Unsupervised domain adaptation (UDA) deals with the task  that  labeled  training  and  unlabeled  test  data  col- lected  from  source  and  target  domains,  respectively. In this paper, we particularly address the practical and challenging scenario of imbalanced cross-domain data. That is, we do not assume the label numbers across do- mains  to  be  the  same,  and  we  also  allow  the  data  in each domain to be collected from multiple datasets/sub- domains. To solve the above task of imbalanced domain adaptation , we propose a novel algorithm of Domain- constraint Transfer Coding (DcTC) . Our DcTC is able to exploit latent subdomains within and across data do- mains,  and  learns  a  common  feature  space  for  joint adaptation and classification purposes. Without assum- ing balanced cross-domain data as most existing UDA approaches do, we show that our method performs fa- vorably  against  state-of-the-art  methods  on  multiple cross-domain visual classification tasks.

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This paper studies joint subchannel allocation, power allocation, and beamforming for simultaneous wireless information and power transfer (SWIPT) in multiuser downlink orthogonal frequency-division multiple access (OFDMA) systems. We formulate a multi-objective optimization (MOO) problem where the objectives are to maximize both the information rate and the harvested power for all users in the system. We approach the MOO problem with two proposed methods, i.e., semidefinite relaxation based weighted aggregation (SDR-WA) and multi-objective genetic algorithm (MOGA). Simulation compares the achievable Pareto optimal solution set yielded by these methods, and illustrates the tradeoffs of the sum information rate vs. the sum harvested power in the system.

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Graphics-intensive mobile games are becoming increasingly popular, but such applications place high demand on device CPUs and GPUs. The design of current mobile systems results in unnecessary energy waste due to lack of consideration of application phases and user attention (a “demand-level” gap) and because each processor administers power management autonomously (a “processor-level” gap). This paper proposes a user-centric CPU-GPU governing framework which aims to reduce energy consumption without significantly impacting the user experience. To bridge the gap at the demand level, we identify the user demand at runtime and accordingly determine appropriate governing policies for the respective processors. On the other hand, to bridge the gap at the processor level, the proposed framework interprets the frequency scaling intents of processors based on the observation of the CPU-GPU interaction and the processor status. We implemented our framework on a Samsung Galaxy S4, and conducted extensive experiments with real-world 3D gaming apps. Experimental results showed that, for an application being highly interactive and frequent phase changing, our proposed framework can reduce energy consumption by 45.1% compared with state-of-the-art policy without significantly impacting the user experience.

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This paper studies the information-theoretic secrecy rates of wireless two-way relay systems where two users wish to exchange information through a single relay with an eavesdropper observing all communications. We formulate and compare the achievable secrecy rates of the system that employs one of the three common relay protocols: conventional decode-and-forward (DF), DF with network coding (NC), and compute-and-forward (CF) based on physical-layer network coding (PNC). We show that CF based on PNC achieves the highest secrecy rate at high signal-to-noise ratio (SNR), while, interestingly, the other two protocols have mixed performance depending on the power allocation scheme and the network topology. Our study offers insights into designing wireless two-way relay protocols from a secrecy perspective.

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Acquiring the knowledge about the volume of customers for places and time of interest has several benefits such as determining the locations of new retail stores and planning advertising strategies. This paper aims to estimate the number of potential customers of arbitrary query locations and any time of interest in modern urban areas. Our idea is to consider existing established stores as a kind of sensors because the near-by human activities of the retail stores characterize the geographical properties, mobility patterns, and social behaviors of the target customers. To tackle the task based on store sensors, we develop a method called Potential Customer Estimator (PCE), which models the spatial and temporal correlation between existing stores and query locations using geographical, mobility, and features on location-based social networks. Experiments conducted on NYC Foursquare and Gowalla data, with three popular retail stores, Starbucks, McDonald's, and Dunkin' Donuts exhibit superior results over state-of-the-art approaches.

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Understanding the implications in smartphone usage and the power breakdown among hardware components has led to various energy-efficient designs for mobile systems. While energy consumption has been extensively explored, one critical dimension is often overlooked - unperceived activities that could steal a significant amount of energy behind users' back potentially. In this paper, we conduct the first exploration of unperceived activities in mobile systems. Specifically, we design a series of experiments to reveal, characterize, and analyze unperceived activities invoked by popular resident applications when an Android smartphone is left unused. We draw possible solutions inspired by the exploration and demonstrate that even an immediate remedy can mitigate energy dissipation to some extent.

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Mobile systems will increasingly feature emerging OLED displays, whose power consumption is highly dependent on the image content. Existing OLED power-saving techniques change users' visual experience or degrade images' visual quality in exchange for power reduction, or seek a chance to also enhance image quality by employing a compound objective function. This paper presents a win-win scheme that always enhances image quality and reduces power consumption simultaneously. We define metrics to assess the profit and the cost for potential image enhancement and power reduction. Then, we propose algorithms that ensure the transformation of images into their quality-enhanced power-saving versions. Finally, the proposed scheme is realized as a practical camera application on mobile devices. The results of experiments conducted on a commercial tablet with a popular image database are very encouraging and provide valuable insights for future research and practices.

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