Ridge-Adjusted Slack Variable Optimization for Supervised Classification

This paper presents an iterative classification algorithm called Ridge-adjusted Slack Variable Optimization (RiSVO). RiSVO is an iterative procedure with two steps: (1) A working subset of the training data is selected so as to reject "extreme" patterns. (2) the decision vector and threshold value are obtained by minimizing the energy function associated with the slack variables. From a computational perspective, we have established a sufficient condition for the "inclusion property" among successive working sets, which allows us to save computation time. Most importantly, under the inclusion property, the monotonic reduction of the energy function can be assured in both substeps at each iteration, thus assuring the convergence of the algorithm. Moreover, ridge regularization is incorporated to improve the robustness and better cope with over-fitting and ill-conditioned problems. To verify the proposed algorithm, we conducted simulations on three data sets from the UCI database: adult, shuttle and bank. Our simulation shows stability and convergence of the RiSVO method. The results also show improvement of performance over the SVM classifier

Learning to Select for MIMO Radar based on Hybrid Analog-Digital Beamforming

In this paper, we propose an energy-efficient radar beampattern design framework for a Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a machine learning approach to synthesize the probing beampattern based on a small number of RF chains and antennas. By leveraging a combination of softmax neural networks, the proposed solution is able to achieve a desirable beampattern with high accuracy

Innovative Applications of Natural Language Processing and Digital Media in Theatre and Performing Arts

The objective of our research is to investigate new digital techniques and tools, offering the audience innovative, attractive, enhanced and accessible experiences. The project focuses on performing arts, particularly theatre, aiming at designing, implementing, experimenting and evaluating technologies and tools that expand the semiotic code of a performance by offering new opportunities and aesthetic means in stage art and by introducing parallel accessible narrative flows. In our novel paradigm, modern technologies emphasize the stage elements providing a multilevel, intense and immersive theatrical experience. Moreover, lighting, video projections, audio clips and digital characters are incorporated, bringing unique aesthetic features. We also attempt to remove sensory and language barriers faced by some audiences. Accessibility features consist of subtitles, sign language and audio description. The project emphasises on natural language processing technologies, embedded communication and multimodal interaction to monitor automatically the time flow of a performance. Based on this, pre-designed and directed stage elements are being mapped to appropriate parts of the script and activated automatically by using the virtual "world" and appropriate sensors, while accessibility flows are dynamically synchronized with the stage action. The tools above are currently adapted within two experimental theatrical plays for validation purposes. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p

Experimental User-Centered Evaluation of an Open Hypermedia System and Web Information Seeking Environments

This paper presents an experimental user-centered evaluation of two hypermedia system architectures, each representing a different interaction model and information-seeking environment. The first system is a hypermedia digital library based on the World Wide Web. This system represents an interaction model in which information seekers consistently use a single interface (i.e. a Web browser) to access different information seeking strategies (ISSs). The second system is a similar library (in terms of content and organisation) that is based on an agent-based Open Hypermedia System (OHS). This library encourages an interaction model in which multiple user interfaces and information seeking strategies may be used in a more parallel fashion. Several researchers have suggested that information seeking may be more effective in systems that allow the parallel use of multiple information seeking strategies. On the other hand, the ease of use of the simple click-and-go-to interaction model introduced by the Web and the consistency of its interface appears to be more attractive for most information seekers. The aim of this paper is to examine and discuss these hypotheses critically. Although general conclusions cannot be drawn from the experiment, the results present some useful indications. A first indication is that information seeking environments that support multiple seeking strategies through multiple interfaces may be more effective and efficient for some information seeking tasks. Also, results taken from a questionnaire given to users of the OHS indicate that complex interaction models may not be prohibitively difficult to use, even for inexperienced information seekers

Blind separation of reflections using the image mixtures ratio

A new method for the blind separation of linear image mix-tures is presented in this paper. Such mixtures often oc-cur, when, for example, we photograph a scene through a semireflecting medium (windshield or glass). The proposed method requires two mixtures of two scenes captured under different illumination conditions. We show that the bound-ary values of the ratio of the two mixtures can lead to an accurate estimation of the separation matrix. The technique is very simple, fast, and reliable, as it does not depend on iterative procedures. The method effectiveness is tested on both artificially mixed images and real images. 1

Efficient Support Vector Machine Classification Using Prototype Selection and Generation

Part 7: Optimization-SVM (OPSVM)International audienceAlthough Support Vector Machines (SVMs) are considered effective supervised learning methods, their training procedure is time-consuming and has high memory requirements. Therefore, SVMs are inappropriate for large datasets. Many Data Reduction Techniques have been proposed in the context of dealing with the drawbacks of k-Nearest Neighbor classification. This paper adopts the concept of data reduction in order to cope with the high computational cost and memory requirements in the training process of SVMs. Experimental results illustrate that Data Reduction Techniques can effectively improve the performance of SVMs when applied as a preprocessing step on the training data