Scalable audio streaming over the internet with network-aware rate-distortion optimization
Reliable audio streaming over the unreliable Internet is a challenging task. Because during a connected session, the bandwidth can vary greatly and the data packets can be lost in the transmission, it is difficult to guarantee a smooth quality of the delivered audio over the Internet. In this work, a system for streaming of scalable coded audio over the Internet is proposed. Scalable audio coding generates a bitstream consisting a number of data units (DUs), where a subset of the DUs can be extracted to reconstruct audio at a lower quality level. A network aware ratedistortion optimization model is proposed so that the server can easily adapt to the network bandwidth fluctuation by sending less packets when the network is congested. The server can also deal with severe packet loss by only retransmitting the more important data packets. By using the scalable audio coding and network aware delivery scheme, the quality of the audio streaming over severe network condition is improved dramatically.

title: The seamlessly multiplexed embedded codec (SMEC) and its application in image coding
abstract: The landmark JPEG 2000 image compression standard offers not only superior compression performance, but also incredible flexibility. The compressed bitstream of JPEG 2000 can be flexibly reorganized to another bitstream of different bitrate, resolution, and spatial region of interest (ROI) or a combination of any of the above. Such flexibility is achieved by multiplexing the compressed bitstream pieces of multiple code-blocks together into a combined bitstream, with the length of the code-block bitstream piece (LOCB) embedded in the combined bitstream. The LOCB serves both to reorganize the bitstream, and to decode the bitstream. It represents a significant overhead, especially since there is no correlation between the neighbor LOCBs. In this work, we introduce seamless multiplexing, and separate the information needed for the reorganization, i.e., the LOCB, from the compressed bitstream itself by using the decoder pointer to multiplex the bitstream pieces. As a result, the compressed bitstream consists of code-block bitstream pieces seamlessly concatenated to each other. With seamless multiplexing, only the compressed bitstream (without LOCB) needs to be delivered to the receiving client. It results in better compression performance and higher granularity of access. Another benefit of seamless multiplexing is that the relative coding orders of the code-blocks are preserved in the bitstream reorganization. As a result, the seamlessly multiplexed embedded codec (SMEC) may utilize the dependencies among the code-blocks in the coding, thus further boost the compression performance.

title: Image compression with a hybrid wavelet-fractal coder
abstract: A hybrid wavelet-fractal coder (WFC) for image compression is proposed. The WFC uses the fractal contractive mapping to predict the wavelet coefficients of the higher resolution from those of the lower resolution and then encode the prediction residue with a bitplane wavelet coder. The fractal prediction is adaptively applied only to regions where the rate saving offered by fractal prediction justifies its overhead. A rate-distortion criterion is derived to evaluate the fractal rate saving and used to select the optimal fractal parameter set for WFC. The superior performance of the WFC is demonstrated with extensive experimental results.

title: Arbitrarily shaped video-object coding by wavelet
abstract: Video-object coding is one of the most important functionalities proposed by MPEG-4. We propose a new wavelet method to encode the texture of an arbitrarily shaped object, both for the still and for the video-object. The method uses the shape adaptive wavelet transform (SA-DWT) in MPEG-4 still object coding, but with a computationally more efficient lifting implementation. The transformed object coefficients are then quantized and entropy encoded with a partial bit-plane embedded coder, which greatly improves the coding efficiency. We denote the coding algorithm as the video-object wavelet (VOW) coder. Experimental results show that VOW significantly outperforms MPEG-4 in still-object coding, and achieves a comparable performance in video-object coding in terms of PSNR. Moreover, the VOW decoded object looks better subjectively, with less annoying blocking artifacts than that of MPEG-4.

title: Distributed hosting of Web content with erasure coding and unequal weight assignment
abstract: By sharing and hosting personal Web content in a peer-to-peer (P2P) fashion, we may increase the bandwidth of retrieval and improve the reliability of retrieval. The cost is that the Web content has to be replicated to and stored in the peers, consuming valuable network bandwidth and peer storage space. In this work, we develop two technologies, namely hierarchical content organization with unequal weight assignment and erasure coding, to reduce the amount of content to be distributed, yet still maintain the retrieval speed up and reliability. Significant improvement over the ordinary Web server is demonstrated.

title: An embedded still image coder with rate-distortion optimization
abstract: It is well known that the fixed rate coder achieves optimality when all coefficients are coded with the same rate-distortion (R-D) slope. In this paper, we show that the performance of the embedded coder can be optimized in a rate-distortion sense by coding the coefficients with decreasing R-D slope. We denote such a coding strategy as rate-distortion optimized embedding (RDE). RDE allocates the available coding bits first to the coefficient with the steepest R-D slope, i.e., the largest distortion decrease per coding bit. The resultant coding bitstream can be truncated at any point and still maintain an optimal R-D performance. To avoid the overhead of coding order transmission, we use the expected R-D slope, which can be calculated from the coded bits and is available in both the encoder and the decoder. With the probability estimation table of the QM-coder, the calculation of the R-D slope can be just a lookup table operation. Experimental results show that the rate-distortion optimization significantly improves the coding efficiency in a wide bit rate range.

title: Construct robust rule sets for classification
abstract: We study the problem of computing classification rule sets from relational databases so that accurate predictions can be made on test data with missing attribute values. Traditional classifiers perform badly when test data are not as complete as the training data because they tailor a training database too much. We introduce the concept of one rule set being more robust than another, that is, able to make more accurate predictions on test data with missing attribute values. We show that the optimal class association rule set is as robust as the complete class association rule set. We then introduce the k-optimal rule set, which provides predictions exactly the same as the optimal class association rule set on test data with up to k missing attribute values. This leads to a hierarchy of k-optimal rule sets in which decreasing size corresponds to decreasing robustness, and they all more robust than a traditional classification rule set. We introduce two methods to find k-optimal rule sets, i.e. an optimal association rule mining approach and a heuristic approximate approach. We show experimentally that a k-optimal rule set generated by the optimal association rule mining approach performs better than that by the heuristic approximate approach and both rule sets perform significantly better than a typical classification rule set (C4.5Rules) on incomplete test data.

title: Rule Evaluations, Attributes, and Rough Sets: Extension and a Case Study
abstract: Manually evaluating important and interesting rules generated from data is generally infeasible due to the large number of rules extracted. Different approaches such as rule interestingness measures and rule quality measures have been proposed and explored previously to extract interesting and high quality association rules and classification rules. Rough sets theory was originally presented as an approach to approximate concepts under uncertainty. In this paper, we explore rough sets based rule evaluation approaches in knowledge discovery. We demonstrate rule evaluation approaches through a real-world geriatric care data set from Dalhousie Medical School. Rough set based rule evaluation approaches can be used in a straightforward way to rank the importance of the rules. One interesting system developed along these lies in HYRIS (HYbrid Rough sets Intelligent System). We introduce HYRIS through a case study on survival analysis using the geriatric care data set.

title: OACerts: Oblivious Attribute Certificates
abstract: We propose oblivious attribute certificates (OACerts), an attribute certificate scheme in which a certificate holder can select which attributes to use and how to use them. In particular, a user can use attribute values stored in an OACert obliviously, i.e., the user obtains a service if and only if the attribute values satisfy the policy of the service provider, yet the service provider learns nothing about these attribute values. This way, the service provider's access control policy is enforced in an oblivious fashion. To enable the oblivious access control using OACerts, we propose a new cryptographic primitive called oblivious commitment-based envelope (OCBE). In an OCBE scheme, Bob has an attribute value committed to Alice and Alice runs a protocol with Bob to send an envelope (encrypted message) to Bob such that: 1) Bob can open the envelope if and only if his committed attribute value satisfies a predicate chosen by Alice and 2) Alice learns nothing about Bob's attribute value. We develop provably secure and efficient OCBE protocols for the Pedersen commitment scheme and comparison predicates as well as logical combinations of them

title: Private collaborative forecasting and benchmarking
abstract: Suppose a number of hospitals in a geographic area want to learn how their own heart-surgery unit is doing compared with the others in terms of mortality rates, subsequent complications, or any other quality metric. Similarly, a number of small businesses might want to use their recent point-of-sales data to cooperatively forecast future demand and thus make more informed decisions about inventory, capacity, employment, etc. These are simple examples of cooperative benchmarking and (respectively) forecasting that would benefit all participants as well as the public at large, as they would make it possible for participants to avail themselves of more precise and reliable data collected from many sources, to assess their own local performance in comparison to global trends, and to avoid many of the inefficiencies that currently arise because of having less information available for their decision-making. And yet, in spite of all these advantages, cooperative benchmarking and forecasting typically do not take place, because of the participants' unwillingness to share their information with others. Their reluctance to share is quite rational, and is due to fears of embarrassment, lawsuits, weakening their negotiating position (e.g., in case of over-capacity), revealing corporate performance and strategies, etc. The development and deployment of <i>private</i> benchmarking and forecasting technologies would allow such collaborations to take place without revealing any participant's data to the others, reaping the benefits of collaboration while avoiding the drawbacks. Moreover, this kind of technology would empower smaller organizations who could then cooperatively base their decisions on a much broader information base, in a way that is today restricted to only the largest corporations. This paper is a step towards this goal, as it gives protocols for forecasting and benchmarking that reveal to the participants the desired answers yet do not reveal to any participant any other participant's private data. We consider several forecasting methods, including linear regression and time series techniques such as moving average and exponential smoothing. One of the novel parts of this work, that further distinguishes it from previous work in secure multi-party computation, is that it involves floating point arithmetic, in particular it provides protocols to securely and efficiently perform division.

title: RACCOON: a peer-based system for data integration and sharing
abstract: Recent database applications see the emerging need to support data integration in distributed, peer-to-peer environments, in which autonomous peers (sources) connected by a network are willing to exchange data and services with each other. To address related research challenges, we are developing a system called "RACCOON", which allows different sources to integrate and share their data. We use an application to show several important features of the RACCOON system. The system also suggests semantic mappings for the user to choose. We show the two different querying modes, particularly how a query is expanded using the semantic mappings in the extended querying mode to compute as many answers to the query as possible.