Automatic attribute discovery methods have gained in popularity to extract sets of visual attributes from images or videos for various tasks. Despite their good performance in some classification tasks, it is difficult to evaluate whether the attributes discovered by these methods are meaningful and which methods are the most appropriate to discover attributes for visual descriptions. In its si...

Attribute reduction of an information system is a key problem in rough set theory and its applications. Using computational intelligence (CI) tools to solve such problems has recently fascinated many researchers. CI tools are practical and robust for many real-world problems, and they are rapidly developed nowadays. However, some classes of CI tools, like memory-based heuristics, have not been ...

We present several extensions to the ciphertext-policy attribute-based encryption (CP-ABE) scheme, first introduced by Bethencourt, et. al. (2007), to support operation in a distributed environment with multiple attribute authorities. Unlike other efforts in creating a multi-authority attribute-based encryption schemes our extensions allow for each authority to be designated a subset of attribu...

We present attribute bagging (AB), a technique for improving the accuracy and stability of classi#er ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classi#ers ar...

One of the biggest risks in software requirements engineering is the risk of overemphasizing one quality attribute requirement (e.g., performance) at the expense of others at least as important (e.g., evolvability and portability). This paper describes an exploratory knowledge-based tool for identifying potential conflicts among quality attributes early in the software/system life cycle. The Qu...

In this paper, we propose a joint semantic preserving action attribute learning framework for action recognition from depth videos, which is built on multistream deep neural networks. More specifically, this paper describes the idea to explore action attributes learned from deep activations. Multiple stream deep neural networks rather than conventional hand-crafted low-level features are employ...

Attribute clustering has been previously employed to detect statistical dependence between subsets of variables. Clusters of variables can be appropriately used for detecting highly dependent domain variables and then reducing the complexity of learning Bayesian networks. We propose a novel attribute clustering algorithm motivated by research of complex networks, called the Star Discovery algor...

In this paper, we study power conservation techniques for multi-attribute queries on wireless data broadcast channels. Indexing data on broadcast channels can improve client filtering capability, while clustering and scheduling can reduce both access time and tune-in time. Thus, indexing techniques should be coupled with clustering and scheduling methods to reduce the battery power consumption ...

Value Difference Metric (VDM) is one of the widely used distance functions to define the distance between a pair of instances with nominal attributes only. Many approaches have been proposed to improve the performance of VDM. In this paper, we focus on the attribute selection approach and propose another improved Value Difference Metric. We call it Selective Value Difference Metric (SVDM). In o...