リバースエンジニアリングのための特徴を考慮した四辺形メッシュ分割

学位の種別:課程博士University of Tokyo(東京大学

Machine Learning Methods for Finding Textual Features of Depression from Publications

Depression is a common but serious mood disorder. In 2015, WHO reports about 322 million people were living with some form of depression, which is the leading cause of ill health and disability worldwide. In USA, there are approximately 14.8 million American adults (about 6.7% percent of the US population) affected by major depressive disorder. Most individuals with depression are not receiving adequate care because the symptoms are easily neglected and most people are not even aware of their mental health problems. Therefore, a depression prescreen system is greatly beneficial for people to understand their current mental health status at an early stage. Diagnosis of depressions, however, is always extremely challenging due to its complicated, many and various symptoms. Fortunately, publications have rich information about various depression symptoms. Text mining methods can discover the different depression symptoms from literature. In order to extract these depression symptoms from publications, machine learning approaches are proposed to overcome four main obstacles: (1) represent publications in a mathematical form; (2) get abstracts from publications; (3) remove the noisy publications to improve the data quality; (4) extract the textual symptoms from publications. For the first obstacle, we integrate Word2Vec with LDA by either representing publications with document-topic distance distributions or augmenting the word-to-topic and word-to-word vectors. For the second obstacle, we calculate a document vector and its paragraph vectors by aggregating word vectors from Word2Vec. Feature vectors are calculated by clustering word vectors. Selected paragraphs are decided by the similarity of their distances to feature vectors and the document vector to feature vectors. For the third obstacle, one class SVM model is trained by vectored publications, and outlier publications are excluded by distance measurements. For the fourth obstacle, we fully evaluate the possibility of a word as a symptom according to its frequency in entire publications, and local relationship with its surrounding words in a publication

Efficient 3D Semantic Segmentation with Superpoint Transformer

We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition point clouds into a hierarchical superpoint structure, which makes our preprocessing 7 times faster than existing superpoint-based approaches. Additionally, we leverage a self-attention mechanism to capture the relationships between superpoints at multiple scales, leading to state-of-the-art performance on three challenging benchmark datasets: S3DIS (76.0% mIoU 6-fold validation), KITTI-360 (63.5% on Val), and DALES (79.6%). With only 212k parameters, our approach is up to 200 times more compact than other state-of-the-art models while maintaining similar performance. Furthermore, our model can be trained on a single GPU in 3 hours for a fold of the S3DIS dataset, which is 7x to 70x fewer GPU-hours than the best-performing methods. Our code and models are accessible at github.com/drprojects/superpoint_transformer.Comment: Accepted at ICCV 2023. Camera-ready version with Appendix. Code available at github.com/drprojects/superpoint_transforme

Temporospatial Context-Aware Vehicular Crash Risk Prediction

With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

Integration of ROS2 with a simulation environment

Dissertação de mestrado integrado em Engenharia InformáticaCurrently, the University of Minho owns a driving simulator, from now on referred to as Driving Simulator Mockup 2-Wheeler (DSM-2W), which mimics a real driving environment for motorcycles. This simulator can reproduce diverse driving scenarios, like driving on different roads, traffic, and weather conditions, and is mostly used to test how the driver reacts to stimulus from subsystems under test in a particular scenario. The simulator has several components, namely, the Mock-up, which represents the motorcycle physically, the software responsible for the simulation environment, that is also projected on a screen, called SILAB [1] as well as several other subsystems and respective software, which all together form a complex distributed system. SILAB creates realistic graphic environments, has different models to control the behavior of other drivers and pedestrians, generates 3D sounds, and facilitates the personalization of the simulation scenario. Robot Operating System 2 (ROS2) [2] provides a set of tools and software libraries that facilitate the develop ment of robot systems and applications. With the increasing reliance on software, sensors, and actuators in the automotive domain, it makes sense to view cars [3] and motorcycles as robots. Therefore, it also makes sense to use ROS2 in the simulation domain to solve the problems at hand. This dissertation describes how ROS2, a well-known and accepted middleware for robotic applications, can also play a role in these contexts acting as a universal interface between motorcycle simulators and external subsystems and thereby significantly improving the system’s expansibility and those subsystems’ portability and reusability.A Universidade do Minho possui um simulador de motas, denominado Driving Simulator Mockup 2-Wheeler (DSM-2W), que imita um ambiente real de condução de motas. Esta ferramenta consegue reproduzir diversos cenários de condução, como conduzir em diferentes condições de estrada, tráfego, bem como em diferentes condições meteorológicas. Esta ferramenta é sobretudo usada para testar como o condutor reage a estímulos de vários sub-sistemas em teste em cenários particulares. O simulador possui diversos componentes, o Mock-up, que representa a mota fisicamente, o software responsável pela projeção do ambiente de simulação no ecrã, chamado SILAB [1], mais um conjunto de sub-sistemas e o respetivo software, que no conjunto formam um complexo sistema distribuído. O SILAB cria ambientes de simulação realistas, tem diferentes modelos para controlar o comportamento dos outros condutores e dos pedestres, gera sons 3D e facilita a personalização do cenário da simulação. O Robot Operating System 2 (ROS2) possui um conjunto de ferramentas e bibliotecas para desenvolver aplicações para robôs [2]. Com o aumento do uso de software, sensores, e atuadores no contexto automóvel, faz sentido equiparar veículos automóveis [3] e motas a robôs Portanto, também faz sentido usar o ROS2 para resolver problemas neste contexto. O objetivo desta dissertação passa por mostrar como o ROS2, um middleware bastante utilizado em aplicações para robôs, pode ter um papel importante em contextos de simulação ao atuar como uma interface universal entre sub-sistemas a testar e um simulador de motas e consequentemente melhorar a extensibilidade do simulador e a portabilidade e reusabilidade desses sub-sistemas

A K-MEANS CLUSTERING BASED SHAPE RETRIEVAL TECHNIQUE FOR 3D MESH MODELS

Due to the large size of shape databases, importance of effective and robust method in shape retrieval has been increased. Researchers mainly focus on finding descriptors which is suitable for rigid models. Retrieval of non-rigid models is a still challenging field which needs to be studied more. For non-rigid models, descriptors that are designed should be insensitive to different poses. For non-rigid model retrieval, we propose a new method which first divides a model into clusters using geodesic distance metric and then computes the descriptor using these clusters. Mesh segmentation is performed using a skeleton-based K-means clustering method.  Each cluster is represented by an area based descriptor which is invariant to scale and orientation. Finally, similar objects for the input model are retrieved. Articulated objects from human to animals are used for this study’s experiments for the validation of the proposed retrieval algorithm