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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

Out-of-distribution Object Detection through Bayesian Uncert...

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International Conference on Information Fusion

作者： Tianhao Zhang Shenglin Wang Nidhal Bouaynaya Radu Calinescu Lyudmila Mihaylova Automatic Control and Systems Engineering The University of Sheffield UK Department of Electrical and Computer Engineering Rowan University USA Department of Computer Science University of York UK

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset.

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Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

Non-Convex Potential Games for Finding Global Solutions to S...

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European control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

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Deep Learning-Driven Culvert Monitoring: A Novel Approach for Flash Flood Mitigation through Visual Blockage Analysis 9

Deep Learning-Driven Culvert Monitoring: A Novel Approach fo...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Samuel, Betty Elezebeth Alghamdi, Sultan Priya, T. Deepalakshmi, R. Kannan, Sathish Selvaraju, Mayakannan Department of Computer Science And Information Technology Jazan Saudi Arabia King Abdulaziz University Faculty of Engineering Department of Electrical And Computer Engineering Jeddah Saudi Arabia Npr College of Engineering & Technology Department of Mathematics Dindigul Tamilnadu Natham India The Tamilnadu Dr. Ambedkar Law University Department of Inter-Disciplinary Studies Tamilnadu Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamilnadu Chennai602105 India Vidyaa Vikas College of Engineering And Technology Department of Mechanical Engineering Tamilnadu Namakkal India

ISBN: (纸本)9798350394474

The primary cause of urban flash floods is often cited as trash clogging culverts. Flash floods can be avoided with the help of intelligent video analytic (IVA) methods that can extract information about blockages in order to make maintenance-related decisions in a timely manner. Knowing the percentage of culverts that are visually blocked can help prioritise maintenance at heavily blocked locations. In this paper, we introduce a deep learning-powered segmentation-classification pipeline for automatically detecting and segmenting culvert openings and then classifying them into one of four categories based on the degree to which they are visually blocked. The deep learning models underwent training using datasets sourced from the Visual Hydraulics Blockage Dataset (VHD) and Images of Culverts Blockage (ICOB). The outcomes revealed that Mask R-CNN attains the highest performance in segmentation, achieving an mAP@75 score of 77.2%. In contrast, NASNet excelled in classification tasks, with a remarkable 81.2% accuracy on the test data. To underscore the practical importance of these findings, one potential application is demonstrated, which involves using the models for monitoring visual obstructions. © 2024 IEEE.

关键词： computer vision

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Predictive HR Analytics: Using Machine Learning to Forecast Workforce Trends and Needs 7

Predictive HR Analytics: Using Machine Learning to Forecast ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Angulakshmi, M. Madhumithaa, N. Dokku, Srinivasa Rao Pachar, Sunita Sneha, K. Sahaya Lenin, D. Vellore Institute of Technology School of Computer Science Engineering and Information systems Vellore India Faculty of Management Srmist Katangulatthur Tamilnadu Chennai India Pvp Siddhartha Institute of Technology Department of Business Administration Kanuru Andhra Pradesh Vijayawada520007 India Gla University Ibm Department India Saveetha School of Engineering Simats Department of Ece TN Chennai602105 India

ISBN: (纸本)9798350350067

The present research paper aims to analyze the use of predictive HR analytics integrated with machine learning algorithms for the anticipation of workforce demand and requirements. Thus, based on Kaggle's IBM HR Analytics and Employee Satisfaction Index datasets, the study uses Decision Trees and Random Forest approaches to develop the model and predict essential HR parameters, including attrition rates, job satisfaction, and performance levels. This paper illustrates that with the use of machine learning, it is possible to extract valuable information from the data concerning employees and apply them to the field of Human Resource Management to improve the efficiency of decision-making. The results identify the future opportunities of predictive analytics by illustrating its application for HR functions to reduce the efforts toward proactive management, enhance the methods for recruitment, and enhance the approaches to retention of employees. Thus, the findings of this study enrich the body of knowledge regarding the application of machine learning in improving the field of human resource management, specifically in identifying the best approaches for connecting HR efforts with the strategic objectives of organizations. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust Adaptive Backstepping control Design for MIMO Electrically Driven Robot Manipulators Using RBF Neural Network With Disturbance 26

Robust Adaptive Backstepping Control Design for MIMO Electri...

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26th International computer science and engineering Conference, ICSEC 2022

作者： Jamil, Muhammad Usman Ahmad, Irfan Buatoom, Uraiwan Assumption University Department of Electrical and Computer Engineering Samut Prakan Thailand Faculty of Science and Arts Chanthaburi Thailand

ISBN: (纸本)9781665491983

In this paper, a robust adaptive (RA) radial basis function (RBF) neural network (NN) based backstepping control design is proposed for multi-input multi-output (MIMO) electrically driven robot manipulators (EDRM) with completely unmodeled dynamics, unknown nonlinearities, disturbance, and virtual control inputs. The proposed research methodology guarantees the controller's resilience even in the presence of parameter changes. The main idea of the backstepping control design is to reduce the error to zero via the use of parameter adjustment rules and a virtual control technique. The recursive backstepping design method treats specific system signals as virtual inputs to smaller subsystems. This novel control strategy guarantees the boundedness of the trajectory tracking error and also weight updates of NN. The key advantage of our control strategy is that it eliminates the requirement for regression matrices, the linear in parameter (LIP) assumption, and an offline learning phase. The results of proposed robust adaptive control methodology with unknown disturbance are compared with conventional proportional-derivative (PD) control scheme, i.e., without backstepping. © 2022 IEEE.

关键词： Backstepping

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Faster Greedy Optimization of Resistance-based Graph Robustness 14

Faster Greedy Optimization of Resistance-based Graph Robustn...

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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Predari, Maria Kooij, Robert Meyerhenke, Henning Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands

ISBN: (纸本)9781665456616

The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G. We consider the optimization problem of adding k new edges to G such that the resulting graph has minimal total effective resistance (i. e., is most robust). The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion;yet, this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in established generic greedy heuristics. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph-and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process large graphs for which the application of the state-of-the-art greedy approach was infeasible before. As far as we know, we are the first to be able to process graphs with 100K+ nodes in the order of minutes. © 2022 IEEE.

关键词： Optimization

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Performance Evaluation of Laser-Induced Screen Printed Electrodes for Electrochemical Detection 9

Performance Evaluation of Laser-Induced Screen Printed Elect...

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9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Hisham, Balqis Nurnadia Badrol Ralib, Aliza Aini Md Nordin, Anis Nurashikin Haotung, Lun Rahim, Rosminazuin Ab Zain, Zainiharyati Mohd Iium Kuliyyah of Engineering Department of Electrical and Computer Engineering Kuala Lumpur Malaysia Jabil Circuit Sdn. Bhd Penang Malaysia University Teknologi Mara UiTM Faculty of Applied Science Selangor Malaysia

ISBN: (纸本)9798350343380

Screen printing is a well-known technique for producing disposable and low-cost screen-printed carbon electrode (SPCE) sensor. SPCE sensors have been used in many applications such as disease detection, toxin detection, disease monitoring for the environment, and food analysis. Despite its outstanding advantages, electrochemical-based SPCE sensors have relatively low sensitivity as compared to chromatography-based and fluorimetry-based conventional sensors. A modification by improving the electrode's materials may be implemented to produce an effective and sensitive SPCE sensor. Thus, in this study, a comparison of material between laser-induced graphene screen-printed electrode (LIG-SPE) and currently existing and widely used screen-printed carbon electrode (SPCE) is presented. Cyclic Voltammetry (CV) was conducted to compare the performance of the fabricated sensors for electrochemical detection and the R2 calibration curves were performed to determine the linearity. The results shows that LIG SPE has better linearity (R2 is 0.9596) compared to SPCE. © 2023 IEEE.

关键词： Graphene

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Uncovering the Link Between Deep Learning and Data science Practices 3

Uncovering the Link Between Deep Learning and Data Science P...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rajarajeswari, S. Kolekar, Vikas K. Singh, Manpreet Naval, Preeti Savitha, R. Seth, Jyoti Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Electrical and Electronics Engineering Faculty of Engineering and Technology Karnataka Ramnagar562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Facts technological know-how has emerge as a vital device for agencies around the sector to understand their customers, optimize their operations, and stay aggressive. But, groups often lack an understanding of the technical factors of records technology, and the way new and rising deep gaining knowledge of strategies can be implemented to resolve their business demanding situations. This paper examines the relationship between deep mastering and records science, in addition to the ways wherein companies can use each to their full potential. It covers the basics of ways deep mastering works, and the way corporations can take benefit of it. Additionally, it discusses how information science satisfactory practices should be integrated right into a deep getting to know software, and the way businesses can make sure they may be leveraging these strategies within the maximum efficient manner. Ultimately, this paper highlights the significance of statistics tradition and the way it can force the adoption of the cutting-edge facts technological know-how and deep getting to know techniques. © 2023 IEEE.

关键词： adoption deep getting highlights technological

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Implementing Machine Learning Techniques to Enhance Real-Time Data Analysis Services 3

Implementing Machine Learning Techniques to Enhance Real-Tim...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jaisudha, J. Dhabliya, Dharmesh Narasimhayya, B.E. Singh, Akhilendra Pratap Sinha, Sanjay Kumar Vashisht, Nitish Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

System learning techniques have emerged to automatically extract data from massive volumes of statistics and enable predictive analytics. Gadget-gaining knowledge has, in particular, been applied to records-driven real-time analytics offerings to facilitate accurate, short, and efficient selection-making. This paper discusses how machine studying strategies can be implemented to improve the accuracy and speed of real-time facts analysis. Conventional statistics analysis models are regularly misguided because of latency troubles and the absence of contextual statistics. Machine getting-to-know techniques have been followed to lessen latency issues, contain contextual facts, and enhance the accuracy of predictions. The paper offers a top-level view of some commonly used machine learning algorithms, such as supervised getting to know, unsupervised mastering, decision timber, neural networks, and support vector machines. It also explains how they can beautify actual-time analytics services. It also covers how strategies, including regularization and dimensionality discount, may be applied to enhance the model's overall performance. The paper gives case research of real-time analytics packages in finance, healthcare, and retail to illustrate the efficacy and added cost of gadget learning in facts analytics offerings. © 2023 IEEE.

关键词： Support vector machines

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Recognition of Handwritten Tamazight Characters Using ResNet, MobileNet and VGG Transfer Learning 23

Recognition of Handwritten Tamazight Characters Using ResNet...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Manai, Elyes Ghayoula, Ridha Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

The Tamazight civilization stands as a significant cultural entity, marked by its linguistic diversity, historical legacy, and scriptural traditions, which collectively enrich the cultural tapestry of North Africa. Among these traditions, the Tamazight handwritten script assumes particular importance, embodying centuries of cultural identity and artistic expression. Recognizing the imperative of safeguarding this cultural heritage, our study focuses on Tamazight handwritten character recognition. Leveraging the strategic application of Transfer Learning, we explore its efficacy in this domain. Transfer Learning presents a robust framework wherein pre-existing models are adapted for specific tasks despite limited data availability. Our research employs three prominent Transfer Learning architectures: VGG, ResNet, and MobileNet. Through a rigorous comparative analysis, we discern the efficacy of these methodologies in the context of Tamazight handwritten character recognition. Our findings underscore the potential of Transfer Learning to significantly augment the accuracy and efficiency of script recognition systems, thereby advancing the overarching objective of preserving and propagating the Tamazight cultural heritage. © 2024 IOS Press. All rights reserved.

关键词： Transfer learning

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