TCL Research America

San Jose, CA, United States

TCL Research America

San Jose, CA, United States

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Wang H.,TCL Research America
APSIPA Transactions on Signal and Information Processing | Year: 2017

We are currently living in a world dominated by mobile apps and connected devices. State-of-the-art mobile phones and tablets use apps to organize knowledge and information, control devices, and/or complete transactions via local, web, and cloud services. However, users are challenged to select a suite of apps, from the millions available today, that is right for them. Apps are increasingly differentiated only by the user experience and a few specialized functions; therefore, many apps are needed in order to cover all of the services a specific user needs, and the user is often required to frequently switch between apps to achieve a specific goal. User experience is further limited by the inability of apps to effectively interoperate, since relevant user data are often wholly contained within the app. This limitation significantly undermines the continuous (function) flow across apps to achieve a desired goal. The result is a disjointed user experience requiring app switching and replicating data among apps. With these limitations in mind, it appears as if the current mobile experience is nearing its full potential but failing to leverage the full power of modern mobile devices. In this paper, we present a vision of the future where apps are no longer the dominant customer interaction in the mobile world. The alternative that we propose would orchestrate the mobile experience by using a moment-first model that would leverage machine learning and data mining to bridge a user's needs across app boundaries, matching context, and knowledge of the user with ideal services and interaction models between the user and device. In this way, apps would be employed at a function level, while the overall user experience would be optimized, by liberating user data outside of the app container and intelligently orchestrating the user experience, to fulfill the needs of the moment. We introduce the concept of a functional entry-point and apply the simple label FUNN to it (which was named FUNC in (Wang, 2014)). We further discuss how a number of learning models could be utilized in building this relationship between the user, FUNN, and context to enable search, recommendations and presentation of FUNNs through a multi-modal human-machine interface that would better fulfill users' needs. Two examples are showcased to demonstrate how this vision is being implemented in home entertainment and driving scenarios. In conclusion, we envision moving forward into a FUNN-based mobile world with a much more intelligent user experience model. This in turn would offer the opportunity for new relationships and business models between software developers, OS providers, and device manufacturers. © The Authors, 2017.


Zhou L.,Nanjing University of Posts and Telecommunications | Yang Z.,Nanjing University of Posts and Telecommunications | Wen Y.,Nanyang Technological University | Wang H.,TCL Research America | Guizani M.,Qatar University
IEEE Transactions on Wireless Communications | Year: 2013

Most existing Quality of Experience (QoE)-driven multimedia resource allocation methods assume that the QoE model of each user is known to the controller before the start of the multimedia playout. However, this assumption may be invalid in many practical scenarios. In this paper, we address the resource allocation problem with incomplete information where the realized mean opinion score (MOS) can only be observed over time, but the underlying QoE model and playout time are unknown. We consider two variants of this problem: 1) the form of the QoE model is known but the parameters are unknown; 2) both the form and the parameters of the QoE model are unknown. For both cases, we develop dynamic resource allocation schemes based on online test-optimization strategy. Simply speaking, one first spends appropriate time on testing the QoE model, then optimizes the sum of the MOS in the remaining playout time. The highlight of this paper lies in resolving the inherent tension between the test and optimization by jointly considering the uncertainties of QoE model and playout time. Furthermore, we derive tight bounds on the MOS loss incurred by the proposed schemes in comparison with the optimal scheme that knows the QoE model a priori and prove that the performance gap, as the playout time tends to infinity, asymptotically shrinks to zero. © 2013 IEEE.


Zhou L.,Nanjing University of Posts and Telecommunications | Yang Z.,Nanjing University of Posts and Telecommunications | Wang H.,TCL Research America | Guizani M.,Qatar University
IEEE Journal on Selected Areas in Communications | Year: 2014

Adaptive wireless video scheduling has been widely studied to improve network performance. However, the majority of existing scheduling algorithms assume that they are able to converge instantaneously to adapt to a dynamic network state, that is, the execution time of the scheduling can be ignored. Nevertheless, due to the limited computation capacity of wireless nodes, this assumption is very difficult, sometimes even impossible, to satisfy in practice. This motivates us to address in this paper the following challenging question: what is the effect of the execution time on the scheduling performance? To this end, we first characterize the scheduling as a stochastic optimization problem that enables us to open up a new degree of performance to exploit in a tractable manner. Next, we build a connection between the execution time and video quality, and rigorously prove that the execution time is disadvantageous to the stability region, but advantageous to the flow balance. Therefore, these results are helpful to shed insights on fundamental scheduling guidelines on designing an efficient video transmission system. © 1983-2012 IEEE.


Li H.,University of Vermont | Wu X.,University of Vermont | Li Z.,TCL Research America | Ding W.,University of Massachusetts Boston
Proceedings - IEEE International Conference on Data Mining, ICDM | Year: 2013

Group feature selection makes use of structural information among features to discover a meaningful subset of features. Existing group feature selection algorithms only deal with pre-given candidate feature sets and they are incapable of handling streaming features. On the other hand, feature selection algorithms targeted for streaming features can only perform at the individual feature level without considering intrinsic group structures of the features. In this paper, we perform group feature selection with streaming features. We propose to perform feature selection at the group and individual feature levels simultaneously in a manner of a feature stream rather than a pre-given candidate feature set. In our approach, the group structures are fully utilized to reduce the cost of evaluating streaming features. We have extensively evaluated the proposed method. Experimental results have demonstrated that our proposed algorithms statistically outperform state-of-the-art methods of feature selection in terms of classification accuracy. © 2013 IEEE.


Li H.,University of Vermont | Wu X.,University of Vermont | Li Z.,TCL Research America
Proceedings of the ACM Symposium on Applied Computing | Year: 2014

Currently, mobile devices built with powerful embedded sensors create new opportunities for data mining applications such as monitoring user activity. In this paper, we target at user recognition based on sensor data of remote control, in which activity recognition determines a user's action that is in favor of collecting one's individual sensor data to identify different users. This new problem faces two challenges: first, sensor data is sensitive and constantly changing which is difficult to obtain meaningful features; second, streaming sensor data for online learning is usually imbalanced on which traditional classifiers are not well performed. To address these challenges, we introduce an efficient activity recognition algorithm by exploring the physical appearance of sensor data, and then an online incremental classifier to deal with imbalanced data streams by adaptively generating training data. Extensive online and offline experiments demonstrate that our proposed method outperforms state-of-the-art algorithms in terms of accuracy. Copyright 2014 ACM.


Li H.,University of Vermont | Wu X.,University of Vermont | Li Z.,TCL Research America | Ding W.,University of Massachusetts Boston
Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013 | Year: 2013

Standard feature selection algorithms deal with given candidate feature sets at the individual feature level. When features exhibit certain group structures, it is beneficial to conduct feature selection in a grouped manner. For high-dimensional features, it could be far more preferable to online generate and process features one at a time rather than wait for generating all features before learning begins. In this paper, we discuss a new and interesting problem of online group feature selection from feature streams at both the group and individual feature levels simultaneously from a feature stream. Extensive experiments on both real-world and synthetic datasets demonstrate the superiority of the proposed algorithm. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.


Zhou L.,Key Laboratory of Broadband Wireless Communication and Sensor Network Technology | Wang H.,TCL Research America | Guizani M.,Qatar University
IEEE Transactions on Communications | Year: 2012

In this work, we investigate the impact of mobility on video streaming over multi-hop wireless networks by utilizing a class of scheduling schemes. We show that node spatial mobility has the ability to improve video quality and reduce the transmission delay without the help of advanced video coding techniques. To describe a practical mobile scenario, we consider a random walk mobility model in which each node can randomly and independently choose its mobility direction, and all the nodes can identically and uniformly visit the entire network. The contributions of this work are twofold: 1) It studies the optimal node velocity for the mobile video system. In this case, it is possible to achieve almost constant transmission delay and video quality as the number of nodes increases; 2) It derives an achievable quality-delay tradeoff range for different node velocities. Therefore, it is helpful to shed insights on network design and fundamental guidelines on establishing an efficient mobile video transmission system. © 1972-2012 IEEE.


Fleites F.C.,Senzari Inc. | Wang H.,TCL Research America | Chen S.-C.,Florida International University
IEEE Transactions on Multimedia | Year: 2015

Smart TVs have realized the convergence of TV, Internet, and PC technologies, but still do not provide a seamless content interaction for TV-enabled shopping. To purchase interesting items displayed in a TV show, consumers must resort to a store or the Web, which is an inconvenient way of purchasing products. The fundamental challenge in realizing such a use case consists of understanding the multimedia content being streamed. Such a challenge can be realized by utilizing object detection to facilitate content understanding though it has to be executed as a computationally bound process so that consumers are provided with a responsive and exciting user interface. To this end, we propose a computational-and temporal-aware multimedia abstraction framework that facilitates the efficient execution of object detection tasks. Given computational and temporal rate constraints, the proposed framework selects the optimal video frames that best represent the video content and allows the execution of the object detection task as a computationally bound process. In this sense, the framework is computationally scalable as it can adapt to the given constraints and generate optimal abstraction results accordingly. Additionally, the framework utilizes 'object views' as the basis for the frame selection process, which depict salient information and are represented as regions of interest (ROI). In general, an ROI can be a whole frame or a region that discards background information. Experimental results demonstrate the computational scalability of the proposed framework and the benefits of using the regions of interest as the basis of the abstraction process. © 1999-2012 IEEE.


Zhu Q.,University of Miami | Li Z.,TCL Research America | Wang H.,TCL Research America | Yang Y.,Florida International University | Shyu M.-L.,University of Miami
Proceedings - 2013 IEEE International Symposium on Multimedia, ISM 2013 | Year: 2013

Most content-based recommender systems focus on analyzing the textual information of items. For items with images, the images can be treated as another information modality. In this paper, an effective method called MSLIM is proposed to integrate multimodal information for content-based item recommendation. It formalizes the probelm into a regularized optimization problem in the least-squares sense and the coordinate gradient descent is applied to solve the problem. The aggregation coefficients of the items are learned in an unsupervised manner during this process, based on which the k-nearest neighbor (k-NN) algorithm is used to generate the top-N recommendations of each item by finding its k nearest neighbors. A framework of using MSLIM for item recommendation is proposed accordingly. The experimental results on a self-collected handbag dataset show that MSLIM outperforms the selected comparison methods and show how the model parameters affect the final recommendation results. © 2013 IEEE.


Zhu Q.,University of Miami | Shyu M.-L.,University of Miami | Wang H.,TCL Research America
Proceedings - 2013 IEEE International Symposium on Multimedia, ISM 2013 | Year: 2013

Most video recommender systems limit the content to the metadata associated with the videos, which could lead to poor results since metadata is not always available or correct. Meanwhile, the visual information of videos is typically not fully explored, which is especially important for recommending new items with limited metadata information. In this paper, a novel content-based video recommendation framework, called Video Topic, that utilizes a topic model is proposed. It decomposes the recommendation process into video representation and recommendation generation. It aims to capture user interests in videos by using a topic model to represent the videos, and then generates recommendations by finding those videos that most fit to the topic distribution of the user interests. Experimental results on the Movie Lens dataset validate the effectiveness of Video Topic by evaluating each of its components and the whole framework. © 2013 IEEE.

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