Vývoj trénovatelných strategií řízení pro dialogové systémy

Abstraktní Vývoj trénovatelných strategií řízení pro dialogové systémy Thanh Le Řeč je nejpřirozenějším a nejefektivnějším způsobem mezilidské komunikace. Hlasové dialogové systémy (Spoken Dialogue Systems, SDS) se pokouší uvést tento způsob interakce do počítačových systému, aby pro komunikaci se stroji nebylo nutné naučit se používat speciální vstupní zařízení jako je klávesnice a myš. Nepřesnosti v automatickém rozpoznávání řeči však způsobují inherentní nejednoznačnost mluveného vstupu, takže stav dialogu (přání uživatele) nelze znát s absolutní jistotou a konstrukce SDS není triviální. Pro práci s nejistotou v dialogu byly navrženy statistické přístupy, které udržují pravděpodobnostní rozdělení přes všechny možné stavy dialogu. Na základě tohoto rozdělení se systém učí, jak komunikovat s uživateli a splnit jejich cíle co nejefektivnějším způsobem. V kontextu techniky zpětnovazebního učení (Reinforcement Learning, RL) se proces učení chápe jako optimalizace strategie volby akce podmíněné aktuálním stavem. Protože prostor možných stavů dialogu je velký i ve velmi omezených SDS, ...Abstract Development of trainable policies for spoken dialogue systems Thanh Le In human­human interaction, speech is the most natural and effective manner of communication. Spoken Dialogue Systems (SDS) have been trying to bring that high level interaction to computer systems, so with SDS, you could talk to machines rather than learn to use mouse and keyboard for performing a task. However, as inaccuracy in speech recognition and inherent ambiguity in spoken language, the dialogue state (user's desire) can never be known with certainty, and therefore, building such a SDS is not trivial. Statistical approaches have been proposed to deal with these uncertainties by maintaining a probability distribution over every possible dialogue state. Based on these distributions, the system learns how to interact with users, somehow to achieve the final goal in the most effective manner. In Reinforcement Learning (RL), the learning process is understood as optimizing a policy of choosing action conditioned on the current belief state. Since the space of dialogue...Institute of Formal and Applied LinguisticsÚstav formální a aplikované lingvistikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

FACTORS AFFECTING CAN THO RESIDENTS’ INTENTION TO VISIT DALAT IN THE COVID-19 ERA

The paper examines the factors affecting the intentions of Can Tho residents to travel to Dalat as the COVID-19 pandemic is gradually brought under control. The data were collected from a survey of 213 Can Tho residents and analyzed with Cronbach’s alpha, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. The results show that the intentions of Can Tho residents to travel to Dalat depend on four factors: (1) attitude toward Dalat tourism, (2) subjective norms, (3) perceived behavioral controls, and (4) perceived health risk. In particular, the attitudinal factor significantly influences Dalat travel intentions. Based on the research results, the article proposes four recommendations for managers, corresponding to the four influencing factors, to increase the intentions of Can Tho residents to travel to Dalat in the COVID-19 era

Exploiting No-Regret Algorithms in System Design

We investigate a repeated two-player zero-sum game setting where the column player is also a designer of the system, and has full control on the design of the payoff matrix. In addition, the row player uses a no-regret algorithm to efficiently learn how to adapt their strategy to the column player's behaviour over time in order to achieve good total payoff. The goal of the column player is to guide her opponent to pick a mixed strategy which is favourable for the system designer. Therefore, she needs to: (i) design an appropriate payoff matrix $A$ whose unique minimax solution contains the desired mixed strategy of the row player; and (ii) strategically interact with the row player during a sequence of plays in order to guide her opponent to converge to that desired behaviour. To design such a payoff matrix, we propose a novel solution that provably has a unique minimax solution with the desired behaviour. We also investigate a relaxation of this problem where uniqueness is not required, but all the minimax solutions have the same mixed strategy for the row player. Finally, we propose a new game playing algorithm for the system designer and prove that it can guide the row player, who may play a \emph{stable} no-regret algorithm, to converge to a minimax solution

Displacement and equilibrium mesh-free formulation based on integrated radial basis functions for dual yield design

This paper presents displacement and equilibrium mesh-free formulation based on integrated radial basis functions(iRBF) for upper and lower bound yield design problems. In these approaches, displacement and stress fields are approximated by the integrated radial basis functions, and the equilibrium equations and boundary conditions are imposed directly at the collocation points. In this paper it has been shown that direct nodal integration of the iRBF approximation can prevent volumetric locking in the kinematic formulation, and instability problems can also be avoided. Moreover, with the use of the collocation method in the static problem, equilibrium equations and yield conditions only need to be enforced at the nodes, leading to the reduction in computational cost. The mean value of the approximated upper and lower bound is found to be in excellent agreement with the available analytical solution, and can be considered as the actual collapse load multiplier for most practical engineering problems, for which exact solution is unknown

Achieving Better Regret against Strategic Adversaries

We study online learning problems in which the learner has extra knowledge about the adversary's behaviour, i.e., in game-theoretic settings where opponents typically follow some no-external regret learning algorithms. Under this assumption, we propose two new online learning algorithms, Accurate Follow the Regularized Leader (AFTRL) and Prod-Best Response (Prod-BR), that intensively exploit this extra knowledge while maintaining the no-regret property in the worst-case scenario of having inaccurate extra information. Specifically, AFTRL achieves $O(1)$ external regret or $O(1)$ \emph{forward regret} against no-external regret adversary in comparison with $O(\sqrt{T})$ \emph{dynamic regret} of Prod-BR. To the best of our knowledge, our algorithm is the first to consider forward regret that achieves $O(1)$ regret against strategic adversaries. When playing zero-sum games with Accurate Multiplicative Weights Update (AMWU), a special case of AFTRL, we achieve \emph{last round convergence} to the Nash Equilibrium. We also provide numerical experiments to further support our theoretical results. In particular, we demonstrate that our methods achieve significantly better regret bounds and rate of last round convergence, compared to the state of the art (e.g., Multiplicative Weights Update (MWU) and its optimistic counterpart, OMWU)

A 6DOF super element for nonlinear analysis of composite frames with partial interaction and semi-rigid connections

This paper presents a displacement-based finite element formulation for nonlinear analysis of steel - concrete composite planar frames subjected to combined action of gravity and lateral loads. A 6DOF super element is proposed for modeling composite beam, allowing for partial interaction between the steel beam and the concrete slab, semi-rigid nature of beam to column composite connection and material nonlinearity. The load control method and the displacement control method are utilized for tracing the structural equilibrium paths, and the direct method is utilized for solving the nonlinear problem. Numerical examples, concerning a two-span continuous composite beam, a portal composite frame and a six-storey composite frame, are performed. The results are compared with experience data or theoretical results from other studies and are discussed for influences of the factors mentioned above on behaviour of composite beams and composite frames

Last Round Convergence and No-Instant Regret in Repeated Games with Asymmetric Information

This paper considers repeated games in which one player has more information about the game than the other players. In particular, we investigate repeated two-player zero-sum games where only the column player knows the payoff matrix A of the game. Suppose that while repeatedly playing this game, the row player chooses her strategy at each round by using a no-regret algorithm to minimize her (pseudo) regret. We develop a no-instant-regret algorithm for the column player to exhibit last round convergence to a minimax equilibrium. We show that our algorithm is efficient against a large set of popular no-regret algorithms of the row player, including the multiplicative weight update algorithm, the online mirror descent method/follow-the-regularized-leader, the linear multiplicative weight update algorithm, and the optimistic multiplicative weight update

VulCurator: A Vulnerability-Fixing Commit Detector

Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, including commit messages, code changes and issue reports for vulnerability-fixing commit classifica- tion. Our experimental results show that VulCurator outperforms the state-of-the-art baselines up to 16.1% in terms of F1-score. VulCurator tool is publicly available at https://github.com/ntgiang71096/VFDetector and https://zenodo.org/record/7034132#.Yw3MN-xBzDI, with a demo video at https://youtu.be/uMlFmWSJYOE.Comment: accepted to ESEC/FSE 2022, Tool Demos Trac

Late Holocene Morphodynamic Feedback in Can Gio Mangrove Tide-Dominated River Mouth Systems, Vietnam

Can Gio (CG), a mangrove forest with a dense network of tidal creeks, gradually grew and spread seaward on a coastal platform, which was built since about 8 ka before present (BP). Along with this development, a sand ridge began to form and moved back with the shoreline withdrawal landward in the Late Holocene. This mangrove environment is likely abandoned from the mainland but was, however, the place for settlement of ancient Vietnamese a few centuries BC. The CG mangrove forest was severely destroyed during the American War and was restored since 1980. However, the historical change of the landscape along the Saigon-Dong Nai River (SG-DNR) since the Late Holocene is not completely unraveled. By analyzing sediment cores with a multiproxy approach, we investigated the recent geological development with regard to the variation of the intensity of the East Asian palaeomonsoon and regarding the accommodation space, as both regulate the development of this coastal environment. A recently significant shift in the coastline, mainly due to a change of hydroclimatic factors, was observed. A continuous coastline retreat occurred over the last millennium, changing the depositional environment and reshaping the CG mangrove landscape. Along the present coast and tidal channels, partially strong erosion and bank failures occur, alternating with accretion at other coastal sections. This development tends to increase progressivel