Effective and Efficient Similarity Search in Scientific Workflow Repositories

International audienceScientific workflows have become a valuable tool for large-scale data processing and analysis. This has led to the creation of specialized online repositories to facilitate worflkow sharing and reuse. Over time, these repositories have grown to sizes that call for advanced methods to support workflow discovery, in particular for similarity search. Effective similarity search requires both high quality algorithms for the comparison of scientific workflows and efficient strategies for indexing, searching, and ranking of search results. Yet, the graph structure of scientific workflows poses severe challenges to each of these steps. Here, we present a complete system for effective and efficient similarity search in scientific workflow repositories, based on the Layer Decompositon approach to scientific workflow comparison. Layer Decompositon specifically accounts for the directed dataflow underlying scientific workflows and, compared to other state-of-the-art methods, delivers best results for similarity search at comparably low runtimes. Stacking Layer Decomposition with even faster, structure-agnostic approaches allows us to use proven, off-the-shelf tools for workflow indexing to further reduce runtimes and scale similarity search to sizes of current repositories

Expert Object Recognition in video

A recent computer vision technique for object classification in still images is the biologically-inspired Expert Object Recognition (EOR). This thesis adapts and extends the EOR approach for use with segmented video data. Properties of this data, such as segmentation masks and the visibility of an object over multiple frames, are exploited to decrease human supervision and increase accuracy. Several types of runtime learning are facilitated: class-level learning in which object types that are not included in the training set are given artificial classes; viewpoint-level learning in which novel views of training objects are associated with existing classes; and instance-level learning of images that are somewhat similar to training images. The architecture of EOR, consisting of feature extraction, clustering, and cluster-specific principal component analysis, is retained. However, the K-means clustering algorithm used in EOR is replaced in this system by an augmented version of Fuzzy K-means. This algorithm is incrementally run over the lifetime of the system, and automatically determines an appropriate number of partitions based on the data in memory and on a system parameter. In addition, the edge and line-based feature extraction of EOR is replaced with a global application of the principal component analysis, which increases accuracy when used with segmented video data. Classification output for the system consists of a multi-class hypothesis for each tracked object, from which a single-class hard hypothesis may be determined. The system, named VEOR (video expert object recognition), is designed for and tested with noisy, automatically segmented real-world data, consisting of both videos and still images of vehicle (car, pickup truck, and van) profiles

From Manifesta to Krypta: The Relevance of Categories for Trusting Others

In this paper we consider the special abilities needed by agents for assessing trust based on inference and reasoning. We analyze the case in which it is possible to infer trust towards unknown counterparts by reasoning on abstract classes or categories of agents shaped in a concrete application domain. We present a scenario of interacting agents providing a computational model implementing different strategies to assess trust. Assuming a medical domain, categories, including both competencies and dispositions of possible trustees, are exploited to infer trust towards possibly unknown counterparts. The proposed approach for the cognitive assessment of trust relies on agents' abilities to analyze heterogeneous information sources along different dimensions. Trust is inferred based on specific observable properties (Manifesta), namely explicitly readable signals indicating internal features (Krypta) regulating agents' behavior and effectiveness on specific tasks. Simulative experiments evaluate the performance of trusting agents adopting different strategies to delegate tasks to possibly unknown trustees, while experimental results show the relevance of this kind of cognitive ability in the case of open Multi Agent Systems