Many research highlights the incorporation of various dimensions in stock price prediction, encompassing fundamental, technical, float, and news/community-based information analysis. However, differing opinions may hinder rapid investment decision-making. Therefore, this research aims to utilize stock price trend prediction models to assist investors in making more effective decisions upon receiving news/community-based information since it is more accessible and comprehensible to many investors. This research gathers stock and technical data from Yahoo Finance!, fundamental data from Macro Trends, text data for employee reviews from Glassdoor for various companies in the analysis. The study employs Random Forest, Extreme Gradient Boosting, Recurrent Neural Network, and Long Short-Term Memory to develop stock price trend prediction models, aiming to provide future guidance for investors' decision-making processes.

In this paper, we present an asynchronous display method, coined image queue, which allows operators to search through a large amount of data gathered by autonomous robot teams. We discuss and investigate the advantages of an asynchronous display for foraging tasks with emphasis on Urban Search and Rescue. The image queue approach mines video data to present the operator with a relevant and comprehensive view of the environment in order to identify targets of interest such as injured victims. It fills the gap for comprehensive and scalable displays to obtain a network-centric perspective for UGVs. We compared the image queue to a traditional synchronous display with live video feeds and found that the image queue reduces errors and operator's workload. Furthermore, it disentangles target detection from concurrent system operations and enables a call center approach to target detection. With such an approach we can scale up to very large multi-robot systems gathering huge amounts of data that is then distributed to multiple operators.

Using classical methods the time course of workload over a work session is rarely observed. Subjective measures of necessity are for the entire session rather than instantaneous. While this distinction makes little difference to researchers wanting to compare task difficulty, it is important to designers of systems that wish to schedule operator attention because they must predict workload. The present study provides an experimental setting in which intervals of continuous work and uninterrupted rest can be precisely controlled. No differences in probability of error or latency of response were found for the length of break/rest intervals or their location in a sequence of intervals. The local utilization within a break/work interval (ratio of work/(work + rest)), however, was significant, suggesting that utilization effects are strictly local and that simple algorithms could be used to optimize human-system performance for such tasks.

Small and Medium-sized Enterprises (SMEs) play a vital and pervasive role in the current development of Taiwan’s economy. Recently, the application of Enterprise Resource Planning (ERP) systems have enabled large enterprises to have direct contact with their clients via e-commerce technology, which has led to even fiercer competition among the SMEs. This study develops and tests a theoretical model including critical factors which influence ERP adoption in Taiwan’s SMEs. Specifically, four dimensions, including CEO characteristics, innovative technology characteristics, organizational characteristics, and environmental characteristics, are empirically examined. The results of a mail survey indicate that the CEO’s attitude towards information technology (IT) adoption, the CEO’s IT knowledge, the employees’ IT skills, business size, competitive pressure, cost, complexity, and compatibility are all important determinants in ERP adoption for SMEs. The authors’ results are compared with research on IT adoption in SMEs based in Singapore and the United States, while implications of the results are also discussed.