1,463 research outputs found
Incorporating soft computing techniques into a probabilistic intrusion detection system
Blurring-Sharpening Process Models for Collaborative Filtering
Collaborative filtering is one of the most fundamental topics for recommender
systems. Various methods have been proposed for collaborative filtering,
ranging from matrix factorization to graph convolutional methods. Being
inspired by recent successes of graph filtering-based methods and score-based
generative models (SGMs), we present a novel concept of blurring-sharpening
process model (BSPM). SGMs and BSPMs share the same processing philosophy that
new information can be discovered (e.g., new images are generated in the case
of SGMs) while original information is first perturbed and then recovered to
its original form. However, SGMs and our BSPMs deal with different types of
information, and their optimal perturbation and recovery processes have
fundamental discrepancies. Therefore, our BSPMs have different forms from SGMs.
In addition, our concept not only theoretically subsumes many existing
collaborative filtering models but also outperforms them in terms of Recall and
NDCG in the three benchmark datasets, Gowalla, Yelp2018, and Amazon-book. In
addition, the processing time of our method is comparable to other fast
baselines. Our proposed concept has much potential in the future to be enhanced
by designing better blurring (i.e., perturbation) and sharpening (i.e.,
recovery) processes than what we use in this paper.Comment: Accepted by SIGIR 202
Non-Iterative Tone Mapping With High Efficiency and Robustness
This paper proposes an efficient approach for tone mapping, which provides a high perceptual image quality for diverse scenes. Most existing methods, optimizing images for the perceptual model, use an iterative process and this process is time consuming. To solve this problem, we proposed a new layer-based non-iterative approach to finding an optimal detail layer for generating a tone-mapped image. The proposed method consists of the following three steps. First, an image is decomposed into a base layer and a detail layer to separate the illumination and detail components. Next, the base layer is globally compressed by applying the statistical naturalness model based on the statistics of the luminance and contrast in the natural scenes. The detail layer is locally optimized based on the structure fidelity measure, representing the degree of local structural detail preservation. Finally, the proposed method constructs the final tone-mapped image by combining the resultant layers. The performance evaluation reveals that the proposed method outperforms the benchmarking methods for almost all the benchmarking test images. Specifically, the proposed method improves an average tone mapping quality index-II (TMQI-II), a feature similarity index for tone-mapped images (FSITM), and a high-dynamic range-visible difference predictor (HDR-VDP)-2.2 by up to 0.651 (223.4%), 0.088 (11.5%), and 10.371 (25.2%), respectively, compared with the benchmarking methods, whereas it improves the processing speed by over 2611 times. Furthermore, the proposed method decreases the standard deviations of TMQI-II, FSITM, and HDR-VDP-2.2, and processing time by up to 81.4%, 18.9%, 12.6%, and 99.9%, respectively, when compared with the benchmarking methods.11Ysciescopu
Multiple Classifier Fusion using k-Nearest Localized Templates
Abstract. This paper presents a method for combining classifiers that uses knearest localized templates. The localized templates are estimated from a training set using C-means clustering algorithm, and matched to the decision profile of a new incoming sample by a similarity measure. The sample is assigned to the class which is most frequently represented among the k most similar templates. The appropriate value of k is determined according to the characteristics of the given data set. Experimental results on real and artificial data sets show that the proposed method performs better than the conventional fusion methods
Single-Copy Certification of Two-Qubit Gates without Entanglement
A quantum state transformation can be generally approximated by single- and
two-qubit gates. This, however, does not hold with noisy intermediate-scale
quantum technologies due to the errors appearing in the gate operations, where
errors of two-qubit gates such as controlled-NOT and SWAP operations are
dominated. In this work, we present a cost efficient single-copy certification
for a realization of a two-qubit gate in the presence of depolarization noise,
where it is aimed to identify if the realization is noise-free, or not. It is
shown that entangled resources such as entangled states and a joint measurement
are not necessary for the purpose, i.e., a noise-free two-qubit gate is not
needed to certify an implementation of a two-qubit gate. A proof-of-principle
demonstration is presented with photonic qubits.Comment: 8 pages. arXiv admin note: text overlap with arXiv:1812.0208
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