arXiv subjects

Benyuan Liu

Publications and source records attributed to Benyuan Liu.

At least 37 records · Page 2Linked to original sources

arXiv2019

Multi-Stream Single Shot Spatial-Temporal Action Detection

We present a 3D Convolutional Neural Networks (CNNs) based single shot detector for spatial-temporal action detection tasks. Our model includes: (1) two short-term appearance and motion streams, with single RGB and optical flow image input separately, in order to capture the spatial and temporal information for the current frame; (2) two long-term 3D ConvNet based stream, working on sequences of continuous RGB and optical flow images to capture the context from past frames. Our model achieves strong performance for action detection in video and can be easily integrated into any current two-stream action detection methods. We report a frame-mAP of 71.30% on the challenging UCF101-24 actions dataset, achieving the state-of-the-art result of the one-stage methods. To the best of our knowledge, our work is the first system that combined 3D CNN and SSD in action detection tasks.

cs.CV↗

arXiv2018

A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System

We introduce a game-theoretic framework to ex- plore revenue sharing in an Edge-Cloud computing system, in which computing service providers at the edge of the Internet (edge providers) and computing service providers at the cloud (cloud providers) co-exist and collectively provide computing resources to clients (e.g., end users or applications) at the edge. Different from traditional cloud computing, the providers in an Edge-Cloud system are independent and self-interested. To achieve high system-level efficiency, the manager of the system adopts a task distribution mechanism to maximize the total revenue received from clients and also adopts a revenue sharing mechanism to split the received revenue among computing servers (and hence service providers). Under those system-level mechanisms, service providers attempt to game with the system in order to maximize their own utilities, by strategically allocating their resources (e.g., computing servers). Our framework models the competition among the providers in an Edge-Cloud system as a non-cooperative game. Our simulations and experiments on an emulation system have shown the existence of Nash equilibrium in such a game. We find that revenue sharing mechanisms have a significant impact on the system-level efficiency at Nash equilibria, and surprisingly the revenue sharing mechanism based directly on actual contributions can result in significantly worse system efficiency than Shapley value sharing mechanism and Ortmann proportional sharing mechanism. Our framework provides an effective economics approach to understanding and designing efficient Edge-Cloud computing systems.

cs.GT↗

arXiv2017

User Tolerance and Self-Regulation in Congestion Control

In response to poor quality of service (QoS), users self-regulate, i.e. they immediately release bandwidth and abandon network. However, there are studies that show users are willing to tolerate poor QoS for some time to evaluate if network performance will improve before abandoning the network. In this paper, we investigate how users willingness to wait for improved QoS may influence network activities, such as network pricing, bandwidth allocation, network revenue, and performance. We develop and employ a self-regulation model that includes user evaluation of QoS before deciding to abandon or stay in the network. This model considers these two factors: user tolerance of low QoS and the price per unit a user is willing to pay. Our investigation uncovers a double edged problem network may be populated with lower paying users, who are also dissatisfied. These lower paying users drive the price higher than the price produced by conventional solution for network congestion. This leads to our proposal for a market informed congestion control scheme, where network resolves congestion based on user profile that is defined by their ability to pay and demand for bandwidth.

cs.NI↗

arXiv2017

Pricing and Revenue Sharing for Secondary Data Market

Recent advances in technology enable public or commercial establishments and individual data plan subscribers to operate as Wi-Fi providers, offering Internet access. However, the model of a monthly flat service fee charged by ISP to establishment or individual WFPs offers very little incentives for ISP to provide additional bandwidth to WFPs customers. Thus, these users may experience insufficient bandwidth when there is high demand for bandwidth. In addition, ISP even discourages individual subscribers to provide Internet access through their smartphone because such practices may cause market saturation. To address this, in this paper we propose a dynamic pricing scheme and a revenue sharing mechanism that provide incentives for ISP to support establishment and individual Wi-Fi providers offering Internet access to users. Our revenue sharing model is based on Shapley value mechanism. Importantly, our proposed revenue sharing mechanism captures the power negotiation between ISP and Wi-Fi providers, and how shifts in the power balance between the two entities influence revenue division. Specifically, the model assures that the party who contributes more receives a higher portion of the revenue. In addition to that, the division of revenue eventually converges to a percentage value.

cs.NI↗

arXiv2015

Bayesian De-quantization and Data Compression for Low-Energy Physiological Signal Telemonitoring

We address the issue of applying quantized compressed sensing (CS) on low-energy telemonitoring. So far, few works studied this problem in applications where signals were only approximately sparse. We propose a two-stage data compressor based on quantized CS, where signals are compressed by compressed sensing and then the compressed measurements are quantized with only 2 bits per measurement. This compressor can greatly reduce the transmission bit-budget. To recover signals from underdetermined, quantized measurements, we develop a Bayesian De-quantization algorithm. It can exploit both the model of quantization errors and the correlated structure of physiological signals to improve the quality of recovery. The proposed data compressor and the recovery algorithm are validated on a dataset recorded on 12 subjects during fast running. Experiment results showed that an averaged 2.596 beat per minute (BPM) estimation error was achieved by jointly using compressed sensing with 50% compression ratio and a 2-bit quantizer. The results imply that we can effectively transmit n bits instead of n samples, which is a substantial improvement for low-energy wireless telemonitoring.

cs.IT↗

arXiv2015

Auction-based Incentive Mechanisms for Dynamic Mobile Ad-Hoc Crowd Service

We investigate a type of emerging user-assisted mobile applications or services, referred to as Dynamic Mobile Ad-hoc Crowd Service (DMACS), such as collaborative streaming via smartphones or location privacy protection through a crowd of smartphone users. Such services are provided and consumed by users carrying smart mobile devices (e.g., smartphones) who are in close proximity of each other (e.g., within Bluetooth range). Users in a DMACS system dynamically arrive and depart over time, and are divided into multiple possibly overlapping groups according to radio range constraints. Crucial to the success of such systems is a mechanism that incentivizes users' participation and ensures fair trading. In this paper, we design a multi-market, dynamic double auction mechanism, referred to as M-CHAIN, and show that it is truthful, feasible, individual-rational, no-deficit, and computationally efficient. The novelty and significance of M-CHAIN is that it addresses and solves the fair trading problem in a multi-group or multi-market dynamic double auction problem which naturally occurs in a mobile wireless environment. We demonstrate its efficiency via simulations based on generated user patterns (stochastic arrivals, random market clustering of users) and real-world traces.

cs.NI↗

arXiv2015

TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

Heart rate monitoring using wrist-type photoplethysmographic (PPG) signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this work, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the average absolute error of heart rate estimation was 2.34 beat per minute (BPM), and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.992. This framework is of great values to wearable devices such as smart-watches which use PPG signals to monitor heart rate for fitness.

cs.CY↗

arXiv2015

Reciprocal Recommendation System for Online Dating

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.

cs.SI↗

arXiv2014

QoE Support for Multi-Layered Multimedia Applications

Congestion control protocol and bandwidth allocation problems are often formulated into Network Utility Maximization (NUM) framework. Existing solutions for NUM generally focus on single-layered applications. As applications such as video streaming grow in importance and popularity, addressing user utility function for these multi-layered multimedia applications in NUM formulation becomes vital. In this paper, we propose a new multi-layered user utility model that leverages on studies of human visual perception and quality of experience (QoE) from the fields of computer graphics and human computer interaction (HCI). Using this new utility model to investigate network activities, we demonstrate that solving NUM with multi-layered utility is intractable, and that rate allocation and network pricing may oscillate due to user behavior specific to multi-layered applications. To address this, we propose a new approach for admission control to ensure quality of service (QoS) and quality of experience (QoE).

cs.NI↗

arXiv2014

Py-oopsi: the python implementation of the fast-oopsi algorithm

Fast-oopsi was developed by Joshua Vogelstein in 2009, which is now widely used to extract neuron spike activities from calcium fluorescence signals. Here, we propose detailed implementation of the fast-oopsi algorithm in python programming language. Some corrections are also made to the original fast-oopsi paper.

cs.CE↗

arXiv2014

Blind Recognition of Touched Keys: Attack and Countermeasures

In this paper, we introduce a novel computer vision based attack that discloses inputs on a touch enabled device, while the attacker cannot see any text or popups from a video of the victim tapping on the touch screen. In the attack, we use the optical flow algorithm to identify touching frames where the finger touches the screen surface. We innovatively use intersections of detected edges of the touch screen to derive the homography matrix mapping the touch screen surface in video frames to a reference image of the virtual keyboard. We analyze the shadow formation around the fingertip and use the k-means clustering algorithm to identify touched points. Homography can then map these touched points to keys of the virtual keyboard. Our work is substantially different from existing work. We target password input and are able to achieve a high success rate. We target scenarios like classrooms, conferences and similar gathering places and use a webcam or smartphone camera. In these scenes, single-lens reflex (SLR) cameras and high-end camcorders used in related work will appear suspicious. To defeat such computer vision based attacks, we design, implement and evaluate the Privacy Enhancing Keyboard (PEK) where a randomized virtual keyboard is used to input sensitive information.

cs.CR↗

arXiv2014

Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation

Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signals. Experiments on real-world fetal ECG signals and epilepsy EEG signals showed that the proposed algorithm has good balance between speed and data reconstruction fidelity when compared to state-of-the-art CS algorithms. Further, we implemented the CS-based compression procedure and a low-power compression procedure based on a wavelet transform in Filed Programmable Gate Array (FPGA), showing that the CS-based compression can largely save energy and other on-chip computing resources.

cs.IT↗

arXiv2014

Online Dating Recommendations: Matching Markets and Learning Preferences

Recommendation systems for online dating have recently attracted much attention from the research community. In this paper we proposed a two-side matching framework for online dating recommendations and design an LDA model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large online dating website shows that two-sided matching improves significantly the rate of successful matches by as much as 45%. Finally, using simulated matchings we show that the the LDA model can correctly capture user preferences.

cs.SI↗

arXiv2014

Who is Dating Whom: Characterizing User Behaviors of a Large Online Dating Site

Online dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' dating preferences in order to make better recommendations on potential dates. The message sending and replying actions of a user are strong indicators for what he/she is looking for in a potential date and reflect the user's actual dating preferences. We study how users' online dating behaviors correlate with various user attributes using a large real-world dateset from a major online dating site in China. Many of our results on user messaging behavior align with notions in social and evolutionary psychology: males tend to look for younger females while females put more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. In addition, we observe that the geographic distance between two users and the photo count of users play an important role in their dating behaviors. Our results show that it is important to differentiate between users' true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, and there is significant discrepancy between a user's stated dating preference and his/her actual online dating behavior. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.

cs.SI↗

arXiv2013

Compression via Compressive Sensing : A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals

Telehealth and wearable equipment can deliver personal healthcare and necessary treatment remotely. One major challenge is transmitting large amount of biosignals through wireless networks. The limited battery life calls for low-power data compressors. Compressive Sensing (CS) has proved to be a low-power compressor. In this study, we apply CS on the compression of multichannel biosignals. We firstly develop an efficient CS algorithm from the Block Sparse Bayesian Learning (BSBL) framework. It is based on a combination of the block sparse model and multiple measurement vector model. Experiments on real-life Fetal ECGs showed that the proposed algorithm has high fidelity and efficiency. Implemented in hardware, the proposed algorithm was compared to a Discrete Wavelet Transform (DWT) based algorithm, verifying the proposed one has low power consumption and occupies less computational resources.

cs.IT↗

arXiv2013

Fast Marginalized Block Sparse Bayesian Learning Algorithm

The performance of sparse signal recovery from noise corrupted, underdetermined measurements can be improved if both sparsity and correlation structure of signals are exploited. One typical correlation structure is the intra-block correlation in block sparse signals. To exploit this structure, a framework, called block sparse Bayesian learning (BSBL), has been proposed recently. Algorithms derived from this framework showed superior performance but they are not very fast, which limits their applications. This work derives an efficient algorithm from this framework, using a marginalized likelihood maximization method. Compared to existing BSBL algorithms, it has close recovery performance but is much faster. Therefore, it is more suitable for large scale datasets and applications requiring real-time implementation.

cs.IT↗

arXiv2013

Scan-based Compressed Terahertz Imaging and Real-Time Reconstruction via the Complex-valued Fast Block Sparse Bayesian Learning Algorithm

Compressed Sensing based Terahertz imaging (CS-THz) is a computational imaging technique. It uses only one THz receiver to accumulate the random modulated image measurements where the original THz image is reconstruct from these measurements using compressed sensing solvers. The advantage of the CS-THz is its reduced acquisition time compared with the raster scan mode. However, when it applied to large-scale two-dimensional (2D) imaging, the increased dimension resulted in both high computational complexity and excessive memory usage. In this paper, we introduced a novel CS-based THz imaging system that progressively compressed the THz image column by column. Therefore, the CS-THz system could be simplified with a much smaller sized modulator and reduced dimension. In order to utilize the block structure and the correlation of adjacent columns of the THz image, a complex-valued block sparse Bayesian learning algorithm was proposed. We conducted systematic evaluation of state-of-the-art CS algorithms under the scan based CS-THz architecture. The compression ratios and the choices of the sensing matrices were analyzed in detail using both synthetic and real-life THz images. Simulation results showed that both the scan based architecture and the proposed recovery algorithm were superior and efficient for large scale CS-THz applications.

cs.CV↗

arXiv2013

The Annealing Sparse Bayesian Learning Algorithm

In this paper we propose a two-level hierarchical Bayesian model and an annealing schedule to re-enable the noise variance learning capability of the fast marginalized Sparse Bayesian Learning Algorithms. The performance such as NMSE and F-measure can be greatly improved due to the annealing technique. This algorithm tends to produce the most sparse solution under moderate SNR scenarios and can outperform most concurrent SBL algorithms while pertains small computational load.

cs.IT↗