Autonomously monitoring and analyzing the behavior of the crowd is an open research topic in the transportation field because of its criticality to the safety of people. Real-time identification, tracking, and prediction of crowd behavior are primordial to ensure smooth crowd management operations and the welfare of the public in many public areas, such as public transport stations and streets. This being said, enabling such systems is not a straightforward procedure. First, the complexity brought by the interaction and fusion from individual to group needs to be assessed and analyzed. Second, the classification of these actions might be useful in identifying danger and avoiding any undesirable consequences. The adoption of the Internet-of-things (IoT) in such systems has made it possible to gather a large amount of data. However, it raises diverse compatibility and trustworthiness challenges, among others, hindering the use of conventional service discovery and network navigability processes for enabling crowd management systems. In fact, as the IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices' status, such as availability, battery capacity, and memory usage), traditional methods fail to learn and understand the evolving behavior of the network so as to enable real-time and context-aware service discovery to assign and select relevant IoT devices for monitoring and managing the crowd. In large-scale IoT networks, crowd management systems usually collect large data streams of images from different heterogeneous sources (e.g., CCTVs, IoT devices, or people with their smartphones) in an inadvertent way. Due to the limitations and challenges related to communication bandwidth, storage, and processing capabilities, it is unwise to transfer unselectively all the collected images since some of these images either contain duplicate information, are inaccurate, or might be falsely submitted by end-users; hence, a filtering and quality check mechanism must be put in place. As images can only provide limited information about the crowd by capturing only a snapshot of the scene at a specific point in time with limited context, an extension to deal with videos to enable efficient analysis such as crowd tracking and identification is essential for the success of crowd management systems. In this thesis, we propose to design a smart image enhancement and quality control system for resource pooling and allocation in the Internet-of-Things applied to crowd management systems. We first rely on the Social IoT (SIoT) concept, which defines the relationships among the connected objects, to extract accurate information about the network and enable trustworthy and context-aware service exchange and resource allocation. We investigate the service discovery process in SIoT networks and essentially focus on graph-based techniques while overviewing their utilization in SIoT and discussing their advantages. We also propose an alternative to these scalable methods by introducing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery in a large-scale heterogeneous IoT network to enable efficient crowd management systems. Secondly, we propose to design a smart image selection procedure using an asymmetric multi-modal neural network autoencoder to select a subset of photos with high utility coverage for multiple incoming streams in the IoT network. The proposed architecture enables the selection of high-context data from an evolving picture stream and ensures relevance while discarding images that are irrelevant or falsely submitted by smartphones, for example. The approach uses the photo's metadata, such as geolocation and timestamps, along with the pictures' semantics to decide which photos can be submitted and which ones must be discarded. To extend our framework beyond just images and deal with real-time videos, we propose a transformer-based crowd management monitoring framework called V3Trans-Crowd that captures information from video data and extracts meaningful output to categorize the crowd's behavior. The proposed 3D Video Transformer is inspired from Video Swin-Transformer/VIVIT and provides an improved hierarchical transformer for multi-modal tasks with spatial and temporal fusion layers. Our simulations show that due to its ability to embed the devices' features and relations, the GNN is capable of providing more concise clusters compared to traditional techniques, allowing for better IoT network learning and understanding. Moreover, we show that the GNN approach speeds up the service lookup search space and outperforms the traditional graph-based techniques to select suitable IoT devices for reporting and monitoring. Simulation results for three different multi-modal autoencoder architectures indicate that a hierarchical asymmetric autoencoder approach can yield better results, outperforming the mixed asymmetric autoencoder and a concatenated input autoencoder, while leveraging user-side rendering to reduce bandwidth consumption and computational overhead. Also, performance evaluation for the proposed V3Trans-Crowd model has shown great results in terms of accuracy for crowd behavior classification compared to state-of-the-art methods such as C3D pre-trained, I3D pre-trained, and ResNet 3D pre-trained on the Crowd-11 and MED datasets.

Advances in convolutional neural networks and recurrent neural networks have led to significant improvements in learning on regular grid data domains such as images and texts. However, many real-world datasets, for instance, social networks, citation networks, molecules, point clouds, and 3D meshes, do not lie in such a simple grid. Such data is irregular or non-Euclidean in structure and has complex relational information. Graph machine learning, especially Graph Neural Networks (GNNs), provides a potential for processing such irregular data and being capable of modeling the relation between entities, which is leading the machine learning field to a new era. However, previous state-of-the-art (SOTA) GNNs are limited to shallow architectures due to challenging problems such as vanishing gradients, over-fitting, and over-smoothing. Most of the SOTA GNNs are not deeper than 3 or 4 layers, which restricts the representative power of GNNs and makes learning on large-scale graphs ineffective. Aiming to resolve this challenge, this dissertation discusses approaches to building large-scale and efficient graph machine learning models for learning structured representation with applications to engineering and sciences. This work would present how to make GNNs go deep by introducing architectural designs and how to automatically search GNN architectures by novel neural architecture search algorithms.

Indiana University-Purdue University Indianapolis (IUPUI) / The dramatic improvement in information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. CourseNet working is a next-generation LMS adopting machine learning to add personalization, gamifi cation, and more dynamics to the system. This work tries to come up with two recommender systems that can help improve CourseNetworking services. The first one is a social recommender system helping CourseNetworking to track user interests and give more relevant recommendations. Recently, graph neural network (GNN) techniques have been employed in social recommender systems due to their high success in graph representation learning, including social network graphs. Despite the rapid advances in recommender systems performance, dealing with the dynamic property of the social network data is one of the key challenges that is remained to be addressed. In this research, a novel method is presented that provides social recommendations by incorporating the dynamic property of social network data in a heterogeneous graph by supplementing the graph with time span nodes that are used to define users long-term and short-term preferences over time. The second service that is proposed to add to Rumi services is a hashtag recommendation system that can help users label their posts quickly resulting in improved searchability of content. In recent years, several hashtag recommendation methods are proposed and de veloped to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This work investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation and recommends the one with the best performance to use in a hashtag recommender system.

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京都大学 / 新制・課程博士 / 博士(薬学) / 甲第23844号 / 薬博第851号 / 新制||薬||242(附属図書館) / 京都大学大学院薬学研究科薬学専攻 / (主査)教授 山下 富義, 教授 石濱 泰, 教授 金子 周司 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

Single-cell RNA sequencing and in situ sequencing were combined in a recent study of the developing human heart to explore the transcriptional landscape at three developmental stages. However, the method used in the study to create the spatial cellular maps has some limitations. It relies on image segmentation of the nuclei and cell types defined in advance by single-cell sequencing. In this study, we applied a new unsupervised approach based on graph neural networks on the in situ sequencing data of the human heart to find spatial gene expression patterns and detect novel cell and sub-cell types. In this thesis, we first introduce some relevant background knowledge about the sequencing techniques that generate our data, machine learning in single-cell analysis, and deep learning on graphs. We have explored several graph neural network models and algorithms to learn embeddings for spatial gene expression. Dimensionality reduction and cluster analysis were performed on the embeddings for visualization and identification of biologically functional domains. Based on the cluster gene expression profiles, locations of the clusters in the heart sections, and comparison with cell types defined in the previous study, the results of our experiments demonstrate that graph neural networks can learn meaningful representations of spatial gene expression in the human heart. We hope further validations of our clustering results could give new insights into cell development and differentiation processes of the human heart.

In this work we present a novel approach to network embedding for neural architecture search - info-NAS. The model learns to predict the output fea- tures of a trained convolutional neural network on a set of input images. We use the NAS-Bench-101 search space as the neural architecture dataset, and the CIFAR-10 as the image dataset. For the purpose of this task, we extend an existing unsupervised graph variational autoencoder, arch2vec, by jointly training on unlabeled and labeled neural architectures in a semi-supervised manner. To evaluate our approach, we analyze how our model learns on the data, compare it to the original arch2vec, and finally, we evaluate both mod- els on the NAS-Bench-101 search task and on the performance prediction task. 1

<p dir="ltr">Topological Data Analysis (TDA) with its roots embedded in the field of algebraic topology has successfully found its applications in computational biology, drug discovery, machine learning and in many diverse areas of science. One of its cornerstones, persistent homology, captures topological features latent in the data. Recent progress in TDA allows us to integrate these finer topological features into traditional machine learning and deep learning pipelines. However, the utilization of topological methods within a conventional deep learning framework remains relatively uncharted. This thesis presents four scenarios where computational topology tools are employed to advance machine learning.</p><p dir="ltr">The first one involves integrating persistent homology to explore high-dimensional cytometry data. The second one incorporates Extended persistence in a supervised graph classification framework and demonstrates leveraging TDA in cases where data naturally aligns with higher-order elements by extending graph neural networks to higher-order networks, applied specifically in non-manifold mesh classification. The third and fourth scenarios delve into enhancing graph neural networks through multiparameter persistence.</p>

[pt] O problema de Districting-and-routing é um problema estratégico no qual porções geográficas devem ser agregadas em regiões de entrega, e cada região de entrega possui um custo de roteamento estimado. Seu objetivo é de minimizar esses custos, além de garantir a divisão da região em distritos. A simulação para obter uma boa aproximação é muito custosa computacionalmente, enquanto mecanismos como buscas locais exigem que esse cálculo seja feito de forma muito eficiente, tornando essa estratégia de aproximação inviável para uma solução metaheurística. Grande parte das soluções existentes para esse problema utilizam de formulas de aproximação contínua para mensurar os custos de roteamento, funções essas que são rápidas de serem calculadas porém cometem erros significativos. Em contraste, propomos uma Rede Neural em Grafo (Graph Neural Network - GNN) que é usada como oráculo por um algoritmo de otimização. Nossos experimentos computacionais executados com dados de cidades do Reino Unido mostram que a GNN é capaz de produzir previsões de custos mais precisas em tempo computacional aceitável. O uso desse estimator na busca local impacta positivamente a qualidade das soluções, levando a uma economia de 10,35 por cento no custo de entrega estimado em relação a função Beardwood, que é comumente usada nesse cenários, e ganhos similares em comparação com outros métodos de aproximação. / [en] The districting-and-routing problem is a strategic problem in which basic geographical units (e.g., zip codes) should be aggregated into delivery regions, and each delivery region is characterized by a routing cost estimated over an extended planning horizon. The objective is to minimize the expected routing costs while ensuring regional separability through the definition of the districts. Repeatedly simulating routing costs on a set of scenarios while searching for good districts can be computationally intensive, so existing solution approaches for this problem rely on approximation functions. In contrast, we propose to rely on a graph neural network (GNN) trained on a set of demand scenarios, which is then used within an optimization approach to infer routing costs while solving the districting problem. Our computational experiments on various metropolitan areas show that the GNN produces accurate cost predictions. Moreover, using this better estimator during the search positively impacts the quality of the districting solutions and leads to 10.35 percent delivery-cost savings over the commonly-used Beardwood estimator and similar gains compared to other approximation methods.

Predicting formation energies of crystals is a common but computationally expensive task. In this work, it is therefore investigated how a neural network can be used as a tool for predicting formation energies with less computational cost compared to conventional methods. The investigated model shows promising results in predicting formation energies, reaching below a mean absolute error of 0.05 eV/atom with less than 4000 training datapoints. The model also shows great transferability, being able to reach below an MAE of 0.1 eV/atom with less than 100 training points when transferring from a pre-trained model. A drawback of the model is however that it is relying on descriptions of the crystal structures that include interatomic distances. Since these are not always accurately known, it is investigated how inaccurate structure descriptions affect the performance of the model. The results show that the quality of the descriptions definitely worsen the accuracy. The less accurate descriptions can however be used to reduce the search space in the creation of phase diagrams, and the proposed workflow which combines conventional density functional theory and machine learning shows a reduction in time consumption of more than 50 \% compared to only using density functional theory for creating a ternary phase diagram.