Model-as-a-Service (MaaS): A Survey

Due to the increased number of parameters and data in the pre-trained model exceeding a certain level, a foundation model (e.g., a large language model) can significantly improve downstream task performance and emerge with some novel special abilities (e.g., deep learning, complex reasoning, and human alignment) that were not present before. Foundation models are a form of generative artificial intelligence (GenAI), and Model-as-a-Service (MaaS) has emerged as a groundbreaking paradigm that revolutionizes the deployment and utilization of GenAI models. MaaS represents a paradigm shift in how we use AI technologies and provides a scalable and accessible solution for developers and users to leverage pre-trained AI models without the need for extensive infrastructure or expertise in model training. In this paper, the introduction aims to provide a comprehensive overview of MaaS, its significance, and its implications for various industries. We provide a brief review of the development history of "X-as-a-Service" based on cloud computing and present the key technologies involved in MaaS. The development of GenAI models will become more democratized and flourish. We also review recent application studies of MaaS. Finally, we highlight several challenges and future issues in this promising area. MaaS is a new deployment and service paradigm for different AI-based models. We hope this review will inspire future research in the field of MaaS.Comment: Preprint. 3 figures, 1 table

Towards Correlated Sequential Rules

The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or useful sequential patterns in a large number of sequences. However, simply being aware of utility-eligible patterns is insufficient for making predictions. To compensate for this deficiency, high-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns based on the appearance of premise sequential patterns. It has numerous applications, such as product recommendation and weather prediction. However, the existing algorithm, known as HUSRM, is limited to extracting all eligible rules while neglecting the correlation between the generated sequential rules. To address this issue, we propose a novel algorithm called correlated high-utility sequential rule miner (CoUSR) to integrate the concept of correlation into HUSRM. The proposed algorithm requires not only that each rule be correlated but also that the patterns in the antecedent and consequent of the high-utility sequential rule be correlated. The algorithm adopts a utility-list structure to avoid multiple database scans. Additionally, several pruning strategies are used to improve the algorithm's efficiency and performance. Based on several real-world datasets, subsequent experiments demonstrated that CoUSR is effective and efficient in terms of operation time and memory consumption.Comment: Preprint. 7 figures, 6 table

Metaverse Security and Privacy: An Overview

Metaverse is a living space and cyberspace that realizes the process of virtualizing and digitizing the real world. It integrates a plethora of existing technologies with the goal of being able to map the real world, even beyond the real world. Metaverse has a bright future and is expected to have many applications in various scenarios. The support of the Metaverse is based on numerous related technologies becoming mature. Hence, there is no doubt that the security risks of the development of the Metaverse may be more prominent and more complex. We present some Metaverse-related technologies and some potential security and privacy issues in the Metaverse. We present current solutions for Metaverse security and privacy derived from these technologies. In addition, we also raise some unresolved questions about the potential Metaverse. To summarize, this survey provides an in-depth review of the security and privacy issues raised by key technologies in Metaverse applications. We hope that this survey will provide insightful research directions and prospects for the Metaverse's development, particularly in terms of security and privacy protection in the Metaverse.Comment: IEEE BigData 2022. 10 pages, 2 figure

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Interaction in Metaverse: A Survey

Human-computer interaction (HCI) emerged with the birth of the computer and has been upgraded through decades of development. Metaverse has attracted a lot of interest with its immersive experience, and HCI is the entrance to the Metaverse for people. It is predictable that HCI will determine the immersion of the Metaverse. However, the technologies of HCI in Metaverse are not mature enough. There are many issues that we should address for HCI in the Metaverse. To this end, the purpose of this paper is to provide a systematic literature review on the key technologies and applications of HCI in the Metaverse. This paper is a comprehensive survey of HCI for the Metaverse, focusing on current technology, future directions, and challenges. First, we provide a brief overview of HCI in the Metaverse and their mutually exclusive relationships. Then, we summarize the evolution of HCI and its future characteristics in the Metaverse. Next, we envision and present the key technologies involved in HCI in the Metaverse. We also review recent case studies of HCI in the Metaverse. Finally, we highlight several challenges and future issues in this promising area.Comment: Preprint. 3 figures, 3 table

Web 3.0: The Future of Internet

With the rapid growth of the Internet, human daily life has become deeply bound to the Internet. To take advantage of massive amounts of data and information on the internet, the Web architecture is continuously being reinvented and upgraded. From the static informative characteristics of Web 1.0 to the dynamic interactive features of Web 2.0, scholars and engineers have worked hard to make the internet world more open, inclusive, and equal. Indeed, the next generation of Web evolution (i.e., Web 3.0) is already coming and shaping our lives. Web 3.0 is a decentralized Web architecture that is more intelligent and safer than before. The risks and ruin posed by monopolists or criminals will be greatly reduced by a complete reconstruction of the Internet and IT infrastructure. In a word, Web 3.0 is capable of addressing web data ownership according to distributed technology. It will optimize the internet world from the perspectives of economy, culture, and technology. Then it promotes novel content production methods, organizational structures, and economic forms. However, Web 3.0 is not mature and is now being disputed. Herein, this paper presents a comprehensive survey of Web 3.0, with a focus on current technologies, challenges, opportunities, and outlook. This article first introduces a brief overview of the history of World Wide Web as well as several differences among Web 1.0, Web 2.0, Web 3.0, and Web3. Then, some technical implementations of Web 3.0 are illustrated in detail. We discuss the revolution and benefits that Web 3.0 brings. Finally, we explore several challenges and issues in this promising area.Comment: ACM Web Conference 202