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16th international conference on Computational Linguistics and intelligent Text Processing, CICLing 2015

16th International Conference on Computational Linguistics a...

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16th Annual conference on intelligent Text Processing and Computational Linguistics, CICLing 2015

ISBN: (纸本)9783319181165

the proceedings contain 50 papers. the special focus in this conference is on Sentiment Analysis, Emotion Detection, Social Network Analysis and Natural Language Generation. the topics include: Building large Arabic multi-domain resources for sentiment analysis;modelling public sentiment in twitter;trending sentiment-topic detection on twitter;feature selection for twitter sentiment analysis;an iterative emotion classification approach for microblogs;aspect-based sentiment analysis using tree kernel based relation extraction;sentiment classification with graph sparsity regularization;detecting emotion stimuli in emotion-bearing sentences;sentiment-bearing new words mining;exploiting emoticons and latent polarities;identifying temporal information and tracking sentiment in cancer patients’ interviews;using stylometric features for sentiment classification;automated linguistic personalization of targeted marketing messages mining user-generated text on social media;inferring aspect-specific opinion structure in product reviews using co-training;summarizing customer reviews through aspects and contexts;twitter recommendation based on users#x2019;personal interests;detection of opinion spam with character n-grams;content-based recommender system enriched with wordnet synsets;active learning based weak supervision for textual survey response classification;detecting and disambiguating locations mentioned in twitter messages;satisfying poetry properties using constraint handling rules;a multi-strategy approach for lexicalizing linked open data;enhancing graph-based techniques for summary extraction with sentiment polarity;experiments with query expansion for entity finding;improving cross language information retrieval using corpus based query suggestion approach and information extraction with active learning.

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Potentials of few-shot learning for quality monitoring in laser welding of hairpin windings 16

Potentials of few-shot learning for quality monitoring in la...

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16th CIRP conference on intelligent Computation in Manufacturing engineering, CIRP ICME 2022

作者： Raffin, Tim Mayr, Andreas Baader, Marcel Laube, Nadine Kühl, Alexander Franke, Jörg Egerlandstr. 9 Erlangen91058 Germany

For the analysis of high-dimensional data in electromechanical manufacturing, data-driven techniques such as deep learning show great potential. However, the vast amount of required data poses a major barrier to the industrial adoption of said techniques. In the production of electric traction drives, this situation is further exacerbated by short innovation cycles. One of these innovations is hairpin technology, which is increasingly being employed by automotive manufacturers and suppliers. In contrast to classical deep learning approaches, few-shot learning aims at generalizing well despite limited data availability. this makes it ideal for the ramp-up phase of new production technologies for which no large legacy datasets exist yet. therefore, this paper presents an empirical evaluation of few-shot learning techniques in the context of hairpin stator production. In doing so, we evaluate and benchmark several few-shot learning architectures and traditional approaches for quality monitoring of laser-welded hairpin windings based on image data. © 2023 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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AutoTrain: An Efficient Auto-training System for Small-scale Image Classification 16

AutoTrain: An Efficient Auto-training System for Small-scale...

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16th IEEE international Wireless Communications and Mobile Computing conference, IWCMC 2020

作者： Yi, Shaoxiao Zou, Junwei Ren, Wei Luo, Hong Beijing Key Lab of Intelligent Telecomm. Software and Multimedia Beijing Univ. of Posts and Telecomm. Beijing100876 China School of Electronic Engineering Beijing University of Posts and Telecomm. Beijing100876 China

ISBN: (纸本)9781728131290

Machine learning has become the most promising research field. However, the involved models usually require complex, tedious and expensive manual intervention. the automated machine learning technology plays a significant role in mitigating this issue. However, the current studies ignore the importance of automation in data preprocessing. In this paper, we propose an efficient automatic training system, AutoTrain, to solve small-scale image classification problems. First, we design sample equalization in data augmentation to improve the performance of training on uneven data. then, a Bayesian optimization-based strategy controller is introduced to rapidly find the strategy applied in data augmentation. Additionally, we present a dynamic adjustment model to fit tasks with different scales and complexity. Finally, experimental results show that the AutoTrain's training speed is about 3 times faster on average than the conventional methods. And the avergae accuracy of AutoTrain has 2% improved to the conventional methods. © 2020 IEEE.

关键词： Image classification

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Knowledge distillation via entropy map for scene text detection 16

Knowledge distillation via entropy map for scene text detect...

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16th IEEE international conference on Computer Science and Education, ICCSE 2021

作者： Chen, Jianwei Lai, Yongxuan Zeng, Yifeng Yang, Fan Xiamen University Department of Automation Xiamen China Xiamen University Department of Software Engineering Xiamen China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen University Xiamen China

ISBN: (纸本)9781665414685

In recent years, state-of-the-art scene text detectors use cumbersome segmentation models as their detection frameworks, but this type of detection model is difficult to deploy to mobile devices with limited computing resources. An effective method to achieve a good compromise between detection accuracy and model complexity is knowledge distillation. the existing knowledge distillation methods cannot make full use of the output characteristics of the segmentation-based text detection, that is, each pixel on the segmentation map represents the probability that the pixel belongs to the text. the results of the entropy map corresponding to the visualized segmentation map show that the degree of confusion between the information entropy map of the student network and the teacher network reflects the gap in generalization ability. In this work, we propose a novel knowledge distillation via entropy map(KDEM). Specifically, the entropy map of the teacher network segmentation map is used as knowledge to guide student network learning. In order to eliminate the possible adverse effects of entropy in non-target regions, we multiply the information entropy map of the teacher network with the mask of the text region to extract the knowledge related to the target. Experiments on three benchmark datasets: MSRA-TD500, ICDAR 2015 and Total-Text, show that our proposed knowledge distillation via entropy map consistently improve the F1-score of the student network and is better than the other three mainstream knowledge distillation methods. © 2021 IEEE.

关键词： Distillation

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Predicting the solidification time of low pressure die castings using geometric feature-based machine learning metamodels 16

Predicting the solidification time of low pressure die casti...

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16th CIRP conference on intelligent Computation in Manufacturing engineering, CIRP ICME 2022

作者： Rosnitschek, Tobias Erber, Maximilian Alber-Laukant, Bettina Hartmann, Christoph Volk, Wolfram Rieg, Frank Tremmel, Stephan Engineering Design and CAD University of Bayreuth Universitaetsstr. 30 Bayreuth95447 Germany Metal Forming and Casting Technical Univeristy of Munich Walther-Meiner-Str. 4 Garching85748 Germany

Casting process simulations are commonly used to predict and avoid defect formation. their integration into structural optimization can enable automated structure- and process-optimized castings. Nevertheless, these simulations are time-consuming and computationally expensive. therefore, this paper used graph theory and skeletonization techniques to extract geometric features from arbitrary 3D geometries and transferred them to machine learning-metamodels. this method can replace casting process simulation for the prediction of directional solidification in low-pressure die casting. automated machine learning and hyperparameter optimization were used to systemize the search for well-suited neural network architectures. Two examples were used to train the metamodels, which are subsequently evaluated by a further test example, unknown to the training data and compared to the simulation results. the results showed an accuracy on unknown geometries over 60 % and thus emphasized that neural network metamodels are capable of replacing time-consuming casting process simulation for specific objectives. © 2023 Elsevier B.V.. All rights reserved.

关键词： Structural optimization

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A multiobjective analysis of adaptive clustering algorithms for the definition of RBF neural network centers in regression problems

A multiobjective analysis of adaptive clustering algorithms ...

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13th international conference on intelligent data engineering and automated learning, ideal 2012

作者： Veroneze, Rosana Gonçalves, André R. Von Zuben, Fernando J. School of Electrical and Computer Engineering University of Campinas Campinas São Paulo Brazil

ISBN: (纸本)9783642326387

A variety of clustering algorithms have been applied to determine the internal structure of Radial Basis Function Neural Networks (RBFNNs). k-means algorithm is one of the most common choice for this task, although, like many other clustering algorithms, it needs to receive the number of prototypes a priori. this is a nontrivial procedure, mainly for real-world applications. An alternative is to use algorithms that automatically determine the number of prototypes. In this paper, we performed a multiobjective analysis involving three of these algorithms, which are: Adaptive Radius Immune Algorithm (ARIA), Affinity Propagation (AP), and Growing Neural Gas (GNG). For each one, the parameters that most influence the resulting number of prototypes composed the decision space, while the RBFNN RMSE and the number of prototypes formed the objective space. the experiments found that ARIA solutions achieved the best results for the multiobjective metrics adopted in this paper. © 2012 Springer-Verlag.

关键词： Clustering algorithms

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Design of a New automated Fault Detector based on artificial intelligence and Big data Techniques 16

Design of a New Automated Fault Detector based on artificial...

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16th international learning and Technology conference, L and T 2019

作者： Rahouma, Kamel H. Afify, Farag M. Hamed, Hesham F.A. Electrical Engineering Department Faculty of Engineering Minia University Minia Egypt

Concerns over energy and environmental issues around the world have led to the worldwide focus on energy use reduction. Studies in each area of energy have shown that residential and commercial construction sectors consume more power than other sectors such as industry, agriculture, services, and transportation. Studies of energy consumption in building sectors have reported that energy savings of 10% to 30% can be obtained by using artificial intelligence (AI), the system would be capable of detecting and analyzing anomalies in energy usage pattern assessing, diagnosing and suggesting the best solution in suitable time. this paper proposes to integrate and hybridize between AI techniques and big data algorithms which can enhance monitoring and controlling building systems, increasing comfort and decreasing efficiently the running costs. In addition, the authors suggest a tool which aims to automatically detect abnormal energy consumption by using AI and big data which are produced by the Building Management System (BMS). this happens by designing a software application that is called Fault Detection Tool (FDT) which automatically detects the abnormalities of energy consumption, optimizes the use of different resources and analyzes faults, complaints and time taken to terminate them. Experimental results show that with the proposed approach, it is possible to accurately detect anomalous patterns in building energy consumption. this tool will be a part of an artificial intelligent decision-making system. © 2019 the Authors. Published by Elsevier B.V.

关键词： Energy utilization

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Neuromorphic predictive systems based on deep learning 16th

Neuromorphic predictive systems based on deep learning

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16th international conference on engineering Applications of Neural Networks, EANN 2015

作者： Kasabov, Nikola School of Computing and Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9783319239811

the current development of the third generation of artificial neural networks-the spiking neural networks (SNN) [1, 5, 9] along with the technological development of highly parallel neuromorphic hardware systems of millions of artificial spiking neurons as processing elements [2, 3], makes it possible to model big and fast data in a fast on-line manner, enabling large-scale problem solving across domain areas including building better predictive systems. the latter topic is covered in this talk. the talk first presents some principles of deep learning inspired by the human brain, such as automated feature selection, ‘chain fire’, polychronisation. these principles are implemented in a recent evolving SNN (eSNN) architecture called NeuCube [4] and its software development system that is made available from: www. kedri. ***. nz/neucube/. these principles allow for an eSNN system to predict events and outcomes, so that once the eSNN is trained on whole spatio-temporal patterns, it can be made to spike early, when only a part of a new pattern is presented as input data. the talk presents a methodology for the design and implementation of NeuCubebased eSNN systems for deep learning and early and accurate outcome prediction from large-scale spatio-/spectro temporal data, referred here as spatiotemporal data machines (STDM). A STDM has modules for: preliminary data analysis, data encoding, pattern learning, classification, regression, prediction and knowledge discovery. this is illustrated on early event prediction tasks using benchmark large spatio/spectro-temporal data with different spatial/temporal characteristics, such as: EEG data for brain computer interfaces;personalised and climate date for stroke occurrence prediction and for the prediction of ecological and seismic events [6–8]. the talk discusses implementation on highly parallel neuromorphic hardware platforms such as the Manchester SpiNNaker [2] and the Eth Zurich chip [3, 10]. the STDM are not only signif

关键词： Neural networks

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automated Corneal Segmentation of Anterior Segment Photographed Images using Centroid-Based Active Contour Model 16

Automated Corneal Segmentation of Anterior Segment Photograp...

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16th international learning and Technology conference, L and T 2019

作者： Daud, Marizuana M. Diyana, W. Mimi Zaki, W. Hussain, Aini Mutalib, Haliza Md Saad, M. Hanif Razak, Aishah Centre of Integrated Systems Engineering and Advanced Technologies Faculty of Engineering and Built Environment University Kebangsaan Malaysia Bangi43600 Malaysia Optometry and Vision Sciences Programme School of Healthcare Sciences Faculty of Health Sciences University Kebangsaan Malaysia Kuala Lumpur50300 Malaysia Visual and Digital Production Effat University Jeddah21478 Saudi Arabia

In this paper, we propose an automatic Active Contour Model (ACM) using corneal centroid information for corneal segmentation. the algorithm was validated on 259 of Anterior Segment Photographed Images (ASPIs) that were captured using a smartphone. the proposed system comprises of four components;a) data collection, b) pre-processing, c) corneal segmentation using proposed automated centroid-based ACM, and (d) feature extraction. there were five corneal shape parameters employed, namely Horizontal Visible Iris Diameter (HVID), Vertical Visible Iris Diameter (VVID), eccentricity, asphericity and solidity. Each of these parameters has its own significant features in measuring the corneal shape quantitatively, which could potentially assist the ophthalmologists during a screening process. Using all ASPIs, the findings show that this method is competent in segmenting 231 corneal regions while Circular Hough Transform method only manage to segment 165 regions successfully. these segmentation results demonstrate a bright potential towards the development of intelligent classification system for ocular diseases. © 2019 the Authors. Published by Elsevier B.V.

关键词： Image segmentation

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Adaboost-based Graph Convolutional Networks with Channel Attention for Intrusion Detection in Internet-of-things 21

Adaboost-based Graph Convolutional Networks with Channel Att...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Long, Jing Luo, Cuiting Chen, Ruxin Liang, Wei Qin, Zheng Hunan Normal University College of Information Science and Engineering Changsha410081 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

ISBN: (纸本)9798350329223

With the increasing proliferation of Internet of things (IoT) and industrial big data technologies, while IoT scenarios offer intelligent services, cyberspace faces various types of threats and attacks. Despite Graph Convolutional Networks (GCN) having been demonstrated as a potent tool for learning graph data representations, many current IoT intrusion detection methods tend to overlook the adoption of graph-related techniques, given the non-graph-structured nature of most IoT traffic data. this leads to the model design overlooking the potential correlations among variables, thus limiting the accuracy of intrusion detection. In this study, we introduce an integrated intrusion detection model that fuses graph learning with a dual attention mechanism(A-GCA). Initially, our research adopts a combination of GCN and Convolutional Neural Networks (CNN) to deeply capture intricate interactions between data nodes. To further enhance the model's feature-learning capability, we incorporate a channel attention mechanism for emphasizing features with pivotal information. Additionally, a multi-head attention mechanism is adopted to amalgamate a wealth of contextual information, ensuring more vivid feature representations. Regarding the training strategy, we employ a variant of Adaboost which uniquely steers weak classifiers from focusing on high-precision fitting of raw data towards precise binary classification tasks, aiming to augment the model's overall performance in intrusion detection. Evaluations and performance analyses using real-world datasets UNSW-NB15 and CICIDS2017 indicate that the proposed method achieves improvements of 2.5% and 3.7% over the best baseline model in the accuracy and F1-score, respectively, relative to the baseline, surpassing other approaches. © 2023 IEEE.

关键词： Adaptive boosting

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