátal-Gomory closure, total dual integrality and integer Lagrangian duality, are studied for the case of integer semidefinite programming. On the practical side, we introduce an improved branch-and-cut approach for ISDPs and a cutting-plane augmented Lagrangian method for solving semidefinite programs with a large number of cutting planes. Throughout the thesis, we apply our results to a wide range of combinatorial optimization problems, among which the quadratic cycle cover problem, the quadratic traveling salesman problem and the graph partition problem. Our approaches lead to novel, strong and efficient solution strategies for these problems, with the potential to be extended to other problem classes
Gabriel Vacariu (c2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy
Unbelievable similar ideas to my ideas published long before..
Learning from imperfect data : incremental learning and Few-shot Learning
In recent years, artificial intelligence (AI) has achieved great success in many fields, e.g., computer vision, speech recognition, recommendation engines, and neural language processing. Although impressive advances have been made, AI algorithms still suffer from an important limitation: they rely on large-scale datasets. In contrast, human beings naturally possess the ability to learn novel knowledge from real-world and imperfect data such as a small number of samples or a non-static continual data stream. Attaining such an ability is particularly appealing. Specifically, an ideal AI system with human-level intelligence should work with the following imperfect data scenarios. 1)~The training data distribution changes while learning. In many real scenarios, data are streaming, might disappear after a given period of time, or even can not be stored at all due to storage constraints or privacy issues. As a consequence, the old knowledge is over-written, a phenomenon called catastrophic forgetting. 2)~The annotations of the training data are sparse. There are also many scenarios where we do not have access to the specific large-scale data of interest due to privacy and security reasons. As a consequence, the deep models overfit the training data distribution and are very likely to make wrong decisions when they encounter rare cases. Therefore, the goal of this thesis is to tackle the challenges and develop AI algorithms that can be trained with imperfect data. To achieve the above goal, we study three topics in this thesis. 1)~Learning with continual data without forgetting (i.e., incremental learning). 2)~Learning with limited data without overfitting (i.e., few-shot learning). 3)~Learning with imperfect data in real-world applications (e.g., incremental object detection). Our key idea is learning to learn/optimize. Specifically, we use advanced learning and optimization techniques to design data-driven methods to dynamically adapt the key elements in AI algorithms, e.g., selection of data, memory allocation, network architecture, essential hyperparameters, and control of knowledge transfer. We believe that the adaptive and dynamic design of system elements will significantly improve the capability of deep learning systems under limited data or continual streams, compared to the systems with fixed and non-optimized elements. More specifically, we first study how to overcome the catastrophic forgetting problem by learning to optimize exemplar data, allocate memory, aggregate neural networks, and optimize key hyperparameters. Then, we study how to improve the generalization ability of the model and tackle the overfitting problem by learning to transfer knowledge and ensemble deep models. Finally, we study how to apply incremental learning techniques to the recent top-performance transformer-based architecture for a more challenging and realistic vision, incremental object detection.Künstliche Intelligenz (KI) hat in den letzten Jahren in vielen Bereichen große Erfolge erzielt, z. B. Computer Vision, Spracherkennung, Empfehlungsmaschinen und neuronale Sprachverarbeitung. Obwohl beeindruckende Fortschritte erzielt wurden, leiden KI-Algorithmen immer noch an einer wichtigen Einschränkung: Sie sind auf umfangreiche Datensätze angewiesen. Im Gegensatz dazu besitzen Menschen von Natur aus die Fähigkeit, neuartiges Wissen aus realen und unvollkommenen Daten wie einer kleinen Anzahl von Proben oder einem nicht statischen kontinuierlichen Datenstrom zu lernen. Das Erlangen einer solchen Fähigkeit ist besonders reizvoll. Insbesondere sollte ein ideales KI-System mit Intelligenz auf menschlicher Ebene mit den folgenden unvollkommenen Datenszenarien arbeiten. 1)~Die Verteilung der Trainingsdaten ändert sich während des Lernens. In vielen realen Szenarien werden Daten gestreamt, können nach einer bestimmten Zeit verschwinden oder können aufgrund von Speicherbeschränkungen oder Datenschutzproblemen überhaupt nicht gespeichert werden. Infolgedessen wird das alte Wissen überschrieben, ein Phänomen, das als katastrophales Vergessen bezeichnet wird. 2)~Die Anmerkungen der Trainingsdaten sind spärlich. Es gibt auch viele Szenarien, in denen wir aus Datenschutz- und Sicherheitsgründen keinen Zugriff auf die spezifischen großen Daten haben, die von Interesse sind. Infolgedessen passen die tiefen Modelle zu stark an die Verteilung der Trainingsdaten an und treffen sehr wahrscheinlich falsche Entscheidungen, wenn sie auf seltene Fälle stoßen. Daher ist das Ziel dieser Arbeit, die Herausforderungen anzugehen und KI-Algorithmen zu entwickeln, die mit unvollkommenen Daten trainiert werden können. Um das obige Ziel zu erreichen, untersuchen wir in dieser Arbeit drei Themen. 1)~Lernen mit kontinuierlichen Daten ohne Vergessen (d. h. inkrementelles Lernen). 2) ~ Lernen mit begrenzten Daten ohne Überanpassung (d. h. Lernen mit wenigen Schüssen). 3) ~ Lernen mit unvollkommenen Daten in realen Anwendungen (z. B. inkrementelle Objekterkennung). Unser Leitgedanke ist Lernen lernen/optimieren. Insbesondere verwenden wir fortschrittliche Lern- und Optimierungstechniken, um datengesteuerte Methoden zu entwerfen, um die Schlüsselelemente in KI-Algorithmen dynamisch anzupassen, z. B. Auswahl von Daten, Speicherzuweisung, Netzwerkarchitektur, wesentliche Hyperparameter und Steuerung des Wissenstransfers. Wir glauben, dass das adaptive und dynamische Design von Systemelementen die Leistungsfähigkeit von Deep-Learning-Systemen bei begrenzten Daten oder kontinuierlichen Streams im Vergleich zu Systemen mit festen und nicht optimierten Elementen erheblich verbessern wird. Genauer gesagt untersuchen wir zunächst, wie das katastrophale Vergessensproblem überwunden werden kann, indem wir lernen, Beispieldaten zu optimieren, Speicher zuzuweisen, neuronale Netze zu aggregieren und wichtige Hyperparameter zu optimieren. Dann untersuchen wir, wie die Verallgemeinerungsfähigkeit des Modells verbessert und das Overfitting-Problem angegangen werden kann, indem wir lernen, Wissen zu übertragen und tiefe Modelle in Ensembles zusammenzufassen. Schließlich untersuchen wir, wie man inkrementelle Lerntechniken auf die jüngste transformatorbasierte Hochleistungsarchitektur für eine anspruchsvollere und realistischere Vision, inkrementelle Objekterkennung, anwendet
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