185 citations · 349 across the 16 of their papers we have counts for
16 papers
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Jaemin Shin, Hyungjun Yoon, Seungjoo Lee +4
Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features s…
Automation Slicing and Testing for in-App Deep Learning Models
Hao Wu, Yuhang Gong, Xiaopeng Ke +5
Intelligent Apps (iApps), equipped with in-App deep learning (DL) models, are emerging to offer stable DL inference services. However, App marketplaces have trouble auto testing iA…
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Cong Guo, Yuxian Qiu, Jingwen Leng +6
Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the o…
Boosting Mobile CNN Inference through Semantic Memory
Yun Li, Chen Zhang, Shihao Han +4
Human brains are known to be capable of speeding up visual recognition of repeatedly presented objects through faster memory encoding and accessing procedures on activated neurons.…
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
Yuanchun Li, Ziqi Zhang, Bingyan Liu +2
The knowledge of a deep learning model may be transferred to a student model, leading to intellectual property infringement or vulnerability propagation. Detecting such knowledge r…
Dual-side Sparse Tensor Core
Yang Wang, Chen Zhang, Zhiqiang Xie +3
Leveraging sparsity in deep neural network (DNN) models is promising for accelerating model inference. Yet existing GPUs can only leverage the sparsity from weights but not activat…
LEAP: TrustZone Based Developer-Friendly TEE for Intelligent Mobile Apps
Lizhi Sun, Shuocheng Wang, Hao Wu +5
ARM TrustZone is widely deployed on commercial-off-the-shelf mobile devices for secure execution. However, many Apps cannot enjoy this feature because it brings many constraints to…
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
Yuanchun Li, Jiayi Hua, Haoyu Wang +2
Deep learning models are increasingly used in mobile applications as critical components. Unlike the program bytecode whose vulnerabilities and threats have been widely-discussed,…
Dynamic Slicing for Deep Neural Networks
Ziqi Zhang, Yuanchun Li, Yao Guo +2
Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constr…
Fast Hardware-Aware Neural Architecture Search
Li Lyna Zhang, Yuqing Yang, Yuhang Jiang +2
Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresse…
Approximate Query Service on Autonomous IoT Cameras
Mengwei Xu, Xiwen Zhang, Yunxin Liu +3
Elf is a runtime for an energy-constrained camera to continuously summarize video scenes as approximate object counts. Elf's novelty centers on planning the camera's count actions…
Video Analytics with Zero-streaming Cameras
Mengwei Xu, Tiantu Xu, Yunxin Liu +1
Low-cost cameras enable powerful analytics. An unexploited opportunity is that most captured videos remain "cold" without being queried. For efficiency, we advocate for these camer…
A First Look at Deep Learning Apps on Smartphones
Mengwei Xu, Jiawei Liu, Yuanqiang Liu +3
We are in the dawn of deep learning explosion for smartphones. To bridge the gap between research and practice, we present the first empirical study on 16,500 the most popular Andr…
DeepCache: Principled Cache for Mobile Deep Vision
Mengwei Xu, Mengze Zhu, Yunxin Liu +2
We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision. DeepCache benefits model execution efficiency by exploiting temporal locali…
Learning-aided Stochastic Network Optimization with Imperfect State Prediction
Longbo Huang, Minghua Chen, Yunxin Liu
We investigate the problem of stochastic network optimization in the presence of imperfect state prediction and non-stationarity. Based on a novel distribution-accuracy curve predi…
Mitigating Redundant Data Transfers for Mobile Web Applications via App-Specific Cache Space
Xuanzhe Liu, Yun Ma, Shuailiang Dong +4
Redundant transfer of resources is a critical issue for compromising the performance of mobile Web applications (a.k.a., apps) in terms of data traffic, load time, and even energy…