Online social networks nowadays enjoy their worldwide prosperity, as they have revolutionized the way for people to discover, to share, and to distribute information. With millions of registered users and the proliferation of user-generated contents, the social networks become "giants", likely eligible to carry on any research tasks. However, the giants do have their Achilles Heel: extreme data sparsity. Compared with the massive data over the whole collection, individual posting documents, (e.g., a microblog less than 140 characters), seem to be too sparse to make a difference under various research scenarios, while actually they are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. We formulate a socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users. An efficient algorithm is designed to learn the proposed factor graph model. Finally we propagate term counts to smooth documents based on the estimated influence. Experimental results on Twitter and Weibo datasets validate the effectiveness of the proposed model. By leveraging the smoothed language model with social factors, our approach obtains significant improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP results.
Recent literatures have demonstrated the feasibility and applicability of light-to-camera communications. They either use this new technology to realize specific applications, e.g., localization, by sending repetitive signal patterns, or consider non-line-of-sight scenarios. We however notice that line-of-sight light-to-camera communications has a great potential because it provides a natural way to enable visual association, i.e., visually associating the received information with the transmitter’s identity. Such capability benefits broader applications, such as augmented reality, advertising, and driver assistance systems. Hence, this paper designs, implements, and evaluates RollingLight, a line-of-sight light-to-camera communication system that enables a light to talk to diverse off-the-shelf rolling shutter cameras. To boost the data rate and enhance reliability, RollingLight addresses the following practical challenges. First, its demodulation algorithm allows cameras with heterogeneous sampling rates to accurately decode high-order frequency modulation in real-time. Second, it incorporates a number of de- signs to resolve the issues caused by inherently unsynchronized light-to-camera channels. We have built a prototype of Rolling- Light with USRP-N200, and also implemented a real system with Arduino Mega 2560, both tested with a range of different camera receivers. We also implement a real iOS application to examine our real-time decoding capability. The experimental results show that, even to serve commodity cameras with a large variety of frame rates, RollingLight can still deliver a throughput of 11.32 bytes per second.
Improving the performance of storage systems without losing the reliability and sanity/integrity of file systems is a major issue in storage system designs. In contrast to existing storage architectures, we consider a PCM-based storage architecture to enhance the reliability of storage systems. In PCM-based storage systems, the major challenge falls on how to prevent the frequently updated (meta)data from wearing out their residing PCM cells without excessively searching and moving metadata around the PCM space and without extensively updating the index structures of file systems. In this work, we propose an adaptive wear-leveling mechanism to prevent any PCM cell from being worn out prematurely by selecting appropriate data for swapping with constant search/sort cost. Meanwhile, the concept of indirect pointers is designed in the proposed mechanism to swap data without any modification to the file system's indexes. Experiments were conducted based on well-known benchmarks and realistic workloads to evaluate the effectiveness of the proposed design, for which the results are encouraging.
Linear spectral mixture analysis (LSMA) has been received wide interests for spectral unmixing in the remote sensing community. This paper introduces a framework called MKL-SMA (Multiple Kernel Learning-based Spectral Mixture Analysis) that integrates a newly proposed multiple kernel learning method into the training process of LSMA. MKL-SMA allows us to adopt a set of nonlinear basis kernels to better characterize the data so that it can enrich the discriminant capability in classification. Because a single kernel is often insufficient to well present all the data characteristics, MKL-SMA has the advantage of providing a broader range of representation flexibilities; it also eases the kernel selection process because the kernel combination parameters can be learned automatically. Unlike most MKL approaches where complex nonlinear optimization problems are involved in their training process, we derived a closed-form solution of the kernel combination parameters in MKL-SMA. Our method is thus efficient for training and easy to implement. The usefulness of MKL-SMA is demonstrated by conducting real hyperspectral image experiments for performance evaluation. Promising results manifest the effectiveness of the proposed MKL-SMA.
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Multi-user MIMO (MU-MIMO) has recently been specified in wireless standards, e.g., LTE-Advance and 802.11ac, to allow an access point (AP) to transmit multiple unicast streams simultaneously to different clients. These protocols however have no specific mechanism for multicasting. Existing systems hence simply allow a single multicast transmission, as a result underutilizing the AP’s multiple antennas. Even worse, in most of systems, multicast is by default sent at the base rate, wasting a considerable link margin available for delivering extra information. To address this inefficiency, we present the design and implementation of HybridCast, a MU-MIMO system that enables joint unicast and multicast. HybridCast efficiently leverages the unused MIMO capability and link margin to send unicast streams concurrently with a multicast session, while ensuring not to harm the achievable rate of multicasting. We evaluate the performance of HybridCast via both testbed experiments and simulations. The results show that HybridCast always outperforms single multicast transmission. The average throughput gain for 4-antenna AP scenarios is 6.22× and 1.54× when multicast is sent at the base rate and the best rate of the bottleneck receiver, respectively.
Multi-user multiple input and multiple output (MU- MIMO) is one predominate approach to improve the wireless capacity. However, since the aggregate capacity of MU-MIMO heavily depends on the channel correlations among the mobile users in a beamforming group, unwisely selecting beamforming groups may result in reduced overall capacity, instead of increas- ing it. How to select users into a beamforming group becomes the bottleneck of realizing the MU-MIMO gain. The fundamental challenge for user selection is the large searching space, and hence there exists a tradeoff between search complexity and achievable capacity. Previous works have proposed several low complexity heuristic algorithms, but they suffer a significant capacity loss. In this paper, we present a novel MU-MIMO MAC, called SIEVE . The core of SIEVE design is its scalable multi-user selection module that provides a knob to control the aggressiveness in searching the best beamforming group. SIEVE maintains a central database to track the channel and the coherence time for each mobile user, and largely avoids unnecessary computing with a progressive update strategy. Our evaluation, via both small-scale testbed experiments and large- scale trace-driven simulations, shows that SIEVE can achieve around 90% of the capacity compared to exhaustive search.
In real-world video surveillance applications, one often needs to recognize face images from a very long distance. Such recognition tasks are very challenging, since such images are typically with very low resolution (VLR). However, if one simply downsamples high-resolution (HR) training images for recognizing the VLR test inputs, or if one directly upsamples the VLR inputs for matching the HR training data, the resulting recognition performance would not be satisfactory. In this paper, we propose a joint face hallucination and recognition approach based on sparse representation. Given a VLR input image, our method is able to synthesize its person-specific HR version with recognition guarantees. In our experiments, we consider two different face image datasets. Empirical results will support the use of our approach for both VLR face recognition. In addition, compared to state-of-the-art super-resolution (SR) methods, we will also show that our method results in improved quality for the recovered HR face images.
Computational modeling of musical timbre is important for a variety of music information retrieval applications. While considerable progress has been made to recognize musical genres and instruments, relatively little attention has been paid to modeling playing techniques, which affect timbre in more subtle ways. In this paper, we contribute to this area of research by systematically evaluating various audio features and processing methods for multi-class playing technique classification, considering up to nine distinct playing techniques of bowed string instruments. Specifically, a collection of 6,759 chamber-recorded single notes of four bowed string instruments and a collection of 33 real-world solo violin recordings are used in the evaluation. Our evaluation shows that using sparse features extracted from the magnitude spectra and phase derivatives including group delay function (GDF) and instantaneous frequency deviation (IFD) leads to significantly better performance than using a combination of state-of-the-art temporal, spectral, cepstral and harmonic feature descriptors. For playing technique classification of violin singe notes, the former approach attains 0.915 macro-average F-score under a tenfold cross validation setting, while the latter only attains 0.835. Moreover, sparse modeling of magnitude and phase-derived spectra also performs well for single-note joint instrument-technique classification (F-score 0.770) and for playing technique classification of real-world violin solos (F-score 0.547). We find that phase information is particularly important in discriminating playing techniques with subtle differences, such as playing with different bowing positions (i.e., normal, sul tasto, and sul ponticello). A systematic investigation of the effect of parameters such as window sizes, hop factors, window types for phase-derived features is also reported to provide more insights.
We present a clustering approach, MK-SOM, that carries out cluster-dependent feature selection, and partitions images with multiple feature representations into clusters. This work is motivated by the observations that human visual systems (HVS) can receive various kinds of visual cues for interpreting the world. Images identified by HVS as the same category are typically coherent to each other in certain crucial visual cues, but the crucial cues vary from category to category. To account for this observation and bridge the semantic gap, the proposed MK-SOM integrates multiple kernel learning (MKL) into the training process of self-organizing map (SOM), and associates each cluster with a learnable, ensemble kernel. Hence, it can leverage information captured by various image descriptors, and discoveries the cluster-specific characteristics via learning the per-cluster ensemble kernels. Through the optimization iterations, cluster structures are gradually revealed via the features specified by the learned ensemble kernels, while the quality of these ensemble kernels is progressively improved owing to the coherent clusters by enforcing SOM. Besides, MK-SOM allows the introduction of side information to improve performance, and it hence provides a new perspective of applying MKL to address both unsupervised and semisupervised clustering tasks. Our approach is comprehensively evaluated in the two applications. The superior and promising results manifest its effectiveness.