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Leveraging SHAP and CBR for Dimensionality Reduction on the Psychology Prediction Dataset 30

Leveraging SHAP and CBR for Dimensionality Reduction on the ...

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Wilkerson, Zachary Leake, David Crandall, David Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Effective dimensionality reduction for feature spaces can benefit the accuracy, efficiency, and/or explain-ability of models using the features. One task of the Explainable AI Challenge of the 2022 International Conference on Case-Based Reasoning is to apply explanation methods for informed feature pruning to refine an artificial neural network model for depression screening. This paper explores how explanations provided by SHAP values can guide feature pruning. It presents and evaluates four approaches developed for this task: 1) iterative feature pruning based on SHAP values, 2) using case-based reasoning to guide search through potential feature prunings, 3) pruning based on using Hamming distance between a reference case and patient cases to partition the case base, and 4) evaluating feature prunings in light of semi-factual and counterfactual cases. Results show that both using case-based reasoning and absolute SHAP values can guide feature pruning to improve model accuracy, with best performance occurring when SHAP values inform feature pruning selection for case-based reasoning-based methods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Case based reasoning

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling 12

A Comparison Study on Generative Adversarial Networks (GANs)...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model's effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image. © 2024 IEEE.

关键词： Generative adversarial networks

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CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation 18th

CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Ad...

引用

18th Robotics: Science and Systems, RSS 2022

作者： Chen, Zheng Pushp, Durgakant Liu, Lantao Luddy School of Informatics Computing and Engineering Indiana University-Bloomington IN47408 United States

ISBN: (纸本)9780992374785

Traversability prediction is a fundamental perception capability for autonomous navigation. The diversity of data in different domains imposes significant gaps to the prediction performance of the perception model. In this work, we make efforts to reduce the gaps by proposing a novel coarse-to-fine unsupervised domain adaptation (UDA) model – CALI. Our aim is to transfer the perception model with high data efficiency, eliminate the prohibitively expensive data labeling, and improve the generalization capability during the adaptation from easyto-obtain source domains to various challenging target domains. We prove that a combination of a coarse alignment and a fine alignment can be beneficial to each other and further design a first-coarse-then-fine alignment process. This proposed work bridges theoretical analyses and algorithm designs, leading to an efficient UDA model with easy and stable training. We show the advantages of our proposed model over multiple baselines in several challenging domain adaptation setups. To further validate the effectiveness of our model, we then combine our perception model with a visual planner to build a navigation system and show the high reliability of our model in complex natural environments where no labeled data is available. The robot navigation demonstration can be seen in this video: https://***/watch?v=Nqsegaq x-o. © 2022, MIT Press Journals. All rights reserved.

关键词： Alignment

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AutoNLP: A Framework for Automated Model Selection in Natural Language Processing 18

AutoNLP: A Framework for Automated Model Selection in Natura...

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Saleem, Shehan Kumarapathirage, Sapna University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo06 Sri Lanka

ISBN: (纸本)9789893347928

Although numerous open-source tools exist for machine learning with tabular data, there is a scarcity of comparable resources tailored specifically for NLP. The lack of transparency in the inner workings of existing AutoNLP tools is a significant obstacle in scientific research. AutoML tools are known for their ability to perform model selection with little human intervention, improving the accuracy and reliability of the results. However, conducting a model space search among pre-trained NLP models can be computationally infeasible, making it challenging to determine the optimal NLP model for a given dataset. This research aims to enhance the performance of NLP model selection. Our approach has resulted in higher accuracy than existing methods on the dataset that was created. In our future work, we plan to benchmark our algorithm against datasets created by other researchers to validate its effectiveness. Additionally, we intend to use the same system to perform model selection among popular large language Transformer models. © 2023 ITMA.

关键词： Machine learning

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Generation and Evaluation of Creative Images from Limited Data: A Class-to-Class VAE Approach 13

Generation and Evaluation of Creative Images from Limited Da...

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13th International Conference on Computational Creativity, ICCC 2022

作者： Ye, Xiaomeng Zhao, Ziwei Leake, David Crandall, David Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

ISBN: (纸本)9789895416042

Generating novel items with desired characteristics requires creativity. One method to achieve this is through creative transformations. Deep learning network methods provide an interesting potential substrate for this task. This paper presents a method for network-based generation of novel images by applying variational autoencoders (VAEs) to learn features, which are then perturbed based on a class-to-class (C2C) method for learning of inter-class similarity and difference information, enabling generating creative samples. Our method learns the pattern between classes, applies this pattern to samples of a source class, and generates new samples of a target class. This study also proposes a general approach to evaluating the creativity of sample generators for classification domains, by evaluating the samples generated by the generator trained in a one-shot setting. The evaluation approach requires only classification labels but not human assessments of creativity. An experiment in two image domains supports that the samples generated by our method satisfy two of Boden’s creativity criteria: being valuable (falling into desired categories) and novel (samples show high variance). © 2022 Proceedings of the 13th International Conference on Computational Creativity, ICCC 2022. All rights reserved.

关键词： Deep learning

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Case-Based Explanation: Making the Implicit Explicit 30

Case-Based Explanation: Making the Implicit Explicit

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Leake, David Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Case-based explanation (CBE) is seen by many as a compelling method for explaining black-box systems, and is advocated and pursued in substantial research in the CBR community. A 2022 position paper by Jonathan Dodge, "The Case Against Case-Based Explanation, " takes a contrasting view, arguing that the use of CBE should be limited to explaining processes based on k-NN or similar approaches. This position paper takes Dodge's points as a jumping-off point to examine the nature and applicability of case-based explanation and to make explicit some of the premises which are fundamental to successful case-based explanation. It considers requirements for the domains to which CBE should be applied, the possible variants of CBE, and the knowledge it requires-both in the system and in the recipient of the explanation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Case based reasoning

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Exploring Quantum Processors in Decentralized Edge computing for Improved Data Processing

Exploring Quantum Processors in Decentralized Edge Computing...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Harshad Pitkar Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems.

关键词： Quantum computing Accuracy Program processors Computational modeling Autoencoders Machine learning Real-time systems Computational efficiency Internet of Things Edge computing

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Metamaterial-Inspired Phased Array with High-Gain Beam-Steering for Future Cellular Networks

Metamaterial-Inspired Phased Array with High-Gain Beam-Steer...

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2024 IEEE International Symposium on Phased Array Systems and Technology, ARRAY 2024

作者： Basherlou, Haleh Jahanbakhsh Parchin, Naser Ojaroudi See, Chan Hwang Abd-Alhameed, Raed A. School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom University of Bradford Faculty of Engineering and Informatics BradfordBD7 1DP United Kingdom

ISBN: (纸本)9798350392142

This paper investigates the features of a new metamaterial-inspired phased array that exhibits high-gain and steerable radiation capabilities, catering to the needs of future cellular networks. Its schematic incorporates eight modified dipole elements arranged in a 1 × 8 linear configuration. To enhance the radiation properties of the designed dipole resonators, 2 × 2 metamaterial unit cells are positioned alongside each dipole resonator. The dipole resonators are compact in size and employ microstrip-line feeds. The suggested smartphone phased array design achieves an impedance bandwidth of 28-32 GHz with -10 dB, offering over 80% total efficiencies and 13 dBi radiation gains. This promising performance aligns with the essential requirements of future cellular communications. © 2024 IEEE.

关键词： Antenna phased arrays

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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System Support for Environmentally Sustainable computing in Data Centers

System Support for Environmentally Sustainable Computing in ...

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IEEE Computer Society Annual Symposium on VLSI

作者： Fan Chen Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Modern data centers suffer from a growing carbon footprint due to insufficient support for environmental sus-tainability. While hardware accelerators and renewable energy have been utilized to enhance sustainability, addressing Quality of Service (QoS) degradation caused by renewable energy supply and hardware recycling remains challenging: (1) prior accelerators exhibit significant carbon footprints due to limited reconfigurability and inability to adapt to renewable energy fluctuations; (2) integrating recycled NAND flash chips in data centers poses challenges due to their short lifetime, increasing energy consumption; (3) the absence of a sustainability estimator impedes data centers and users in evaluating and improving their environmental impact. This study aims to improve system support for environmentally sustainable data centers by proposing a reconfigurable hardware accelerator for intensive computing primitives and developing a fractional NAND flash cell to extend the lifetime of recycled flash chips while supporting graceful capacity degradation. We also introduce a sustainability estimator to evaluate user task energy consumption and promote sustainable practices. We present our preliminary results and recognize this as an ongoing initiative with significant potential to advance environmentally sustainable computing in data centers and stimulate further exploration in this critical research domain.

关键词： Data centers Renewable energy sources Microprocessors Green products Computer architecture Quality of service Systems support

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