Toward affective dialogue management using partially observable Markov decision processes

Designing and developing affective dialogue systems have recently received much interest from the dialogue research community. A distinctive feature of these systems is affect modeling. Previous work was mainly focused on showing system's emotions to the user in order to achieve the designer's goal such as helping the student to practice nursing tasks or persuading the user to change their dietary behavior. A challenging problem is to infer the user's affective state and to adapt the system's behavior accordingly. This thesis addresses this problem from an engineering perspective using Partially Observable Markov Decision Process (POMDP) techniques and a Rapid Dialogue Prototyping Methodology (RDPM). We argue that the POMDPs are suitable for use in designing affective dialogue management models for three main reasons. First, the POMDP model allows for realistic modeling of the user's affective state, the user's intention, and other (user's) hidden state components by incorporating them into the state space. Second, recent dialogue management research has shown that the POMDP-based dialogue manager is able to cope well with uncertainty that can occur at many levels inside a dialogue system from speech recognition, natural language understanding to dialogue management. Third, the POMDP environment can be used to create a simulated user which is useful for learning and evaluation of competing dialogue strategies. In the first part of this thesis, we first present the RDPM for a quick production of frame-based dialogue models for traditional (i.e., non-affect sensitive) singleapplication dialogue systems. The usability of the RDPM has been validated through the implementation of several prototype dialogue systems. We then present a novel approach to developing interfaces for multi-application systems which are dialogue systems that allow the user to navigate between a large set of applications smoothly and transparently. The work in this part provides an essential infrastructure for implementing our prototype POMDP-based dialogue manager. In the second part, we first describe a factored POMDP approach to affective dialogue management. This approach illustrates that POMDPs are an elegant model for building affective dialogue systems. Further, the POMDP-based dialogue strategy outperforms all other known strategies from the literature when tested with smallscale dialogue problems. However, a well-known drawback of POMDP-based dialogue managers is that computing a near-optimal dialogue policy is extremely computationally expensive. We then propose a tractable hybrid DDN-POMDP method to tackle many of these scalability problems. The central contribution of our method (compared with other POMDP-based dialogue management methods from the literature) is the ability to handle frame-based dialogue problems with hundreds of slots and hundreds of slot values

A quasi-3D hyperbolic shear deformation theory for functionally graded plates

A quasi-3D hyperbolic shear deformation theory for functionally graded plates is developed. The theory accounts for both shear deformation and thickness-stretching effects by a hyperbolic variation of all displacements across the thickness, and satisfies the stress-free boundary conditions on the top and bottom surfaces of the plate without requiring any shear correction factor. The benefit of the present theory is that it contains a smaller number of unknowns and governing equations than the existing quasi-3D theories, but its solutions compare well with 3D and quasi-3D solutions. Equations of motion are derived from the Hamilton principle. Analytical solutions for bending and free vibration problems are obtained for simply supported plates. Numerical examples are presented to verify the accuracy of the present theory

A Deep Learning-Based Aesthetic Surgery Recommendation System

We propose in this chapter a deep learning-based recommendation system for aesthetic surgery, composing of a mobile application and a deep learning model. The deep learning model built based on the dataset of before- and after-surgery facial images can estimate the probability of the perfection of some parts of a face. In this study, we focus on the most two popular treatments: rejuvenation treatment and eye double-fold surgery. It is assumed that the outcomes of our history surgeries are perfect. Firstly a convolutional autoencoder is trained by eye images before and after surgery captured from various angles. The trained encoder is utilized to extract learned generic eye features. Secondly, the encoder is further trained by pairs of image samples, captured before and after surgery, to predict the probability of perfection, so-called perfection score. Based on this score, the system would suggest whether some sorts of specific aesthetic surgeries should be performed. We preliminarily achieve 88.9 and 93.1% accuracy on rejuvenation treatment and eye double-fold surgery, respectively

Boosting Punctuation Restoration with Data Generation and Reinforcement Learning

Punctuation restoration is an important task in automatic speech recognition (ASR) which aim to restore the syntactic structure of generated ASR texts to improve readability. While punctuated texts are abundant from written documents, the discrepancy between written punctuated texts and ASR texts limits the usability of written texts in training punctuation restoration systems for ASR texts. This paper proposes a reinforcement learning method to exploit in-topic written texts and recent advances in large pre-trained generative language models to bridge this gap. The experiments show that our method achieves state-of-the-art performance on the ASR test set on two benchmark datasets for punctuation restoration.Comment: Accepted at INTERSPEECH 2023, 6 page

An effective algorithm for reliability-based optimization of stiffened Mindlin plate

Nowadays, stiffened plates have been widely used in many branches of structural engineering such as aircraft, ships, bridges, buildings etc... In comparison with common bending plate structures, stiffened plates not only have larger bending stiffness but also use less amount of material. Hence, it usually has higher economic efficiency. However, to obtain high effectiveness in solving the design problems of the stiffened plate, the reliability-based optimization problems need to be established together with the ordinary numerical computing methods. Therefore, the paper presents an approach to establish and solve the reliability-based optimization problem for the stiffened Mindlin plate. To analyze the behavior of Mindlin plate, we use the recently proposed CS-DSG3 element. The random variables are chosen to be elastic modulus, density of mass and external force. The design variables are the thickness, the width and the height of the stiffened plate. The objective function can be the strain energy or the mass of the structure and subjected to the constraints of displacement or vibration frequency. The reliability-based optimization algorithm used in this paper is a three-step closed loop: 1) Estimating the random variables by the Reliability Index (RI) method; 2) Solving the optimization problem using Sequential Quadratic Programming (SQP) method; 3) Checking and estimating the reliability by the first-order reliability method (FORM) in which the limit state function is the limit of displacement or vibration frequency of the structure