Enhancing Source-Location Privacy in Sensor Network Routing
- L. Zhang
- Computer ScienceIEEE International Conference on Distributed…
- 6 June 2005
This paper provides a formal model for the source-location privacy problem in sensor networks and examines the privacy characteristics of different sensor routing protocols, and devised new techniques to enhance source- location privacy that augment these routing protocols.
Stereoscopic image generation based on depth images for 3D TV
Results are presented to show that the proposed system provides an improvement in image quality of stereoscopic virtual views while maintaining reasonably good depth quality.
Critical success factors of enterprise resource planning systems implementation success in China
- L. Zhang, Matthew K.O. Lee, Zhe Zhang, Probir Banerjee
- Business, Computer Science36th Annual Hawaii International Conference on…
- 6 January 2003
This study attempts to study critical success factors affecting enterprise resource planning (ERP) systems implementation success in China with focus on both generic and unique factors.
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
- Xiangyu Zhao, L. Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, Dawei Yin
- Computer ScienceKnowledge Discovery and Data Mining
- 19 February 2018
This paper model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedback.
Deep reinforcement learning for page-wise recommendations
- Xiangyu Zhao, Long Xia, L. Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang
- Computer ScienceACM Conference on Recommender Systems
- 7 May 2018
A principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page is proposed and a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users is proposed.
An End-to-End Measurement of Certificate Revocation in the Web's PKI
- Yabing Liu, Will Tome, Christo Wilson
- Computer ScienceACM/SIGCOMM Internet Measurement Conference
- 28 October 2015
A close look at certificate revocations in the Web's PKI is taken, finding that a surprisingly large fraction of the certificates served have been revoked, and that obtaining certificate revocation information can often be expensive in terms of latency and bandwidth for clients.
Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM
- Guangming Zhu, L. Zhang, Peiyi Shen, Juan Song
- Computer ScienceIEEE Access
- 17 March 2017
This paper presents a multimodal gesture recognition method based on 3-D convolution and convolutional long-short-term-memory (LSTM) networks and finds that it can be considered as an optional skill to prevent overfitting when no pre-trained models exist.
Dynamic Service Provisioning in Elastic Optical Networks With Hybrid Single-/Multi-Path Routing
- Zuqing Zhu, W. Lu, L. Zhang, N. Ansari
- Computer ScienceJournal of Lightwave Technology
- 1 January 2013
The simulation results have demonstrated that the proposed HSMR schemes can effectively reduce the bandwidth blocking probability (BBP) of dynamic RMSA, as compared to two benchmark algorithms that use single-path routing and split spectrum.
Protecting Receiver-Location Privacy in Wireless Sensor Networks
- Y. Jian, Shigang Chen, Zhan Zhang, L. Zhang
- Computer ScienceIEEE INFOCOM - 26th IEEE International…
- 1 May 2007
A location-privacy routing protocol (LPR) that is easy to implement and provides path diversity combined with fake packet injection is proposed, able to minimize the traffic direction information that an adversary can retrieve from eavesdropping.
Deep Reinforcement Learning for List-wise Recommendations
- Xiangyu Zhao, L. Zhang, Zhuoye Ding, Dawei Yin, Y. Zhao, Jiliang Tang
- Computer ScienceArXiv
- 30 December 2017
This paper proposes a novel recommender system with the capability of continuously improving its strategies during the interactions with users and introduces an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online.
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