Zobrazeno 1 - 10
of 349
pro vyhledávání: '"Winslett, Marianne"'
Modern power grids are undergoing significant changes driven by information and communication technologies (ICTs), and evolving into smart grids with higher efficiency and lower operation cost. Using ICTs, however, comes with an inevitable side effec
Externí odkaz:
http://arxiv.org/abs/2405.13965
We show that as a side effect of building code requirements, almost all commercial buildings today are vulnerable to a novel data exfiltration attack, even if they are air-gapped and secured against traditional attacks. The new attack uses vibrations
Externí odkaz:
http://arxiv.org/abs/2206.12944
Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency. However, in order to map deep neural network (DNN) based object detection models to edge devices, one typically needs to compress such
Externí odkaz:
http://arxiv.org/abs/2110.13713
Autor:
Chen, Yao, Long, Xin, He, Jiong, Chen, Yuhang, Tan, Hongshi, Zhang, Zhenxiang, Winslett, Marianne, Chen, Deming
The pervasive adoption of Deep Learning (DL) and Graph Processing (GP) makes it a de facto requirement to build large-scale clusters of heterogeneous accelerators including GPUs and FPGAs. The OpenCL programming framework can be used on the individua
Externí odkaz:
http://arxiv.org/abs/2005.08466
Autor:
Ganesh, Prakhar, Chen, Yao, Lou, Xin, Khan, Mohammad Ali, Yang, Yin, Sajjad, Hassan, Nakov, Preslav, Chen, Deming, Winslett, Marianne
Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and, thus, are too resource-hungry and computation-intensive
Externí odkaz:
http://arxiv.org/abs/2002.11985
Autor:
Li, Zijian, Cai, Ruichu, Chai, Kok Soon, Ng, Hong Wei, Vu, Hoang Dung, Winslett, Marianne, Fu, Tom Z. J., Xu, Boyan, Yang, Xiaoyan, Zhang, Zhenjie
Data-driven models are becoming essential parts in modern mechanical systems, commonly used to capture the behavior of various equipment and varying environmental characteristics. Despite the advantages of these data-driven models on excellent adapti
Externí odkaz:
http://arxiv.org/abs/1910.06761
Elasticity is highly desirable for stream processing systems to guarantee low latency against workload dynamics, such as surges in data arrival rate and fluctuations in data distribution. Existing systems achieve elasticity following a resource-centr
Externí odkaz:
http://arxiv.org/abs/1711.01046
Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or
Externí odkaz:
http://arxiv.org/abs/1502.07526
Autor:
Fu, Tom Z. J., Ding, Jianbing, Ma, Richard T. B., Winslett, Marianne, Yang, Yin, Zhang, Zhenjie
In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given
Externí odkaz:
http://arxiv.org/abs/1501.03610
Autor:
Ding, Jianbing, Fu, Tom Z. J., Ma, Richard T. B., Winslett, Marianne, Yang, Yin, Zhang, Zhenjie, Chao, Hongyang
A cloud-based data stream management system (DSMS) handles fast data by utilizing the massively parallel processing capabilities of the underlying platform. An important property of such a DSMS is elasticity, meaning that nodes can be dynamically add
Externí odkaz:
http://arxiv.org/abs/1501.03619