5651829
Utilizing Domain Knowledge in Neuroevolution
We propose a method called Rule-based ESP (RESP) for utilizing prior knowledge evolving Artificial Neural Networks (ANNs). First, KBANN-likete chniques are used to transform a set of rules into an ANN, then the ANN is trained using the Enforced Subpopulations (ESP) neuroevolution method. Empirical results in the Prey Capture domain show that RESP can reach higher level of performance than ESP. The results also suggest that incremental learning is not necessary with RESP, and it is often easier to design a set of rules than an incremental evolution scheme. In addition, an experiment with some of the rules deleted suggests that RESP is robust even with an incomplete knowledge base. RESP therefore provides a robust methodology for scaling up neuroevolution to harder tasks by utilizing existing knowledge about the domain.
5975278
The Knowledge Required to Interpret Noun Compounds
This paper presents a novel approach to the estimation of user’s affective states in Human-Computer Interaction. Most of the present approaches divide emotions strictly between positive or negative. However, recent discoveries in the field of Emotional Intelligence show that emotions should be rather perceived as context-sensitive engagements with the world. This leads to a need to specify whether the emotions conveyed in a conversation are appropriate for a situation they are expressed in. In the proposed method we use a system for affect analysis on textual input to recognize users emotions and a Web mining technique to verify the contextual appropriateness of those emotions. On this basis a conversational agent can choose to either sympathize with the user or help them manage their emotions. Finally, the results of evaluation of the proposed method with two different conversational agents are discussed, and perspectives for further development of the method are proposed.
1153368
Indirect anaphora resolution as semantic path search
Anaphora occur commonly in natural language text, and resolving them is essential for capturing the knowledge encoded in text. Indirect anaphora are especially challenging to resolve because the referring expression and the antecedent are related by unstated background knowledge. Such anaphora need to be resolved properly in order to automatically capture the knowledge expressed in natural language. Resolving indirect anaphora has been treated as a unique problem that requires special-purpose methods, and these methods have had limited success in precision and recall. In this study, we used a generic tool for finding semantic paths between two concepts to resolve these anaphora, and it achieved approximately twice the recall of the best previous system without loss of precision. A series of ablation study showed that the biggest increase in recall came from an abductive stopping criterion of the search.
1692629
Representing roles and purpose
Ontology designers often distinguish Entities (things that are) from Events (things that happen). It is not obvious how this division admits Roles (things that are, but only in the context of things that happen). For example, Person might be considered an Entity, while Employee is a Role. A Person remains a Person independent of the Events in which he participates. Someone is an Employee only by virtue of participating in an Employment Event. The problem of how to represent Roles is not new, but there is little consensus on a solution. In this paper, we present an ontology that finds a place for Roles as well as a representation that allows Roles to be related to Entities and Events to express the teleological notion of purpose.
5146657
Interpreting Loosely Encoded Questions
Knowledge-based question-answering systems have become quite competent and robust at answering a wide range of questions in different domains, however in order to ask questions correctly, one needs to have intimate knowledge of the structure of the knowledge base, and typical users lack this knowledge. We address this problem by developing a system that uses the content of the knowledge base to automatically align a user's encoding of a query to the structure of the knowledge base. Our preliminary evaluation shows the system detects and corrects most misalignments, and users are able to pose most questions quickly.
4017008
Gene symbol disambiguation using knowledge-based profiles
MOTIVATION
The ambiguity of biomedical entities, particularly of gene symbols, is a big challenge for text-mining systems in the biomedical domain. Existing knowledge sources, such as Entrez Gene and the MEDLINE database, contain information concerning the characteristics of a particular gene that could be used to disambiguate gene symbols.


RESULTS
For each gene, we create a profile with different types of information automatically extracted from related MEDLINE abstracts and readily available annotated knowledge sources. We apply the gene profiles to the disambiguation task via an information retrieval method, which ranks the similarity scores between the context where the ambiguous gene is mentioned, and candidate gene profiles. The gene profile with the highest similarity score is then chosen as the correct sense. We evaluated the method on three automatically generated testing sets of mouse, fly and yeast organisms, respectively. The method achieved the highest precision of 93.9% for the mouse, 77.8% for the fly and 89.5% for the yeast.


AVAILABILITY
The testing data sets and disambiguation programs are available at http://www.dbmi.columbia.edu/~hux7002/gsd2006
14087397
Statistical Model Order Reduction for Interconnect Circuits Considering Spatial Correlations
In this paper, we propose a novel statistical model order reduction technique, called statistical spectrum model order reduction (SS-MOR) method, which considers both intra-die and inter-die process variations with spatial correlations. The SSMOR generates order-reduced variational models based on given variational circuits. The reduced model can be used for fast statistical performance analysis of interconnect circuits with variational input sources, such as power grid and clock networks. The SSMOR uses statistical spectrum method to compute the variational moments and Monte Carlo sampling method with the modified Krylov subspace reduction method to generate the variational reduced models. To consider spatial correlations, we apply orthogonal decomposition to map the correlated random variables into independent and uncorrelated variables. Experimental results show that the proposed method can deliver about 100x speedup over the pure Monte Carlo projection-based reduction method with about 2% of errors for both means and variances in statistical transient analysis.
12382521
On the design and performance of prefix-preserving IP traffic trace anonymization
Even though real-world Internet traffic traces are crucial for network research, only a tiny percentage of traffic traces collected are made public. One major reason why traffic trace owners hesitate to make the traces publicly available is the concern that the confidential and private information may be inferred from the trace. In this paper we focus on the problem of anonymizing IP addresses in a trace. More specifically, we are interested in prefixpreserving anonymization in which the prefix relationship among IP addresses is preserved in the anonymized trace, making such a trace usable in situations where such prefix relationships are important. The goal of our work is two fold. First, we are interested in analyzing the security properties inherent in prefix-preserving IP address anonymization. Through the analysis of IP traffic traces, we investigate the effect of some types of attacks on the security of the prefix-preserving anonymization process. We also derive results for the optimum manner in which an attack should proceed which provides a bound on the performance of attacks in general. Second, we observe that an existing scheme used for prefix-preserving anonymization, TCPdpriv, has some drawbacks that limit its use in a large-scale, distributed setting. We develop an alternative cryptography-based, prefixpreserving anonymization technique to address these drawbacks while maintaining the same level of anonymity as TCPdpriv.
228217
Localized on-chip power delivery network optimization via sequence of linear programming
In this paper, we propose an efficient algorithm to reduce the voltage noises for on-chip power/ground (P/G) networks of VLSI. The new method is based on the sequence of linear programming (SLP) method as the optimization engine and a localized scheme via partitioning for dealing with large circuits. We show that by directly optimizing the decap area as the objective function and using the time-domain adjoint method, SLP can deliver much better quality than existing methods based on the merged time-domain adjoint method. The partitioning strategy further improves the scalability of the proposed algorithm and makes it efficient for large circuits. The resulting algorithm is general enough for any P/G network. Experimental results demonstrate the advantage of the proposed method over existing state-of-the-art methods in terms of solution quality at a mild computation cost increase
10379627
Medical video mining for efficient database indexing, management and access
To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.