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An improved Chinese text multi-label classification method based on CNN

Journal of Physics: Conference Series 2020年第1期1619卷

作者： Yuanxia Xin Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430065 Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan 430065 Hubei China

Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm.

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MedShapeNet - a large-scale dataset of 3D medical shapes for computer vision

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2025年第1期70卷 71-90页

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Viet Duc Vu Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Maechler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Kuestner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Loeffler, Maximilian T. Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nuernberger, Andreas Pedrosa, Joo Ferreira, Carlos Aresta, Guilherme Cunha, Antonio Campilho, Aurelio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Roehrig, Rainer Hoelzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, Andre Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hoerst, Fabian Brehmer, Alexan Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Girardetstr 2 D-45131 Essen Germany Graz Univ Technol Inst Comp Graph & Vis ICG Inffeldgasse 16c A-8010 Graz Austria Comp Algorithms Med Lab Cafe Graz Austria Univ Med Essen AoR Canc Res Ctr Cologne Essen CCCE Hufelandstr 55 D-45147 Essen Germany Univ Med Essen Univ Hosp Essen Ctr Virtual & Extended Real Med ZvRM Hufelandstr 55 D-45147 Essen Germany Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Swiss Fed Inst Technol Lausanne EPFL Comp Vis Lab Lausanne Switzerland Univ Hosp RWTH Aachen Dept Oral & Maxillofacial Surg Aachen Germany Shanghai Jiao Tong Univ Renji Hosp Sch Med Dept Radiol Shanghai Peoples R China AGH Univ Sci & Technol Dept Measurement & Elect Krakow Poland Univ Appl Sci Western Switzerland HES SO Valais Inst Informat Syst Sierre Switzerland Univ Basel Dept Biomed Engn Ctr Med Image Anal & Nav CIAN Allschwil Switzerland Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA ZhejiangLab Res Ctr Connected Healthcare Big Data Hangzhou Zhejiang Peoples R China Graz Univ Technol Inst Mech Graz Austria Justus Liebig Univ Giessen Univ Hosp Giessen Dept Diagnost & Intervent Radiol Giessen Germany Mehran Univ Engn & Technol Dept Mech Engn Jamshoro Sindh Pakistan Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Hosp Canadian Surg Technol & Adv Robot CSTAR London ON Canada Shanghai Jiao Tong Univ Inst Biomed Mfg & Life Qual Engn Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Med Univ Graz Dept Cardiac Surg Graz Austria Techn Univ Munchen Univ Klinikum Geschaftsfuhrender Oberarzt Abt Intervent & Diagn Munich Germany Icometrix Leuven Belgium Tech Univ Munich Dept Informat Garching Germany Univ Zurich Dept Quantitat Biomed Zurich Switzerland Tech Univ Dortmund Fac Stat Dortmund Germany Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Techn Univ Munchen Geschaftsfuhrender Oberarzt Abt Interv

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods: We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results: By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions: MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://***/.

关键词： 3D medical shapes benchmark anatomy education shapeomics augmented reality virtual reality

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Acoustic and Kinematic Examination of Dysarthria in Cantonese Patients of Parkinson's Disease 11

Acoustic and Kinematic Examination of Dysarthria in Cantones...

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11th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Sun, Yue Ng, Manwa L. Lian, Chongyuan Wang, Lan Yang, Feng Yan, Nan Guizhou Univ Coll Big Data & Informat Engn Guiyang Guizhou Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sy Shenzhen Peoples R China Univ Hong Kong Speech Sci Lab Hong Kong Peoples R China Shenzhen Childrens Hosp Dept Speech Therapy Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen Peoples R China

ISBN: (纸本)9781538656273

Hypokinetic dysarthria is one of the core symptoms of Parkinson's disease, characterized by reduced loudness, slurred speech and distorted consonant productions. Dopaminergic medication for Parkinson's disease (PD) has been proven to be effective in treating limb and gross motor movement problems. however, the literature sees contradict-ory findings regarding dopaminergic effects on speech problems associated with PD. Previous perceptual and acoustic studies of PD hypokinetic dysarthria mostly involved heterogeneous population and variations of speech tasks which complicated data interpretation. Also, the lack of kinematic data limited our understanding of the details of articulation errors associated with PD, as well as the medication effects. Electromagnetic articulography (EMA) enabled the examina-tion of PD articulatory patterns with high degree of accuracy and safety. The aim of the present study was to address these inconsistencies by providing an integrative description of basic kinematic and acoustic parameters of speech production about the dopaminergic effect on early PD speech by using EMA. The results revealed a significant improvement of articulatory function on dopaminergic effects for PD speech, evidenced by increased vowel space, the velocity and accelerated velocity initiation and coordination of articulation during bilabial or alveolar productions.

关键词： Parkinson's disease hypokinetic dysarthria dopaminergic medication kinematic analysis acoustic analysis

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Recovery and repair schemes for shift-XOR regenerating codes

arXiv

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arXiv 2019年

作者： Fu, Ximing Yang, Shenghao Xiao, Zhiqing School of Science and Engineering Chinese University of Hong Kong Shenzhen China University of Science and Technology of China China School of Science and Engineering Chinese University of Hong Kong Shenzhen Guangdong518172 China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Chinese University of Hong Kong Shenzhen Guangdong518172 China Shenzhen Research Institute of Big Data Shenzhen Guangdong518172 China Tsinghua University

Recovery and repair schemes are proposed for shift-exclusive-or (shift-XOR) product-matrix (PM) regenerating codes, which outperform those of the existing PM codes in terms of both communication and computation costs. In particular, for the minimum bandwidth regenerating (MBR) codes, our recovery and repair schemes have the optimal transmission bandwidth, zero decoding auxiliary data space and lower time complexity;for the minimum storage regenerating (MSR) codes, our recovery and repair schemes have smaller transmission bandwidth, smaller decoding auxiliary data space and lower time complexity. Moreover, our schemes involve only XOR operations in recovery and repair and are practical for system implementation. Technically, a procedure is first proposed for solving a system of shift-XOR equations, which plays a similar fundamental role as Gaussian elimination for solving systems of linear equations, and is of independent interest. The recovery and repair of shift-XOR MBR/MSR codes are then decomposed into a sequence of systems of shift-XOR equations, and hence can be solved by a sequence of calls to the procedure for solving a system of shift-XOR equations. As the decomposition of the recovery and repair depends only on the PM construction, but not the specific shift and XOR operations, our recovery and repair schemes can be extended to other MBR/MSR codes using the PM construction. Copyright © 2019, The Authors. All rights reserved.

关键词： Matrix algebra

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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A java code protection scheme via dynamic recovering runtime instructions 18th

A java code protection scheme via dynamic recovering runtime...

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18th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2018

作者： Jiajia, Sun Jinbao, Gao Yu-an, Tan Yu, Zhang Xiao, Yu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China China University of Mining and Technology Beijing100083 China Department of Computer Science and Technology Shandong University of Technology ZiboShandong255022 China

ISBN: (纸本)9783030050627

As Android operating system and applications on the device play important roles, the security requirements of Android applications increased as well. With the upgrade of Android system, Android runtime mode (ART mode) has gradually become the mainstream architecture of the Android operating system. ART introduces several improvements in Android, but it also introduces new ways to enhance malicious activities. This paper proposed a confidential finer granularity protection scheme for application programs under ART mode of ROOT Android devices. Taking Java method as the protection granularity, the protection scheme increased the accuracy of protecting targets. In addition, the protection scheme provided a more thorough protection for applications by combining dynamic loading technology and encryption technology in ART mode, and improved the security of Android applications. Experiments showed that the proposed protection scheme is effective. © Springer Nature Switzerland AG 2018.

关键词： Android (operating system)

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Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework

arXiv

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arXiv 2018年

作者： Liu, Yang Key Laboratory of Big Data Analysis and Processing of Henan province Intelligent Technology and Application Engineering Research Centre of Henan province College of Computer Science and Information Engineering Henan University Kaifeng475004 China

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Correction to: Incentive mechanisms for mobile crowd sensing based on supply-demand relationship

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Peer-to-Peer Networking and Applications 2019年第3期13卷 1069-1069页

作者： Xu, Jia Lu, Wei Xu, Lijie Yang, Dejun Li, Tao Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Computer Science Colorado School of Mines Golden USA

Correction is needed in the original article. The university name in affiliation (1) is changed from “University of Posts and Telecommunications” to “Nanjing University of Posts and Telecommunications”.

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Impact of Prior Knowledge and data Correlation on Privacy Leakage: A Unified Analysis

arXiv

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arXiv 2019年

作者： Li, Yanan Ren, Xuebin Yang, Shusen Yang, Xinyu National Engineering Laboratory for Big Data Analytics [NEL-BDA Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Ministry of Education Key Lab for Intelligent Networks and Network Security [MOE KLINNS Lab Xi’an Jiaotong University Xi’an Shaanxi710049 China

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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An Improved Iterated Hybrid Search for DNA Codes Design

An Improved Iterated Hybrid Search for DNA Codes Design

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Zhenghui Liu Bin Wang Changjun Zhou Xiaopeng Wei Qiang Zhang Zhixiang Yin Xianwen Fang Zhonglong Zheng Key Laboratory of Advanced Design and Intelligent Ministry of Education Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Huaian China College of Mathematics Zhejiang Normal University Jinhua China

ISBN: (纸本)9781538693810;9781538693803

DNA codes have a significant effect on the efficiency of DNA computing and other applications. Designing DNA codes is to obtain a certain sets of DNA codes with satisfying certain combinatorial constraints. Most works focus on algorithmic design for DNA codes. In this paper, Simulated Annealing and Clique Search are modified and combined into an iterated hybrid search to improve the lower bound for DNA codes set. Over 160 instances are considered, and the iterated hybrid search method is able to yield improvements for some lower bounds with n ≤ 16. Comparing with previous works, the proposed algorithm is more efficient for constructing DNA codes set.

关键词： biocomputing DNA iterative methods search problems simulated annealing biology computing tabu search DNA simulated annealing iterative methods Codes Computational Molecular Biology Lower bound DNA Computation Search methods

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