Perez-Hurtado, IgnacioOrellana-Martin, DavidMartinez-del-Amor, Miguel A.Valencia-Cabrera, Luis...
22页
查看更多>>摘要：Membrane computing is a massively parallel and non-deterministic bioinspired computing paradigm whose models are called P systems. Validating and testing such models is a challenge which is being overcome by developing simulators. Regardless of their heterogeneity, such simulators require to read and interpret the models to be simulated. To this end, P Lingua is a high-level P system definition language which has been widely used in the last decade. The P-Lingua ecosystem includes not only the language, but also libraries and software tools for parsing and simulating membrane computing models. Each version of P Lingua supported new types or variants of P systems. This leads to a shortcoming: Only a predefined list of variants can be used, thus making it difficult for researchers to study custom ones. Moreover, derivation modes cannot be user-defined, i.e, the way in which P system computations should be generated is determined by the simulation algorithm in the source code. The main contribution of this paper is a completely new design of the P-Lingua language, called P-Lingua 5, in which the user can define custom variants and derivation modes, among other improvements such as including procedural programming and simulation directives. It is worth mentioning that it has backward-compatibility with previous versions of the language. A completely new set of command-line tools is provided for parsing and simulating P-Lingua 5 files. Finally, several examples are included in this paper covering the most common P system types. (c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
查看更多>>摘要：Data selection, which includes feature and instance selection, is often an important step in building prediction systems. Genetic algorithms (GA) frequently allow finding better solutions than classical methods in many areas. This is also true for the instance selection task. The main difficulties and challenges in GA-based instance selection are high computational complexity and decreasing performance with the dataset size growth. This is caused by the fact that each instance is encoded in one chromosome position. Hence bigger datasets result in longer chromosomes. The main contribution of this paper addresses the above problems in a three-step process. In the first step the dataset is divided into several consistent regions by fuzzy clustering. Then GA-based instance selection is performed independently within each cluster. Finally ensemble voting provides seamless aggregation of the partial results from the overlapping clusters. This improves dataset exploitation by more localized search and also takes the advantage of ensemble methods. This method significantly improves the predictive model performance and data reduction in comparison to instance selection performed on the whole training dataset.(c) 2021 Elsevier Inc. All rights reserved.
Tung, N. T.Nguyen, Trinh D. D.Fourier-Viger, PhilippeNguyen, Ngoc-Thanh...
22页
查看更多>>摘要：In contrast to frequent itemset mining (FIM) algorithms that focus on identifying itemsets with high occurrence frequency, high-utility itemset mining algorithms can reveal the most profitable sets of items in transaction databases. Several algorithms were proposed to perform the task efficiently. Nevertheless, most of them ignore the item categorizations. This useful information is provided in many real-world transaction databases. Previous works, such as CLH-Miner and ML-HUI Miner were proposed to solve this limitation to dis-cover cross-level and multi-level HUIs. However, the CLH-Miner has a long runtime and high memory usage. To address these drawbacks, this study extends tight upper bounds to propose effective pruning strategies. A novel algorithm named FEACP (Fast and Efficient Algorithm for Cross-level high-utility Pattern mining) is introduced, which adopts the proposed strategies to efficiently identify cross-level HUIs in taxonomy-based data-bases. It can be seen from a thorough performance evaluation that FEACP can identify use-ful itemsets of different abstraction levels in transaction databases with high efficiency, that is up to 8 times faster than the state-of-the-art algorithm on the tested sparse data-bases and up to 177 times on the tested dense databases. FEACP reduces memory usage by up to half over the CLH-Miner algorithm.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：To solve the issue of texture preservation in the image style transfer process, this paper presents a novel style transfer method for images that often contain tiny details but are easily noticed by human subjects (e.g., human faces). We aim to achieve content preserving style transfer via an appropriate trade-off between detail preservation and style transfer. To this end, we utilize wavelet transformation with a deep neural network for decoupled style and detail synthesis. Additionally, style transfer should involve a one-toone correspondence of semantic structures of scenes and avoid noticeable unnatural looking style transitions around them. To address the above issue, we leverage an attention mechanism and semantic segmentation for matching and design a novel content loss with local one-to-one correspondence for producing content-preserving stylized results. Finally, we employ wavelet transform to perform feature optimization (FO) to repair some imperfect results. We perform various experiments with Qabf evaluation and a user study to validate our proposed method and show its superiority over state-of-the-art methods for ensemble and texture preservation.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Convolutional neural networks (CNNs) have surpassed humans in many computer vision areas. However, the redundancy of CNNs inhibits its application in the embedded device. In this paper, a simple shared channel weight convolution (SCWC) approach is proposed to reduce the number of parameters. Multiplication is much more complex than accumulation, so reducing multiplication is more significant than reducing accumulation in CNNs. The proposed SCWC can reduce the number of multiplications by the distributive property due to the structured channel parameter sharing. Furthermore, the fully trained CNN can be directly compressed without tedious training from scratch. To evaluate the performance of the proposed SCWC, five competitive benchmark datasets for image classification and object detection, CIFAR-10, CIFAR-100, ImageNet, CUB-200-2011 and PASCAL VOC are adopted. Experiments demonstrate that the proposed SCWC can reduce about 50% of parameters and multiplications for ResNet50 with only 0.13% accuracy loss, and reduce about 70% of parameters and multiplications for VGG16 with 1.64% accuracy loss on ImageNet, which is better than many other pruning methods for CNNs. The accuracy loss is very small because of the soft parameter sharing. Moreover, the proposed SCWC is also effective for object detection and fine-grained image classification tasks.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：With the successful applications of DNNs in many real-world tasks, model's robustness has raised public concern. Recently the robustness of deep models is often evaluated by purposely generated adversarial samples, which is time-consuming and usually dependent on the specific attacks and model structures. Addressing the problem, we propose a generic evaluation metric ROBY, a novel attack-independent robustness measurement based on the model's feature distribution. Without prior knowledge of adversarial samples, ROBY uses inter-class and intra-class statistics to capture the features in the latent space. Models with stronger robustness always have larger distances between classes and smaller distances in the same class. Comprehensive experiments have been conducted on ten state-of-the-art deep models and different datasets to verify ROBY's effectiveness and efficiency. Compared with other evaluation metrics, ROBY better matches the robustness golden standard attack success rate (ASR), with significantly less computation cost. To the best of our knowledge, ROBY is the first light-weighted attack-independent robustness evaluation metric general to a wide range of deep models. The code of it can be downloaded at https://github.com/Allen-piexl/ROBY.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：The main steam temperature (MST) in power plants suffers from nonlinearity and large time delay, which cause large overshoot and long settling time under widely used cascade proportion integration differentiation (PID) controller. In order to cope with the negative effects, we propose a stochastic configuration network (SCN) based cascade generalized predictive control (GPC) scheme to improve the performance of the MST. A three-layer SCN is employed to model the MST process. The SCN is constructed by two phases, i.e., initial phase and real-time phase. The initial phase determines the structure and primary parameters of the learner model using the stochastic configuration algorithm. The realtime phase employs weighted recursive least squares (WRLS) for building the real-time MST process model for GPC design. Taking into account some constraints of the MST process, Karush-Kuhn-Tucker (KKT) conditions are applied for solving the constrained receding-horizon optimization problem. The derived explicit solutions of GPC avoid the implicit form which usually has to be solved iteratively. Comparative simulations demonstrate the superiority of the proposed SCN based cascade GPC (SCN-CGPC). Finally, the proposed SCN-CGPC is implemented via a standalone external MST control system in a power plant. The effectiveness and practicability are validated with the real-world application.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Ensemble learning combines many algorithms or models to obtain better predictive perfor-mance. Ensembles have produced the winning algorithm in competitions such as the Netflix Prize. They are used in climate modeling and relied upon to make daily forecasts. Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods presents challenges because the class labels or the ground truth is unknown. Thus, traditional ensemble techniques that use the class labels cannot be used for this task. We use Item Response Theory (IRT) - a class of models used in educational psychomet-rics - to construct an unsupervised anomaly detection ensemble. IRT's latent trait compu-tation lends itself to anomaly detection because the latent trait can be used to uncover the hidden ground truth. Using a novel IRT mapping to the anomaly detection problem, we construct an ensemble that can downplay noisy, non-discriminatory methods and accentu-ate sharper methods. We demonstrate the effectiveness of the IRT ensemble using two real data repositories and show that it outperforms other ensemble techniques. We find that the IRT ensemble performs well even if the set of anomaly detection methods have low cor-relation values.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：A matching in a bipartite graph with parts X and Y is called envy-free, if no unmatched vertex in X is a adjacent to a matched vertex in Y. Every perfect matching is envy-free, but envy-free matchings exist even when perfect matchings do not. We prove that every bipartite graph has a unique partition such that all envy-free matchings are contained in one of the partition sets. Using this structural theorem, we provide a polynomial-time algorithm for finding an envy-free matching of maximum cardinality. For edge-weighted bipartite graphs, we provide a polynomial-time algorithm for finding a maximum-cardinality envy-free matching of minimum total weight. We show how envy-free matchings can be used in various fair division problems with either continuous resources ("cakes") or discrete ones. In particular, we propose a symmetric algorithm for proportional cake cutting, an algorithm for 1-out-of-(2n 2) maximin-share allocation of discrete goods, and an algorithm for 1-out-of-[2n=3j maximin-share allocation of discrete bads among n agents. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：This study focuses on optimizing the sampling strategies for WSNs to estimate spatiotemporal signals. Existing deep-learning-based approaches for signal estimation tend to collect samples from pre-determined sensing locations, due to which the performance of signal estimation relies heavily on selected handcrafted features. Instead of fixing sensing locations, we propose a semi-binarized variational autoencoder to simultaneously optimize the sampling strategy and evaluate the signal estimated from the sampled sensing locations. The proposed framework is composed of a backpropagatable binarized encoding layer to optimize sensing locations and a generative model to estimate the complete signal from these sparse samples. Moreover, a feature-level discrepancy was proposed to further optimize the sampling locations with respect to the estimation error. The experiments were conducted using four publicly available datasets with three evaluation metrics (mean square error, standard deviation, and peak signal-to-noise ratio). The 10-fold cross validation and two-sample t-test were utilized to analyze the experimental results, which demonstrate the significant improvement achieved by the proposed method.CO 2021 Elsevier Inc. All rights reserved.
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