The widespread implementation of social distancing measures and remote work due to the COVID-19 pandemic has significantly altered societal dynamics, leading to an increased reliance on social media platforms for expressing sentiment. However, existing sentiment analysis models face challenges in comprehending the complexities of English tweets and nuances within social media conversations. To address this, our study proposes an innovative ensemble framework for sentiment analysis on social media, integrating Tiny Bert, a lightweight variant of BERT, into a dynamic bootstrap aggregation and stacking ensemble with extreme gradient boosting as a meta-learner. This framework aims to improve sentiment analysis efficiency while managing computational costs effectively. Our experiments demonstrate promising results, achieving an accuracy, precision, recall, and F1-score of 96.34%, 96.39%, 96.34%, and 96.35% respectively. These findings advance sentiment analysis tailored for the dynamic landscape of social media, enabling the identification of key pandemic discourse sentiments and informing public health interventions. The study underscores the importance of AI in extracting insights from COVID-19 tweets, contributing to a deeper understanding of societal impacts and highlighting its role in addressing global health challenges.

The prediction of utterances in two-party conversations is a crucial technology for realizing natural turn-taking between humans and virtual agents. Recently, Voice Activity Projection (VAP) models, capable of a unified approach to various turn-taking events, have gained attention. This study investigates the incorporation of non-verbal features to enhance the performance of VAP models. Our results indicate that the integration of non-verbal features leads to significantly better performance in the VAP models, particularly in aspects of turnshift prediction, overlap prediction, and backchannel prediction. Moreover, we explored the performance of VAP models using only single-speaker features, targeting their implementation in virtual agents. The findings demonstrate the feasibility of adequately predicting turn-taking from the user to the spoken dialogue system. The study also outlines the potential for further performance enhancement by integrating a variety of language and non-verbal features.

Flexible paper answer sheets are widely employed in various examinations due to cost-effectiveness. However, optical marks on flexible paper often encounter challenges such as irregular shapes, non-uniform arrangement, deformation, and scanning noise, rendering automatic optical mark recognition (OMR) a formidable task. This paper introduces a multi-layer feature energy model based on the Bayesian global optimization method. The model seamlessly integrates the localization of individual marks and a division model to effectively address the problem of locating and segmenting optical marks with varying shapes and arrangements, even in the presence of deformation and diverse noise disturbances. Furthermore, the model incorporates the pixel occupancy ratio to achieve optical mark recognition. A comprehensive dataset comprising 31,940 instances of optical marks with diverse shapes and arrangements was meticulously created. This dataset achieved an impressive single-mark localization accuracy of 97.07% and an outstanding recognition accuracy of 97.80%. These results underscore the proposed method's remarkable flexibility and noise resilience in solving multiple-choice recognition problems.

Background: Code s generation tools such as GitHub Copilot have received attention due to their performance in generating code. Generally, a prior analysis of their performance is needed to select new code-generation tools from a list of candidates. Without such analysis, there is a higher risk of selecting an ineffective tool, which would negatively affect software development productivity. Additionally, conducting prior analysis of new code generation tools is often time-consuming. Aim: To use a new code generation tool without prior analysis but with low risk, we propose to evaluate the new tools during software development (i.e., online optimization). Method: We apply the bandit algorithm (BA) approach to help select the best code suggestion or generation tool among a list of candidates. Developers evaluate whether the result of the tool is correct or not. When code generation and evaluation are repeated, the evaluation results are saved. We utilize the stored evaluation results to select the best tool based on the BA approach. In our preliminary analysis, we evaluated five tools with 164 code-generation cases using BA. Result: BA approach selected ChatGPT as the best tool as the evaluation proceeded, and during the evaluation, the average accuracy by BA approach outperformed the second-best performing tool. Our results reveal the feasibility and effectiveness of BA in assisting the selection of best-performing code suggestion or generation tools.

Various clone detection methods have been proposed, with results varying depending on the combination of the methods and hyperparameters used (i.e., configurations). To help select a suitable clone detection configuration, we propose two Bandit Algorithm (BA) based methods that can help evaluate the configurations used dynamically while using detection methods. Our analysis showed that the two proposed methods, the naïve method and BANC (BA considering Negative Cases), identified the best configurations from four used code clone detection methods with high probability.

In future 6G Vehicle-to-Everything (V2X) Network, task offloading of mobile edge computing (MEC) systems will face complex challenges in high mobility, dynamic environment. We herein propose a Multi-Agent Deep Reinforcement Learning algorithm (MADRL) with cloud-edge-vehicle collaborations to address these challenges. Firstly, we build the model of the task offloading problem in the cloud-edge-vehicle system, which meets low-latency, low-energy computing requirements by coordinating the computational resources of connected vehicles and MEC servers. Then, we reformulate this problem as a Markov Decision Process and propose a digital twin-assisted MADRL algorithm to tackle it. This algorithm tackles the problem by treating each connected vehicle as a agent, where the observations of agents are defined as the current local environmental state and global digital twin information. The action space of agents comprises discrete task offloading targets and continuous resource allocation. The objective of this algorithm is to improve overall system performance, taking into account collaborative learning among the agents. Experimental results show that the MADRL algorithm performed well in computational efficiency and energy consumption compared with other strategies.