17542443
Using Relative Entropy for Authorship Attribution
Authorship attribution is the task of deciding who wrote a particular document. Several attribution approaches have been proposed in recent research, but none of these approaches is particularly satisfactory; some of them are ad hoc and most have defects in terms of scalability, effectiveness, and efficiency. In this paper, we propose a principled approach motivated from information theory to identify authors based on elements of writing style. We make use of the Kullback-Leibler divergence, a measure of how different two distributions are, and explore several different approaches to tokenizing documents to extract style markers. We use several data collections to examine the performance of our approach. We have found that our proposed approach is as effective as the best existing attribution methods for two class attribution, and is superior for multi-class attribution. It has lower computational cost and is cheaper to train. Finally, our results suggest this approach is a promising alternative for other categorization problems.
42961259
Authorship Attribution Via Combination of Evidence
Authorship attribution is a process of determining who wrote a particular document. We have found that different systems work well for particular sets of authors but not others. In this paper, we propose three authorship attribution systems, based on different ways of combining existing methodologies. All systems show better effectiveness than the state-of-art methods.
15518950
Effective and Scalable Authorship Attribution Using Function Words
Techniques for identifying the author of an unattributed document can be applied to problems in information analysis and in academic scholarship. A range of methods have been proposed in the research literature, using a variety of features and machine learning approaches, but the methods have been tested on very different data and the results cannot be compared. It is not even clear whether the differences in performance are due to feature selection or other variables. In this paper we examine the use of a large publicly available collection of newswire articles as a benchmark for comparing authorship attribution methods. To demonstrate the value of having a benchmark, we experimentally compare several recent feature-based techniques for authorship attribution, and test how well these methods perform as the volume of data is increased. We show that the benchmark is able to clearly distinguish between different approaches, and that the scalability of the best methods based on using function words features is acceptable, with only moderate decline as the difficulty of the problem is increased.
32435431
Regularized Semi-supervised Classification on Manifold
Semi-supervised learning gets estimated marginal distribution PX with a large number of unlabeled examples and then constrains the conditional probability p(y | x) with a few labeled examples. In this paper, we focus on a regularization approach for semi-supervised classification. The label information graph is first defined to keep the pairwise label relationship and can be incorporated with neighborhood graph which reflects the intrinsic geometry structure of PX. Then we propose a novel regularized semi-supervised classification algorithm, in which the regularization term is based on the modified Graph Laplacian. By redefining the Graph Laplacian, we can adjust and optimize the decision boundary using the labeled examples. The new algorithm combines the benefits of both unsupervised and supervised learning and can use unlabeled and labeled examples effectively. Encouraging experimental results are presented on both synthetic and real world datasets.
15935019
Statistical interval-valued fuzzy systems via linear regression
In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system. In order to make the type-2 fuzzy logic system reasonable and reliable, a new simple statistical linear method to decide interval-valued fuzzy membership functions and a new probability type reduce reasoning method for the interval-valued fuzzy logic system are proposed in this paper. An example of statistical interval-valued FLS is performed and results show that the developed method is more accurate to design a fuzzy logic system than type-1 method and computation is efficient.
12919460
Decay rate for a PH/M/2 queue with shortest queue discipline
In this paper, we consider a PH/M/2 queue in which each server has its own queue and arriving customers join the shortest queue. For this model, it has been conjectured that the decay rate of the tail probabilities for the shortest queue length in the steady state is equal to the square of the decay rate for the queue length in the corresponding PH/M/2 model with a single queue. We prove this fact in the sense that the tail probabilities are asymptotically geometric when the difference of the queue sizes and the arrival phase are fixed. Our proof is based on the matrix analytic approach pioneered by Neuts and recent results on the decay rates.
1405072
Effective document clustering for large heterogeneous law firm collections
Computational resources for research in legal environments have historically implied remote access to large databases of legal documents such as case law, statutes, law reviews and administrative materials. Today, by contrast, there exists enormous growth in lawyers' electronic work product within these environments, specifically within law firms. Along with this growth has come the need for accelerated knowledge management---automated assistance in organizing, analyzing, retrieving and presenting this content in a useful and distributed manner.In cases where a relevant legal taxonomy is available, together with representative labeled data, automated text classification tools can be applied. In the absence of these resources, document clustering offers an alternative approach to organizing collections, and an adjunct to search.To explore this approach further, we have conducted sets of successively more complex clustering experiments using primary and secondary law documents as well as actual law firm data. Tests were run to determine the efficiency and effectiveness of a number of essential clustering functions. After examining the performance of traditional or hard clustering applications, we investigate soft clustering (multiple cluster assignments) as well as hierarchical clustering. We show how these latter clustering approaches are effective, in terms of both internal and external quality measures, and useful to legal researchers. Moreover, such techniques can ultimately assist in the automatic or semi-automatic generation of taxonomies for subsequent use by classification programs.
10708118
Fault modeling and simulation for crosstalk in system-on-chip interconnects
System-on-chips (SOCs) using ultra deep sub-micron (DSM) technologies and GHz clock frequencies have been predicted by the 1997 SIA Road Map. Recent studies, as well as experiments reported in this paper, show significant crosstalk effects in long on-chip interconnects of GHz DSM chips. Recognizing the importance of high-speed, reliable interconnects in GHz SOCs, we address in this paper the problem of testing for glitch and delay errors caused by crosstalk in buses and interconnects between components of a SOC. Since it is not possible to explicitly test for all the possible process variations and defects that can lead to crosstalk errors in SOC interconnects, we present an abstract model, Maximum Aggressor (MA) fault model, and its test requirements. The attractiveness of the model is that it can abstract crosstalk defects in interconnects with a linear number of faults, while the corresponding MA tests provide complete coverage for all level defects related to cross-coupling capacitance the interconnects. A SPICE-level fault simulation methodology is presented which allows simulation of a small subset of the potentially exponential number of defects. The simulation methodology also enables validation of the proposed fault model and the resulting test set.