The Continuous Use Intentions and Antecedents of Novice Players in the Social Network Online Games

Social network online games (SNOGs) make players have a positive usage status (such as: entertainment, and enjoyment). Yet, it may also produce a negative usage status (such as: technostress). Users who are new to social network online games are termed novice gamers. Based on the theory of Technological-Personal-Environmental (TPE), this research proposes a framework to explain the adoption of social network online games from the novice players’ perspective, and conducts qualitative in-depth interviews with them to define the key factors for the continuous usage intention on social network online games. This research plans to use online questionnaires and structural equation modeling (SEM) to verify models and hypotheses in order to obtain antecedents of the continuous usage intention for novice players in social network online games and related impact

Application of Serious Game Model on Simulation Training for Decision Makings of Project Management

This study develops a novel serious game foster decision making skills in project management based on role playing. It provides features such as accident event handling, resource allocation, selections of subcontractors, procurement and stock management, adjustment of project scheduling, and performance evaluations to be exercised by user online. In doing so, users can strengthen their project skills by managing a virtual project. Importantly, the proposed game provides users with virtual experiences to implement management and control tasks of a construction project, which is often difficult to offer in conventional training. The proposed system is an online game developed by using advanced web development tools with integrations of MySQL for project data management and MS project for project schedule update on the server side. Users are presented with a virtual project in dynamic scene image depicting a construction site, which changes as the game progresses. There are also images present various construction events. During game play, users can function as a project manager whom is responsible for making various decisions involving accident handling and resource allocation as the virtual construction project progresses. Results of the decisions made are obtained by real-time simulation based on project data, then are visually and interactively replied to the users to understand the subsequent effects of their actions. In this training mode, decision-based simulation can provide realistic and reliable consequences of their decisions. The virtual reality feature of this computer game offers an economic and viable alternative to actual project management experiences

Principles of Increasing the Interactivity of Mobile Applications of Smart Parking

Mobile applications (APPs) are adopted and downloaded widely around the world. Those APPs are those for examples linking to enterprise applications, management information systems, education systems, and healthcare systems. Thus, the importance of potential development APPs is hung. The objective of this study is to elicit design principles for ensuring the interactivity of smart parking APPs to be used especially in the city. This study conducted a systematic review on the past literature of interface design and case studies on seven smart parking APPs on the market in order to develop a design framework for smart parking APPs, which include a list of design principles. Then, four experts of APP design were involved in a series of interviews to improve the readability and usability of the framework. Finally, this study identified 11 principles for improving the interactivity of smart parking APPs, which are useful to the situation when users need to interact with a smart phone urgently in a small-touch-screen environment

Estimating Trust Strength For Supporting Effective Recommendation Services

In the age of information explosion, Internet facilitates product searching and collecting much more convenient for users. However, it is time-consuming and exhausting for users to deal with large amounts of product information. In response, various recommendation approaches have been developed to recommend products that match users’ preferences and requirements. In addition to the well-known collaborative filtering recommendation approach, the trust-based recommendation approach is the emerging one. The reason is that most of online communities allow users to express their trust on other users. Based on the analysis of trust relationships, the trust-based recommendation approach finds out and consults the opinions of more reliable users and therefore makes better recommendations. Existing trust-based recommendation techniques consider all trust relationships in a given trust network equally important and give them the same trust strength. However, in a real-world setting, trust relationships may be of various strengths. In response, in this study, we propose a mechanism for trust strength estimation on the basis of the machine learning approach and estimate the trust strength for each existing trust relationship in a given trust network. To overcome the sparsity of the trust network, we also develop a modified trust propagation method to expand the original trust network. Finally, we perform a series of experiments to demonstrate the performance of our trust-based recommendation approach based on the trust strength estimation mechanism. Our empirical evaluation results show that our proposed approach outperforms our benchmark techniques, i.e., the traditional collaborative filtering approach and the original trust-based one

The North System for Formosa Speech Recognition Challenge 2023

This report provides a concise overview of the proposed North system, which aims to achieve automatic word/syllable recognition for Taiwanese Hakka (Sixian). The report outlines three key components of the system: the acquisition, composition, and utilization of the training data; the architecture of the model; and the hardware specifications and operational statistics. The demonstration of the system has been made public at https://asrvm.iis.sinica.edu.tw/hakka_sixian

SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts

Large language models (LLMs) have gained considerable attention for Artificial Intelligence Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direct adaptation of continuous speech to LLMs that process discrete tokens remains an unsolved challenge, hindering the application of LLMs for speech generation. The advanced speech LMs are in the corner, as that speech signals encapsulate a wealth of information, including speaker and emotion, beyond textual data alone. Prompt tuning has demonstrated notable gains in parameter efficiency and competitive performance on some speech classification tasks. However, the extent to which prompts can effectively elicit generation tasks from speech LMs remains an open question. In this paper, we present pioneering research that explores the application of prompt tuning to stimulate speech LMs for various generation tasks, within a unified framework called SpeechGen, with around 10M trainable parameters. The proposed unified framework holds great promise for efficiency and effectiveness, particularly with the imminent arrival of advanced speech LMs, which will significantly enhance the capabilities of the framework. The code and demos of SpeechGen will be available on the project website: \url{https://ga642381.github.io/SpeechPrompt/speechgen}Comment: Work in progress. The first three authors contributed equall

An Exploration of In-Context Learning for Speech Language Model

Ever since the development of GPT-3 in the natural language processing (NLP) field, in-context learning (ICL) has played an important role in utilizing large language models (LLMs). By presenting the LM utterance-label demonstrations at the input, the LM can accomplish few-shot learning without relying on gradient descent or requiring explicit modification of its parameters. This enables the LM to learn and adapt in a black-box manner. Despite the success of ICL in NLP, little work is exploring the possibility of ICL in speech processing. This study proposes the first exploration of ICL with a speech LM without text supervision. We first show that the current speech LM does not have the ICL capability. With the proposed warmup training, the speech LM can, therefore, perform ICL on unseen tasks. In this work, we verify the feasibility of ICL for speech LM on speech classification tasks.Comment: The first two authors contributed equall