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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chen Shen Hongfei Lin Zhiheng Li Yonghe Chu Zhengguang Li Zhihao Yang School of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Software Technology Institute Dalian Jiaotong University Dalian China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Detecting adverse drug events from Twitter is expected to reveal unreported side effects, thereby complementing current spontaneous reporting systems. However, existing studies usually only use word embeddings as the input for deep learning models, which ignores the structural information of sentences. In addition, deep learning models usually require a large number of cases for training, but the scale of annotated corpora that can be used for this task is limited. In order to solve the above problems, we propose a graph-boosted framework, that constructs the text into a graph structure. By using pre-trained graph embeddings and word embeddings for model training, our proposed framework provides richer semantic and structural information for prediction. The experimental results show that the proposed method can be used in different deep learning models and bring improvements when using the TwiMed corpus of different scales.

关键词： Drugs Social networking (online) Blogs Training Task analysis Deep learning computer science

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FlashSchema: Achieving High Quality XML Schemas with Powerful Inference Algorithms and Large-scale Schema Data

FlashSchema: Achieving High Quality XML Schemas with Powerfu...

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International Conference on Data engineering

作者： Yeting Li Jialun Cao Haiming Chen Tingjian Ge Zhiwu Xu Qiancheng Peng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China The Hong Kong University of Science and Technology Hong Kong China University of Massachusetts Lowell United States College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Getting high quality XML schemas to avoid or reduce application risks is an important problem in practice, for which some important aspects have yet to be addressed satisfactorily in existing work. In this paper, we propose a tool FlashSchema for high quality XML schema design, which supports both one-pass and interactive schema design and schema recommendation. To the best of our knowledge, no other existing tools support interactive schema design and schema recommendation. One salient feature of our work is the design of algorithms to infer k-occurrence interleaving regular expressions, which are not only more powerful in model capacity, but also more efficient. Additionally, such algorithms form the basis of our interactive schema design. The other feature is that, starting from large-scale schema data that we have harvested from the Web, we devise a new solution for type inference, as well as propose schema recommendation for schema design. Finally, we conduct a series of experiments on two XML datasets, comparing with 9 state-of-the-art algorithms and open-source tools in terms of running time, preciseness, and conciseness. Experimental results show that our work achieves the highest level of preciseness and conciseness within only a few seconds. Experimental results and examples also demonstrate the effectiveness of our type inference and schema recommendation methods.

关键词： XML Inference algorithms Tools Semantics Libraries Data models

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WaveCNet: Wavelet integrated CNNs to suppress aliasing effect for noise-robust image classification

arXiv

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arXiv 2021年

作者： Li, Qiufu Shen, Linlin Guo, Sheng Lai, Zhihui Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China MyBank Ant Group Hangzhou310012 China

Though widely used in image classification, convolutional neural networks (CNNs) are prone to noise interruptions, i.e. the CNN output can be drastically changed by small image noise. To improve the noise robustness, we try to integrate CNNs with wavelet by replacing the common down-sampling (maxpooling, strided-convolution, and average pooling) with discrete wavelet transform (DWT). We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet). During the down-sampling, WaveCNets apply DWT to decompose the feature maps into the low-frequency and high-frequency components. Containing the main information including the basic object structures, the low-frequency component is transmitted into the following layers to generate robust high-level features. The high-frequency components are dropped to remove most of the data noises. The experimental results show that WaveCNets achieve higher accuracy on ImageNet than various vanilla CNNs. We have also tested the performance of WaveCNets on the noisy version of ImageNet, ImageNet-C and six adversarial attacks, the results suggest that the proposed DWT/IDWT layers could provide better noise-robustness and adversarial robustness. When applying WaveCNets as backbones, the performance of object detectors (i.e., faster R-CNN and RetinaNet) on COCO detection dataset are consistently improved. We believe that suppression of aliasing effect, i.e. separation of low frequency and high frequency information, is the main advantages of our approach. The code of our DWT/IDWT layer and differentWaveCNets are available at https://***/CVISZU/ WaveCNet. © 2021, CC BY.

关键词： Signal sampling

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Capturing dynamics of information diffusion in SNS: A survey of methodology and techniques

arXiv

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arXiv 2021年

作者： Li, Huacheng Xia, Chunhe Wang, Tianbo Wen, Sheng Chen, Chao Xiang, Yang School of Computer Science and Engineering Beihang University No. 37 Xueyuan Road Haidian Beijing China School of Cyber Science and Technology Beihang University No. 37 Xueyuan Road Haidian Beijing China Department of Computer Science and Software Engineering Swinburne University of Technology 1 James Cook Drive TownsvilleQLD Australia College of Science and Engineering James Cook University John Street HawthornVIC Australia Digital Research & Innovation Capability Platform Swinburne University of Technology John Street HawthornVIC Australia

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing.) Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： Taxonomies

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Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

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IEEE Access 2020年 8卷 153546-153560页

作者： Malik, Rahul Khamparia, Aditya Garg, Sahil Gupta, Deepak Choi, Bong Jun Hossain, M. Shamim School of Computer Science and Engineering Lovely Professional University Phagwara India Electrical Engineering Department École de Technologie Supérieure MontréalQC Canada Department of Computer Science Engineering Maharaja Agrasen Institute of Technology New Delhi India School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

There is a need for improvement of tools to deal with large volumes of multimedia data effectively. In particular, real-time data processing is one of the major problems for multimedia data computing in remote sensing systems. Such big data systems have to offer effective management and computational efficiency for applications in real-time. In this paper, we propose a large-scale geological processing method for aerial Light Detection and Ranging (LiDAR) clouds containing multimedia data that ensures mobility and timeliness. By utilizing Spark and Cassandra, our proposed approach can significantly reduce the execution time of the time-consuming process. We investigate fast ground-only raster generation from huge LiDAR datasets. We observed that filtered cloud data ensuing from impartial consideration of neighboring zones could lead to classification errors on the boundaries. Therefore, an integrated approach is proposed to correct these errors to improve the classification consistency, achieve faster processing time, provide automatic error correction, obtain Digital Terrain Models (DTM), and minimize user intervention. These features can provide a framework for an on-demand DTM output and scalable application services. Furthermore, the proposed approach can expect to benefit other real-time applications in LiDAR systems. © 2013 IEEE.

关键词： Error correction

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Retraction notice to "Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review" [Heliyon 10 (2024) e36743]

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Heliyon 2025年第9期11卷

作者： Sagheer Abbas Muhammad Asif Abdur Rehman Meshal Alharbi Muhammad Adnan Khan Nouh Elmitwally Department of Computer Science Prince Mohammad Bin Fahd University Al-Khobar KSA Department of Computer Science Education University Lahore Attock Campus Pakistan School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University 11942 Alkharj Saudi Arabia Riphah School of Computing & Innovation Faculty of Computing Riphah International University Lahore Campus Lahore 54000 Pakistan School of Computing Skyline University College University City Sharjah 1797 Sharjah United Arab Emirates Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13120 Republic of Korea Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza 12613 Egypt School of Computing and Digital Technology Birmingham City University Birmingham B4 7XG UK

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A Feature Subset Selection Method Based on the Combination of PCA and Improved GA

A Feature Subset Selection Method Based on the Combination o...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Yinghao Li Kai Shi Fuqiang Qiao Hongyu Luo School of Computer Science and Engineering Tianjin University of Technology China College of Software and Communication Tianjin Sino-German University of Applied Sciences China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

Nowadays, intrusion detection data has redundant features, which leads to unsatisfactory detection results. This paper proposes a feature selection method based on the combination of principal component analysis (PCA) and improved Genetic algorithm(GA). First, PCA is used to reduce the dimensionality of the intrusion data, and the cosine similarity is used to calculate angles between the principal component and the original feature; the feature with high cosine similarity to the principal component is selected and the genetic algorithm is input, and the improved inter-group crossover strategy is adopted to reduce the probability of entering the local optimum. The fitness function of the genetic algorithm is the detection rate, and the optimal feature subset is selected as the intrusion detection data feature. Experimental results show that this method can improve the efficiency of intrusion detection and improve the classification ability of the classifier.

关键词： Machine learning algorithms Diversity reception Intrusion detection Probability Feature extraction Principal component analysis Genetic algorithms

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A New Cluster Validity Index for Stock Clustering Based on Efficient Frontier

A New Cluster Validity Index for Stock Clustering Based on E...

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Yahui Lu Minghao Li Xiaochu Tang Hui Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen P.R.China College of Management Shenzhen University Shenzhen P.R.China

ISBN: (数字)9781728141114

ISBN: (纸本)9781728141121

Clustering is an unsupervised learning method to discover meaningful information by grouping similar objects together. It is a great challenge to valuate the results of stock clustering. In this paper, we propose a specific index IBEF(Index Based on Efficient Frontier) to evaluate the results of stock clustering based on the concept of efficient frontier. IBEF is defined by the difference between two efficient frontier curves. One curve is built by all stocks and the other curve is built by center stock of each cluster. If the clustering result is good, the two curves are close to each other and IBEF value will be small. Our experiments on different correlation coefficients and clustering methods show that IBEF is a proper validity index comparing with other indexes.

关键词： Indexes Portfolios Clustering methods Approximation algorithms Correlation Clustering algorithms Unsupervised learning

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Efficient reinforcement learning in continuous state and action spaces with Dyna and policy approximation

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Frontiers of computer science 2019年第1期13卷 106-126页

作者： Shan ZHONG Quan LIU Zongzhang ZHANG Qiming FU School of Computer Science and Technology Soochow UniversitySuzhou 215000China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou 215006China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000China College of Electronic &Information Engineering Suzhou University of Science and TechnologySuzhou 215000China

Dyna is an effective reinforcement learning (RL) approach that combines value function evaluation with model ***,existing works on Dyna mostly discuss only its efficiency in RL problems with discrete action *** paper proposes a novel Dyna variant,called Dyna-LSTD-PA,aiming to handle problems with continuous action ***-LSTD-PA stands for Dyna based on least-squares temporal difference (LSTD)and policy ***-LSTD-PA consists of two simultaneous,interacting *** learning process determines the probability distribution over action spaces using the Gaussian distribution;estimates the underlying value function,policy,and model by linear representation;and updates their parameter vectors online by LSTD(,t).The planning process updates the parameter vector of the value function again by using ofttine LSTD(2).Dyna-LSTD-PA also uses the Sherman-Morrison formula to improve the efficiency of LSTD(,t),and weights the parameter vector of the value function to bring the two processes ***,the global error bound is derived by considering approximation,estimation,and model ***,Dyna-LSTD-PA outperforms two representative methods in terms of convergence rate,success rate,and stability performance on four benchmark RL problems.

关键词： problem solving control methods heuristic search methods dynamic programming

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LEARNING-AUGMENTED DATA STREAM ALGORITHMS 8

LEARNING-AUGMENTED DATA STREAM ALGORITHMS

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8th International Conference on Learning Representations, ICLR 2020

作者： Jiang, Tanqiu Li, Yi Lin, Honghao Ruan, Yisong Woodruff, David P. Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Zhiyuan College Shanghai Jiao Tong University Shanghai200240 China Department of Software Engineering Xiamen University Fujian Xiamen361000 China Computer Science Department Carnegie Mellon University PittsburghPA15213 United States

The data stream model is a fundamental model for processing massive data sets with limited memory and fast processing time. Recently Hsu et al. (2019) incorporated machine learning techniques into the data stream model in order to learn relevant patterns in the input data. Such techniques led to the training of an oracle to predict item frequencies in the streaming model. In this paper we explore the full power of such an oracle, showing that it can be applied to a wide array of problems in data streams, sometimes resulting in the first optimal bounds for such problems, and sometimes bypassing known lower bounds without such an oracle. We apply the oracle to counting distinct elements on the difference of streams, estimating frequency moments, estimating cascaded aggregates, and estimating moments of geometric data streams. For the distinct elements problem, we bypass the known lower bound and obtain a new space-optimal algorithm. For estimating the p-th frequency moment for 0 2 we obtain a quadratic savings in memory, bypassing known lower bounds without an oracle. We empirically validate our results, demonstrating also our improvements in practice. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Learning systems

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