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NRC publication: Ye

Speeding Up Problem Solving by Abstraction: A Graph Oriented Approach

This paper presents a new perspective on the traditional AI task of problem solving and the techniques of abstraction and refinement. The new perspective is based on the well known, but little exploited, relation between problem solving and the task of finding a path in a graph between two given nodes. The graph oriented view of abstraction suggests two new families of abstraction techniques, algebraic abstraction and STAR abstraction. The first is shown to be extremely sensitive to the exact manner in which problems are represented. STAR abstraction, by contrast, is very widely applicable and leads to significant speedup in all our experiments. The reformulation of traditional refinement techniques as graph algorithms suggests several enhancements, including an optimal refinement algorithm, and one radically new technique: alternating search direction. Experiments comparing these techniques on a variety of problems show that alternating opportunism (AltO) a variant of the new technique, is uniformly superior to all the others

Regression by Feature Projections

This paper describes a machine learning method, called Regression by Feature Projections (RFP), for predicting a real-valued target feature. In RFP training is based on simply storing the projections of the training instances on each feature separately. Prediction of the target value for a query point is obtained through two approximation procedures executed sequentially. The first approximation process is to find the individual predictions of features by using the K-nearest neighbor algorithm (KNN). The second approximation process combines the predictions of all features. During the first approximation step, each feature is associated with a weight in order to determine the prediction ability of the feature at the local query point. The weights, found for each local query point, are used in the second step and enforce the method to have an adaptive or context-sensitive nature. We have compared RFP with the KNN algorithm. Results on real data sets show that RFP is much faster than KNN, yet its prediction accuracy is comparable with the KNN algorithm

A Layered Detection Method for Malware Identification

Part 5: Session 5: Monitor, Diagnose, and Then OptimizeInternational audienceIn recent years, millions of new malicious programs are produced by Pa mature industry of malware production. These programs have tremendous challenges on the signature-based anti-virus products and pose great threats on network and information security. Machine learning techniques are applicable for detecting unknown malicious programs without knowing their signatures. In this paper, a Layered Detection (LD) method is developed to detect malwares with a two-layer framework. The Low-Level-Classifiers (LLC) are employed to identify whether the programs perform any malicious functions according to the API-calls of the programs. The Up-level-Classifier (ULC) is applied to detect malwares according to the low level function identification. The LD method is compared with many classical classification algorithms with comprehensive test datasets containing 16135 malwares and 1800 benign programs. The experiments demonstrate that the LD method outperforms other algorithms in terms of detection accuracy