8575653
Using Shortest Path Algorithms to Identify Criminal Associations
Frequently in criminal investigations, law enforcement agencies face the problem of identifying associations between a group of entities such as individuals and organizations. In this paper we present a link analysis technique to solve such a problem. This approach uses shortest path algorithms to find the strongest associations between two or more given entities. The experimental results have demonstrated that our approach is potentially useful in terms of quality and efficiency. Specifically, we found that the two-tree Priority-First Search algorithm in most cases was the fastest algorithm to find shortest paths and the paths found consisted of meaningful criminal associations around 80 percent of the time.
16185032
Using Social Contextual Information to Match Criminal Identities
Criminal identity matching is crucial to crime investigation in law enforcement agencies. Existing techniques match identities that refer to the same individuals based on simple identity features. These techniques are subject to several problems. First, there is an effectiveness trade-off between the false negative and false positive rates. The improvement of one rate usually lowers the other. Second, in some situations such as identity theft, simple-feature-based techniques are unable to match identities that have completely different identity feature values. We argue that the information about the social context of an individual may provide additional information for revealing the individual’s identity, helping improve the effectiveness of identity matching techniques. We define two types of social contextual features: role-based personal features and social group features. Experiments showed that social contextual features, especially the structural similarity and the relational similarity, significantly improved the precision without lowering the recall of criminal identity matching tasks.
1639751
Expertise visualization: An implementation and study based on cognitive fit theory
Expertise management systems are being widely adopted in organizations to manage tacit knowledge. These systems have successfully applied many information technologies developed for document management to support collection, processing, and distribution of expertise information. In this paper, we report a study on the potential of applying visualization techniques to support more effective and efficient exploration of the expertise information space. We implemented two widely applied dimensionality reduction visualization techniques, the self-organizing map (SOM) and multidimensional scaling (MDS), to generate compact but distorted (due to the dimensionality reduction) map visualizations for an expertise data set. We tested cognitive fit theory in our context by comparing the SOM and MDS displays with a standard table display for five tasks selected from a low-level, domain-independent visual task taxonomy. The experimental results based on a survey data set of research expertise of the business school professors suggested that using both SOM and MDS visualizations is more efficient than using the table display for the associate, compare, distinguish, and cluster tasks, but not the rank task. Users generally achieved comparable effectiveness for all tasks using the tabular and map displays in our study. © 2006 Elsevier B.V. All rights reserved.
17196137
Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks
Effective and efficient link analysis techniques are needed to help law enforcement and intelligence agencies fight organized crimes such as narcotics violation, terrorism, and kidnapping. In this paper, we propose a link analysis technique that uses shortest-path algorithms, priority-first-search (PFS) and two-tree PFS, to identify the strongest association paths between entities in a criminal network. To evaluate effectiveness, we compared the PFS algorithms with crime investigators' typical association-search approach, as represented by a modified breadth-first-search (BFS). Our domain expert considered the association paths identified by PFS algorithms to be useful about 70% of the time, whereas the modified BFS algorithm's precision rates were only 30% for a kidnapping network and 16.7% for a narcotics network. Efficiency of the two-tree PFS was better for a small, dense kidnapping network, and the PFS was better for the large, sparse narcotics network.
7877784
On the Topology of the Dark Web of Terrorist Groups
In recent years, terrorist groups have used the WWW to spread their ideologies, disseminate propaganda, and recruit members. Studying the terrorist websites may help us understand the characteristics of these websites and predict terrorist activities. In this paper, we propose to apply network topological analysis methods on systematically collected the terrorist website data and to study the structural characteristics at the Web page level. We conducted a case study using the methods on three collections of Middle-Eastern, US domestic, and Latin-American terrorist websites. We found that these three networks have the small-world and scale-free characteristics. We also found that smaller size websites which share same interests tend to make stronger inter-website linkage relationships.
215893227
CrimeNet explorer: a framework for criminal network knowledge discovery
Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for netw...
19232033
Protein Threading by Linear Programming: Theoretical Analysis and Computational Results
In a previous paper (Xu, Li, Kim, and Xu, Journal of Bioinformatics and Computational Biology, vol. 1, no. 1, pp. 95–117, 2003), we have used an integer programming approach to implement a protein threading program RAPTOR for protein 3D structure prediction, based on the threading model treating pairwise contacts rigorously and allowing variable gaps. We have solved the integer program by the canonical branch-and-bound method. In this paper we present a branch-and-cut method, a careful theoretical analysis of our formulation and why our approach is so effective. The result of cutting plane analysis is that two types of well-known cuts for this problem are already implied in the constraint set, which provides us some intuition that our formulation would be very effective. Experimental results show that for about 99 percent of real threading instances, the linear relaxations of their integer programs solve to integral optimal solutions directly. For the rest one percent of real instances, the integral solutions can be obtained with only several branch nodes. Experimental results also show that no special template or sequence features result in more possibilities of fractional solutions. This indicates that extra effort to seek for cutting planes to strengthen the existing formulation is unnecessary.
36507
Ranking definitions with supervised learning methods
This paper is concerned with the problem of definition search. Specifically, given a term, we are to retrieve definitional excerpts of the term and rank the extracted excerpts according to their likelihood of being good definitions. This is in contrast to the traditional approaches of either generating a single combined definition or simply outputting all retrieved definitions. Definition ranking is essential for the task. Methods for performing definition ranking are proposed in this paper, which formalize the problem as either classification or ordinal regression. A specification for judging the goodness of a definition is given. We employ SVM as the classification model and Ranking SVM as the ordinal regression model respectively, such that they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined. An enterprise search system based on this method has been developed and has been put into practical use. Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods of using heuristic rules or employing the conventional information retrieval method of Okapi. This is true both when the answers are paragraphs and when they are sentences. Experimental results also show that SVM or Ranking SVM models trained in one domain can be adapted to another domain, indicating that generic models for definition ranking can be constructed.
19187096
Traveling salesman problem of segments
In this paper, we present a polynomial time approximation scheme (PTAS) for a variant of the traveling salesman problem (called segment TSP) in which a traveling salesman tour is sought to traverse a set of n ∊-separated segments in two dimensional space. Our results are based on an interesting combinatorial result which bounds the total number of entry points in an optimal TSP tour and a generalization of Arora's technique5 for Euclidean TSP (of a set of points). The randomized version of our algorithm takes O(n2(log n)O(1/∊2)) time to compute a (1+∊)-approximation with probability ≥l/2, and can be derandomized with an additional factor of O(n2).
16148439
Solving the brachytherapy seed localization problem using geometric and linear programming techniques
We propose a technique to solve the Brachytherapy Seed Localization problem in prostate brachytherapy. Our algorithm is based on novel geometric approaches to exploit the special structure of the problem combined with a number of key observations which help us formulate it as an Integer Program. We solve the equivalent linear program and present a variant of randomized rounding to yield an integral solution to our problem. The algorithm is efficient and performs extremely well in practice. We discuss in detail the underlying theory and performance evaluations based on our implementation.
14580037
An architectural framework for providing reliability and security support
This paper explores hardware-implemented error-detection and security mechanisms embedded as modules in a hardware-level framework called the reliability and security engine (RSE), which is implemented as an integral part of a modern microprocessor. The RSE interacts with the processor through an input/output interface. The CHECK instruction, a special extension of the instruction set architecture of the processor, is the interface of the application with the RSE. The detection mechanisms described here in detail are: (I) the memory layout randomization (MLR) module, which randomizes the memory layout of a process in order to foil attackers who assume a fixed system layout, (2) the data dependency tracking (DDT) module, which tracks the dependencies among threads of a process and maintains checkpoints of shared memory pages in order to rollback the threads when an offending (potentially malicious) thread is terminated, and (3) the instruction checker module (ICM), which checks an instruction for its validity or the control-flow of the program just as the instruction enters the pipeline for execution. Performance simulations for the studied modules indicate low overhead of the proposed solutions.