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19th international Conference on Computational Science and Its Applications (ICCSA)

作者： Mazzei, Mauro CNR Ist Anal Sistemi & Informat Antonio Ruberti Via Taurini 19 I-00185 Rome Italy

ISBN: (纸本)9783030243029

The analysis, image recognition, the classification in remote sensing data always up to date is for the insiders a very useful system for the management, planning, protection of our territory. Technological evolution the succession of applications and tools in GIS environment, have refined more and more techniques allowing us to computerize much of the territory bringing an enrichment of geographical data distributed more or less updated, remote sensing, digital orthoimage help us in the areas where the local computerization is not always updated. In this paper I want to demonstrate that an approach based on machine learning applied to territorial analysis using dynamic matrices, provides for the definition of numerical indices assigned to each recognized pattern. The numerical indices will be the elements of recognition where the territorial computerization is insufficient. The data from remote sensing or digital orthoimage are variable matrices where the numerical indices have to express their potential. The goal is to educate the recognition in matrix form to obtain a dynamic topology where the computerization has not been updated.

关键词： data mining Spatial data mining Spatial data analysis Spatial statistical model

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Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task 26

Improving End-to-End Text Image Translation From the Auxilia...

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26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Ma, Cong Zhang, Yaping Tu, Mei Han, Xu Wu, Linghui Zhao, Yang Zhou, Yu Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit NLPR 95 Zhongguan East Rd Beijing 100190 Peoples R China Zhongke Fanyu Technol Co Ltd Fanyu AI Lab Beijing 100190 Peoples R China Samsung Res China Beijing SRC B Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

End-to-end text image translation (TIT), which aims at translating the source language embedded in images to the target language, has attracted intensive attention in recent research. However, data sparsity limits the performance of end-to-end text image translation. Multi-task learning is a non-trivial way to alleviate this problem via exploring knowledge from complementary related tasks. In this paper, we propose a novel text translation enhanced text image translation, which trains the end-to-end model with text translation as an auxiliary task. By sharing model parameters and multi-task training, our model is able to take full advantage of easily-available large-scale text parallel corpus. Extensive experimental results show our proposed method outperforms existing end-to-end methods, and the joint multi-task learning with both text translation and recognition tasks achieves better results, proving translation and recognition auxiliary tasks are complementary. (1)

关键词： Training Image recognition Text recognition Optical character recognition Multitasking Decoding machine translation

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SIM-CNN: Self-supervised Individualized Multimodal learning for stress Prediction on Nurses Using Biosignals 1st

SIM-CNN: Self-supervised Individualized Multimodal Learning ...

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1st international workshop on machine learning for Multimodal Healthcare data (ML4MHD)

作者： Eom, Sunmin Eom, Sunwoo Washington, Peter Univ Hawaii Manoa Honolulu HI 96822 USA

ISBN: (纸本)9783031476785;9783031476792

Precise stress recognition from biosignals is inherently challenging due to the heterogeneous nature of stress, individual physiological differences, and scarcity of labeled data. To address these issues, we developed SIM-CNN, a self-supervised learning (SSL) method for personalized stress-recognition models using multimodal biosignals. SIM-CNN involves training a multimodal 1D convolutional neural network (CNN) that leverages SSL to utilize massive unlabeled data, optimizing individual parameters and hyperparameters for precision health. SIM-CNN is evaluated on a real-world multimodal dataset collected from nurses that consists of 1,250 h of biosignals, 83 h of which are explicitly labeled with stress levels. SIM-CNN is pre-trained on the unlabeled biosignal data with next-step time series forecasting and fine-tuned on the labeled data for stress classification. Compared to SVMs and baseline CNNs with an identical architecture but without self-supervised pre-training, SIM-CNN shows clear improvements in the average AUC and accuracy, but a further examination of the data also suggests some intrinsic limitations of patient-specific stress recognition using biosignals recorded in the wild.

关键词： Self-Supervised learning Multimodality Precision health stress Biosignals

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Action2Activity: Recognizing Complex Activities from Sensor data 24

Action2Activity: Recognizing Complex Activities from Sensor ...

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1st international workshop on Social Influence Analysis / 24th international Joint Conference on Artificial Intelligence (IJCAI)

作者： Liu, Ye Nie, Liqiang Han, Lei Zhang, Luming Rosenblum, David S. Natl Univ Singapore Sch Comp Singapore Singapore Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781577357384

As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a mid-level feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.

关键词： Artificial intelligence

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Scaling Activity recognition Using Channel state Information Through Convolutional Neural Networks and Transfer learning 1

Scaling Activity Recognition Using Channel State Information...

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1st international workshop on Challenges in Artificial Intelligence and machine learning for Internet of Things (AIChallengeIoT)

作者： Brinke, Jeroen Klein Meratnia, Nirvana Univ Twente Enschede Netherlands

ISBN: (纸本)9781450370134

Unobtrusive sensing is receiving much attention in recent years, as it is less obtrusive and more privacy-aware compared to other monitoring technologies. Human activity recognition is one of the fields in which unobtrusive sensing is heavily researched, as this is especially important in health care. In this regard, investigating WiFi signals, and more specifically 802.11n channel state information, is one of the more prominent research fields. However, there is a challenge in scaling it up. Transfer learning is rarely applied, and when applied, it is done on filtered/modified data or extracted features. This paper focuses on two aspects. First, convolutional networks are used across multiple participants, days and activities and analysis is done based on these results. Secondly, it looks into the possibility of applying transfer learning based on raw channel state information over multiple participants and activities over multiple days. Results show channel state information is accurate for single participants (F-1-score of 0.90), but sensitive to different participants and fluctuating WiFi signals over days (F-1-score of 0.25-0.35). Furthermore, results show both clustering and transfer learning can be applied to increase the performance to 0.80 when using minimal resources and retraining. CCS CONCEPTS Computer systems organization -> Sensor networks;Human-centered computing -> Ubiquitous and mobile computing;Computing methodologies -> Cross-validation.

关键词： datasets channel state information human activity recognition remote sensing deep learning convolutional neural networks transfer learning

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Dynamically organizing KDD processes

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international JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2001年第3期15卷 451-473页

作者： Zhong, N Liu, CN Ohsuga, S Maebashi Inst Technol Dept Informat Engn Maebashi Gumma 371 Japan Beijing Polytech Univ Dept Comp Sci Beijing 100022 Peoples R China Waseda Univ Sch Sci & Engn Dept Informat & Comp Sci Shinjuku Ku Tokyo 169 Japan

How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and data mining). Within the framework of the KDD process and the GLS (Global learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system by dynamically organizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontology to describe KDD agents, in the style of GOER (Object Oriented Entity Relationship) data model. Based on this ontology of KDD agents, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain;(2) tackle the complexity of KDD process;and (3) support evolution of KDD data, knowledge and process. The GLS system, as a multistrategy and multiagent KDD system based on the methodology, increases both autonomy and versatility.

关键词： KDD process knowledge discovery automation intelligent agents multistrategy learning AI planning

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Kernel whitening for one-class classification

Kernel whitening for one-class classification

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1st international workshop on pattern recognition with Support Vector machines

作者： Tax, DMJ Juszczak, P Fraunhofer Inst FIRST IDA D-12489 Berlin Germany Delft Univ Technol Fac Appl Phys Pattern Recognit Grp NL-2628 CJ Delft Netherlands

ISBN: (纸本)354044016X

In one-class classification one tries to describe a class of target data and to distinguish it from all other possible outlier objects. Obvious applications are areas where outliers are very diverse or very difficult or expensive to measure, such as in machine diagnostics or in medical applications. In order to have a good distinction between the target objects and the outliers, good representation of the data is essential. The performance of many one-class classifiers critically depends on the scaling of the data and is often harmed by data distributions in (nonlinear) subspaces. This paper presents a simple preprocessing method which actively tries to map the data to a spherical symmetric cluster and is almost insensitive to data distributed in subspaces. It uses techniques from Kernel PCA to rescale the data in a kernel feature space to unit variance. This transformed data can now be described very well by the Support Vector data Description, which basically fits a hypersphere around the data. The paper presents the methods and some preliminary experimental results.

关键词： novelty detection one-class classification kernel PCA feature extraction

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Minimizing Training data for Reliable Writer Identification in Medieval Manuscripts 2nd

Minimizing Training Data for Reliable Writer Identification ...

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： Cilia, Nicole Dalia De stefano, Claudio Fontanella, Francesco Molinara, Mario Scotto di Freca, Alessandra University of Cassino and Southern Lazio Via G. Di Biasio 43 Cassino FR Italy

ISBN: (纸本)9783030307530

Palaeography aims to study ancient documents and the identification of the people who participated in the handwriting process of a given document is one of the most important problems. To this aim, expert paleographers typically analyze handwriting features such as letter heights and widths, distances between characters and angles of inclination. With the aim of achieving more precise measures and also thanks to the availability of high-quality digital images, paleographers are starting to use digital tools. In this context, in previous studies, we proposed a pattern recognition system for distinguishing the writers of mediaeval books and also investigated which is the minimum amount of training data needed to achieve satisfactory results in terms of accuracy. In this paper, we present a reject option that allows us to implement a highly-reliable system for writer identification, trained on a reduced set of data. The experimental results, performed on two sets of digital images from medieval Bibles, show that rejecting only a few samples it is possible to strongly reduce the error rate. © 2019, Springer Nature Switzerland AG.

关键词： Digital devices

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AISD 2024 - Proceedings of the 1st international workshop of Young Scientists on Artificial Intelligence for Sustainable Development

AISD 2024 - Proceedings of the 1st International Workshop of...

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1st international workshop of Young Scientists on Artificial Intelligence for Sustainable Development, AISD 2024

The proceedings contain 19 papers. The topics discussed include: artificial intelligence application in renewable energy sources;intelligent system for visual testing of software products;verifying the economic potential of low-carbon energy using artificial intelligence in transport;enhancing the efficiency of decision support systems in the warehousing sector;data transformation review in deep learning;comparative analysis of CNN architecture for emotion classification on human faces;content analysis of court decisions: a GPT-4 based sentence-by-sentence data generation and association rules mining;exploring the future of UAE judiciary: ai integration, bias mitigation, and systemic enhancements;comparative analysis of stress factors of humanities and technical specialities students;integrated approach to the international aspects of online dispute resolution formation;fuzzy audit system of enterprise activity;and does expectations affect inflation forecasting abilities of machine learning techniques: case of Ukraine.

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Forklift Truck Activity recognition from CAN data 2nd

Forklift Truck Activity Recognition from CAN Data

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2nd international workshop on IoT streams for data-Driven Predictive Maintenance, IoT streams 2020, and 1st international workshop on IoT, Edge, and Mobile for Embedded machine learning, ITEM 2020, co-located with ECML/PKDD 2020

作者： Chen, Kunru Pashami, Sepideh Nowaczyk, Slawomir Johansson, Emilia sternelöv, Gustav Rögnvaldsson, Thorsteinn Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden Toyota Material Handling Europe Mjölby Sweden

ISBN: (纸本)9783030667696

machine activity recognition is important for accurately estimating machine productivity and machine maintenance needs. In this paper, we present ongoing work on how to recognize activities of forklift trucks from on-board data streaming on the controller area network. We show that such recognition works across different sites. We first demonstrate the baseline classification performance of a Random Forest that uses 14 signals over 20 time steps, for a 280-dimensional input. Next, we show how a deep neural network can learn low-dimensional representations that, with fine-tuning, achieve comparable accuracy. The proposed representation achieves machine activity recognition. Also, it visualizes the forklift operation over time and illustrates the relationships across different activities. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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