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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Moosaei, Hossein Ketabchi, Saeed Syed, Mujahid N. Bazikar, Fatemeh Department of Informatics Faculty of Science Jan Evangelista Purkyně University Ústí nad Labem Czech Republic Department of Applied Mathematics Faculty of Mathematical Sciences University of Guilan Rasht Iran Department of Industrial and Systems Engineering Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals KSA Dhahran31261 Saudi Arabia Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran

ISBN: (纸本)9783031503191

The augmented Lagrangian method can be used for finding the least 2 - norm solution of a linear programming problem. This approach’s primary advantage is that it leads to the minimization of an unconstrained problem with a piecewise quadratic, convex, and differentiable objective function. However, this function lacks an ordinary Hessian, which precludes the use of a fast Newton method. In this paper, we apply the smoothing techniques and solve an unconstrained smooth reformulation of this problem using a fast Newton method. Computational results and comparisons are illustrated through multiple numerical examples to show the effectiveness of the proposed algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Newton-Raphson method

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Word-Based Turkish Lip Reading System with Two Stream 3dcnn and Bilstm

SSRN

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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Landscape Rippling: Context-based water-mediated interaction design

Landscape Rippling: Context-based water-mediated interaction...

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作者： Lin, Weiyue Hong, Haoran She, Yingying Yang, Baorong School of Informatics Xiamen University Xiamen China School of Software and Microelectronics Peking University Beijing China Graduate School of Frontier Sciences The University of Tokyo Chiba Japan School of Film Xiamen University Xiamen China College of Computer Engineering Jimei University Xiamen China

With a core purpose of helping users to understand the context, a water interface provides possibility for enhancing user experience in interaction process. Starting from analyzing existing water-mediated interaction approaches, we proposed a water-mediated interaction design model and a corresponding user experience model, aiming to eliminate the boundary between users and the context with water as the medium. According to the proposed model, we implemented a water-mediated interaction system Landscape Rippling, with the painting "A Panorama of Rivers and Mountains" as its context. Ultimately, user experience tests of the interaction system demonstrate the effectiveness of this water-mediated interaction design model. © 2022 John Wiley & Sons, Ltd.

关键词： Augmented reality

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Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone 5

Long Short Term Memory Networks for Stroke Activity Recognit...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2021

作者： Siagian, Pandapotan Fernando, Erick Del Institute Of Technology Faculty Of Informatics And Electrical Engineering Computer Engineering Study Program North Sumatera 22381 Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

Aims this study to analyze rehabilitation exercises using sensor data embedded in smartphones is widely used to recognize human activities regularly to gain a better understanding of human behavior. However, it is rarely used to measure accelerators of patient movement in hospital wards. This paper proposes an optimized neural network-based feature extraction approach based on the Long Short Term Memory technique to recognize human activity using data from a tri-axial accelerometer. The activity to be measured is the Range of Motion for patient rehabilitation, and rehabilitation exercises are one of the most important steps for recovery. The results of the softmax regression trained on the dataset set have 1, 076, 105 records and 12 class attributes (class distribution). The experimental results on the SAR dataset show that our Long Short Term Memory-based approach is practical and successful, with an accuracy of 89.74%. © 2021 IEEE.

关键词： Smartphones

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Active vision: A promising technology for achieving zero-defect manufacturing

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Procedia computer Science 2024年 232卷 2821-2830页

作者： Konstantinos A. Tsintotas Ioannis Kansizoglou Fotios K. Konstantinidis Spyridon G. Mouroutsos Georgios Ch. Syrakoulis Foivos Psarommatis Yiannis Aloimonos Antonios Gasteratos Department of Production and Management Engineering Democritus University of Thrace 12 Vas. Sophias Xanthi GR-671 32 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Xanthi GR-671 00 Greece Department of Informatics SIRIUS University of Oslo Gaustadalleen 23B 0373 Oslo Noway Research Center on Production Engineering and Management (CIGIP) Universitat Polite`cnica de València Spain Department of Computer Science University of Maryland College Part MD-207 42 United States

Quality constitutes the cutting-edge of contemporary industry, much more sharpened with the emergence of Industry 4.0. Yet, the assurance of delivering high-quality products remains a challenging problem that requires cautious controls and timely actions through the entire phases of the production cycle. To that end, zero-defect manufacturing (ZDM) has given prominence to a comprehensive methodology that ensures quality control and defects eradication at every phase of the procedure, prioritizing the continuous supervision of each distinctive component to satisfy the corresponding quality assurance standards. Continuous supervision and detection of possible flaws are subjective to exploiting high-tech sensors for high-precision monitoring and assessment. Considering the above, adopting active vision technology can further enhance the promising capabilities of ZDM. The paper highlights the active vision's benefits to the ZDM strategy and introduces a practical framework for applying such technology through the different stages. Innovations and challenges that have to be taken into consideration are also discussed.

关键词： Active Vision Zero Defect Manufacturing Industry 4.0 Machine Vision Manufacturing

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Nam-Cr: Noise-Aware Multisensor Cloud Removal for Optical Remote Sensing Imagery by Fusing Sar Data

SSRN

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SSRN 2024年

作者： Xu, Meng Zhang, Ruihua Liu, Shuyi Jia, Xiuping Li, Qingquan Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Guangdong Key Laboratory of Urban Informatics Shenzhen University Shenzhen518060 China College of Civil and Transportation Engineering Shenzhen University Shenzhen518060 China

Optical remote sensing images frequently suffer from extensive cloud coverage, resulting in the loss of valuable raw data and severely hindering their Earth observation capacity. Cloud removal methods employing auxiliary synthetic aperture radar (SAR) data have been explored in recent years instead of using optical cloud-free reference images from the same area on a different date, where uncertainties arise due to surface changes and unavailability of cloud-free images is often encountered. However, the inherent speckle noise in SAR data brings challenges in interpreting texture information from SARs into optical images for cloud removal. In this study, we propose a data-driven noise-aware multisensor cloud removal (NAM-CR) method for multispectral images by fusing SAR data. The network is composed of a self-attention multimodal aggregator (SMMA) and deep-shallow feature cross-fusion (DSFF) modules for better integration of the texture information from SAR data and spectral information from optical images. To further reduce speckle noise effects, the proposed method is equipped with a novel cloud-guided noise optimization loss function. We trained and tested our method on 100,000 patch triplets from the SEN12MS-CR dataset containing global all-season Sentinel optical and SAR images. The experimental results demonstrate that NAM-CR outperforms the other five state-of-the-art cloud removal methods and achieves outstanding performance with the highest PSNR of 34.22 dB and minimal parameter usage. The source code is accessible to the public at http://***/. © 2024, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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Classification of Handwritten Chinese Numbers with Convolutional Neural Networks 5

Classification of Handwritten Chinese Numbers with Convoluti...

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5th International Conference on Pattern Recognition and Image Analysis, IPRIA 2021

作者： Ameri, Rasoul Alameer, Ali Ferdowsi, Saideh Abolghasemi, Vahid Nazarpour, Kianoush Shahrood University of Technology Faculty of Computer Engineering Shahrood Iran School of Biological Sciences Queen's University Belfast Belfast United Kingdom School of Computer Science Electronic Engineering University of Essex Colchester United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9781665426596

Deep learning methods have become the key ingredient in the field of computer vision;in particular, convolutional neural networks (CNNs). Appropriating the network architecture and data pre-processing have significant impact on performance. This paper focuses on the classification of handwritten Chinese numbers. Firstly, we applied various methods of pre-processing to our collected image dataset. Secondly, we customised a CNN-based architecture with minimal number of layers and parameters specifically for the task. Experimental results showed that our proposed methods provides superior classification rate of 99.1%. Our results also show that the proposed method has competitive performance compared to smaller neural networks with fewer parameters, e.g. Squeezenet and deeper networks with a larger size and number of parameters, e.g., pre-trained GoogLeNet and MobileNetV2. © 2021 IEEE.

关键词： Convolutional neural networks

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On the Robustness of Adversarial Training Against Uncertainty Attacks

arXiv

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arXiv 2024年

作者： Ledda, Emanuele Scodeller, Giovanni Angioni, Daniele Piras, Giorgio Cinà, Antonio Emanuele Fumera, Giorgio Biggio, Battista Roli, Fabio Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Via Dodecaneso 35 Genova16146 Italy Department of Electric and Electronic Engineering University of Cagliari Via Marengo 3 Cagliari09100 Italy

In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of its wide use, assumes high relevance for security-sensitive applications. Within these scenarios, it becomes fundamental to guarantee good (i.e., trustworthy) uncertainty measures, which downstream modules can securely employ to drive the final decision-making process. However, an attacker may be interested in forcing the system to produce either (i) highly uncertain outputs jeopardizing the system’s availability or (ii) low uncertainty estimates, making the system accept uncertain samples that would instead require a careful inspection (e.g., human intervention). Therefore, it becomes fundamental to understand how to obtain robust uncertainty estimates against these kinds of attacks. In this work, we reveal both empirically and theoretically that defending against adversarial examples, i.e., carefully perturbed samples that cause misclassification, additionally guarantees a more secure, trustworthy uncertainty estimate under common attack scenarios without the need for an ad-hoc defense strategy. To support our claims, we evaluate multiple adversarial-robust models from the publicly available benchmark RobustBench on the CIFAR-10 and ImageNet datasets. © 2024, CC BY.

关键词： Adversarial machine learning

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PLS-SEM Approach and Sentiment Analysis for Identifying Significant Factors of Customer Satisfaction

PLS-SEM Approach and Sentiment Analysis for Identifying Sign...

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Data Science and Its Applications (ICoDSA), International Conference on

作者： Marastika Wicaksono Aji Bawono Diana Purwitasari Mauridhi Hery Purnomo Departement of Technology Management Sepuluh Nopember Institute of Technology Surabaya East Java Departement of Informatics Engineering Sepuluh Nopember Institute of Technology Surabaya East Java Departement of Electrical Engineering Departement of Computer Engineering Sepuluh Nopember Institute of Technology Surabaya East Java

Reviews have a direct impact on customer satisfaction. The aim of this study is to dissect and analyze a collection of 775 negative and 557 positive comment reviews, drawn from four distinct e-commerce platforms. By classifying these remarks into positive and negative sentiments, this research endeavors to illuminate underlying trends permeating these marketplaces. The research methodology employed involves field observations of online shopping experiences, utilizing data derived from 254 e-commerce customers. These data were collected via validated questionnaires and subsequently analyzed using the partial least squares structural equation modeling approach, employing the lavaan r library within the R programming environment. The questionnaire results produced a rating scale from 1 to 5, categorizing responses from “very satisfied” to “less satisfied”, effectively illustrating both positive and negative commentary. The field comment data collected was coordinated with comment data extracted from four marketplace trading accounts. This data comment customer was analyzed using a range of comparative models such as k-nearest neighbors, multinomial naive bayes, stochastic gradient descent, and decision trees to conduct sentiment analysis. The findings reveal that the naive bayes method generates the greatest accuracy in sentiment analysis, registering an accuracy value of 0.886. Moreover, the analysis executed through r programming indicates that the e-service quality model yields the most robust results, reflected by an adjusted r-square value of 0.885. This study exerts a notable impact on service quality, as evidenced by a coefficient value of 0.865 and a perceived reputation score of 0.162.

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Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

arXiv

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arXiv 2024年

作者： Hou, Junlin Liu, Sicen Bie, Yequan Wang, Hongmei Tan, Andong Luo, Luyang Chen, Hao The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Department of Engineering Shenzhen MSU-BIT University Shenzhen China The Department of Biomedical Informatics Harvard University Cambridge United States The Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to explain black-box models after training, have raised concerns about their fidelity to model predictions. In contrast, Self-eXplainable AI (S-XAI) offers a compelling alternative by incorporating explainability directly into the training process of deep learning models. This approach allows models to generate inherent explanations that are closely aligned with their internal decision-making processes, enhancing transparency and supporting the trustworthiness, robustness, and accountability of AI systems in real-world medical applications. To facilitate the development of S-XAI methods for medical image analysis, this survey presents a comprehensive review across various image modalities and clinical applications. It covers more than 200 papers from three key perspectives: 1) input explainability through the integration of explainable feature engineering and knowledge graph, 2) model explainability via attention-based learning, concept-based learning, and prototype-based learning, and 3) output explainability by providing textual and counterfactual explanations. This paper also outlines desired characteristics of explainability and evaluation methods for assessing explanation quality, while discussing major challenges and future research directions in developing S-XAI for medical image analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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