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检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

共 2255 条记录，以下是1421-1430 订阅

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Safe Facial Multi-attribute Feature Fusion Analysis Based on Embedded Multi-Task learning 22

Safe Facial Multi-attribute Feature Fusion Analysis Based on...

引用

22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Liu, Haibo Li, Qianmu Meng, Shunmei Rong, Zhenbang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Wuyi Univ Intelligent Mfg Dept Jiangmen Peoples R China

ISBN: (纸本)9781728116648

As the main branch of face analysis, facial multi-attribute analysis has a very broad application prospect in the fields of public security monitoring and human-computer interaction. Most deep learning methods have problems such as face alignment, complex network structure, and low computation speed. This paper analyses the potential correlation between facial multi-attributes, and proposes a Embedded Multi-Task learning-based Safe Facial Multi-attribute Feature Fusion analysis model. Using Embedded Multi-Task learning strategy and feature fusion method, a single neural network is used to compute the facial region bounding box and analysis multiple facial attributes. This method realizes rapid analysis of multi-attributes of unconstrained faces.

关键词： Public safety Facial multi-attribute Feature fusion Embedded Multi-Task learning

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Modeling and Training of Neural Processing Systems 22

Modeling and Training of Neural Processing Systems

引用

22nd ACM/IEEE international conference on Model Driven engineering Languages and Systems (MODELS)

作者： Kusmenko, Evgeny Nickels, Sebastian Pavlitskaya, Svetlana Rumpe, Bernhard Timmermanns, Thomas Rhein Westfal TH Aachen Chair Software Engn Aachen Germany

ISBN: (纸本)9781728125350

The field of deep learning has become more and more pervasive in the last years as we have seen varieties of problems being solved using neural processing techniques. Image analysis and detection, control, speech recognition, translation are only a few prominent examples tackled successfully by neural networks. Thereby, the discipline imposes a completely new problem solving paradigm requiring a rethinking of classical software development methods. The high demand for deep learning technology has led to a large amount of competing frameworks mostly having a Python interface - a quasi standard in the community. Although, existing tools often provide great flexibility and high performance, they still lack to deliver a completely domain oriented problem view. Furthermore, using neural networks as reusable building blocks with clear interfaces in productive systems is still a challenge. In this work we propose a domain specific modeling methodology tackling design, training, and integration of deep neural networks. Thereby, we distinguish between three main modeling concerns: architecture, training, and data. We integrate our methodology in a component-based modeling toolchain allowing one to employ and reuse neural networks in large software architectures.

关键词： deep learning neural networks model-driven software engineering

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A Method for Classification of Heavy Mineral Based on Machine learning

A Method for Classification of Heavy Mineral Based on Machin...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Huizhen Hao Xutong Jiang Yuhu Sun Wanchun Dou Qing Gu Software Institute Nanjing University Nanjing P. R. China School of Information and Communication Engineering Nanjing Institute of Technology Nanjing P. R. China State Key Laboratory for Novel Software Technology Nanjing University Nanjing P. R. China

The following topics are dealt with: learning (artificial intelligence); neural nets; cloud computing; scheduling; optimisation; feature extraction; data privacy; parallel processing; resource allocation; security of ... 详细信息

ISBN: (纸本)9781728176499;9781728176505

关键词： Machine learning algorithms Text analysis Text recognition Prototypes Machine learning Feature extraction Classification algorithms

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Hybrid Model of Vehicle Recognition Based on Convolutional Neural Network

Hybrid Model of Vehicle Recognition Based on Convolutional N...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Chan Su Jianguo Wei School of software Nanchang University Nanchang China

ISBN: (纸本)9781728176499;9781728176505

With the improvement of people's living standard, the number of cars on the road has increased dramatically. Vehicle recognition is greatly significant for intelligent traffic management. In this paper, a hybrid model of vehicle recognition algorithm based on VGG16-softmax hybrid model is proposed. The convolutional neural network called VGG16 is used, Imagenet is used for pre-training, migration learning is used to migrate parameters to the new training model, variational auto-encoder is used for data reconstruction, and finally softmax multi-classifier is used for classification. Experiments show that this method can save time, get better vehicle feature of details and higher accuracy.

关键词： Training Uncertainty Transfer learning Training data Feature extraction data models Pattern recognition

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Electroencephalography Based Machine learning Framework for Anxiety Classification 2nd

Electroencephalography Based Machine Learning Framework for ...

引用

2nd international conference on intelligent Technologies and Applications, INTAP 2019

作者： Arsalan, Aamir Majid, Muhammad Anwar, Syed Muhammad Department of Computer Engineering University of Engineering and Technology Taxila Taxila Pakistan University of Central Florida OrlandoFL32816 United States

ISBN: (纸本)9789811552311

Anxiety is a psycho-physiological phenomenon related to the mental health of a person. Persistence of anxiety for an extended period of time can manifest into anxiety disorder, which is a root cause of multiple mental health issues. Therefore, accurately detecting anxiety is vital using methods that are automated, efficient and independent of user bias. To this end, we present an experimental study for the classification of human anxiety using electroencephalography (EEG) signals acquired from a commercially available four channel headset. EEG data of 28 participants are acquired for a duration of three minutes. Five different feature groups in time domain are extracted from the acquired EEG signals. Wrapper method of feature selection is applied, which selects features from two feature groups among the five feature groups initially extracted. Classification is performed using logistic regression (LR), random forest (RF), and multilayer perceptron (MLP) classifiers. We have achieved a classification accuracy of 78.5% to classify human anxiety by using the RF classifier. Our proposed scheme outperforms when compared with existing methods of anxiety/stress classification. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Introducing Context and Context-awareness in data Integration: Identifying the Problem and a Preliminary Case Study on Informed Consent 20

Introducing Context and Context-awareness in Data Integratio...

引用

Proceedings of the 22nd international conference on Information Integration and Web-based Applications & Services

作者： Christophe Debruyne Trinity College Dublin Dublin Ireland

ISBN: (纸本)9781450389228

data integration is the process of selecting, preprocessing, and transforming data from heterogeneous sources in data-driven projects. This process also requires the most time, effort, resources. data integration is such an involved process due to the many informed decisions one has to make. These decisions are influenced by the complex context of a data-driven project. We argue that using said context could facilitate the decision-making processes and even automate some integration steps. However, the problem we identify in this paper is that the context of a data-driven project is tacit and, therefore, not easily accessible by humans and certainly not by software agents. From the SotA, however, we observe that current models represent the context in crude and simplistic terms. These context models are furthermore built for specific tasks or application domains such as query optimization or a smart home. The current state of affairs is thus is not fit for intelligent data integration. Next to identifying the problem, we postulate that solving this problem requires two steps: formalizing context and using that context for building context-aware agents. We illustrate this notion of "context-aware data integration" with preliminary results obtained with a use case in the domain of GDPR, more specifically the generation of datasets that takes into account informed consent.

关键词： Ontology engineering Context Context-aware data Integration

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Efficient Inference in Multi-task Cox Process Models 22

Efficient Inference in Multi-task Cox Process Models

引用

22nd international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Aglietti, Virginia Damoulas, Theodoros Bonilla, Edwin, V Univ Warwick Alan Turing Inst Coventry W Midlands England UNSW CSIROs Data61 Sydney NSW Australia

We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGGPs, whose log intensities are given by linear combinations of latent functions drawn from Gaussian process priors. The combination coefficients are also drawn from Gaussian processes and can incorporate additional dependencies. We derive closed-form expressions for the moments of the intensity functions and develop an efficient variational inference algorithm that is orders of magnitude faster than competing deterministic and stochastic approximations of multivariate LGGPs, coregionalization models, and multi-task permanental processes. Our approach outperforms these benchmarks in multiple problems, offering the current state of the art in modeling multivariate point processes.

关键词： Stochastic systems

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Travel Time Prediction in Missing data Areas: Feature-based Transfer learning Approach

Travel Time Prediction in Missing Data Areas: Feature-based ...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Sayda Elmi Kian-Lee Tan School of Computing National University of Singapore Singapore

ISBN: (纸本)9781728176505

Travel time prediction based on deep learning techniques has received much attention in recent years. It is of great importance to advanced traffic management systems but remains a challenging problem as historical data are available in some but not all areas. Our preliminary experiments on New York City road network demonstrated that GPS pings recorded by in-vehicle GPS devices providing speed, position and time, do not cover the whole road network, thus, travel time in some regions could not be predicted accurately. However, predicting accurate travel time in each and every region in a city is fundamental to intelligent transportation systems. To predict travel time of target areas where little historical traffic data are available, we propose a transfer learning framework that exploits historical data of some source areas which are data-abundant regions. To accurately predict travel time in source areas, we propose a hybrid architecture that models the spatial and temporal closeness as well as the road network typology. Experimental results on New York City road network show that our proposed model can achieve competitive performance comparing to baseline methods and classic regression models.

关键词： Deep learning Roads Computational modeling High performance computing Urban areas Transfer learning Predictive models

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Automatic Modulation Classification Using Combination of Variational Mode Decomposition and Multifractal Analysis

Automatic Modulation Classification Using Combination of Var...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Bingyang Li Wen Wang Xiaofei Zhang Meng Zhang Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China The State Radio Monitoring Center Beijing China

ISBN: (纸本)9781728176505

Automatic modulation classification (AMC) plays a key role in intelligent wireless communication. AMC is widely used in civil and military field for its ability of identifying the modulation type of unknown signal without any prior knowledge. To improve the classification accuracy of feature based AMC at low signal-to-noise (SNR), a novel modulation classification scheme based on Variational Mode Decomposition (VMD) and multifractal analysis is proposed in this paper. The proposed scheme applies VMD to decompose the original signal into multiple intrinsic mode functions(IMFs). Then, multifractal analysis is used to extract multifractal features from each IMF, which combine with statistical features extracted from each IMF as the input of Extreme learning Machine (ELM). In this paper, we use SMW200A Vector Signal Generator to generate real RF data to verify the effectiveness of the proposed scheme, and simulate the additive white Gaussian noise (AWGN) to verify the robustness of the scheme to noise. The results show that the proposed scheme can reach an average accuracy of 95% when the SNR is 0dB.

关键词： Radio frequency Wireless communication Extreme learning machines High performance computing Modulation Feature extraction Signal generators

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A subjectivity-aware algorithm for label aggregation in crowdsourcing 22

A subjectivity-aware algorithm for label aggregation in crow...

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22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Wu, Ming Li, Qianmu Wang, Shuo Hou, Jun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Wuyi Univ Intelligent Mfg Dept Jiangmen Peoples R China

ISBN: (纸本)9781728116648

Crowdsourcing has already attracted a wide attention in the field of machine learning and its related fields. A large amount of labeled data can be obtained quickly and cheaply on crowdsourcing platforms. To deal with the problem that labels collected from crowds are usually noisy due to the low accuracy of non-expert online workers, we use quality control methods to improve the qualities of crowd data. Unfortunately, current quality control methods only consider the instance difficulty or the worker reliability to account for the variety of labels to the same instance, and these methods did not take subjectivity of workers into consideration which also effects the responses. In this paper, we present a novel subjectivity-aware algorithm for label aggregation, which also model the difficulty of instances and reliability of workers as latent parameters. This method is an EM-like algorithm, which not only infers the ground truth of the instances, but also simultaneously estimates the latent parameters. Experimental results on real-world datasets show that our method outperforms the state-of-the-art ground truth inference algorithms.

关键词： crowdsourcing quality control subjectivity difficulty reliability EM algorithms

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