45 research outputs found

    ON EXPRESSIVENESS, INFERENCE, AND PARAMETER ESTIMATION OF DISCRETE SEQUENCE MODELS

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    Huge neural autoregressive sequence models have achieved impressive performance across different applications, such as NLP, reinforcement learning, and bioinformatics. However, some lingering problems (e.g., consistency and coherency of generated texts) continue to exist, regardless of the parameter count. In the first part of this thesis, we chart a taxonomy of the expressiveness of various sequence model families (Ch 3). In particular, we put forth complexity-theoretic proofs that string latent-variable sequence models are strictly more expressive than energy-based sequence models, which in turn are more expressive than autoregressive sequence models. Based on these findings, we introduce residual energy-based sequence models, a family of energy-based sequence models (Ch 4) whose sequence weights can be evaluated efficiently, and also perform competitively against autoregressive models. However, we show how unrestricted energy-based sequence models can suffer from uncomputability; and how such a problem is generally unfixable without knowledge of the true sequence distribution (Ch 5). In the second part of the thesis, we study practical sequence model families and algorithms based on theoretical findings in the first part of the thesis. We introduce neural particle smoothing (Ch 6), a family of approximate sampling methods that work with conditional latent variable models. We also introduce neural finite-state transducers (Ch 7), which extend weighted finite state transducers with the introduction of mark strings, allowing scoring transduction paths in a finite state transducer with a neural network. Finally, we propose neural regular expressions (Ch 8), a family of neural sequence models that are easy to engineer, allowing a user to design flexible weighted relations using Marked FSTs, and combine these weighted relations together with various operations

    Modeling Individual Activity and Mobility Behavior and Assessing Ridesharing Impacts Using Emerging Data Sources

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    Predicting individual mobility behavior is one of the major steps of transportation planning models. Accurate prediction of individual mobility behavior will be beneficial for transportation planning. Although previous studies have used different data sources to model individual mobility behaviors, they have several limitations such as the lack of complete mobility sequences and travel mode information, limiting our ability to accurately predict individual movements. In recent years, the emergence of GPS-based floating car data (FCD) and on-demand ride-hailing service platforms can provide innovative data sources to understand and model individual mobility behavior. Compared to the previously used data sources such as mobile phone and social media data, mobility data extracted of the new data sources contain more specific, detailed, and longitudinal information of individual travel mode and coordinates of the visited locations. This dissertation explores the potential of using GPS-based FCD and on-demand ride-hailing service data with different modeling techniques towards understanding and predicting individual mobility and activity behaviors and assessing the ridesharing impacts through three studies

    Input Output HMM for Indoor Temperature Prediction in Occupancy Management Under User Preferences

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    In this paper, a probabilistic machine learning method is proposed to predict the indoor temperature of an office environment. An IOHMM-based model is developed to represent the office environment under different circumstances of heating sources. One year of time series data is observed and studied to learn the dynamics of the indoor thermal states. The uncertainty associated with the changing aspects of the indoor temperature and its dependence on the outdoor temperature is considered in the model development. The well-known Baum Welch and forward-backward algorithms are adapted to learn the model parameters. Then, the Viterbi algorithm is used to predict the maximum path of hidden states, leading to predicting the most likely future temperatures. A numerical application is presented to demonstrate the model development steps and the training and testing results. Finally, the model's performance is validated using leave-one-out cross-validation, which shows that the model has a prediction accuracy of about 78%

    DevOps for Trustworthy Smart IoT Systems

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    ENACT is a research project funded by the European Commission under its H2020 program. The project consortium consists of twelve industry and research member organisations spread across the whole EU. The overall goal of the ENACT project was to provide a novel set of solutions to enable DevOps in the realm of trustworthy Smart IoT Systems. Smart IoT Systems (SIS) are complex systems involving not only sensors but also actuators with control loops distributed all across the IoT, Edge and Cloud infrastructure. Since smart IoT systems typically operate in a changing and often unpredictable environment, the ability of these systems to continuously evolve and adapt to their new environment is decisive to ensure and increase their trustworthiness, quality and user experience. DevOps has established itself as a software development life-cycle model that encourages developers to continuously bring new features to the system under operation without sacrificing quality. This book reports on the ENACT work to empower the development and operation as well as the continuous and agile evolution of SIS, which is necessary to adapt the system to changes in its environment, such as newly appearing trustworthiness threats

    Multi-Robot Symbolic Task and Motion Planning Leveraging Human Trust Models: Theory and Applications

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    Multi-robot systems (MRS) can accomplish more complex tasks with two or more robots and have produced a broad set of applications. The presence of a human operator in an MRS can guarantee the safety of the task performing, but the human operators can be subject to heavier stress and cognitive workload in collaboration with the MRS than the single robot. It is significant for the MRS to have the provable correct task and motion planning solution for a complex task. That can reduce the human workload during supervising the task and improve the reliability of human-MRS collaboration. This dissertation relies on formal verification to provide the provable-correct solution for the robotic system. One of the challenges in task and motion planning under temporal logic task specifications is developing computationally efficient MRS frameworks. The dissertation first presents an automaton-based task and motion planning framework for MRS to satisfy finite words of linear temporal logic (LTL) task specifications in parallel and concurrently. Furthermore, the dissertation develops a computational trust model to improve the human-MRS collaboration for a motion task. Notably, the current works commonly underemphasize the environmental attributes when investigating the impacting factors of human trust in robots. Our computational trust model builds a linear state-space (LSS) equation to capture the influence of environment attributes on human trust in an MRS. A Bayesian optimization based experimental design (BOED) is proposed to sequentially learn the human-MRS trust model parameters in a data-efficient way. Finally, the dissertation shapes a reward function for the human-MRS collaborated complex task by referring to the above LTL task specification and computational trust model. A Bayesian active reinforcement learning (RL) algorithm is used to concurrently learn the shaped reward function and explore the most trustworthy task and motion planning solution

    Probabilistic Graphical Models for ERP-Based Brain Computer Interfaces

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    An event related potential (ERP) is an electrical potential recorded from the nervous system of humans or other animals. An ERP is observed after the presentation of a stimulus. Some examples of the ERPs are P300, N400, among others. Although ERPs are used very often in neuroscience, its generation is not yet well understood and different theories have been proposed to explain the phenomena. ERPs could be generated due to changes in the alpha rhythm, an internal neural control that reset the ongoing oscillations in the brain, or separate and distinct additive neuronal phenomena. When different repetitions of the same stimuli are averaged, a coherence addition of the oscillations is obtained which explain the increase in amplitude in the signals. Two ERPs are mostly studied: N400 and P300. N400 signals arise when a subject tries to make semantic operations that support neural circuits for explicit memory. N400 potentials have been observed mostly in the rhinal cortex. P300 signals are related to attention and memory operations. When a new stimulus appears, a P300 ERP (named P3a) is generated in the frontal lobe. In contrast, when a subject perceives an expected stimulus, a P300 ERP (named P3b) is generated in the temporal – parietal areas. This implicates P3a and P3b are related, suggesting a circuit pathway between the frontal and temporal–parietal regions, whose existence has not been verified. Un potencial relacionado con un evento (ERP) es un potencial eléctrico registrado en el sistema nervioso de los seres humanos u otros animales. Un ERP se observa tras la presentación de un estímulo. Aunque los ERPs se utilizan muy a menudo en neurociencia, su generación aún no se entiende bien y se han propuesto diferentes teorías para explicar el fenómeno. Una interfaz cerebro-computador (BCI) es un sistema de comunicación en el que los mensajes o las órdenes que un sujeto envía al mundo exterior proceden de algunas señales cerebrales en lugar de los nervios y músculos periféricos. La BCI utiliza ritmos sensorimotores o señales ERP, por lo que se necesita un clasificador para distinguir entre los estímulos correctos y los incorrectos. En este trabajo, proponemos utilizar modelos probabilísticos gráficos para el modelado de la dinámica temporal y espacial de las señales cerebrales con aplicaciones a las BCIs. Los modelos gráficos han sido seleccionados por su flexibilidad y capacidad de incorporar información previa. Esta flexibilidad se ha utilizado anteriormente para modelar únicamente la dinámica temporal. Esperamos que el modelo refleje algunos aspectos del funcionamiento del cerebro relacionados con los ERPs, al incluir información espacial y temporal.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

    Improving classification of error related potentials using novel feature extraction and classification algorithms for an assistive robotic device

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    We evaluated the proposed feature extraction algorithm and the classifier, and we showed that the performance surpassed the state of the art algorithms in error detection. Advances in technology are required to improve the quality of life of a person with a severe disability who has lost their independence of movement in their daily life. Brain-computer interface (BCI) is a possible technology to re-establish a sense of independence for the person with a severe disability through direct communication between the brain and an electronic device. To enhance the symbiotic interface between the person and BCI its accuracy and robustness should be improved across all age groups. This thesis aims to address the above-mentioned issue by developing a novel feature extraction algorithm and a novel classification algorithm for the detection of erroneous actions made by either human or BCI. The research approach evaluated the state of the art error detection classifier using data from two different age groups, young and elderly. The performance showed a statistical difference between the aforementioned age groups; therefore, there needs to be an improvement in error detection and classification. The results showed that my proposed relative peak feature (RPF) and adaptive decision surface (ADS) classifier outperformed the state of the art algorithms in detecting errors using EEG for both elderly and young groups. In addition, the novel classification algorithm has been applied to motor imagery to improve the detection of when a person imagines moving a limb. Finally, this thesis takes a brief look at object recognition for a shared control task of identifying utensils in cooperation with a prosthetic robotic hand

    Modelling individual accessibility using Bayesian networks: A capabilities approach

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    The ability of an individual to reach and engage with basic services such as healthcare, education and activities such as employment is a fundamental aspect of their wellbeing. Within transport studies, accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. The data landscape however, has changed over the last decade providing an unprecedented quantity of transport related data at an individual level. Mobility data from dfferent sources have the potential to contribute to the understanding of individual accessibility and its relation to phenomena such as social exclusion. At the same time, the unlabelled nature of such data present a considerable challenge, as a non-trivial step of inference is required if one is to deduce the transportation modes used and activities reached. This thesis develops a novel framework for accessibility modelling using the CA as theoretical foundation. Within the scope of this thesis, this is used to assess the levels of equality experienced by individuals belonging to different population groups and its link to transport related social exclusion. In the proposed approach, activities reached and transportation modes used are considered manifestations of individual hidden capabilities. A modelling framework using dynamic Bayesian networks is developed to quantify and assess the relationships and dynamics of the different components in fluencing the capabilities sets. The developed approach can also provide inferential capabilities for activity type and transportation mode detection, making it suitable for use with unlabelled mobility data such as Automatic Fare Collection Systems (AFC), mobile phone and social media. The usefulness of the proposed framework is demonstrated through three case studies. In the first case study, mobile phone data were used to explore the interaction of individuals with different public transportation modes. It was found that assumptions about individual mobility preferences derived from travel surveys may not always hold, providing evidence for the significance of personal characteristics to the choices of transportation modes. In the second case, the proposed framework is used for activity type inference, testing the limits of accuracy that can be achieved from unlabelled social media data. A combination of the previous case studies, the third case further defines a generative model which is used to develop the proposed capabilities approach to accessibility model. Using data from London's Automatic Fare Collection Systems (AFC) system, the elements of the capabilities set are explicitly de ned and linked with an individual's personal characteristics, external variables and functionings. The results are used to explore the link between social exclusion and transport disadvantage, revealing distinct patterns that can be attributed to different accessibility levels
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