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A matrix-based implementation of DE algorithm: The compensation and deficiency 30th

A matrix-based implementation of DE algorithm: The compensat...

30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017

作者： Pan, Jeng-Shyang Meng, Zhenyu Xu, Huarong Li, Xiaoqing Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Department of Computer Science and Technology Xiamen University of Technology Xiamen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783319600413

Differential Evolution has become a very popular continuous optimization algorithm since its inception as its simplicity, easy coding and good performance over kinds of optimization problems. Difference operator in donor vector calculation is the key feature of DE algorithm. Usually, base vector and difference vectors selection in calculating a donor usually cost extra lines of condition judgement. Moreover, these vectors are not equally selected from the individual population. These lead to more perturbation in optimization performance. To tackling this disadvantage of DE implementation, a matrix-based implementation of DE algorithm is advanced herein this paper. Three commonly used DE implementation approaches in literature are also presented and contrasted. CEC2013 test suites for real-parameter optimization are used as the test-beds for these comparison. Experiment results show that the proposed matrix-based implementation of DE algorithm performs better on optimization performance than the common implementation schemes of DE algorithm with similar time complexity. © Springer International Publishing AG 2017.

关键词： Global optimization

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Developing Global Digital Economy after COVID-19: Preface for ITQM 2020 & 2021

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Procedia computer science 2021年第0期199卷 1-9页

作者： Liu, Yubin Shi, Yong Wang, Yong Ergu, Daji Berg, Daniel Tien, James Li, Jianping Tian, Yingjie Southwest Minzu University Sichuan Chengdu610041 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Engineering University of Miami Coral GablesFL33124 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Welcome to the Eighth International Conference on Information technology and Quantitative management (ITQM 2020 & 2021), July 9-11, Chengdu, China. The theme of ITQM 2020 & 2021 is “Developing Global Digital Economy after COVID-19”. ITQM 2020 & 2021 is organized by International Academy of Information technology and Quantitative management (IAITQM), Southwest Minzu University, Chinese Academy of sciences and University of Nebraska at Omaha. IAITQM was formally inaugurated on June 3, 2012 with more than 50 founding members from China, United States, Australia, Japan, Lithuania, Poland, Romania, Spain, Singapore, South Korea, The Netherlands, Turkey and other countries. The International Conference on Information technology and Quantitative management (ITQM), established by IAITQM, is a global forum for exchanging research findings and case studies that bridge the latest information technology and quantitative management techniques. It explores how the use of information technology techniques to improve quantitative management and how the development of management tools can reshape the development of information technology. The First International Conference on Information technology and Quantitative management (ITQM 2013) took place in Suzhou, China. The Second International Conference on Information technology and Quantitative management (ITQM 2014) was held in Moscow, Russia. The Third International Conference on Information technology and Quantitative management (ITQM 2015) was held at Rio de Janeiro, Brazil. The Forth International Conference on Information technology and Quantitative management (ITQM 2016) was held at Asan, Korea. The Fifth International Conference on Information technology and Quantitative management (ITQM 2017) was held at New Delhi, India. The Sixth International Conference on Information technology and Quantitative management (ITQM 2018) was hosted at Omaha, USA. The Seventh International Conference on Information technology and Quantitative

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Hybrid Learning Network: A Novel Architecture for Fast Learning

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Procedia computer science 2017年 122卷 622-628页

作者： Ying Liu Chao Xiang School of Computer and Engineering University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

There’re many effective architectures of the artificial neural network(ANN). For which the training is a hard work. The cost for training an ANN increases exponentially when the ANN gets deeper or wider. We therefore propose a novel architecture, the Hybrid Learning Network(HLN), to achieve a fast learning with good stablity. The HLN can learn from both labeled data and unlabeled data at the same time in a hybrid learning manner. It uses a Self Organizing Map unified by the specially designed nonlinear function as the sparsity mask for a hidden layer to improve the training speed. We experiment our architecture on a synthetic dataset to test its regression capability against the traditional architecture, the result is promising.

关键词： hybrid learning neural network sparsity mask self organizing map fast learning

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Improving Ranking-based Recommendation by Social Information and Negative Similarity

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Procedia computer science 2015年 55卷 732-740页

作者： Ying Liu Jiajun Yang School of Computer and Control University of Chinese Academy of Sciences Beijing 100049 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100049 China

Recommender system is able to suggest items that are likely to be preferred by the user. Traditional recommendation algorithms use the predicted rating scores to represent the degree of user preference, called rating-based recommendation methods. Recently, ranking-based algorithms have been proposed and widely used, which use ranking to present the user preference rather than rating scores. In this paper, we propose two novel methods to overcome the weaknesses in VSRank, a state-of-the-art ranking-based algorithm. Firstly, a novel similarity measure is proposed to make better use of negative similarity; secondly, social network information is integrated into the model to smooth ranking. Experimental results on a publicly available dataset demonstrate that the proposed methods outperform the existing widely used ranking-based algorithms and rating-based algorithms considerably.

关键词： Recommendation system Ranking-based recommendation Collaborative filtering

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Design and Development of FPGA-based High-Performance Radar data Stream mining System

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Procedia computer science 2015年 55卷 876-885页

作者： Ying Liu Pengshan Ma Hongyuan Cui School of Computer and Control University of Chinese Academy of Sciences Beijing 101408 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Radar signal sorting is a key technique in electronic reconnaissance systems and is currently an important research direction in radar signal processing. Clustering, one of data mining techniques, has been adopted to solve radar sorting problem. However, none of the existing clustering-based methods is able to perform real-time analysis on high speed radar stream. Therefore, in this paper, we propose, design and implement a high performance FPGA-based data stream mining system to perform real-time radar signal sorting on continuous data stream. Firstly, a density-based clustering algorithm is proposed for radar signal sorting; secondly, FPGA-based program of the clustering algorithm is designed and implemented; thirdly, the FPGA board is designed and implemented. Experiments are performed on the board we designed. The results show that the proposed system can achieve real-time radar signal sorting on FPGA, and the resource consumption on FPGA is very low. The clustering algorithm is efficient in terms of accuracy.

关键词： data mining clustering signal processing FPGA parallel computing

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Creating knowledge and Wisdom via big data Analytics: Preface for ITQM 2017

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Procedia computer science 2017年 122卷 1-9页

作者： Ahuja, Vandana Shi, Yong Khazanchi, Deepak Abidi, Naseem Tian, Yingjie Berg, Daniel Tien, James M. Jaypee Business School A-10 Sector-62 Noida Uttar Pradesh201 307 India School of Economics and Management University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha Chinese Academy of Sciences OmahaNE68182 United States College of Engineering University of Miami Coral GablesFL33124 United States

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Deep Feature Inpainting for Unsupervised Visual Anomaly Detection 10

Deep Feature Inpainting for Unsupervised Visual Anomaly Dete...

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10th International Conference on Information technology and Quantitative management, ITQM 2023

作者： Yang, Jie Lyu, Mengjin Qi, Zhiquan Tian, Yingjie Shi, Yong School of Mathematical Science University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska OmahaNE68182 United States School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. In this paper, we address the problem of unsupervised visual anomaly detection from the perspective of image feature restoration. We propose deep feature inpainting (DFI) where we develop a deep learning model that can automatically inpainting the normal image patterns or features from the anomalous images by eliminating the irregular structures or patterns. By comparing the anomalous features and their reconstructed counterparts, we can identify and analyze the anomalous structures in the images. Extensive experiments have been carried out to demonstrate the effectiveness of the approach. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative management.

关键词： Anomaly detection

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Deep Learning Based Image Quality Assessment: A Survey 10

Deep Learning Based Image Quality Assessment: A Survey

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10th International Conference on Information technology and Quantitative management, ITQM 2023

作者： Yang, Jie Lyu, Mengjin Qi, Zhiquan Shi, Yong School of Mathematical Science University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska OmahaNE68182 United States School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Image quality assessment (IQA) is the problem of measuring the perceptual quality of images, which is crucial for many image-related applications. It is a difficult task due to the coupling of various degradation and the scarcity of annotations. To facilitate a better understanding of IQA, we survey the recent advances in deep learning based IQA methods, which have demonstrated remarkable performance and innovation in this field. We classify the IQA methods into two main groups: reference-based and reference-free methods. Reference-based methods compare query images with reference images, while reference-free methods do not. We further subdivide reference-based methods into full-reference and reduced-reference methods, depending on the amount of information they need from the reference images, and reference-free methods into single-input, pair-input, and multimodal-input methods, according to the form of input they use. The advantages and limitations of each category are analyzed and some representative examples of state-of-the-art methods are provided. We conclude our paper by highlighting some of the future directions and open challenges in deep learning based IQA. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative management.

关键词： Deep learning

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features 38

Not All Diffusion Model Activations Have Been Evaluated as D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques 38

Suppress Content Shift: Better Diffusion Features via Off-th...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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