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ONE-PIXEL SHORTCUT: ON thE learning PREFERENCE OF DEEP NEURAL NETWORKS 11

ONE-PIXEL SHORTCUT: ON THE LEARNING PREFERENCE OF DEEP NEURA...

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11th international conference on learning Representations, ICLR 2023

作者： Wu, Shutong Chen, Sizhe Xie, Cihang Huang, Xiaolin Department of Automation Shanghai Jiao Tong University China Computer Science and Engineering University of California Santa Cruz United States

Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Existing work adds ∞-bounded perturbations to the original sample so that the trained model generalizes poorly. Such perturbations, however, are easy to eliminate by adversarial training and data augmentations. In this paper, we resolve this problem from a novel perspective by perturbing only one pixel in each image. Interestingly, such a small modification could effectively degrade model accuracy to almost an untrained counterpart. Moreover, our produced One-Pixel Shortcut (OPS) could not be erased by adversarial training and strong augmentations. To generate OPS, we perturb in-class images at the same position to the same target value that could mostly and stably deviate from all the original images. Since such generation is only based on images, OPS needs significantly less computational cost than the previous methods using DNN generators. Based on OPS, we introduce an unlearnable dataset called CIFAR-10-S, which is indistinguishable from CIFAR-10 by humans but induces the trained model to extremely low accuracy. Even under adversarial training, a ResNet-18 trained on CIFAR-10-S has only 10.61% accuracy, compared to 83.02% by the existing error-minimizing method. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Pixels

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Unscrambling Representation and Recognizing Gaits in an intelligent Attendance System 4

Unscrambling Representation and Recognizing Gaits in an Inte...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Prathik, K. Girija, J. Bangalore Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350300888

Traditional approaches to managing attendance have been entirely transformed by the advent of automated and intelligent solutions due to the development of innovative attendance systems. this research introduces a revolutionary intelligent attendance system that uses unscrambled representation and gait identification using an Inception v3 model based on transfer learning to precisely identify people based on their gait patterns and record attendance. By employing transfer learning, the proposed method improves the pre-trained weights of the Inception v3 model using the outdoor gait dataset, enabling it to capture and analyze the distinctive features of human gait accurately. By separating and storing many components that go into a person's gait pattern, incorporating disentangled representation improves the system's accuracy and produces more accurate identification results. the intelligent attendance system has many benefits compared to conventional attendance management strategies. the proposed model streamlines the attendance procedures and reduces administrative expenses by removing the dependency on traditional techniques like ID cards, PINs, or biometrics. the proposed system is non-intrusive in operation, protecting user privacy and convenience. MATLAB is used for programming, and OpenCV is used to process and analyze the video and image data from the database. the system completes comprehensive testing on an outdoor gait dataset with a remarkable accuracy of 94%, proving its reliability and usefulness in practical situations. © 2023 IEEE.

关键词： MATLAB

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Classification of Water Quality Index Using Machine learning Algorithm for Well Assessment: A Case Study in Dili, Timor-Leste 11

Classification of Water Quality Index Using Machine Learning...

引用

11th international conference on Advanced Informatics: Concept, theory and Application, ICAICTA 2024

作者： Da Costa, Zulmira Ximenes Ikeda, Keisuke Nagawaki, Takumi Nishida, Yuichi Tamura, Satoshi Boogaard, Floris Comelis Graduate School of Engineering Gifu University Gifu501-1193 Japan National University of Timor Lorosa'e Faculty of Engineering Science and Technology Hera Timor-leste Graduate School of Natural Science and Technology Gifu University Gifu501-1193 Japan Gifu University Faculty of Engineering Gifu501-1193 Japan Hanze University of Applied Sciences Research Centre for Built Environment NoorderRuimte Groningen9747AS Netherlands Deltares Utrecht Netherlands

ISBN: (纸本)9798331520311

this paper investigate to use of information technology, i.e. machine learning algorithms for water assessment in Timor-Leste. It is essential to access clean water to ensure the safety for humans and others livings in this world. the Water Quality Index (WQI) is the standard tool for assessing water quality, which can be calculated from physicochemical and microbiological parameters. However, in developing countries, it is continuing need to bring water and energy for the most disadvantaged, make it necessary to find new solutions. In such case, missing-value imputation and machine learning models are useful for classifying water samples into suitable or unsuitable with significant accuracy. Some imputation methods were tested, and four machine learning algorithms were explored: logistic regression, support vector machine, random forest, and Gaussian naïve Bayes. We obtained a dataset with 368 observations from 26 groundwater sampling points in Dili city of Timor-Leste. According to experimental results, it is found that 64% of the water samples are suitable for human consumption. We also found k-NN imputation and random forest method were the clear winners, achieving 96% accuracy with three-fold cross validation. the analysis revealed that some parameters significantly affected the classification results. © 2024 IEEE.

关键词： Adversarial machine learning

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Semantic Segmentation of PolSAR data Using CNN Based Deep learning Models 11

Semantic Segmentation of PolSAR Data Using CNN Based Deep Le...

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11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Garg, Rajat Singh, Kartikay Singh, Isha Kumar, Anil Kumar, Shashi Tiwari, Rajeev School of Computer Science & Engineering Iilm University Greater Noida India School of Computer Science University of Petroleum and Energy Studies Dehradun India Prsd Iirs Isro Dehradun India

ISBN: (纸本)9798350318159

the rapid advancement in Deep learning models which were originally designed for computer vision has unlocked a multitude of opportunities for addressing complex visual problems. Originally, these models were designed to tackle problems such as computer vision or image processing but now-a-days, it has opened new horizons in the domains of remote sensing and geoinformatics. this research aims to present a comprehensive comparison of semantic segmentation outcomes generated by multiple deep learning models. the comparative analysis between FPN, UNET, and DeepLabv3+ on the L band Uninhabited Ariel Vehicle Synthetic Aperture Radar (UAVSAR) dataset of the Houston region in the Texas, USA show that the DeepLabv3+ outperforms the FPN and UNET models with Xception as a backbone. the overall accuracy of FPN, UNET and DeepLabv3+ is 84.62%, 86.52%, and 87.78% respectively. © 2023 IEEE.

关键词： Synthetic aperture radar

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Zero-Shot Meta-learning for Small-Scale data From Human Subjects 11

Zero-Shot Meta-Learning for Small-Scale Data From Human Subj...

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11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Jiang, Julie Lerman, Kristina Ferrara, Emilio University of Southern California Viterbi School of Engineering Information Sciences Institute Los AngelesCA United States University of Southern California Annenberg School of Communication Viterbi School of Engineering Information Sciences Institute Los AngelesCA United States

ISBN: (纸本)9798350302639

While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled. Existing methods applied to such data often do not easily generalize to out-of-sample subjects. Instead, models must make predictions on test data that may be drawn from a different distribution, a problem known as zero-shot learning. To address this challenge, we develop an end-to-end framework using a meta-learning approach, which enables the model to rapidly adapt to a new prediction task with limited training data for out-of-sample test data. We use three real-world small-scale human subjects datasets (two randomized control studies and one observational study), for which we predict treatment outcomes for held-out treatment groups. Our model learns the latent treatment effects of each intervention and, by design, can naturally handle multitask predictions. We show that our model performs the best holistically for each held-out group and especially when the test group is distinctly different from the training group. Our model has implications for improved generalization of small-size human studies to the wider population. © 2023 IEEE.

关键词： Zero-shot learning

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Research on intelligent Interactive Regulation Framework Based on Hierarchical and Zonal Structure Supporting Large-scale New Adjustable Load Resources 11

Research on Intelligent Interactive Regulation Framework Bas...

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11th China international conference on Electricity Distribution, CICED 2024

作者： Wang, Jiaying Lu, Chunguang Deng, Lan Xie, Danyu State Grid Zhejiang Marketing Service Center Hangzhou311202 China State Grid Jiaxing Power Supply Company Jiaxing314000 China State Grid Taizhou Electric Power Supply Company Taizhou318000 China

ISBN: (数字)9798350368345

ISBN: (纸本)9798350368345

In this paper, an intelligent interactive regulation framework based on hierarchical and partitioned structure is proposed to support a new type of large-scale flexible load resource. Driven by the 'Double Carbon' target, the construction of New Power Systems has been accelerated, and the proportion of electricity in terminal energy consumption has increased significantly. Firstly, this paper analyzes the influence of new loads such as electric vehicle and data center on the Operation Mechanism and balance mode of power system. then, the paper discusses the current research situation of power grid hierarchical partition methods, and points out the shortcomings of existing methods. In this paper, a new intelligent interactive regulation framework is proposed, and the cluster cooperative and interactive regulation strategy of adjustable load resources is proposed. the feasibility of the proposed model is verified by a case study. © 2024 IEEE.

关键词： Energy consumption data centers Electricity Electric vehicles Regulation Power grids Security Carbon Optimization Load modeling

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UNIVERSAL VISION-LANGUAGE DENSE RETRIEVAL: learning A UNIFIED REPRESENTATION SPACE FOR MULTI-MODAL RETRIEVAL 11

UNIVERSAL VISION-LANGUAGE DENSE RETRIEVAL: LEARNING A UNIFIE...

引用

11th international conference on learning Representations, ICLR 2023

作者： Liu, Zhenghao Xiong, Chenyan Lv, Yuanhuiyi Liu, Zhiyuan Yu, Ge Department of Computer Science and Technology Northeastern University Shenyang China Microsoft Research Redmond United States Department of Computer Science and Technology Tsinghua University Beijing China Institute for Artificial Intelligence Tsinghua University Beijing China State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China

this paper presents Universal Vision-Language Dense Retrieval (UniVL-DR), which builds a unified model for multi-modal retrieval. UniVL-DR encodes queries and multi-modality resources in an embedding space for searching candidates from different modalities. To learn a unified embedding space for multi-modal retrieval, UniVL-DR proposes two techniques: 1) Universal embedding optimization strategy, which contrastively optimizes the embedding space using the modality-balanced hard negatives;2) Image verbalization method, which bridges the modality gap between images and texts in the raw data space. UniVL-DR achieves the state-of-the-art on the multi-modal open-domain question answering benchmark, WebQA, and outperforms all retrieval models on the two subtasks, text-text retrieval and text-image retrieval. It demonstrates that universal multi-modal search is feasible to replace the divide-and-conquer pipeline with a united model and also benefits single/cross modality tasks. All source codes of this work are available at https://***/OpenMatch/UniVL-DR. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Embeddings

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Question Answering FAQ Bot Using Natural Language Processing 11th

Question Answering FAQ Bot Using Natural Language Processing

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11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Vijay, K. Sowmia, K.R. Ravikumar, S. Arockia Raj, Y. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9789819717231

the BOT retorts with the precise results that help the user for the query and also requests them to rate the response to provide better results. the [frequently answered questions] BOT is a program developed through machine learning and natural language processing. It classifies the queries and results as key-value pairs and maps the response to the user query. It extricates itself by providing additional capabilities such as NLU processes and reinforcement learning. NLU helps the bot to make it convenient for an array of languages to meet the diverse expectations. Reinforcement learning retrieves user rating to provide better performance and search results. the project was developed due to a concern about fetching meticulous responses for specific service-related queries. the overall satisfaction rate of users in getting responses to the questions they post is devastating. the root cause for the proposed project has aimed at providing an environment for a people to find their answers at better accuracy and also respond to their satisfaction. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Global classification of intentional movement across upper limb myoelectric pattern recognition-controlled prosthesis users 11

Global classification of intentional movement across upper l...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Stambaugh, Nathaniel Wright, Zachary A. Coapt LLC Res & Grants Chicago IL 60610 USA

ISBN: (纸本)9781665462921

One idealized vision of advanced upper limb prosthetic control is a plug-and- play design that any new user can don and instantly control attached devices as intended. Traditional body-powered prostheses are likely the closest available option but are limited in their range of motion and can have negative long-term impacts. Basic dual-site myoelectric-controlled prostheses require only minor adjustments prior to users being able to control their prosthetic device, but still requires training to learn despite only having a limited number of motions available. this contrasts with state-of-the-art myoelectric pattern recognition-controlled prostheses where a machine learning algorithm also learns the individual user;specifically, their unique patterns of muscle activity corresponding to prosthesis motions. However, the wide variation in muscle activity patterns both within and between users, mainly due to physiological differences, has been the primary reason why it is difficult to develop a true off-the-shelf prosthesis component for myoelectric pattern recognition control. In this paper, we take a small step towards this vision by investigating statistical and machine learning methods that classify prosthesis motion for any pattern recognition user. Specifically, we use a large dataset of EMG training data collected from 191 users over a six-month period to develop, as a first step, a binary classifier built to simply identify intended motion or no motion for all users. Our results could have an immediate impact on prosthesis performance for current users and justify further development of a potential global classification model which can be used by any persons with upper limb difference who wish to use a myoelectric-controlled prosthesis.

关键词： upper-limb prosthesis pattern recognition control machine learning

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Dermatological Disease Detection Employing Transfer learning 11

Dermatological Disease Detection Employing Transfer Learning

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11th international conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Mathur, Siddharth Jain, Tarun Cse Manipal University Jaipur Jaipur India

ISBN: (纸本)9781665475129

Dermatology has long been regarded as one of science's most difficult terrains, owing to its unpredictability and difficult diagnoses. People in most developing countries find it unnecessarily expensive, preventing people from seeking early diagnosis. With the widespread use of smartphones in developing countries, new avenues for low-cost diagnosis have opened. Images captured by a smartphone's camera can be further processed to assess the disease's name and severity. the proposed system employs computer vision and deep learning techniques to aid the diagnosis of skin diseases. the images are first pre-processed with computer vision to achieve uniformity and remove any unwanted elements that could impede output efficiency. the deep learning section is concerned with data processing and the generation of results. the developed model was effective at classifying skin diseases into 19 categories. Pre-trained models like Xception, Inception V3, Inception V2, and MobileNet were employed to achieve a maximum F1 score of 92.307% from the Xception model © 2023 IEEE.

关键词： Diagnosis

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