Browsing by Author "Computer Information and Graphics Technology, Purdue School of Engineering and Technology"

Now showing 1 - 4 of 4
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    An Adaptive and Modular Blockchain Enabled Architecture for a Decentralized Metaverse
    (IEEE, 2024-04) Cheng, Ye; Guo, Yihao; Xu, Minghui; Hu, Qin; Yu, Dongxiao; Cheng, Xiuzhen; Computer Information and Graphics Technology, Purdue School of Engineering and Technology
    A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse have led to excessive consumption of on-chain resources, raising the threshold for ordinary users to join, thereby losing the human-centered design. Therefore, we propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse to address these issues. The solution includes an adaptive consensus/ledger protocol based on a modular blockchain, which can effectively adapt to the ever-changing scenarios of the metaverse, reduce resource consumption, and provide a secure and reliable interactive environment. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources. Users can establish a temporary trusted environment and rent others’ NFR to meet their computing needs. Finally, we simulate and test our solution based on XuperChain, and the experimental results prove the feasibility of our design.
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    Maximum Density Divergence for Domain Adaptation
    (IEEE, 2021) Li, Jingjing; Chen, Erpeng; Ding, Zhengming; Zhu, Lei; Lu, Ke; Shen, Heng Tao; Computer Information and Graphics Technology, Purdue School of Engineering and Technology
    Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence ("match" in ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations.
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    Methods of Current Knowledge Teaching on the Cybersecurity Example
    (MDPI, 2022-10-22) Nyemkova, Elena; Justice, Connie; Liaskovska, Solomiia; Lakh, Yuriy; Computer Information and Graphics Technology, Purdue School of Engineering and Technology
    Teaching of modern cybersecurity specialists should be up to date and use the newest methods and methodologies in universities as the IT industry is rapidly growing and constantly changing. A good idea is to use methods of management in IT companies as methods for current knowledge teaching of university students. It is also worth engaging students not only in educational international projects but the research projects as well. This work analyzes the method for teaching students, and the Scrum methodology was selected and implemented for educational and research projects. Students participated in both projects, however, Scrum models should be different for them and this is illustrated in the paper. The visualization of collected statistical data of the performed educational project illustrated distributions of students by specialization and by marks. The distributions by marks showed that using the Scrum model for the teaching course significantly increases the marks compared with the average level marks of the students in their specializations.
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    Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
    (IEEE, 2021) Dong, Jiahua; Cong, Yang; Sun, Gan; Yang, Yunsheng; Xu, Xiaowei; Ding, Zhengming; Computer Information and Graphics Technology, Purdue School of Engineering and Technology
    Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most existing methods require effective prior and constraints to explore the intrinsic lesions characterization, which only generates incorrect and rough prediction; 2) they neglect the underlying semantic dependencies among weakly-labeled target enteroscopy diseases and fully-annotated source gastroscope lesions, while forcefully utilizing untransferable dependencies leads to the negative performance. To tackle above issues, we propose a new weakly-supervised lesions transfer framework, which can not only explore transferable domain-invariant knowledge across different datasets, but also prevent the negative transfer of untransferable representations. Specifically, a Wasserstein quantified transferability framework is developed to highlight wide-range transferable contextual dependencies, while neglecting the irrelevant semantic characterizations. Moreover, a novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples. It inhibits the enormous deviation of false pseudo pixel labels under the self-supervision manner. Afterwards, dynamically-searched feature centroids are aligned to narrow category-wise distribution shift. Comprehensive theoretical analysis and experiments show the superiority of our model on the endoscopic dataset and several public datasets.