13530245
A robust codec for transmission of very low bit-rate video over channels with bursty errors
We describe a robust codec for the transmission of very low bit-rate video over channels with a variety of errors, including random and bursty bit errors and packet loss. The codec exploits adaptivity to give good performance with a low overhead. By only protecting macroblocks which would otherwise be poorly concealed by the decoder the codec allows adaptive selection of the parts of video to protect. For protection, it uses multiple description codes which indirectly provide frequency-based adaptivity by protecting the more significant DCT coefficients. Simulations show significant improvements in the performance of the codec when compared to codecs which use intra macroblock updating (raster scan and random) at the same overhead. The codec is efficient in its use of bits and has good error resilience properties both objectively and subjectively over a wide range of conditions. Further, transcoding of the received bit stream to the standard H.263 syntax is relatively easy.
2748729
Partially Supervised Classification of Text Documents
We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents that contains documents from class P and other types of documents, identify the documents from class P in M . The key feature of this problem is that there is no labeled nonP document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised classification. In this paper, we show that this problem can be posed as a constrained optimization problem and that under appropriate conditions, solutions to the constrained optimization problem will give good solutions to the partially supervised classification problem. We present a novel technique to solve the problem and demonstrate the effectiveness of the technique through extensive experimentation.
573509
Boosting the margin: A new explanation for the effectiveness of voting methods
One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance
361066
Efficient agnostic learning of neural networks with bounded fan-in
We show that the class of two-layer neural networks with bounded fan-in is efficiently learnable in a realistic extension to the probably approximately correct (PAC) learning model. In this model, a joint probability distribution is assumed to exist on the observations and the learner is required to approximate the neural network which minimizes the expected quadratic error. As special cases, the model allows learning real-valued functions with bounded noise, learning probabilistic concepts, and learning the best approximation to a target function that cannot be well approximated by the neural network. The networks we consider have real-valued inputs and outputs, an unlimited number of threshold hidden units with bounded fan-in, and a bound on the sum of the absolute values of the output weights. The number of computation steps of the learning algorithm is bounded by a polynomial in 1//spl epsiv/, 1//spl delta/, n and B where /spl epsiv/ is the desired accuracy, /spl delta/ is the probability that the algorithm fails, n is the input dimension, and B is the bound on both the absolute value of the target (which may be a random variable) and the sum of the absolute values of the output weights. In obtaining the result, we also extended some results on iterative approximation of functions in the closure of the convex hull of a function class and on the sample complexity of agnostic learning with the quadratic loss function.
1176859
RankCut - A Domain Independent Forward Pruning Method for Games
Forward pruning, also known as selective search, is now employed in many strong game-playing programs. In this paper, we introduce RankCut – a domain independent forward pruning technique which makes use of move ordering, and prunes once no better move is likely to be available. Since gameplaying programs already perform move ordering to improve the performance of αβ search, this information is available at no extra cost. As RankCut uses additional information untapped by current forward pruning techniques, RankCut is a complementary forward pruning method that can be used with existing methods, and is able to achieve improvements even when conventional pruning techniques are simultaneously employed. We implemented RankCut in a modern open-source chess program, CRAFTY. RankCut reduces the game-tree size by approximately 10%-40% for search depths 8-12 while retaining tactical reliability, when implemented alongside CRAFTY’s existing forward pruning techniques.
37770250
A Theoretical Analysis of Query Selection for Collaborative Filtering
We consider the problem of determining which of a set of experts has tastes most similar to a given user by asking the user questions about his likes and dislikes. We describe a simple and fast algorithm for a theoretical model of this problem with a provable approximation guarantee, and prove that solving the problem exactly is NP-Hard.
10871419
Towards an Information-Theoretic Framework for Analyzing Intrusion Detection Systems
IDS research still needs to strengthen mathematical foundations and theoretic guidelines. In this paper, we build a formal framework, based on information theory, for analyzing and quantifying the effectiveness of an IDS. We firstly present a formal IDS model, then analyze it following an information-theoretic approach. Thus, we propose a set of information-theoretic metrics that can quantitatively measure the effectiveness of an IDS in terms of feature representation capability, classification information loss, and overall intrusion detection capability. We establish a link to relate these metrics, and prove a fundamental upper bound on the intrusion detection capability of an IDS. Our framework is a practical theory which is data trace driven and evaluation oriented in this area. In addition to grounding IDS research on a mathematical theory for formal study, this framework provides practical guidelines for IDS fine-tuning, evaluation and design, that is, the provided set of metrics greatly facilitates a static/dynamic fine-tuning of an IDS to achieve optimal operation and a fine-grained means to evaluate IDS performance and improve IDS design. We conduct experiments to demonstrate the utility of our framework in practice.
52820102
Adaptive frame-skipping for video transcoding
In video transcoding, the compressed video bitstream is often converted to the reduced frame-rate video bitstream in order to reduce bit-rate or implement fast forward and reverse video playback. One way to perform this is to decompress the compressed video bitstream and drop several specific frames, then compress those nonskipped frames to another compressed video bitstream. This method has heavy computation complexity. In this paper, we propose two new methods to reduce the complexity. The first method is simple frame-skipped video transcoder architecture which can reduce frame rate of recompressed video bitstream adaptively and quickly. The second method is bidirection dominant vector selection method which converts the popular I-B-P frame structure of video bitstream to be the I-P frame structure quickly through reusing the decoded incoming motion vectors. Experimental results are reported in this paper to show the efficacy of the proposed methods.
14613627
Spatial Index on Air
With the advent of wireless networking and personal digital devices, the population of mobile users will increase significantly. Broadcasting is particularly suitable for environments having a large number of clients. In this paper, we study the query processing of some typical location-dependent queries, such as window queries and kNN queries, in a broadcast system. To reduce clients' power consumption and provide efficient services, a transformation of the objects is applied based on Hilbert curve. Furthermore, a linear index structure is constructed and several algorithms are devised to answer spatial queries. Experiments are conducted to evaluate the performance of the proposed transformation and related algorithms. Results show that the proposed schemes outperform existing algorithms significantly.
6965660
Simulating Internet worms
The accurate and efficient modeling of Internet worms is a particularly challenging task for network simulation tools. The atypical and aggressive behavior of these worms can easily consume excessive resources, both processing time and storage, within a typical simulator. In particular the selection of random IP addresses, and the sending of packets to the selected hosts, even if they are non-existent or not modeled in the simulation scenario, is challenging for existing network simulation tools. Further, the computation of routing information for these randomly chosen target addresses defeats most caching or on-demand routing methods, resulting in substantial overhead in the simulator. We discuss the design of our Internet worm models in the Georgia Tech Network Simulator, and show how we addressed these issues. We present some results from our Internet worm simulations that show the rate of infection spread for a typical worm under a variety of conditions.
2157536
A framework for constructing features and models for intrusion detection systems
Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, and extensible. Given these requirements and the complexities of today's network environments, we need a more systematic and automated IDS development process rather that the pure knowledge encoding and engineering approaches. This article describes a novel framework, MADAM ID, for Mining Audit Data for Automated Models for Instrusion Detection. This framework uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records taht are processed according to the feature definitions to generate intrusion detection rules. Results from the 1998 DARPA Intrusion Detection Evaluation showed that our ID model was one of the best performing of all the participating systems. We also briefly discuss our experience in converting the detection models produced by off-line data mining programs to real-time modules of existing IDSs.