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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

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TRACKING AND VISUALIZATION OF BENCHTOP ASSEMBLY COMPONENTS USING A RGBD CAMERA 18

TRACKING AND VISUALIZATION OF BENCHTOP ASSEMBLY COMPONENTS U...

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ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Maddipatla, Yashwanth Li, Jiaqiong Zheng, Yi Li, Beiwen Iowa State Univ Dept Mech Engn Ames IA 50011 USA

ISBN: (纸本)9780791887240

The workforce training for part assembly processes usually depends on paper-based instructions, which may not be intuitive and could take a long time for a worker to be fully proficient. This paper aims to improve the efficiency of the training for the part assembly process with the help of an RGBD camera like Kinect. Essentially, the RGBD camera tracks the assembly bench, and an associated computer graphics interface provides the assembly instructions through a virtual model of the real part on the bench. The model is displayed on a computer, and its orientation is manipulated based on its real counterpart, which is tracked by the Kinect system in real-time. A U-net-based full-flow bi-directional fusion network model was trained for real-time 6D pose estimation. The training data was primarily captured with Kinect and Apple iPad Pro's LiDAR. The captured data was processed in Meshlab and Matlab. The training data was also synthetically enriched so the machine learning model could recognize and track the parts in various environmental settings. The virtual model visualization interface is built with the Unity gaming engine and uses the 3D models of the assembly parts. Once set up, the system works seamlessly and reduces the time an operator has to spend assembling parts containing many circuit boards and other smaller components. Our experimental testing showed high accuracy in recognizing and tracking the assembly parts with the trained model.

关键词： Flow (Dynamics) machine learning Manufacturing Matlab Visualization Network models Printed circuit boards Three-dimensional models Engines Computers Testing Lidar

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Artificial Intelligence for securing SDIoT: Challenges and countermeasures 20

Artificial Intelligence for securing SDIoT: Challenges and c...

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20th international Multi-conference on Systems, Signals and Devices, SSD 2023

作者： Zaidoun, Ameur Salem Baccouch, Hajer Guermazi, Abderrahmen Lachiri, Zied National Engineering School of Tunis Signal Images and Information Technologies Lab Tunisia Higher Institute of Technological Studies of Siliana Tunisia National Engineering School of Sfax Computer and Embedded Systems Lab Tunisia Higher Institute of Technological Studies of Sfax Tunisia

ISBN: (纸本)9798350332568

SDIoT (software Defined IoT) is an emerging IoT technology, deployed using SDN architecture;nevertheless, it presents several weaknesses, which can be raised in different layers. To mitigate SDIoT intrusions and attacks, security as used in a common network cannot provide a complete solution and must be enhanced with AI (Artificial Intelligence) based countermeasures tools, mainly machine and deep learning. The paper identifies the salient features of SDIoT technology. Afterwards, it describes the most common attacks against SDIoT networks and possible countermeasures using several deep learning algorithms. In addition, the paper describes datasets used for learning steps. At the end, it discusses the suitability of AI based security tools to some attacks. © 2023 IEEE.

关键词： Deep learning

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Exploring Hyperparameter Usage and Tuning in machine learning Research 2

Exploring Hyperparameter Usage and Tuning in Machine Learnin...

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IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Simon, Sebastian Kolyada, Nikolay Akiki, Christopher Potthast, Martin Stein, Benno Siegmund, Norbert Univ Leipzig Leipzig Germany Bauhaus Univ Weimar Weimar Germany Univ Leipzig ScaDS AI Dresden Leipzig Germany

ISBN: (纸本)9798350301137

The success of machine learning (ML) models depends on careful experimentation and optimization of their hyperparameters. Tuning can affect the reliability and accuracy of a trained model and is the subject of ongoing research. However, little is known on whether and how hyperparameters are used and optimized in research practice. This lack of knowledge not only limits the adoption of best practices for tuning in research, but also affects the reproducibility of published results. Our research systematically analyzes the use and tuning of hyperparameters in ML publications. For this, we analyze 2000 code repositories and their associated research papers from Papers with Code. We compare the use and tuning of hyperparameters of three widely used ML libraries: scikit-learn, TensorFlow, and PyTorch. Our results show that the most of the available hyperparameters remain untouched, and those that have been changed use constant values. In particular, there is a significant difference between tuning hyperparameters and the reporting about it in the corresponding research papers. Our results suggest that there is a need for improved research and reporting practices when using ML methods to improve the reproducibility of published results.

关键词： Hyperparameter Hyperparameter Tuning configuration Settings

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XMODOS: An Explainable Model for Denial of Service Attack Detection

XMODOS: An Explainable Model for Denial of Service Attack De...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Molokwu, Reginald C. Molokwu, Bonaventure C. Molokwu, Victor C. Canadian Institute for Cybersecurity University of New Brunswick Canada Software Engineering Department of Computer Science Concordia University Canada School of Energy Geoscience Infrastructure and Society Hariot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9798350337020

This paper proposes a machine learning-based approach for detecting Denial-of-Service (DoS) attacks using different datasets for training and testing. The study evaluates the performance of the proposed approach on a unique dataset;thereafter, compares it to existing approaches in a bid to demonstrate its superiority in terms of accuracy, false-positive rate, and computational efficiency. Furthermore, the impacts of different machine learning algorithms and hypertuning configurations on the performance of the proposed approach are investigated. Also, via Feature Importance Analysis, this study examines the influence of each feature (present in the dataset) on our proposed model. The contributions of our work herein demonstrate the potentials of the proposed approach in detecting DoS attacks;and our research highlights the importance of employing machine learning in this domain. Future research directions have been suggested based on insights acquired from the investigations of different algorithms and hyperparameters. © 2023 IEEE.

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Predicting software Errors Using Neural Networks 12

Predicting Software Errors Using Neural Networks

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Pandey, Ratnesh Kumar Chand, Jay Srivastava, Ashish Kumar Singh, Narendra Pal Gautam, Rakesh Kumar Invertis University Department of Computer Science & Engineering UP Bareilly India Kamla Nehru Institute of Physical and Social Sciences Department of Computer Science & Engineering UP Sultanpur India Madyanchal Professional University Department of Computer Science & Engineering MP Bhopal India Iimt College of Engineering Department of Computer Science & Engineering UP Greater Noida India Iet Department of Computer Science & Engineering Department of Information Technology UP Lucknow India

ISBN: (纸本)9798350369861

The growing call for online technology software has additionally elevated the call for great and occasional-cost merchandise. Predicting software vulnerabilities is one of the most essential obligations of a very good security gadget. Growth nice at low cost by decreasing ordinary testing and preservation efforts. Early detection of defects in the software program improvement existence Cycle (SDLC) leads to early improvement and transport in the closing period of software program control, which leads to consumer delight and gives self assurance to the development group. in the beyond decade, many machine learning-primarily based blunders prediction software program were proposed to attain excessive accuracy. synthetic Neural Networks (ANN) are considered one of the system gaining knowledge of techniques and encompass maximum of the prediction schemes and models. ∗ This examine provides a vital analysis of the modern facts published due to the fact that 20152018. On the usage of neural networks for software program prediction. This take a look at is based totally on the studies system information from 3 widely used digital libraries (which include IEEE, Elsevier and Springer) and then conducted the subsequent take a look at. 8 most crucial articles were selected in the course of the entire method. studies courses are examined meticulously. ∗ This paintings will help researchers look into the modern-day country of software disorder prediction through focusing on neural networks and provide benchmarks for destiny trends. This white paper uses device getting to know and deep learning to find out the quality device gaining knowledge of algorithms for software defect prediction (SEP). This have a look at uses the NASA Metric statistics program (MDP) dataset to evaluate and predict the overall performance and accuracy of deep getting to know and machine getting to know fashions. the 2 mastering algorithms used are help vector machine (SVM) and logistic regression. Two deep

关键词： Forecasting

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VDDA: An Effective software Vulnerability Detection Model Based on Deep learning and Attention Mechanism 26

VDDA: An Effective Software Vulnerability Detection Model Ba...

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26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chang, Jiaqi Ma, Zhujuan Cao, Binghao Zhu, Erzhou Anhui University School of Computer Science and Technology Hefei China Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350331684

Many of software vulnerability detection methods suffer from problems of dependent on expert experience, rough detection granularity, and incomplete syntax and semantics information on source codes. This paper proposes the VDDA, Vulnerability Detection based on Deep learning and Attention mechanism, an effective software vulnerability detection model based on deep learning and the attention mechanism. In the VDDA, deep learning technology is used to construct the underlying classifier to avoid the feature engineering of traditional machine learning techniques. The Joren slice tool combined with the code attribute graph (CPG) optimization is used to simplify the source code before it is fed to the Bidirectional Long Short-Term Memory (BLSTM) deep model. Meanwhile, the attention mechanism is employed to improve the efficiency and accuracy of vulnerability detection. Experiment results have demonstrated that the proposed VDDA model is more effective than the existing vulnerability detection methods. © 2023 IEEE.

关键词： Semantics

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An Exploratory Analysis of Effect of Adversarial machine learning Attack on IoT-enabled Industrial Control Systems

An Exploratory Analysis of Effect of Adversarial Machine Lea...

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2023 international conference on Smart Computing and Application, ICSCA 2023

作者： Trivedi, Sandeep Anh Tran, Tien Faruqui, Nuruzzaman Hassan, Md. Maruf Deloitte Consulting Llp Houston United States Seoul National University Department of Naval Architecture and Ocean Engineering Seoul City Korea Republic of Daffodil International University Daffodil Smart City Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350347050

machine learning (ML)-based Intrusion Detection Systems (IDS) is an effective technology to automatically detect cyber attacks in the Internet of Things (IoT) dependent Industrial Control Systems (ICS). It is faster, more efficient, and can detect attacks without human intervention. However, ML-based IDSs have introduced another security threat called Adversarial machine learning (AML). An AML attack may cause severe industrial infrastructural and production damage resulting in substantial financial loss. This paper presents an exploratory analysis of initiating an AML attack using adversarial samples created using a Fast Gradient Sign Method (FGSM). The research presented in this paper has been conducted from a dataset generated from a full-fledged singular module of a power distribution industry controlled by IoT-enabled ICSs. We explored the AML attack on Gradient Boosting (GB) and Iterative Dichotomiser 3 (ID3) model and discovered the average classification accuracy, precision, recall, and F1-scores are 87%, 88%, 87.5%, and 87%, respectively. The AML attack reduces the average precision, recall, and F1-score by 20.5%, 20.5%, and 22.5%, respectively, when 50% perturbations are added to 10% samples. © 2023 IEEE.

关键词： Internet of things

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Generative Pre-Trained Transformer (GPT) Models for Irony Detection and Classification 4

Generative Pre-Trained Transformer (GPT) Models for Irony De...

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4th international Informatics and software engineering conference, IISEC 2023

作者： Aytekin, Mustafa Ulvi Ayhan Erdem, O. Gazi University Graduate School of Natural and Applied Sciences Department of Computer Engineering Ankara Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350318036

The tasks of identifying and classifying ironic texts remain an ongoing challenge, necessitating continued exploration for enhanced solutions in NLP. This study delves into assessing the effectiveness of Generative Pre-Trained Transformer (GPT) models, which have emerged in recent years, in handling irony detection and classification tasks through the implementation of zero-shot learning and few-shot learning methods in English texts. Additionally, we compare GPT text embedding models with GloVe, a proven text embedding model, utilizing various machine learning and deep learning approaches. Within this study, we employed the SemEval-2018 Task 3 dataset, curated as part of the Semantic Evaluation 2018 workshop. The most noteworthy achievement in binary classification, namely irony detection, is an F1 score of 68.9%, attained by the text-davinci-003 model through few-shot learning, with access to forty-Two samples for training. In terms of multiclass classification, namely irony classification, the text-embedding-Ada-002 text embedding model, in conjunction with the Gaussian Naive Bayes algorithm, attained the best result with an F1 score of 48.5%. The best results obtained in the study achieved comparable results with previous studies. © 2023 IEEE.

关键词： Embeddings

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international conference on Advances and Applications of Artificial Intelligence and machine learning, ICAAAIML 2023

International Conference on Advances and Applications of Art...

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international conference on Advances and Applications of Artificial Intelligence and machine learning, ICAAAIML 2023

ISBN: (纸本)9789819795062

The proceedings contain 35 papers. The special focus in this conference is on Advances and Applications of Artificial Intelligence and machine learning. The topics include: Leveraging Differently Layered Convolutional Neural Network for Speech Emotion Recognition;enhancing Stock Price Prediction Accuracy Through Deep learning Techniques: A Case Study on Nepal's Stock Market;Decision-Level Fusion of Face and Gait Cues Using Fusion of Median Local Binary Pattern (Median-LBP) and Gabor Scale Average (GSA)-Based PCA → LDA;Analysis of CERN's Project Atlas Data for a Possible Relationship Between Subatomic Particles;machine learning-Based Approach for Diabetic Retinopathy Detection and Grading;Anticipating Customer Loyalty to Health Insurance Using PLS-SEM: The Role of AI Chatbots, Gamified Apps, and Customer Engagement;enhancing Sensitivity Control for Improved Gesture Recognition in Unconstrained Environments;health Risk Predictor Analysis to Assess the Impact of Heavy Alcohol Consumption;genomic Insights into Infertility Using Neural Network;Classification Analysis of Motor Imagery EEG Signals Using Bidirectional LSTM Model;rideGuard: Empowering Riders with Intelligent Accident Detection and Aid;statistical Feature Based Traffic Classification and Quality of Service Assessment in software Defined Networks;An AI and ML-Enhanced Approach with IoT for Pothole Detection to Ensure Pedestrian Safety During Flooding;empowering Health Insurance with Personalization: A Comprehensive Architecture Leveraging Federated learning and BlockChain;a Novel Approach for Performance Evaluation and Effectiveness of Data-Driven Heart Disease Diagnosis;a Hybrid Recurrent Neural Network with Mini-Batch Gradient Descent Method for Multiple Sclerosis Disease Prediction;identifying Real-Time Multi-class Food Detection Through Deep learning Models;endpoint Focused Ideal Attributes of a Cryptocurrency Wallet.

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*** - engineering and Assurance of a Driverless Regional Train 2

*** - Engineering and Assurance of a Driverless Regional Tra...

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IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Zeller, Marc Rothfelder, Martin Klein, Cornel Siemens AG Munich Germany

ISBN: (纸本)9798350301137

Traditional automation technologies alone are not sufficient to enable the fully automated operation of trains. However, Artificial Intelligence (AI) and machine learning (ML) offers great potential to realize the mandatory novel functions to replace the tasks of a human train driver, such as obstacle detection on the tracks. The problem, which still remains unresolved, is to find a practical way to link AI/ML techniques with the requirements and approval processes that are applied in the railway domain. The *** project aims to lay the foundation for the safe use of AI/ML to achieve the driverless operation of rail vehicles and thus addresses this key technological challenge hindering the adoption of unmanned rail transport. The project goals are to develop guidelines and methods for the reliable engineering and safety assurance of ML in the railway domain. Therefore, the project investigates methods to reliable design ML models and to prove the trustworthiness of AI-based functions taking robustness, uncertainty, and transparency aspects of the AI/ML model into account.

关键词： Driverless regional train AI/ML engineering processes safety approval rail autonomous driving

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