In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor control models, integrations of both and so on. In general, the robustness and precision of recognition is one of the key problems for object recognition models. In this paper, inspired by features of human recognition process and their biological mechanisms, a new integrated and dynamic framework is proposed to mimic the semantic extraction, concept formation and feature re-selection in human visual processing. The main contributions of the proposed model are as follows: (1) Semantic feature extraction: Local semantic features are learnt from episodic features that are extracted from raw images through a deep neural network; (2) Integrated concept formation: Concepts are formed with local semantic information and structural information learnt through network. (3) Feature re-selection: When ambiguity is detected during recognition process, distinctive features according to the difference between ambiguous candidates are re-selected for recognition. Experimental results on hand-written digits and facial shape dataset show that, compared with other methods, the new proposed model exhibits higher robustness and precision for visual recognition, especially in the condition when input samples are smantic ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science.

Answer: A Novel Biologically Mechanism-Based Visual Cognition Model Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity

Question: Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose a robust graph matching algorithm inspired in sparsityrelated techniques. We cast the problem, resembling group or collaborative sparsity formulations, as a non-smooth convex optimization problem that can be efficiently solved using augmented Lagrangian techniques. The method can deal with weighted or unweighted graphs, as well as multimodal data, where different graphs represent different types of data. The proposed approach is also naturally integrated with collaborative graph inference techniques, solving general network inference problems where the observed variables, possibly coming from different modalities, are not in correspondence. The algorithm is tested and compared with state-of-the-art graph matching techniques in both synthetic and real graphs. We also present results on multimodal graphs and applications to collaborative inference of brain connectivity from alignment-free functional magnetic resonance imaging (fMRI) data. The code is publicly available.

Answer: Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching

Question: In this paper we present methods for attacking and defending k-gram statistical analysis techniques that are used, for example, in network traffic analysis and covert channel detection. The main new result is our demonstration of how to use a behavior's or process' k-order statistics to build a stochastic process that has those same k-order stationary statistics but possesses different, deliberately designed, (k + 1)order statistics if desired. Such a model realizes a "complexification" of the process or behavior which a defender can use to monitor whether an attacker is shaping the behavior. By deliberately introducing designed (k + 1)-order behaviors, the defender can check to see if those behaviors are present in the data. We also develop constructs for source codes that respect the k-order statistics of a process while encoding covert information. One fundamental consequence of these results is that certain types of behavior analyses techniques come down to an arms race in the sense that the advantage goes to the party that has more computing resources applied to the problem. Points of view in this document are those of the authors and do not necessarily represent the official position of the sponsoring agencies or the U.S. Government. V. Crespi is with the Department of Computer Science, California State University at Los Angeles, Los Angeles CA, 90032 USA. email: vcrespi@calstatela.edu. Crespi's work was partially supported by AFOSR Grant FA9550-07-1-0421 and by NSF Grant HRD-0932421. G. Cybenko is with the Thayer School of Engineering, Dartmouth College, Hanover NH 03755. email: gvc@dartmouth.edu. Cybenko's work was partially supported by Air Force Research Laboratory contracts FA8750-10-1-0045, FA8750-09-1-0174, AFOSR contract FA9550-07-1-0421, U.S. Department of Homeland Security Grant 2006-CS-001-000001 and DARPA Contract HR001-06-1-0033 A. Giani is with the Department of EECS, University of California at Berkeley, Berkeley CA 94720. email: agiani@eecs.berkeley.edu. Giani's's work was partially supported by U.S. Department of Homeland Security Grant 2006-CS001-000001 and DARPA Contract HR001-06-1-0033 when she was a Ph.D. student at Dartmouth April 28, 2011 DRAFT ar X iv :1 10 4. 50 71 v1 [ cs .L G ] 2 7 A pr 2 01 1 CRESPI, CYBENKO, GIANI 2

Answer:
Attacking and Defending Covert Channels and Behavioral Models