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Backdoor Attacks against Deep Neural Networks by Personalized Audio Steganography 26

Backdoor Attacks against Deep Neural Networks by Personalize...

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26th international conference on Pattern Recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Liu, Peng Zhang, Shuyi Yao, Chuanjian Ye, Wenzhe Li, Xianxian Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the world of cyber security, backdoor attacks are widely used. These attacks work by injecting a hidden backdoor into training samples to mislead models into making incorrect judgments for achieving the effect of the attack. However, since the triggers in backdoor attacks are relatively single, defenders can easily detect backdoor triggers of different corrupted samples based on the same behavior. In addition, most current work considers image classification as the object of backdoor attacks, and there is almost no related research on speaker verification. This paper proposes a novel audio steganography-based personalized trigger backdoor attack that embeds hidden trigger techniques into deep neural networks. Specifically, the backdoor speaker verification uses a pre-trained audio steganography network that employs specific triggers for different samples to implicitly write personalized information to all corrupted samples. This personalized method can significantly improve the concealment of the attack and the success rate of the attack. In addition, only the frequency and pitch were modified and the structure of the attacked model was left unaltered, making the attack behavior stealthy. The proposed method provides a new attack direction for speaker verification. Through extensive experiments, we verified the effectiveness of the proposed method.

关键词： Deep learning Training Steganography Neural networks Training data Benchmark testing Behavioral sciences

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META DISCOVERY: LEARNING TO DISCOVER NOVEL CLASSES GIVEN VERY LIMITED data 10

META DISCOVERY: LEARNING TO DISCOVER NOVEL CLASSES GIVEN VER...

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10th international conference on Learning Representations, ICLR 2022

作者： Chi, Haoang Liu, Feng Han, Bo Yang, Wenjing Lan, Long Liu, Tongliang Niu, Gang Zhou, Mingyuan Sugiyama, Masashi National University of Defense Technology China Hong Kong Baptist University Hong Kong University of Technology Sydney Australia The University of Sydney Australia RIKEN AIP Japan The University of Texas Austin United States The University of Tokyo Japan

In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However, the implicit assumptions behind NCD are still unclear. In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes. Based on this finding, NCD is theoretically solvable under certain assumptions and can be naturally linked to meta-learning that has exactly the same assumption as NCD. Thus, we can empirically solve the NCD problem by meta-learning algorithms after slight modifications. This meta-learning-based methodology significantly reduces the amount of unlabeled data needed for training and makes it more practical, as demonstrated in experiments. The use of very limited data is also justified by the application scenario of NCD: since it is unnatural to label only seen-class data, NCD is sampling instead of labeling in causality. Therefore, unseen-class data should be collected on the way of collecting seen-class data, which is why they are novel and first need to be clustered. © 2022 ICLR 2022 - 10th international conference on Learning Representationss. All rights reserved.

关键词： Learning algorithms

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Growth model of Cynoglossus semilaevis based on improved parallel BP neural network 5

Growth model of Cynoglossus semilaevis based on improved par...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Zheng, Jie Hou, Jiakang Tian, Yunchen Li, Zhenzhong Sun, Zhenlong College of Computer and Information Engineering Tianjin Agricultural University Tianjin China Tianjin Agricultural University and Tianjin Key Laboratory of Aquatic Ecology and Aquaculture Tianjin China Shandong Dongrun Instrument Science and Technology Co. Ltd. Shandong China Nantong Longyang Aquatic Products Co. Ltd. Jiangsu China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

models constructed based on parallel BP neural networks are gradually used in data processing and mining because of their multidimensional non-linear fitting capability and better performance in terms of fitting effect and training speed. However, existing parallel BP neural network models are still trained serially within a single map task, which affects the performance of the models. Based on this, a combinatorial division method is proposed to achieve parallel training within map tasks and to construct an improved parallel BP neural network Cynoglossus semilaevis growth model. In this paper, we use the actual culture data under the factory recirculating water culture model to construct Cynoglossus semilaevis growth model. It was concluded from the experiments that values of running time, RMSE and MAPE of Cynoglossus semilaevis growth model based on improved parallel BP neural network were 51s, 6.79 and 1.34%, respectively. © 2022 IEEE.

关键词： data handling

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Performance Analysis of data Trend with the Combination of ARIMA and Dynamic System

Performance Analysis of Data Trend with the Combination of A...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Yijia Ma Siying Wang Haojing Cheng School of Economics and Management Communication University of China Beijing China School of Computer and Cyber Sciences Communication University of China Beijing China

This article combines ARIMA with Dynamic System model to propose a novel method for predicting data trends. The prediction of data trends is often divided into qualitative and quantitative forecasting. Qualitative forecasting relies on the expertise and analytical judgment of professionals, as well as the objective attributes of the phenomenon itself. Quantitative forecasting involves mathematically describing the regularities of phenomena and predicting their development trends. Both qualitative and quantitative forecasting have their advantages and disadvantages. In this study, Dynamic System and ARIMA models are used to represent the qualitative and quantitative research approaches, respectively. The study explores the strengths and weaknesses of these two models in predicting continuous data with peaks, and attempts to integrate them. It is found that the former can better predict macro-level overall trends and peaks, while the latter allows for micro-level, precise predictions.

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Cloud Computing Virtual Machine Migration Algorithm Based on Deep Reinforcement Learning and Demand Awareness

Cloud Computing Virtual Machine Migration Algorithm Based on...

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Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE international conference on

作者： Pingji Zou Lanzhou Vocational Technical College Lanzhou China

ISBN: (纸本)9798350355260

When discussing the dynamic management of virtual machines in cloud computing environment, the current cloud cluster virtual machine management algorithm faces the problems of too single optimization goal, lack of generalization ability and poor stability. In order to solve these problems, we propose a requirement-aware driven virtual machine dynamic management method. This method uses load forecasting technology to monitor the change of users ' demand for virtual machine resources in real time before migration operation, and combines these requirements with the current physical resource status of the cloud cluster as input data to the virtual machine dynamic migration model combined with deep learning and reinforcement learning technology to formulate the best migration strategy. The goal is to improve the quality of service, reduce the risk of violating the service level agreement and realize the balanced allocation of resources. The efficient virtual machine live migration scheme is generated by stimulating the deep reinforcement learning algorithm to achieve the optimization of multiple objectives such as high service quality, resource balance and low energy consumption.

关键词： Cloud computing Heuristic algorithms Clustering algorithms Deep reinforcement learning Prediction algorithms Virtual machines Stability analysis data models Optimization Convergence

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Civil Aviation Passenger Throughput Forecasting Model Based on Machine Learning

Civil Aviation Passenger Throughput Forecasting Model Based ...

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Artificial Intelligence, Deep Learning and Neural Networks (AIDLNN), international conference on

作者： Luyuan Yang Diansheng Yang China Academy of Civil Aviation Science and Technology Beijing Guangzhou Zhishi Information Technology Co. Ltd.

ISBN: (数字)9798331520816

ISBN: (纸本)9798331520823

This article examines a predictive model for estimating civil aviation passenger traffic using the long short-term memory network (LSTM). Analyzing the historical flight data, meteorological data, and holiday information of an international airport between 2010 and 2020 helped create a forecasting model that effectively captures the long-term dependency of time series data. The process of preparing data includes removing data, filling in missing values, and standardizing data, and then combining multi-dimensional data with feature engineering to improve data quality. The outcomes of the study show that the LSTM model performs reliably on the training and validation data, obtaining an MSE of 0.35, RMSE of 0.59, and MAE of *** suggests that the model possesses strong prediction accuracy and generalization capabilities. The study in this paper confirms the efficiency of the LSTM model in predicting civil aviation passenger throughput, offers dependable decision-making support for airport management and flight scheduling, and enhances the model's performance by employing proper data processing and feature selection strategies.

关键词： Training Atmospheric modeling Time series analysis Predictive models Throughput Airports data models Reliability Forecasting Long short term memory

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Explicit and Implicit Section Identification from Clinical Discharge Summaries 16

Explicit and Implicit Section Identification from Clinical D...

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16th international conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abbas, Asim Hussain, Jamil Afzal, Muhammad Bilal, Hafiz Syed Muhammad Lee, Sungyoung Jeon, Seokhee Kyung Hee Univ Ubiquitous Comp Lab Yongin South Korea Sejong Univ Dept Data Sci Sejong South Korea Sejong Univ Dept Software Sejong South Korea Natl Univ Sci & Technol Dept Comp SEECS Islamabad Pakistan Kyung Hee Univ Hapt & VR Lab Yongin South Korea

ISBN: (纸本)9781665426787

In the clinical domain, mostly data is generated in natural language and unstructured format in clinical notes, containing meaningful and hidden information. Various algorithms have been proposed to recognize and identify different sections within those clinical notes for easy conversion into a structured data format for further processing in terms of data storage and retrieval. The algorithm has proposed recognizing and identifying the explicit and implicit defined section heading and the start and end of boundaries for identified sections to enhance clinical notes' information extraction (IE). A section dictionary is constructed contain explicit define section name. An exact term matching approach is used to identify explicit define section and term partially matching procedure is followed utilizing Levenshtein Distance algorithm. We evaluated our discharge summaries provided by Beth Israel Deaconess Medical Center 2010 I2b2 Challenge. The experiments showed that the proposed algorithm achieved a satisfactory score of precision 100%, recall 94.5%, and f-score 97.17% overall. We also perform experiments for the explicit section with a result score of 100% precision, 93.53% recall, and 96.66% f-score, and for the implicitly identified section, we gain precision of 100%, recall 95.46%, and 97.68%. The main goal of proposing this algorithm is to automatically prepare data for data-driven approaches like machine learning and deep learning and enhance the meaningful use of EHR's and CDSS systems, clinical outcomes, and events.

关键词： section extraction section identification clinical section information extraction unstructured documents

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algorithms and methods for extracting knowledge about objects defined by arrays of empirical data using ANN models

Algorithms and methods for extracting knowledge about object...

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2020 international conference on Applied Mathematics, Computational Science and Mechanics: Current Problems, AMCSM 2020

作者： Arzamastsev, A.A. Zenkova, N.A. Kazakov, N.A. Voronezh State University Department of Mathematical and Applied Analysis University Square 1 Voronezh394018 Russia Tambov State University Department of Mathematical Modeling and Information Technology International Str. 33 Tambov392000 Russia

Most data Mining techniques are based on traditional methods of constructing mathematical models and their parametric identification. The article considers the technology of extracting knowledge about objects, given by empirical data arrays. It is based on detecting some characteristic features in the object generating these data. They can include dynamic and static characteristics;delays in various channels;inertia of channels or inertia combined with delay;different channel sensitivity;bypass and certain classes of nonlinearities. We also considered the objects that are characterized by a constant flow of new data, which makes it necessary to retrain the model. As a tool for obtaining knowledge in this work, the apparatus of artificial neural networks (ANNs) is used. It includes a structure builder, which allows you to quickly change the structure of ANN models and machine training algorithms. This enables you to successfully solve parametric identification problems. algorithms and methods of knowledge extraction have been developed for these tasks. Examples that confirm the possibility of their use are given. © Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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Intelligent System for Ceramic Product Design Based on Big data Analysis

Intelligent System for Ceramic Product Design Based on Big D...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Lanyue Huang Qiaohui Chen Xiaobo Chen Qian Peng Key Laboratory of Traditional Heated-form Craft Technology and Digital Design Ministry of Culture and Tourism China Academy of Art Hangzhou Zhejiang China School of Design and Innovation China Academy of Art Hangzhou Zhejiang China Tourism College of Zhejiang Hangzhou Zhejiang China School of Craft Art China Academy of Art Hangzhou Zhejiang China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The design of ceramic products is a crucial and complex process, and the traditional design process has certain drawbacks in product design. This article put forward a ceramic product design intelligent system based on multiple linear regression models to increase the design accuracy and efficiency. The basic model of ceramic product design intelligent system was introduced as multiple regression model. Based on ceramic product design data in large quantities as the learning and modeling data, the designed system can fully consider the relationship and effect among several design variables. The experiments results showed that the accuracy of ceramic product design intelligent system based on multiple linear regression models was better than the original design manner, and the system provided innovative design solutions that were tailored to practical needs and requirements. The system allows designers to consider the effects of multiple factors comprehensively and effectively, thus offering more accurate, innovative, and more market-demanding design solutions for designers, and the system is likely to make a significant contribution to the development and innovation of ceramic product design.

关键词： Training Analytical models Technological innovation Accuracy Computational modeling Linear regression Ceramic products data models Intelligent systems Optimization

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A Context-Enhanced Transformer with Abbr-Recover Policy for Chinese Abbreviation Prediction 22

A Context-Enhanced Transformer with Abbr-Recover Policy for ...

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31st ACM international conference on Information and Knowledge Management (CIKM)

作者： Cao, Kaiyan Yang, Deqing Liu, Jingping Liang, Jiaqing Xiao, Yanghua Wei, Feng Wu, Baohua Lu, Quan Fudan Univ Sch Data Sci Shanghai Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781450392365

Chinese abbreviation prediction is very important for various natural language processing tasks such as query understanding and entity linking, since people tend to use the concise abbreviation rather than the full form (name) to mention an entity. The existing models achieve their predictions through sequence labeling, i.e., the binary classification for each character (token) of the full form. However, they only leverage the semantics of the entity itself, overlooking the label dependencies between the tokens, and the rich information of the entity-related texts. In this paper we proposed a Context-Enhanced Transformer with Abbr-Recover policy, namely CETAR, for Chinese abbreviation prediction. CETAR predicts the abbreviation sequence mainly through an iterative decoding process, of which each round consists of an abbreviation and recovery operation. Our extensive experiments upon both general field and specific domain datasets justify that CETAR outperforms the state-of-the-art baselines including sequence labeling models and sequence generation models. Moreover, we have successfully constructed a Chinese abbreviation dataset from the famous tour website Fliggy, and we also shared it at https://github. com/tolerancecky/abbr-0731. The online A/B test on the Fliggy search system shows that 2.03% of conversion rate improvement has been achieved with the predicted abbreviations.

关键词： Chinese abbreviation prediction Transformer abbreviation and recovery context

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