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检索条件"机构=Dept. Computer Science and Applied Cognitive Science"

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Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection

Diffusion Denoised Smoothing for Certified and Adversarial R...

引用

2023 IJCAI Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning, AISafety-SafeRL 2023

作者： Franco, Nicola Korth, Daniel Lorenz, Jeanette Miriam Roscher, Karsten Günnemann, Stephan Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to make a confident prediction. In this study, we present a novel approach for certifying the robustness of OOD detection within a 2-norm around the input, regardless of network architecture and without the need for specific components or additional training. Further, we improve current techniques for detecting adversarial attacks on OOD samples, while providing high levels of certified and adversarial robustness on in-distribution samples. The average of all OOD detection metrics on CIFAR10/100 shows an increase of-13%/5% relative to previous approaches. Code: https://***/FraunhoferIKS/distro © 2023 CEUR-WS. All rights reserved.

关键词： Machine learning

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Software Defect Predictor and Classifier Tool Using Machine Learning Techniques

Software Defect Predictor and Classifier Tool Using Machine ...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Shankar, Sahana P. Pushp, Divyansh Makadia, Nemi Dubey, Raghav Nayak, Anusha Shashikant Chaudhari, Shilpa Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Karnataka Bangalore India M S Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

The Software testing is critical for ensuring the quality of software, but it can consume more than half of a software project's total expenses. This research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. To improve the efficiency of software testing, this report presents research on software defect prediction using a hybrid machine learning algorithm that combines supervised and unsupervised learning techniques. The study used publicly available datasets to train and evaluate various machine learning models, including an ensemble learning model. A comparative analysis was conducted among KNN, Random Forest, Adaboost, Multilayer Perceptron, CNN, and Gaussian Naïve Bayes on different datasets. Feature selection was performed using various methods, including Recurrent Feature Elimination (RFE), SelectKBest, and Correlation, implemented by a tool created to select any feature selection algorithm and compare the results for various combinations with the above mentioned classifiers. The results achieved by the model are showed in detail for each of the algorithms on the selected datasets. The main objectives of the research are to create a tool to apply various machine learning techniques to the datasets and determine the most effective ones, as well as to explore different feature selection methods for improving the accuracy of defect prediction. © 2023 IEEE.

关键词： Software testing

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Detection of Cancer Cells Using Convolutional Neural Networks (CNNs) 2

Detection of Cancer Cells Using Convolutional Neural Network...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Mohiuddin, Md. Ghouse Reddy, D. Laxmi Sreenivasa Ahmed, Aijaz University Pg College Palamamuru University Dept. of Computer Science Telangana State Mahabubnagar India Chaitanya Bharthi Institute of Technology - Cbit Dept. of It Telangana State Hyderabad India University of Technology & Applied Science Dept. of It Shinas Oman

ISBN: (纸本)9798331542573

The fastest spreading dieses among the cancer dieses throughout the world is the Breast cancer. Identifying it at the prior stage will be helpful for effective treatment. Convolutional Neural Networks (CNNs) is an efficient technique which has demonstrated significant ability in classifying and recognizing patterns in medical images. In this research paper we have explored the application of deep learning models for detecting cancer cells from histopathological images, and focused on various architectures, training methods, and assessment measures. Here we specifically evaluated the functioning of four well-known models of CNN comprising AlexNet, VGG16, InceptionV3 and, ResNet50 by employing the dataset from BreakHis for binary classification of the histopathological images into Malignant and Benign classes. Each model is fine-tuned and trained through transfer learning. Here we used cross-entropy loss with Adam optimizer for binary classification, through which the learning rate is adaptively adjusted, and facilitating efficient handling of large datasets. In this research work for training our models we applied Stratified k-fold cross-validation approach for ensuring stability and generalizability. The effectiveness and performance of the models are assessed with the help of Accuracy, Precision, & Recall, and F1-Score. Comparative analysis indicates that ResNet50 outperforms the other models in detecting cancer cells. Finally, we discussed the potencies and weakness of the applied models for offering valuable perceptions into their efficacy for medical image classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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The metric-aware kernel-width choice for LIME 1

The metric-aware kernel-width choice for LIME

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Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Barrera-Vicen, Aurelio Paluzo-Hidalg, Eduardo Gutiérrez-Naranjo, Miguel A. Dept. Computer Science and Artificial Intelligence University of Seville Spain Dept. Applied Mathematics I University of Seville Spain

Local Interpretable Model-Agnostic Explanations (LIME) are a well-known approach to provide local interpretability to Machine Learning models. LIME uses an exponential smoothing kernel based on the kernel width value, which defines the width of the local neighbourhood. In this paper, we study the influence of the distances for these local explanations, and we explore the choice of kernel width to guarantee a fair performance comparison between the distances. © 2023 CEUR-WS. All rights reserved.

关键词： Lime

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Asynchronous Complete Visibility Algorithm for Luminous Robots on Grid 11

Asynchronous Complete Visibility Algorithm for Luminous Robo...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Kim, Yonghwan Katayama, Yoshiaki Wada, Koichi Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Hosei University Dept. of Applied Informatics Tokyo Japan

ISBN: (纸本)9798350306941

An autonomous mobile robot system is a distributed system consisting of mobile computational entities (called robots) that autonomously and repeatedly perform three operations: Look, Compute, and Move. Various problems related to autonomous mobile robots, such as gathering, pattern formation, or flocking, have been extensively studied to understand the relation between each robot's capabilities and the solvability of these problems. In this study, we focus on the complete visibility problem, which involves relocating all the robots on an infinite grid plane so that each robot is visible to every other robot. We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (not transparent). In this paper, we propose an algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) when a set of robots is given. The algorithm ensures that complete visibility is achieved even when robots operate asynchronously and have no knowledge of the total number of robots on the grid plane using only two colors. © 2023 IEEE.

关键词： Visibility

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Anomaly Detection in Supermarket Refrigeration Systems using Transformer Models: A Comparative Study

Anomaly Detection in Supermarket Refrigeration Systems using...

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2024 Lock-in or log out? Wie digitale Souveranitat gelingt, INFORMATIK 2024

作者： Meyer, Melina Gergeleit, Martin Krechel, Dirk RheinMain University of Applied Sciences Dept. Design Computer Science Media Kurt-Schumacher-Ring 18 Wiesbaden65197 Germany

ISBN: (纸本)9783885797463

This study investigates anomaly detection methods in supermarket refrigeration systems. A transformer-based model is introduced in this field and compared with LSTM autoencoders. The models are trained and evaluated using preprocessed refrigeration data, with parameters optimized for accuracy, recall, precision and F1 score. Our findings aim to enhance system monitoring and maintenance strategies, ultimately improving reliability, energy efficiency, and operational excellence in supermarket refrigeration technology. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Retail stores

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Using a Design Pattern to Build Microservices for Instant Messaging Software to Access IoT Devices 13

Using a Design Pattern to Build Microservices for Instant Me...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Huang, Hsin-Yu Fanjiang, Yong-Yi Tsai, Hsing-Yu Huang, Yung-Shiang Yu, Yi-Shiang Lee-Ming Institute of Technology Dept. of Digital Multimedia New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (纸本)9798350355079

This study introduces a design pattern to build microservices, which provides an opportunity for instant messaging software developers, Internet of Things(IoT) device manufacturers and third parties to deploy themselves belonging to the APIs in microservices. Users can use instant messaging software to access IoT devices through the APIs deployed by the system. Instant messaging software developers, IoT device manufacturers and third parties can develop and deploy APIs in Instant Messaging (IM) microservice and IoT microservice of this system based on the OpenAPI specification document using a design pattern. The goal of this system is for users to use the installed and usually used instant messaging software without having to install and learn new app to access various IoT devices. © 2024 IEEE.

关键词： Instant messaging applications

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Towards fracture risk assessment by deep-learning-based classification of prevalent vertebral fractures

Towards fracture risk assessment by deep-learning-based clas...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Yilmaz, Eren B. Fricke, Tobias Laue, Julian Polzer, Constanze Sedaghat, Sam Hövener, Jan-Bernd Glüer, Claus-Christian Meyer, Carsten Campus Kiel Germany Dept. of Radiology and Neuroradiology UKSH Campus Kiel Germany Dept. of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany Dept. of Computer Science Faculty of Engineering Kiel University Germany

ISBN: (纸本)9781510660359

Osteoporosis is a complex multifactorial skeletal disease and has become a major socioeconomic issue, causing tremendous hospitalization and rehabilitation costs. In addition to age, premature menopause or the use of oral corticosteroids, vertebral fractures are regarded as main risk factors for developing osteoporosis and associated fractures. In this work, we adapt an existing automated pipeline to classify fracture grades of individual vertebrae and the spine as a whole: First, vertebral body centers were identified on CT images by a hierarchical neural network. Next, the fracture grades of individual vertebrae were processed by a multi-head, feed-forward convolutional neural network. The sum of the classified grades was then evaluated according to the German radiology guidelines. A hyperparameter search on validation data showed the most promising results for an output configuration based on three sequentially applied binary classification outputs trained using binary cross-entropy: Grade 0-1 vs 2-3, and 0 vs 1-3, and 0-2 vs 3. In a cross-validation setting on 159 low-dose CT images, our pipeline accurately classified patients to have sum-scores ≥ 2 with sensitivities and specificities of 90 % ± 5.0 % and 87 % ± 2.7 %, respectively. As our method was based on classifying individual vertebrae, we were able to provide both the fracture position and severity to enhance transparency, interpretability and usability. © 2023 SPIE.

关键词： Fracture

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Polarity Classification of Twitter Data Using Machine Learning Approach

Polarity Classification of Twitter Data Using Machine Learni...

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2023 Human-Centered cognitive Systems, HCCS 2023

作者： Bangyal, Waqas Haider Iqbal, Muddesar Bashir, Ahsan Ubakanma, George Kohsar Univeristy Murree Dept. of Computer Science Muree Pakistan London South Bank University School of Engineering School of Engineering Cognitive Systems Research Centre United Kingdom Abasyn University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350359183

Sentiment analysis has become very important nowadays because there are lots of social media platforms peoples are using to express their opinion. Twitter is one of the most popular social media platforms which is used for microblogs. People use to express their opinion on current affairs, and there is a challenge for researchers to classify the sentiment accurately. In this research study, we proposed a greatly efficient technique for the detection of fake news on covid-19. The data set of fake news is taken from the corpus and executes the NLP cycle. In this research, we applied five machine learning to predict the sentiment of fake or real news. Support Vector Machine, Logistic Regression, KNN, Decision Trees, and Random Forest are machine learning classifiers used in this research, and results are compared. © 2023 IEEE.

关键词： Sentiment analysis

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Classification of Depression, Anxiety and Stress from Handwriting and Drawing with Stacking Models 8

Classification of Depression, Anxiety and Stress from Handwr...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Bayrak, Semra Golgiyaz, Sedat Aykut, Murat Karadeniz Technical University Graduate School of Natural and Applied Science Dept. of Computer Engineering Trabzon61080 Turkey Bingöl University Dept. of Computer Engineering Bingöl Turkey Karadeniz Technical University Dept. of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331540104

Recently, emotion analysis from online handwriting and drawings has become an important research topic. This study aims to determine the optimal model combination using dynamic and statistical features obtained from handwriting and drawings for detecting emotional states. The feature vectors are derived from physical (kinematic), statistical and signal processing (spectral, cepstral and frequency domain) analyses. To address the class imbalance problem, ADASYN (Adaptive Synthetic Sampling) and Tomek Links methods were applied together. In this way, the number of samples of the minority class has been increased, while over-sampled classes have been reduced. Dimensionality reduction was realized using PCA, and Gradient Boosting Classifier (GBC) feature selection. After determining the fewest and most effective features, the samples were classified using the two-level stacking ensemble method. eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), GBC, Random Forest (RF) methods are selected for base and meta models. Model and hyperparameter optimization was carried out by Optuna frameork. The performance of the models is evaluated through experiments in publicly available EMOTion recognition from HAndWriting and draWing (EMOTHAW) database. The aim was to determine the emotional states of depression, anxiety and stress with the data obtained from 7 different drawing/writing tasks of 129 people. The results obtained from accuracy, f1-score, precision and recall metrics show that the model provides remarkable performance. © 2024 IEEE.

关键词： Dimensionality reduction

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