Zobrazeno 1 - 10
of 799
pro vyhledávání: '"Zou, Jia"'
Publikováno v:
Proceedings of the 35th International Conference on Scientific and Statistical Database Management (SSDBM 2023)
Protecting sensitive information in diagnostic data such as logs, is a critical concern in the industrial software diagnosis and debugging process. While there are many tools developed to automatically redact the logs for identifying and removing sen
Externí odkaz:
http://arxiv.org/abs/2409.17535
Autor:
Guan, Hong, Wang, Yancheng, Xie, Lulu, Nag, Soham, Goel, Rajeev, Swamy, Niranjan Erappa Narayana, Yang, Yingzhen, Xiao, Chaowei, Prisby, Jonathan, Maciejewski, Ross, Zou, Jia
Effective fraud detection and analysis of government-issued identity documents, such as passports, driver's licenses, and identity cards, are essential in thwarting identity theft and bolstering security on online platforms. The training of accurate
Externí odkaz:
http://arxiv.org/abs/2408.01690
Lane-changing is a critical maneuver of vehicle driving, and a comprehensive understanding of its impact on traffic is essential for effective traffic management and optimization. Unfortunately, existing studies fail to adequately distinguish the imp
Externí odkaz:
http://arxiv.org/abs/2407.18557
The human-like automatic deductive reasoning has always been one of the most challenging open problems in the interdiscipline of mathematics and artificial intelligence. This paper is the third in a series of our works. We built a neural-symbolic sys
Externí odkaz:
http://arxiv.org/abs/2402.09051
The application of contemporary artificial intelligence techniques to address geometric problems and automated deductive proof has always been a grand challenge to the interdiscipline field of mathematics and artificial Intelligence. This is the four
Externí odkaz:
http://arxiv.org/abs/2402.09047
Autor:
Guan, Hong, Gautier, Summer, Ambrish, Rajan Hari, Wang, Yancheng, Xiao, Chaowei, Yang, Yingzhen, Zou, Jia
It is challenging to select the right privacy-preserving mechanism for federated query processing over multiple private data silos. There exist numerous privacy-preserving mechanisms, such as secure multi-party computing (SMC), approximate query proc
Externí odkaz:
http://arxiv.org/abs/2401.12393
Chiral excitation flows have drawn a lot of attention for their unique unidirectionality. Such flows have been studied in three-node networks with synthetic gauge fields (SGFs), while they are barely realized as the number of nodes increases. In this
Externí odkaz:
http://arxiv.org/abs/2312.02009
Taxonomy, Semantic Data Schema, and Schema Alignment for Open Data in Urban Building Energy Modeling
Urban Building Energy Modeling (UBEM) is a critical tool to provide quantitative analysis on building decarbonization, sustainability, building-to-grid integration, and renewable energy applications on city, regional, and national scales. Researchers
Externí odkaz:
http://arxiv.org/abs/2311.08535
Autor:
Zhang, Xiaokai, Zhu, Na, He, Yiming, Zou, Jia, Huang, Qike, Jin, Xiaoxiao, Guo, Yanjun, Mao, Chenyang, Li, Yang, Zhu, Zhe, Yue, Dengfeng, Zhu, Fangzhen, Wang, Yifan, Huang, Yiwen, Wang, Runan, Qin, Cheng, Zeng, Zhenbing, Xie, Shaorong, Luo, Xiangfeng, Leng, Tuo
This is the first paper in a series of work we have accomplished over the past three years. In this paper, we have constructed a consistent formal plane geometry system. This will serve as a crucial bridge between IMO-level plane geometry challenges
Externí odkaz:
http://arxiv.org/abs/2310.18021
Autor:
Zhou, Lixi, Lin, Qi, Chowdhury, Kanchan, Masood, Saif, Eichenberger, Alexandre, Min, Hong, Sim, Alexander, Wang, Jie, Wang, Yida, Wu, Kesheng, Yuan, Binhang, Zou, Jia
Publikováno v:
EDBT 2024
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently. In this visionary paper, we embark on a comprehensive exploration of represen
Externí odkaz:
http://arxiv.org/abs/2310.04696