Xin Wei

Xin Wei

PhD student in Old Dominion University

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy

Published in International Joint Conferences on Artificial Intelligence (IJCAI), 2019

Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget feasible incentive mechanism to learn users’ cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this general problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users’ cost distributions. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works.

Recommended citation: Pengzhan Zhou, Xin Wei, Cong Wang, Yuanyuan Yang. " Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy." International Joint Conferences on Artificial Intelligence (IJCAI), 2019.

k-Level Truthful Incentivizing Mechanism and Generalized k-MAB Problem

Published in IEEE Transactions on Computers (TC), 2021

Multi-armed bandits problem has been widely utilized in economy-related areas. Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users’ cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users’ cost distributions. We further extend the problem to a more generalized k-MAB problem by removing the contextual information of difficulties. CUE-UCB algorithm is proposed to address the online advertisement problem for multi-platforms. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works in sharing economy, and up to 175% increment of utility for online advertising.

Recommended citation: Pengzhan Zhou, Xin Wei, Cong Wang, Yuanyuan Yang. " k-Level Truthful Incentivizing Mechanism and Generalized k-MAB Problem." IEEE Transactions on Computers (TC), 2021.

Visual descriptor extraction from patent figure captions: a case study of data efficiency between BiLSTM and transformer

Published in Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries, 2022

Technical drawings used for illustrating designs are ubiquitous in patent documents, especially design patents. Different from natural images, these drawings are usually made using black strokes with little color information, making it challenging for models trained on natural images to recognize objects. To facilitate indexing and searching, we propose an effective and efficient visual descriptor model that extracts object names and aspects from patent captions to annotate benchmark patent figure datasets. We compared two state-of-the-art named entity recognition (NER) models and found that with a limited number of annotated samples, the BiLSTM-CRF model outperforms the Transformer model by a significant margin, achieving an overall F1=96.60%. We further conducted a data efficiency study by varying the number of training samples and found that BiLSTM consistently beats the transformer model on our task. The proposed model is used to annotate a benchmark patent figure dataset.

Recommended citation: Xin Wei, Jian Wu, Kehinde Ajayi, Diane Oyen. " Visual descriptor extraction from patent figure captions: a case study of data efficiency between BiLSTM and transformer." Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries, 2022.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.