The use of statistical AI techniques in authorship recognition (or stylometry) has contributed to literary and historical breakthroughs. These successes have led to the use of these techniques in criminal investigations and prosecutions.  However, few have studied adversarial attacks and their devastating effect on the robustness of existing classification methods. This paper presents a framework for adversarial attacks including obfuscation attacks, where a subject attempts to hide their identity imitation attacks, where a subject attempts to frame another subject by imitating their writing style.  The major contribution of this research is that it demonstrates that both attacks work very well.  The obfuscation attack reduces the effectiveness of the techniques to the level of random guessing and the imitation attack succeeds with 68-91% probability depending on the stylometric technique used.  These results are made more significant by the fact that the experimental subjects were unfamiliar with stylometric techniques, without specialized knowledge in linguistics, and spent little time on the attacks. This paper also provides another significant contribution to the field in using human subjects to empirically validate the claim of high accuracy for current techniques (without attacks) by reproducing results for three representative stylometric methods.

In this paper we describe the application of a novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspace need to be reconciled and managed automatically. The key feature of our "Generalized Integrated Learning Architecture" (GILA) is a set of integrated learning and reasoning (ILR) systems coordinated by a central meta-reasoning executive (MRE). Each ILR learns independently from the same training example and contributes to problem-solving in concert with other ILRs as directed by the MRE. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Further, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

Language students can increase their effectiveness in learning written Japanese by mastering the visual structure and written technique of Japanese kanji.  Yet, existing kanji handwriting recognition systems do not assess the written technique sufficiently enough to discourage students from developing bad learning habits.  In this paper, we describe our work on Hashigo, a kanji sketch interactive system which achieves human instructor-level critique and feedback on both the visual structure and written technique of students’ sketched kanji.  This type of automated critique and feedback allows students to target and correct specific deficiencies in their sketches that, if left untreated, are detrimental to effective long-term kanji learning.

This paper describes a novel approach based on a combination of techniques in AI, parallel computing, and network science to address an important problem in social sciences and public health: planning and responding in the event of epidemics. Spread of infectious disease is an important societal problem -- human behavior, social networks, and the civil infrastructures all play a crucial role in initiating and controlling such epidemic processes.  We specifically consider the economic and social effects of realistic interventions  proposed and adopted by public health officials and behavioral changes  of  private citizens in the event of a ``flu-like'' epidemic. Our results provide new insights for developing robust public policies that can prove useful for epidemic planning.

This paper introduces the challenge of digital preservation in the   area of engineering design and manufacturing and presents a   methodology to apply knowledge representation and semantic   techniques to develop Digital Engineering Archives.  This work   is part of an ongoing, multi-university, effort to create   Cyber-Infrastructure-Based Engineering Repositories for   Undergraduates (CIBER-U) to support engineering design education.   The technical approach is to use knowledge representation techniques   to create formal models of engineering data elements, workflows and   processes.  With these formal engineering knowledge and processes   can be captured and preserved with some guarantee of long-term   interpretability.  The paper presents examples of how the techniques   can be used to encode specific engineering information     packages and workflows.  These techniques are being integrated   into a semantic Wiki that supports the CIBER-U engineering education   activities across nine universities and involving over 3,500   students since 2006.

We describe an AI-enhanced task management tool developed for a military environment, which differs from office environments in important ways: differing time scales, a focus on teams collaborating on tasks instead of an individual managing her own set of diverse tasks, and a focus on tasklists and standard operating procedures instead of individual tasks. We discuss the Task Assistant prototype, our process for adapting it from an office environment to a military one, and lessons learned about developing AI technology for a high-pressure operational environment.

Personal handwritings can add colors to human communication. Handwriting, however, takes more time and is less favored than typing in the digital age. In this paper we propose an intelligent algorithm which can generate imitations of Chinese handwriting by a person requiring only a very small set of training characters written by the person. Our method first decomposes the sample Chinese handwriting characters into a hierarchy of reusable components, called character components. During handwriting generation, the algorithm tries and compares different possible ways to compose the target character. The likeliness of a given personal handwriting generation result is evaluated according to the captured characteristics of the person's handwriting. We then find among all the candidate generation results an optimal one which can maximize a likeliness estimation. Experiment results show that our algorithm works reasonably well in the majority of the cases and sometimes remarkably well, which was verified through comparison with the groundtruth data and by a small scale user survey.

Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduction with numerous applications in science and engineering. However, the standard PCA suffers from the fact that the principal components (PCs) are usually linear combinations of all the original variables, and it is thus often difficult to interpret the PCs. To alleviate this drawback, various sparse PCA approaches were proposed in literature [15, 6, 17, 28, 8, 25, 18, 7, 16]. Despite success in achieving sparsity, some important properties enjoyed by the standard PCA are lost in these methods such as uncorrelation of PCs and orthogonality of loading vectors. Also, the total explained variance that they attempt to maximize can be too optimistic. In this paper we propose a new formulation for sparse PCA, aiming at finding sparse and nearly uncorrelated PCs with orthogonal loading vectors while explaining as much of the total variance as possible. We also develop a novel augmented Lagrangian method for solving a class of nonsmooth constrained optimization problems, which is well suited for our formulation of sparse PCA. We show that it converges to a feasible point, and moreover under some regularity assumptions, it converges to a stationary point. Additionally, we propose two nonmonotone gradient methods for solving the augmented Lagrangian subproblems, and establish their global and local convergence. Finally, we compare our sparse PCA approach with several existing methods on synthetic, random, and real data, respectively. The computational results demonstrate that the sparse PCs produced by our approach substantially outperform those by other methods in terms of total explained variance, correlation of PCs, and orthogonality of loading vectors.

There have been several highway traffic models proposed based on cellular automata. The simplest one is elementary cellular automaton rule 184. We extend this model to city traffic with cellular automata coupled at intersections using only rules 184, 252, and 136. The simplicity of the model offers a clear understanding of the main properties of city traffic and its phase transitions. We use the proposed model to compare two methods for coordinating traffic lights: a green-wave method that tries to optimize phases according to expected flows and a self-organizing method that adapts to the current traffic conditions. The self-organizing method delivers considerable improvements over the green-wave method. For low densities, the self-organizing method promotes the formation and coordination of platoons that flow freely in four directions, i.e. with a maximum velocity and no stops. For medium densities, the method allows a constant usage of the intersections, exploiting their maximum flux capacity. For high densities, the method prevents gridlocks and promotes the formation and coordination of "free-spaces" that flow in the opposite direction of traffic.

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