9404152
Learning multi-label scene classification
In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive. Such problems arise in semantic scene and document classification and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a field scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a different treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classification; furthermore, our work appears to generalize to other classification problems of the same nature.
9685849
Adaptive data partition for sorting using probability distribution
Many computing problems benefit from dynamic partition of data into smaller chunks with better parallelism and locality. However, it is difficult to partition all types of inputs with the same high efficiency. This paper presents a new partition method in sorting scenario based on probability distribution, an idea first studied by Janus and Lamagna in early 1980's on a mainframe computer. The new technique makes three improvements. The first is a rigorous sampling technique that ensures accurate estimate of the probability distribution. The second is an efficient implementation on modern, cache-based machines. The last is the use of probability distribution in parallel sorting. Experiments show 10-30% improvement in partition balance and 20-70% reduction in partition overhead, compared to two commonly used techniques. The new method reduces the parallel sorting time by 33-50% and outperforms the previous fastest sequential sorting technique by up to 30%.
16080938
Locality phase prediction
As computer memory hierarchy becomes adaptive, its performance increasingly depends on forecasting the dynamic program locality. This paper presents a method that predicts the locality phases of a program by a combination of locality profiling and run-time prediction. By profiling a training input, it identifies locality phases by sifting through all accesses to all data elements using variable-distance sampling, wavelet filtering, and optimal phase partitioning. It then constructs a phase hierarchy through grammar compression. Finally, it inserts phase markers into the program using binary rewriting. When the instrumented program runs, it uses the first few executions of a phase to predict all its later executions.Compared with existing methods based on program code and execution intervals, locality phase prediction is unique because it uses locality profiles, and it marks phase boundaries in program code. The second half of the paper presents a comprehensive evaluation. It measures the accuracy and the coverage of the new technique and compares it with best known run-time methods. It measures its benefit in adaptive cache resizing and memory remapping. Finally, it compares the automatic analysis with manual phase marking. The results show that locality phase prediction is well suited for identifying large, recurring phases in complex programs.
10424667
Miss Rate Prediction Across Program Inputs and Cache Configurations
Improving cache performance requires understanding cache behavior. However, measuring cache performance for one or two data input sets provides little insight into how cache behavior varies across all data input sets and all cache configurations. This paper uses locality analysis to generate a parameterized model of program cache behavior. Given a cache size and associativity, this model predicts the miss rate for arbitrary data input set sizes. This model also identifies critical data input sizes where cache behavior exhibits marked changes. Experiments show this technique is within 2 percent of the hit rate for set associative caches on a set of floating-point and integer programs using array and pointer-based data structures. Building on the new model, this paper presents an interactive visualization tool that uses a three-dimensional plot to show miss rate changes across program data sizes and cache sizes and its use in evaluating compiler transformations. Other uses of this visualization tool include assisting machine and benchmark-set design. The tool can be accessed on the Web at http://www.cs.rochester.edu/research/locality
302118
A Hierarchical Model of Reference Affinity
To improve performance, data reorganization needs locality models to identify groups of data that have reference affinity. Much past work is based on access frequency and does not consider accessing time directly. In this paper, we propose a new model of reference affinity. This model considers the distance between data accesses in addition to the frequency. Affinity groups defined by this model are consistent and have a hierarchical structure. The former property ensures the profitability of data packing, while the latter supports data packing for storage units of different sizes. We then present a statistical clustering method that identifies affinity groups among structure fields and data arrays by analyzing training runs of a program. When used by structure splitting and array regrouping, the new method improves the performance of two test programs by up to 31%. The new data layout is significantly better than that produced by the programmer or by static compiler analysis.
6253791
Array regrouping and structure splitting using whole-program reference affinity
While the memory of most machines is organized as a hierarchy, program data are laid out in a uniform address space. This paper defines a model of reference affinity, which measures how close a group of data are accessed together in a reference trace. It proves that the model gives a hierarchical partition of program data. At the top is the set of all data with the weakest affinity. At the bottom is each data element with the strongest affinity. Based on the theoretical model, the paper presents k-distance analysis, a practical test for the hierarchical affinity of source-level data. When used for array regrouping and structure splitting, k-distance analysis consistently outperforms data organizations given by the programmer, compiler analysis, frequency profiling, statistical clustering, and all other methods we have tried.
16973693
An adaptive handoff priority scheme for wireless MC-CDMA cellular networks supporting realtime multimedia applications
An adaptive handoff priority scheme is proposed for a packet switching multicode (MC)-CDMA cellular network supporting realtime multimedia applications. The scheme jointly considers the physical, link and network layer characteristics, and gives service priority to handoff calls by exploiting the transmission rate adaptability of multimedia calls to the available radio resources. Based on the scheme, a call admission region is derived with the estimated cell capacity and the proposed packet scheduling and partial packet integration scheme and used for call admission control and handoff management with the satisfaction of quality of service requirements for all multimedia traffic, where the QoS parameters include the wireless transmission bit error rate, packet loss rate and delay requirement. Simulation results are given to demonstrate the viability of the proposed scheme.
31888135
An automatic seal imprint verification approach
The automatic verification of seal imprints is a difficult pattern recognition problem and has a lot of applications in the oriental countries. Several approaches have been proposed in the past 10 years, but all of them were hard to implement in practice because of their drawbacks in the adaptability, reliability and practicality. In this paper, a new automatic seal imprint verification approach is presented. Due to the efficient and accurate registration method, a set of effective features and the elaborate implementations, this approach has satisfied most practical requirements and has been used for a practical check verification system in a Chinese bank.
6496359
Implicit user modeling for personalized search
Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word "java" to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine.
6579321
Discovering interesting patterns through user's interactive feedback
In this paper, we study the problem of discovering interesting patterns through user's interactive feedback. We assume a set of candidate patterns (ie, frequent patterns) has already been mined. Our goal is to help a particular user effectively discover interesting patterns according to his specific interest. Without requiring a user to explicitly construct a prior knowledge to measure the interestingness of patterns, we learn the user's prior knowledge from his interactive feedback. We propose two models to represent a user's prior: the log linear model and biased belief model. The former is designed for item-set patterns, whereas the latter is also applicable to sequential and structural patterns. To learn these models, we present a two-stage approach, progressive shrinking and clustering, to select sample patterns for feedback. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach.
12254119
Wireless network security