Adaptive music retrieval–a state of the art

With the development of more and more sophisticated Music Information Retrieval approaches, aspects of adaptivity are becoming an increasingly important research topic. Even though, adaptive techniques have already found their way into Music Information Retrieval systems and contribute to robustness or user satisfaction they are not always identified as such. This paper attempts a structured view on the last decade of Music Information Retrieval research from the perspective of adaptivity in order to increase awareness and promote the application and further development of adaptive techniques. To this end, different approaches from a wide range of application areas that share the common aspect of adaptivity are identified and systematically categorized.     

Evaluating recommender systems from the user’s perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users?? perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS??s ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users?? adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system??s recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system??s overall perceptive qualities and how these qualities influence users?? behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods.     

Ontology-based state representations for intention recognition in human–robot collaborative environments

In this paper, we describe a novel approach for representing state information for the purpose of intention recognition in cooperative human–robot environments. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon a set of predefined high-level state relationships that must be true for future actions to occur, a robot can use the detailed state information described in this paper to infer the probability of subsequent actions occurring. This would allow the robot to better help the human with the task or, at a minimum, better stay out of his or her way.     

Invariance analysis of modified C2 features: case study—handwritten digit recognition

Humans are very efficient in recognizing alphanumeric characters, even in the presence of significant image distortions. Recent advances in visual neuroscience have led to a solid model of object and shape recognition in the visual ventral stream which competes with the state-of-the-art computer vision systems on some standard recognition tasks. A modification of this model is also proposed by adding more biologically inspired properties such as sparsification of features, lateral inhibition and feature localization to enhance its performance. In this study, we show that using features proposed by the modified model results in higher handwritten digit recognition rates compared with the original model over English and Farsi handwritten digit datasets. Our analyses also demonstrate higher invariance of the modified model to various image distortions.     

Universal noiseless and noisy data compression techniques — the state of the art

The technique of data compression demonstrates desirable properties that assure performance improvements of advanced network architectures such as ISDN, serial communication channels, data storage and data archiving. A global trend in telecommunications networks is the conversion from analogue to digital transmission. The design goal of these new networks is that voice, data, and images will be transmitted in fundamentally the same way, requiring neither prior knowledge as to the type of data being transmitted nor operator intervention. However, the bandwidth requirements for digital transmission are generally high. Spectrum bandwidth utilization is a design parameter for these systems that will benefit from the transmission of compressed data. The proposed Integrated Services Digital Network (ISDN) combines digital voice, data, and images on the same network. Since this network has limited bandwidth for transmission of information, the available bandwidth must be used as efficiently as possible.     

Syntax–Aware graph convolutional network for the recognition of chinese implicit inter-sentence relations

The Journal of Supercomputing - In the literature, most previous studies on English implicit inter-sentence relation recognition only focused on semantic interactions, which could not exploit the...     

Recursive “concave–convex” Fisher Linear Discriminant with applications to face,handwritten digit and terrain recognition

In classification, previous studies have shown that an eigenvalue based technique can be cast as an related SVM-type problem and that by solving this SVM-type problem, the performance can be improved significantly. In this paper, we develop a recursive “concave–convex” Fisher Linear Discriminant (DR) (RPFLD) for dimension reduction technique of high-dimensional data to extract as many meaningful features as possible, which incorporates the fundamental idea behind Fisher Linear Discriminant and casts the Fisher Linear Discriminant as a “concave–convex” programming problem based on the hinge loss. The solution of our method follows from solving the related SVM-type optimization problems iteratively, which means the proposed method, can be viewed as the combination of multiple related SVM-type problems. The special formulation of our method provides convenience for constructing sparse multi-class Fisher Linear Discriminant directly. Due to use of a recursive procedure, the number of features available from RPFLD is independent of the number of classes, meaning that in contrast to the original Fisher Linear Discriminant the number of features available from our method has no upper bound. We evaluate our algorithm on the Yale, and ORL face image databases, handwritten digit database and Terrain image dataset. Experimental results show that RPFLD outperforms other Fisher Linear Discriminant algorithms.