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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

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Investigating the Impact of Data Augmentation for Fine-Grain...

Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Rissa Rahmania Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Group (CEG) Faculty of Engineering Universitas Katolik Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products.

关键词： Surveys computer vision Accuracy Image recognition Transformers Feature extraction Data augmentation Artificial intelligence Image classification

Low-loss, nonvolatile tuning of Bragg structures with Sb2Se3

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Low-loss, nonvolatile tuning of Bragg structures with Sb2Se3

2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Nobile, Nicholas A. Lian, Chuanyu Sun, Hongyi Huang, Yi-Siou Mills, Brian Popescu, Cosmin Constantin Callahan, Dennis Hu, Juejun Ríos Ocampo, Carlos A. Youngblood, Nathan Electrical & Computer Engineering Department University of Pittsburgh PittsburghPA15213 United States Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Department of Materials Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Draper Scholar Program The Charles Stark Draper Laboratory Inc CambridgeMA02139 United States The Charles Stark Draper Laboratory Inc CambridgeMA02139 United States

Sb2Se3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop- and pass-band functionalities. Experimental results highlight fabrica... 详细信息

ISBN: (纸本)9781957171395

关键词： Fabry-Perot interferometers

Stochastic Dynamics and Probability Analysis for a Generalized Epidemic Model with Environmental Noise

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arXiv 2024年

作者： Boukanjime, Brahim Maama, Mohamed Laboratory of Mathematical Modeling and Economic Calculations Hassan First University of Settat Morocco Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young’s inequality and Chebyshev’s inequality, we demonstrate the concepts of stochastic ultimate boundedness and stochastic permanence, providing insights into the long-term behavior of the epidemic dynamics under random perturbations. Furthermore, we derive conditions for stochastic extinction, which describes scenarios where the epidemic may eventually die out, and V-geometric ergodicity, which indicates the rate at which the system’s state converges to its equilibrium. Finally, we perform numerical simulations to verify our theoretical results and assess the model’s behavior under different parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic models

A Detailed Analysis of Qualitative and Quantitative Factors in Realization of 6G Communication

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A Detailed Analysis of Qualitative and Quantitative Factors ...

International Conference on Internet of Things: Systems, Management and Security

作者： Mohammad Alja’Afreh Ali Karime Sahel Alouneh Abdulmotaleb El Saddik Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE School of Electrical Engineering and Computer Science University of Ottawa Canada Department of Electrical and Computer Engineering The Royal Military College of Canada Canada Cybersecurity Program College of Engineering AL AIN University UAE Computer Engineering Department German Jordanian University

Undoubtedly, the world has so far faced a pandemic which is reshaping daily lives and business activities. Even at current endemic stages, special focus on maintaining physical distancing norms for curbing the expeditious spread of the disease, many institutions, individuals and industries rely on communications networks or telecoms for ensuring service consistency to avoid complete termination of their business operations and other activities. This has put enormous pressure on mobile networks and communication systems thereby making the technology experts to think more about introducing rapid speed, vast coverage and high connectivity networks. The extensive application of fresh communication networks and enabling technologies have impelled the advent of 6G communication networks. As 6G is still in its inception phase, its complete realization requires a proper and high understanding of diverse quantitative and qualitative factors supporting its deployment. From this standpoint, this survey article intends to deeply explore 6G networks, their significance and prerequisites. This paper provides a succinct theoretical background of 6G technology, and reviews the diverse enabling technologies and existing works undertaken on core technologies. It explores the prevailing gaps in research for providing readers to gain information regarding challenges in perfect 6G network realization and implementation thus paving the road for a successful 6G vision.

关键词： 6G mobile communication Systematics Pandemics Roads Telecommunications Communication networks Security

COULD DROPPING A FEW CELLS CHANGE THE TAKEAWAYS FROM DIFFERENTIAL EXPRESSION?

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arXiv 2023年

作者： Shiffman, Miriam Giordano, Ryan Broderick, Tamara MIT Computational & Systems Biology Program United States MIT Dept. of Electrical Engineering & Computer Science United States UC Berkeley Dept. of Statistics United States

Analysis of differential gene expression plays a fundamental role in biology toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells or samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, generalization is justified under the assumption that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we might hope that any deviation from the assumption is small enough that A) fundamental conclusions of the analysis still hold, and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard statistical tools, if conclusions could be substantively changed by dropping a very small fraction of observations. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of differential expression;namely, we cast the analysis in a differentiable form suitable to the approximation, extend the approximation to models whose hyperparameters depend on the full dataset, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We use our tool to assess the robustness of differential expression for published single-cell RNA-seq data, and discover that thousands of genes can have their results flipped by dropping Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models

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arXiv 2023年

作者： Ghosh, Shantanu Yu, Ke Arabshahi, Forough Batmanghelich, Kayhan Department of Electrical and Computer Engineering Boston University MA United States Intelligent Systems Program University of Pittsburgh PA United States MetaAI Menlo ParkCA United States

We use concept-based interpretable models to mitigate shortcut learning. Existing methods lack interpretability. Beginning with a Blackbox, we iteratively carve out a mixture of interpretable experts (MoIE) and a residual network. Each expert explains a subset of data using First Order Logic (FOL). While explaining a sample, the FOL from biased BB-derived MoIE detects the shortcut effectively. Finetuning the BB with Metadata Normalization (MDN) eliminates the shortcut. The FOLs from the finetuned-BB-derived MoIE verify the elimination of the shortcut. Our experiments show that MoIE does not hurt the accuracy of the original BB and eliminates shortcuts effectively. © 2023, CC BY.

关键词： Deep neural networks

The Michigan Robotics Undergraduate Curriculum: Defining the Discipline of Robotics for Equity and Excellence

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arXiv 2023年

作者： Jenkins, Odest Chadwicke Grizzle, Jessy Atkins, Ella Stirling, Leia Rouse, Elliott Guzdial, Mark Provost, Damen Mann, Kimberly Millunchick, Joanna Robotics and Electrical Engineering & Computer Science United States Undergraduate Education Robotics Institute United States Robotics Institute United States Graduate Programs Robotics Institute United States Robotics and Industrial Engineering United States Robotics and Mechanical Engineering United States Electrical Engineering & Computer Science United States Engineering Education Research Program United States Undergraduate Education College of Engineering United States University of Michigan United States

The Robotics Major at the University of Michigan was successfully launched inthe 2022-23 academic year as an innovative step forward to better servestudents, our communities, and our society. Building on our guiding principleof "Robotics with Respect" and our larger Robotics Pathways model, the MichiganRobotics Major was designed to define robotics as a true academic disciplinewith both equity and excellence as our highest priorities. Understanding thattalent is equally distributed but opportunity is not, the Michigan RoboticsMajor has embraced an adaptable curriculum that is accessible through adiversity of student pathways and enables successful and sustained career-longparticipation in robotics, AI, and automation professions. The results afterour planning efforts (2019-22) and first academic year (2022-23) have beenhighly encouraging: more than 100 students declared Robotics as their major,completion of the Robotics major by our first two graduates, soaringenrollments in our Robotics classes, thriving partnerships with HistoricallyBlack Colleges and Universities. This document provides our original curricularproposal for the Robotics Undergraduate program at the University of Michigan,submitted to the Michigan Association of State Universities in April 2022 andapproved in June 2022. The dissemination of our program design is in the spiritof continued growth for higher education towards realizing equity andexcellence. The most recent version of this document is also available on Google Docsthrough this link: https://***/robotics_majorMSC Codes 97 © 2023, CC BY.

关键词： Curricula

Adaptive GLCM sampling for transformer-based COVID-19 detection on CT

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arXiv 2022年

作者： Jung, Okchul Kang, Dong Un Kim, Gwanghyun Chun, Se Young Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in AI Seoul National University Korea Republic of

The world has suffered from COVID-19 (SARS-CoV-2) for the last two years, causing much damage and change in people’s daily lives. Thus, automated detection of COVID-19 utilizing deep learning on chest computed tomography (CT) scans became promising, which helps correct diagnosis efficiently. Recently, transformer-based COVID-19 detection method on CT is proposed to utilize 3D information in CT volume. However, its sampling method for selecting slices is not optimal. To leverage rich 3D information in CT volume, we propose a transformer-based COVID-19 detection using a novel data curation and adaptive sampling method using gray level co-occurrence matrices (GLCM). To train the model which consists of CNN layer, followed by transformer architecture, we first executed data curation based on lung segmentation and utilized the entropy of GLCM value of every slice in CT volumes to select important slices for the prediction. The experimental results show that the proposed method improve the detection performance with large margin without much difficult modification to the model. © 2022, CC BY.

关键词： COVID-19

DDD: A Perceptually Superior Low-Response-Time DNN-Based Declipper

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DDD: A Perceptually Superior Low-Response-Time DNN-Based Dec...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jayeon Yi Junghyun Koo Kyogu Lee Dept. of Electrical Engineering and Computer Science University of Michigan Dept. of Intelligence and Information Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University Artificial Intelligence Institute Seoul National University

Clipping is a common nonlinear distortion that occurs whenever the input or output of an audio system exceeds the supported range. This phenomenon undermines not only the perception of speech quality but also downstream processes utilizing the disrupted signal. Therefore, a real-time-capable, robust, and low-response-time method for speech declipping (SD) is desired. In this work, we introduce DDD (Demucs-Discriminator-Declipper), a real-time-capable speech-declipping deep neural network (DNN) that requires less response time by design. We first observe that a previously untested real-time-capable DNN model, Demucs, exhibits a reasonable declipping performance. Then we utilize adversarial learning objectives to increase the perceptual quality of output speech without additional inference overhead. Subjective evaluations on harshly clipped speech shows that DDD outperforms the baselines by a wide margin in terms of speech quality. We perform detailed waveform and spectral analyses to gain an insight into the output behavior of DDD in comparison to the baselines. Finally, our streaming simulations also show that DDD is capable of sub-decisecond mean response times, outperforming the state-of-the-art DNN approach by a factor of six.

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