We propose a method of mining most informative features for the event recognition from photo collections. Our goal is to classify different event categories based on the visual content of a group of photos that constitute the event. Such photo groups are typical in a personal photo collection of different events. Visual features are extracted from the images, yet the features from individual images are often noisy and not all of them represent the distinguishing characteristics of an event. We employ the PageRank technique to mine the most informative features from the images that belong to the same event. Subsequently, we classify different event categories using the multiple images of the same event because we argue that they are more informative about the content of an event rather than any single image. We compare our proposed approach with the standard bag of features method (BOF) and observe considerable improvements in recognition accuracy.
A self-aware signal processing architecture is proposed based on adaptive resource escalation which is guided by a multi-objective Genetic Algorithm (GA). The GA prioritizes tasks within a reconfigurable hardware fabric to maintain the quality-of-service and power consumption objectives. Attainment of these objectives is subject to the intrinsic reliability and performance of the computational elements in the resource pool. A health metric at the application layer, such as Peak-Signal-to-Noise Ratio (PSNR) measurement in a Discrete Cosine Transform (DCT) or Measure of Confidence in a Support Vector Machine (SVM) classifier, is used to assess throughput performance. When performance decreases beyond acceptable tolerances, the primary objective is to maximally recover output quality. The secondary objective is to minimize power consumption which also depends upon the input signal characteristics, in addition to the utilized computational resources. An adaptive guidance function for GA-driven recovery is proposed and validated for these objectives. It retains healthy processing elements in the throughput datapath to gracefully-degrade throughput by optimizing resource selection.
This article provides an introduction to plagiarism and the numerous negative aspects associated with it. Some examples from history have also been provided along with their outcomes. There are different types of plagiarism with varying legal and social aspects. The taxonomy of plagiarism is built by classifying it, with respect to the method involved in plagiarism, the form in which it happens or the intention of the plagiarist. The strategies suggested in the literature to avoid plagiarism are organized into individual and organizational levels. Individuals can adopt strategies to build habits of avoiding plagiarism and focus on their original and innovative way of thinking. Similarly, institutions can make policies to cope with plagiarism and hence maintain their reputation. In this paper, the focus is not on mentioning the plagiarism detection methods; rather we believe that building awareness in the people about plagiarism outcomes is more important than teaching them about the different methodologies used for detection. Some students avoid plagiarism detection as if playing a game and it can be only avoided by educating them in ethics. KeywordsPlagiarism, avoiding plagiarism, detecting plagiarism, consequences of plagiarism, types of plagiarism, problems of plagiarism, creativity and plagiarism.
We employ output-discrepancy consensus to mitigate faulty modules of a Triple Modular Redundant (TMR) arrangement using dynamic partial reconfiguration. Traditionally, the fault-handling resilience of a TMR arrangement is limited to fault(s) in a single TMR instance over the entire mission duration. An additional permanent fault in any of two other TMR instances results in mission's failure. However, in this work, a novel Self-Configuring approach for Discrepancy Resolution (SCDR) is developed and assessed. In SCDR, the occurrence of faults in more than one module initiates the repair mechanism, then upon fault recovery, the system is configured into Concurrent Error Detection (CED) mode. The approach is validated by the complete recovery of a TMR realization of 25 stage Finite Impulse Response (FIR) filter implemented on a reconfigurable platform as a case study. The results show that a self-healing circuit can be realized exploiting the dynamic partial reconfiguration capability of FPGAs while requiring a streamlined operational datapath compared to TMR.
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