14593921
Stochastic link and group detection
Link detection and analysis has long been important in the social sciences and in the government intelligence community. A significant effort is focused on the structural and functional analysis of "known" networks. Similarly, the detection of individual links is important but is usually done with techniques that result in "known" links. More recently the internet and other sources have led to a flood of circumstantial data that provide probabilistic evidence of links. Co-occurrence in news articles and simultaneous travel to the same location are two examples.We propose a probabilistic model of link generation based on membership in groups. The model considers both observed link evidence and demographic information about the entities. The parameters of the model are learned via a maximum likelihood search. In this paper we describe the model and then show several heuristics that make the search tractable. We test our model and optimization methods on synthetic data sets with a known ground truth and a database of news articles.
7098682
High-performing feature selection for text classification
This paper reports a controlled study on a large number of filter feature selection methods for text classification. Over 100 variants of five major feature selection criteria were examined using four well-known classification algorithms: a Naive Bayesian (NB) approach, a Rocchio-style classifier, a k-nearest neighbor (kNN) method and a Support Vector Machine (SVM) system. Two benchmark collections were chosen as the testbeds: Reuters-21578 and small portion of Reuters Corpus Version 1 (RCV1), making the new results comparable to published results. We found that feature selection methods based on chi2 statistics consistently outperformed those based on other criteria (including information gain) for all four classifiers and both data collections, and that a further increase in performance was obtained by combining uncorrelated and high-performing feature selection methods.The results we obtained using only 3% of the available features are among the best reported, including results obtained with the full feature set.
10555290
From Lasso regression to Feature vector machine
Lasso regression tends to assign zero weights to most irrelevant or redundant features, and hence is a promising technique for feature selection. Its limitation, however, is that it only offers solutions to linear models. Kernel machines with feature scaling techniques have been studied for feature selection with non-linear models. However, such approaches require to solve hard non-convex optimization problems. This paper proposes a new approach named the Feature Vector Machine (FVM). It reformulates the standard Lasso regression into a form isomorphic to SVM, and this form can be easily extended for feature selection with non-linear models by introducing kernels defined on feature vectors. FVM generates sparse solutions in the nonlinear feature space and it is much more tractable compared to feature scaling kernel machines. Our experiments with FVM on simulated data show encouraging results in identifying the small number of dominating features that are non-linearly correlated to the response, a task the standard Lasso fails to complete.
1743726
Hypertext Categorization using Hyperlink Patterns and Meta Data
Hypertext poses new text classi cation research challenges as hyperlinks, content of linked documents, and meta data about related web sites all provide richer sources of information for hypertext classi cation that are not available in traditional text classi cation. We investigate the use of such information for representing web sites, and the e ectiveness of di erent classi ers (Naive Bayes, Nearest Neighbor, and Foil) in exploiting those representations. We nd that using words in web pages alone often yields suboptimal performance of classi ers, compared to exploiting additional sources of information beyond document content. On the other hand, we also observe that linked pages can be more harmful than helpful when the linked neighborhoods are highly \noisy" and that links have to be used in a careful manner. More importantly, our investigation suggests that meta data which is often available, or can be acquired using Information Extraction techniques, can be extremely useful for improving classi cation accuracy. Finally, the relative performance of the di erent classi ers being tested gives us insights into the strengths and limitations of our algorithms for hypertext classi cation.
14181364
Probabilistic score estimation with piecewise logistic regression
Well-calibrated probabilities are necessary in many applications like probabilistic frameworks or cost-sensitive tasks. Based on previous success of asymmetric Laplace method in calibrating text classifiers' scores, we propose to use piecewise logistic regression, which is a simple extension of standard logistic regression, as an alternative method in the discriminative family. We show that both methods have the flexibility to be piecewise linear functions in log-odds, but they are based on quite different assumptions. We evaluated asymmetric Laplace method, piecewise logistic regression and standard logistic regression over standard text categorization collections (Reuters-21578 and TRECAP) with three classifiers (SVM, Naive Bayes and Logistic Regression Classifier), and observed that piecewise logistic regression performs significantly better than the other two methods in the log-loss metric.
8555900
Unsupervised Learning of Arabic Stemming Using a Parallel Corpus
This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10 K sentences) parallel corpus as its sole training resources. No parallel text is needed after the training phase. Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
17112661
QB or Not QB: An Efficient Execution Verification Tool for Memory Orderings
We study the problem of formally verifying shared memory multiprocessor executions against memory consistency models—an important step during post-silicon verification of multiprocessor machines. We employ our previously reported style of writing formal specifications for shared memory models in higher order logic (HOL), obtaining intuitive as well as modular specifications. Our specification consists of a conjunction of rules that constrain the global visibility order. Given an execution to be checked, our algorithm generates Boolean constraints that capture the conditions under which the execution is legal under the visibility order. We initially took the approach of specializing the memory model HOL axioms into equivalent (for the execution to be checked) quantified boolean formulae (QBF). As this technique proved inefficient, we took the alternative approach of converting the HOL axioms into a program that generates a SAT instance when run on an execution. In effect, the quantifications in our memory model specification were realized as iterations in the program. The generated Boolean constraints are satisfiable if and only if the given execution is legal under the memory model. We evaluate two different approaches to encode the Boolean constraints, and also incremental techniques to generate and solve Boolean constraints. Key results include a demonstration that we can handle executions of realistic lengths for the modern Intel Itanium memory model. Further research into proper selection of Boolean encodings, incremental SAT checking, efficient handling of transitivity, and the generation of unsatisfiable cores for locating errors are expected to make our technique practical.
12472387
CLID: A general approach to validate security policies in a dynamic network
Many researchers have considered security policy management, including how to configure policies manually and even how to automatically generate security policies based on security requirements. Both can be error prone, especially when properties of the network topology change, because security requirements are usually not bound to any particular route path. Our DETER lab emulation results show that conflicts could be caused by these factors. Therefore, a systematic way to validate the correctness of the security policies is essential. This paper presents an approach, CLID (conflict and looping identification and detection), to verify whether a set of security policies (e.g. IPSec/VPN tunnels) satisfy the given security requirements, without causing any conflicts. This approach utilizes the definition of a security policy lo include network routing data as well as traffic selector information, thus it works for general network topologies. We also analyze and justify the correctness of the approach. The paper concludes with our simulation results and addresses future work.
4910050
Empirical Study on Benchmarking Software Development Tasks
Benchmarking is one of the most important methods to learn the best practices for software process improvement. However, in current software process context, benchmarking is mainly for projects rather than software development tasks. Can we benchmark software development tasks? If so, how to? Moreover, benchmarking software development tasks has to deal with multivariate and variable return to scale (VRS). This paper reports practical experience of benchmarking software development tasks under multivariate and VRS constraints using Data Envelopment Analysis (DEA). The analysis of experience data in Institute of Software, Chinese Academy of Sciences (ISCAS) indicates that the ideas and techniques of benchmarking software projects can be deployed at the software development task level. Moreover, results also show that DEA VRS model allows the developers to gain new insight about how to identify the relatively efficient tasks as the task performance benchmark and how to establish different reference sets for each relatively inefficient task under multivariate and VRS constraints. We thus recommend DEA VRS model be used as the default technique for appropriately benchmarking software development tasks. Our results are beneficial to software process improvement. To the best of our knowledge, we believe that it is the first time to report such comprehensive and repeatable results of benchmarking software development tasks using DEA.
2067202
On self adaptive routing in dynamic environments - an evaluation and design using a simple, probabilistic scheme
Recently we have seen an emergent trend of self adaptive routing in both Internet and wireless ad hoc networks. Although there are previous methods for computing the traffic equilibria of self adaptive routing (e.g., selfish routing), these methods use computationally demanding algorithms and require that a precise analytical model of the network be given. Also, it remains an open question how to design an adaptive routing scheme which ensures convergence to traffic equilibria in practice. In this paper we propose a simple, efficient, distributed probabilistic routing scheme for self adaptive routing in dynamic, realistic environments. Using both analysis and extensive simulations, we show that our scheme can converge to the desired traffic equilibrium (either user-optimal or network-optimal) very quickly. We find that user-optimal routing can achieve very close to optimal average latency in dynamic environments, but such performance often comes at the cost of seriously overloading certain links. To avoid link overloads, we improve adaptive routing by optimizing average user latency and link utilization simultaneously. Our evaluation shows that there is a trade-off between optimizing dual objectives, but the degradation in average latency is only marginal for typical link utilization requirements.
15017783
On the Computational Complexity of Sensor Network Localization
Determining the positions of the sensor nodes in a network is essential to many network functionalities such as routing, coverage and tracking, and event detection. The localization problem for sensor networks is to reconstruct the positions of all of the sensors in a network, given the distances between all pairs of sensors that are within some radius r of each other. In the past few years, many algorithms for solving the localization problem were proposed, without knowing the computational complexity of the problem. In this paper, we show that no polynomial-time algorithm can solve this problem in the worst case, even for sets of distance pairs for which a unique solution exists, unless RP = NP. We also discuss the consequences of our result and present open problems.