Young Choon Lee

Miao Hu, Zhenxiao Luo, Amirmohammad Pasdar et al.

Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requirements have been increasingly hindering the traditional cloud computing approach due to high bandwidth consumption and high latency. Edge computing in essence aims to overcome this hindrance by processing most video data making use of edge servers, such as small-scale on-premises server clusters, server-grade computing resources at mobile base stations and even mobile devices like smartphones and tablets; hence, the term edge-based video analytics. However, the actual realization of such analytics requires more than the simple, collective use of edge servers. In this paper, we survey state-of-the-art works on edge-based video analytics with respect to applications, architectures, techniques, resource management, security and privacy. We provide a comprehensive and detailed review on what works, what doesn't work and why. These findings give insights and suggestions for next generation edge-based video analytics. We also identify open issues and research directions.

Zhongli Dong, Young Choon Lee, Albert Y. Zomaya

Blockchain technology is often discussed as if it emerged from nowhere, yet its architectural DNA traces directly to the decentralized computing principles James~N. Gray articulated in 1986. This paper maps the conceptual lineage from Gray's requestor/server model to modern blockchain architectures, showing how his emphasis on modularity, autonomy, data integrity, and standardized communication anticipated the design of systems like Bitcoin and Ethereum, and, more recently, the Web3 movement and Layer-2 scaling architectures. We examine consensus mechanisms, cryptographic foundations, rollup-based Layer-2 protocols, and cross-chain interoperability through this historical lens, identify persistent challenges in scalability and modularity, and outline future directions toward Web4: an intelligent, decentralized internet integrating blockchain, artificial intelligence, and the Internet of Things.

Amirmohammad Pasdar, Young Choon Lee, Seok-Hee Hong

The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services, harmful and dangerous ones have also uncovered the feasible way to penetrate smartphones for malicious behaviors. Thorough application analysis is key to revealing malicious intent and providing more insights into the application behavior for security risk assessments. Such in-depth analysis motivates employing deep neural networks (DNNs) for a set of features and patterns extracted from applications to facilitate detecting potentially dangerous applications independently. This paper presents an Analytic-based deep neural network, Android Malware detection (ADAM), that employs a fine-grained set of features to train feature-specific DNNs to have consensus on the application labels when their ground truth is unknown. In addition, ADAM leverages the transfer learning technique to obtain its adjustability to new applications across smartphones for recycling the pre-trained model(s) and making them more adaptable by model personalization and federated learning techniques. This adjustability is also assisted by federated learning guards, which protect ADAM against poisoning attacks through model analysis. ADAM relies on a diverse dataset containing more than 153000 applications with over 41000 extracted features for DNNs training. The ADAM's feature-specific DNNs, on average, achieved more than 98% accuracy, resulting in an outstanding performance against data manipulation attacks.

Qingye Jiang, Young Choon Lee, Joseph G. Davis et al.

The open source development model has become a paradigm shift from traditional in-house/closed-source software development model, with many successes. Traditionally, open source projects were characterized essentially by their individual volunteer developers. Such tradition has changed significantly with the participation of many organizations in particular. However, there exists a knowledge gap concerning how open source developer communities evolve. In this paper, we present some observations on open source developer communities. In particular, we analyze git repositories of 20 well-known open source projects, with over 3 million commit activities in total. The analysis has been carried out in three respects, productivity, diversity and growth using the Spearman's rank correlation coefficient, diversity index and the Gompertz/logistic curves, respectively. We find out that (a) the Spearman's rank correlation coefficient between active contributors and commit activities reveals how changes in the size of the developer community impacts the productivity of the community; (b) the diversity index of an open source developer community reveals the structure of the community; and (c) the growth of open source developer communities can be described using different phases of growth curves as in many organic matters.

Amirmohammad Pasdar, Zhongli Dong, Young Choon Lee

Blockchain is a form of distributed ledger technology (DLT) where data is shared among users connected over the internet. Transactions are data state changes on the blockchain that are permanently recorded in a secure and transparent way without the need of a third party. Besides, the introduction of smart contracts to the blockchain has added programmability to the blockchain and revolutionized the software ecosystem leading toward decentralized applications (DApps) attracting businesses and organizations to employ this technology. Although promising, blockchains and smart contracts have no access to the external systems (i.e., off-chain) where real-world data and events resides; consequently, the usability of smart contracts in terms of performance and programmability would be limited to the on-chain data. Hence, \emph{blockchain oracles} are introduced to mitigate the issue and are defined as trusted third-party services that send and verify the external information (i.e., feedback) and submit it to smart contracts for triggering state changes in the blockchain. In this paper, we will study and analyze blockchain oracles with regard to how they provide feedback to the blockchain and smart contracts. We classify the blockchain oracle techniques into two major groups such as voting-based strategies and reputation-based ones. The former mainly relies on participants' stakes for outcome finalization while the latter considers reputation in conjunction with authenticity proof mechanisms for data correctness and integrity. We then provide a structured description of patterns in detail for each classification and discuss research directions in the end.