Liusheng Huang

University of Science and Technology of China

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Co-Authors

Hongli Xu

University of Science and Technology of China

188

Wei Yang

University of Science and Technology of China

128

He Huang

Soochow University

Miaomiao Tian

Beijing Institute of Graphic Communication

Zhili Chen

University of Utah

Chunming Qiao

University at Buffalo, State University of New York

Hongli Xu

University of Science and Technology of China

Hong Zhong

Anhui University

Mingjun Xiao

University of Science and Technology Beijing

Wei Yang

University of Science and Technology of China

Cooperative Institutions

University of Science and Technology of China

293

Chinese Academy of Sciences

124

Suzhou University of Science and Technology

Tsinghua University

University of Chinese Academy of Sciences

Peking University

Huazhong University of Science and Technology

Shanghai Jiao Tong University

Wuhan University

Sichuan University

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DopGest: Dual-Frequency Based Ultrasonic Gesture Recognition

Qi Liu Wei Yang Yang Xu Yahui Hu Qijian He

In-air gesture recognition is becoming a popular means of interacting with terminal devices, such as smart phones, tablets and laptops. To implement in-air gesture recognition, many methods have been researched, such as computer visionbased, RF-based, WiFi-based and ultrasonic-based. Compared to other approaches, the ultrasonic-based method shows superiorities in low-cost, robustness and so on. In this paper, we construct a dual-frequency based ultrasonic gesture recognition system called DopGest, which utilizes the Doppler Effect to recognize gestures. Our system is implemented completely on a laptop without any additional equipments, and we use two speakers and one microphone already embedded in laptop as sensors. Most of ultrasonic-based methods exploit single tone, which limit the type of gestures. For instance, they can not recognize horizontal swipe gestures. On the contrary, our system can recognize swipe left and swipe right gestures by using two high tones (18 kHz). In our system, DopGest generates two different high frequency tones from two speakers and detects Doppler Effect caused by hand movements, then it extracts features from Doppler shifts of these two high tones, since laptops generally contain at least two speakers. Compared to computervision based method, our system can work under any light conditions without extra hardware devices and with low power consumption. Experiments show that our system can achieve an average classification accuracy of 88.9%.

10.1109/smartworld.2018.00084

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Citations (4)

Cooperative optimal pricing for stochastic access control in overlaid radio access networks

Telecommunication Systems (2015)

Haoran Zhang Liusheng Huang Hongli Xu Quan Sun

Blocking (statistics)

Dynamic Pricing

Call blocking

Call Admission Control

10.1007/s11235-014-9917-0

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Citations (1)

A Novel Multiparty Quantum Secret Sharing Scheme of Secure Direct Communication Based on Bell States and Bell Measurements

Chinese Physics Letters (2011)

Runhua Shi Liusheng Huang Wei Yang Hong Zhong

We present a novel quantum secret sharing scheme of secure direct communication and analyze its security. This scheme takes Einstein—Podolsky—Rosen (EPR) pairs in Bell states as quantum resources. In order to obtain the direct communication message, all agents only need to perform Bell measurements, not to perform any local unitary operation. The total efficiency in this scheme approaches 100% as the classical information exchanged is unnecessary except for the eavesdropping checks.

Eavesdropping

Quantum teleportation

EPR paradox

10.1088/0256-307x/28/5/050303

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Citations (5)

Breaking A Proxy Signature Scheme From Lattices.

International journal of network security (2012)

Miaomiao Tian Liusheng Huang

Proxy (statistics)

Signature (topology)

Source

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Citations (7)

Privacy-preserving statistical quantitative rules mining

Scalable Information Systems (2007)

Weiwei Jing Liusheng Huang Yifei Yao Weijiang Xu

This paper considers the problem of mining Statistical Quantitative rules (SQ rules) without revealing the private information of parties who compute jointly and share distributed data. Based on several basic tools for Privacy-Preserving Data Mining (PPDM), including secure sum, secure mean and secure frequent itemsets, this paper presents two algorithms to accomplish privacy-preserving SQ rules mining over horizontally partitioned data. One is to securely compute confidence intervals for testing the significance of rules; the other is to securely discover SQ rules.

Private information retrieval

10.5555/1366804.1366892

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Citations (0)

Reinforcement Learning-based Adversarial Attacks on Object Detectors using Reward Shaping

Zhenbo Shi Wei Yang Zhenbo Xu Zhidong Yu Liusheng Huang

In the field of object detector attacks, previous methods primarily rely on fixed gradient optimization or patch-based cover techniques, often leading to suboptimal attack performance and excessive distortions. To address these limitations, we propose a novel attack method, Interactive Reinforcement-based Sparse Attack (IRSA), which employs Reinforcement Learning (RL) to discover the vulnerabilities of object detectors and systematically generate erroneous results. Specifically, we formulate the process of seeking optimal margins for adversarial examples as a Markov Decision Process (MDP). We tackle the RL convergence difficulty through innovative reward functions and a composite optimization method for effective and efficient policy training. Moreover, the perturbations generated by IRSA are more subtle and difficult to detect while requiring less computational effort. Our method also demonstrates strong generalization capabilities against various object detectors. In summary, IRSA is a refined, efficient, and scalable interactive, iterative, end-to-end algorithm.

Markov random field

10.1145/3581783.3612304

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Citations (1)

Cryptanalysis and Improvement of a Certificateless Multi-proxy Signature Scheme

Fundamenta Informaticae (2014)

Miaomiao Tian Wei Yang Liusheng Huang

Certificateless cryptography is a new type of public key cryptography, which removes the certificate management problem in traditional public key cryptography and the key escrow problem in identity-based public key cryptography. Multi-proxy signature

Proxy (statistics)

Signature (topology)

10.3233/fi-2013-976

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Citations (3)

Routing and Photon Source Provisioning in Quantum Key Distribution Networks

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications (2024)

Xu Sun Yangming Zhao Liusheng Huang Chunming Qiao

Quantum key distribution

Provisioning

10.1109/infocom52122.2024.10621389

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Privacy-preserving LOF outlier detection

Knowledge and Information Systems (2013)

Lu Li Liusheng Huang Wei Yang Xiaohui Yao An Liu

Local outlier factor

10.1007/s10115-013-0692-0

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Citations (18)

Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization

arXiv (Cornell University) (2024)

Luyao Gao Jianchun Liu Hongli Xu Xu Sun Qianpiao Ma

End-cloud collaboration offers a promising strategy to enhance the Quality of Service (QoS) in DNN inference by offloading portions of the inference workload from end devices to cloud servers. Despite the potential, the complex model architectures and dynamic network conditions will introduce numerous bubbles (\ie, idle waiting time) in pipeline execution, resulting in inefficient resource utilization and degraded QoS. To address these challenges, we introduce a novel framework named COACH, designed for near bubble-free pipeline collaborative inference, thereby achieving low inference latency and high system throughput. Initially, COACH employs an \textit{offline} component that utilizes an efficient recursive divide-and-conquer algorithm to optimize both model partitioning and transmission quantization, aiming to minimize the occurrence of pipeline bubbles. Subsequently, the \textit{online} component in COACH employs an adaptive quantization adjustment and a context-aware caching strategy to further stabilize pipeline execution. Specifically, COACH analyzes the correlation between intermediate data and label semantic centers in the cache, along with its influence on the quantization adjustment, thereby effectively accommodating network fluctuations. Our experiments demonstrate the efficacy of COACH in reducing inference latency and enhancing system throughput. Notably, while maintaining comparable accuracy, COACH achieves up to 1.7x faster inference and 2.1x higher system throughput than baselines.

10.48550/arxiv.2501.12388

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