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检索条件"任意字段=Conference on Artificial Intelligence and Machine Learning in Defense Applications IV"

共 594 条记录，以下是51-60 订阅

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An AI Framework for Modelling and Evaluating Attribution Methods in Enhanced machine learning Interpretability 47

An AI Framework for Modelling and Evaluating Attribution Met...

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47th IEEE-Computer-Society Annual International conference on Computers, Software, and applications (COMPSAC)

作者： Cuzzocrea, Alfredo Ratul, Qudrat E. Alahy Belmerabet, Islam Serra, Edoardo Univ Calabria IDEA Lab Arcavacata Di Rende Italy Univ Paris City Dept Comp Sci Paris France Boise State Univ Comp Sci Dept Boise ID USA

ISBN: (纸本)9798350326970

In this paper, we propose a general methodology for estimating the degree of the attribution methods precision and generality in machine learning interpretability. Additionally, we propose a technique to measure the attribution consistency between two attribution methods. In our experiments, we focus on the two well-known model agnostic attribution methods, SHAP and LIME, then we evaluate them on two real applications in the attack detection field. Our proposed methodology highlights the fact that both LIME and SHAP are lacking precision, generality, and consistency. Therefore, more inspection is needed in the attribution research field.

关键词： artificial intelligence machine learning Interpretability Intelligent AI Tools

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Feature Selection via Dynamic Graph–based Attention Block in MI–based EEG Signals 13

Feature Selection via Dynamic Graph–based Attention Block i...

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13th International Winter conference on Brain-Computer Interface, BCI 2025

作者： Han, Hyeon-Taek Lee, Dae-Hyeok Kwak, Heon-Gyu Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Brain–computer interface (BCI) technology enables direct interaction between humans and computers by analyzing brain signals. Electroencephalogram (EEG) is one of the non–invasive tools used in BCI systems, providing high temporal resolution for real–time applications. However, EEG signals are often affected by a low signal–to–noise ratio, physiological artifacts, and individual variability, representing challenges in extracting distinct features. Also, motor imagery (MI)–based EEG signals could contain features with low correlation to MI characteristics, which might cause the weights of the deep model to become biased towards those features. To address these problems, we proposed the end–to–end deep preprocessing method that effectively enhances MI characteristics while attenuating features with low correlation to MI characteristics. The proposed method consisted of the temporal, spatial, graph, and similarity blocks to preprocess MI–based EEG signals, aiming to extract more discriminative features and improve the robustness. We evaluated the proposed method using the public dataset 2a of BCI Competition iv to compare the performances when integrating the proposed method into the conventional models, including the DeepConvNet, the M–ShallowConvNet, and the EEGNet. The experimental results showed that the proposed method could achieve the improved performances and lead to more clustered feature distributions of MI tasks. Hence, we demonstrated that our proposed method could enhance discriminative features related to MI characteristics. © 2025 IEEE.

关键词： Electroencephalography

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Assuring autonomous operations in aviation: is use of AI a good idea? 4

Assuring autonomous operations in aviation: is use of AI a g...

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conference on artificial intelligence and machine learning for Multi-Domain Operations applications iv

作者： Bharadwaj, Ramesh Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9781510651036;9781510651029

Autonomous systems, including self-driving cars and unmanned underwater/surface/ground/air/space vehicles, have caught the imagination of the press, the public, and personnel of the U.S. Department of defense (DoD). Moreover, these systems increasingly harness the power of artificial intelligence and machine learning (AI/ML), more specifically Deep learning (DL), for their operation. The stunning performance of deep learning algorithms in comparison to extant methods, including pattern matching, computational linguistics, statistical inferencing, and legacy machine-learning, has taken the world by storm. However, adoption of such systems in safety-critical applications has been the subject of intense debate and scrutiny, because of the propensity of these algorithms for erroneous operation, such as misdirection with seemingly innocuous signage, and their vulnerability to data poisoning and data sparsity attacks, leading to incorrect operation which can be both embarrassing and damaging. This has naturally led the DoD community to ask, "How do we harness this technology being unleashed upon the world, and yet keep our nation and warfighters safe?" Before we answer this question, however, it is important to note that trust is integral to DoD systems, including autonomous systems, and ensuring reliable system operations is paramount. Therefore, we need strategies to equip the DoD to design, build, deploy, and sustain autonomous systems that are trustworthy, secure, reliable, and dependable. In this paper, we investigate issues leading to poor performance and lack of robustness of autonomous systems based on machine learning, and discuss the state of the art for their mitigation.

关键词： Assured Autonomy artificial intelligence machine learning Adversarial Training Aviation Safety

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Recent Trends in artificial intelligence and machine learning Methods Applied to Water Jet Machining 8th

Recent Trends in Artificial Intelligence and Machine Learnin...

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8th International Scientific-Technical conference (MANUFACTURING)

作者： Khan, Rehan Wieczorowski, Michal Qureshi, Ariba Ammar, Muhammad Ahmed, Tauseef Khan, Umair Natl Univ Sci & Technol Islamabad Pakistan Poznan Univ Tech Poznan Poland Univ Teknol PETRONAS Seri Iskandar Darul Ridzuan P Malaysia

ISBN: (纸本)9783031564468;9783031564444

Abrasive Water Jet Machining is a revolutionary unconventional cutting technology that has a wide range of applications in the machining of difficult-to-machine materials. Process parameters are critical in determining the efficiency and economics of a high-quality machining process. As a consequence of advancements in sensor technology, machining operations may now be automated, and the massive amounts of data generated can be used to model and monitor the processes using artificial intelligence (AI) and machine learning (ML) approaches. This paper presents an overview of the current research trends linking the application of AI and ML methods to AWJM processes for enhanced performance metrics, process monitoring and control, and improved variable optimization. Overcoming challenges related to data quality, model interpretability, and system integration will be essential for the successful implementation of AI and ML in the field of water jet machining. The potential future directions in the ever-expanding field of AI and machining processes, particularly AWJM, are also discussed.

关键词： Abrasive Waterjet modelling artificial neural network process parameters

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A Survey on Adversarial Text Attacks on Deep learning Models in Natural Language Processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

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5th International conference on Data Science, machine learning and applications

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different artificial intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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Vertical Federated Image Segmentation 20th

Vertical Federated Image Segmentation

引用

20th International conference on artificial intelligence applications and Innovations (AIAI)

作者： Mandal, Paul K. Leo, Cole Univ Texas Austin Dept Comp Sci Austin TX 78712 USA T2S Solut Aberdeen MD 21017 USA

ISBN: (纸本)9783031632259;9783031632235;9783031632228

There has been a growing concern for both data privacy and acquisition for the purpose of training robust computer vision algorithms. Often, information is located on separate data silos and it can be difficult for a machine learning engineer to consolidate all of it in a fashion that is appropriate for model development. Additionally, some of these localized data regions may not have access to a labelled ground truth, rendering conventional model training impossible. In this paper, we propose a novel architecture that can perform image segmentation in vertical federated environments. This is the first implementation of a federated architecture that can perform vertical federated image segmentation. We utilized a distributed vertical fully convolutional network architecture that is able to train on data where the segmentation maps reside on a different federate than the original image. Our architecture is able to compress the features of an image from 49,152 down to 500, allowing for more efficient communication between the top and bottom model of our federated architecture. We trained our model on 369 images from the CamVid dataset. Our model demonstrates a robust capability for accurate road detection.

关键词： Computer Vision Convolutional Network (CNN) Deep learning Federated learning Fully Convolutional Network (FCN) Image Segmentation machine learning Vertical Federated learning (VFL)

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Comparative evaluations of online and offline tracking algorithms 4

Comparative evaluations of online and offline tracking algor...

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conference on artificial intelligence and machine learning in defense applications iv

作者： Yardimci, Ozan Roketsan Ind Inc Adv Technol & Syst Grp Kemalpasa Mahallesi 21 TR-06780 Ankara Turkey

ISBN: (纸本)9781510655560

In this study, the performance of online and offline tracking algorithms, which are frequently used in the literature, were compared on the defined datasets. Therefore, six different datasets are prepared. The datasets consist of consecutive frames. In each dataset, target has different motion characteristics and the background types are different from each other. In addition, a total of six well known algorithms were used for comparison. These are KCF, MOSSE, CSRT, TLD, Go Turn and Siamese tracking algorithms. In conclusion, especially in cases where the target is small and the SNR value is low, the highest performance is obtained with the KCF algorithm. On the other hand, when the target is big and the SNR value is high, it is observed that the Siamese algorithm better handle changes in the target shape. In this context, considering the real scenarios, it may be possible to use the algorithms in a hybrid way to get the better performance.

关键词： Computer vision deep learning visual target tracking infrared images target detection correlation filter

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Evaluating the Effectiveness of artificial intelligence in Integrated System Architectures to Combat Cybersecurity Threats 7

Evaluating the Effectiveness of Artificial Intelligence in I...

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7th IEEE International conference on Computer and Communication Engineering Technology, CCET 2024

作者： Samonte, Mary Jane C. Goc-Ong, Andrei E. Matoza, Raf Bradey F. Viernes, Ron Gerrovir A. School of Information Technology Mapua University Makati Philippines

ISBN: (纸本)9798350355673

The Internet of Things (IoT) and artificial intelligence (AI) offer powerful solutions for various applications. However, this digitalization boom also increases cybersecurity threats. Organizations are integrating AI into critical systems to combat cybercrime. This study explores the effectiveness of AI techniques like machine learning and deep learning in identifying and defending against cyber threats, particularly those created by AI tools. This research critically evaluates the effectiveness of AI-powered security systems in detecting and responding to a wide range of cyber threats. The study analyzes existing literature on AI in cybersecurity threats and integrated system architecture security. AI is a double-edged sword in cybersecurity, used for both defense and offense. AI integration enhances cyberattacks' effectiveness, potentially disrupting benign AI algorithms. As AI becomes more prevalent, understanding its role in cybersecurity is crucial for developing strong defenses. Studies show promise in integrating AI into the Internet of Things for threat detection and prevention. machine learning and deep learning algorithms are used in Intrusion Detection Systems to identify attacks and improve overall IoT security. © 2024 IEEE.

关键词： Adversarial machine learning

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Explainable AI for Fair Sepsis Mortality Predictive Model 22nd

Explainable AI for Fair Sepsis Mortality Predictive Model

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22nd International conference on artificial intelligence in Medicine (AIME)

作者： Chang, Chia-Hsuan Wang, Xiaoyang Yang, Christopher C. Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA

ISBN: (纸本)9783031665349;9783031665356

artificial intelligence supports healthcare professionals with predictive modeling, greatly transforming clinical decision-making. This study addresses the crucial need for fairness and explainability in AI applications within healthcare to ensure equitable outcomes across diverse patient demographics. By focusing on the predictive modeling of sepsis-related mortality, we propose a method that learns a performance-optimized predictive model and then employs the transfer learning process to produce a model with better fairness. Our method also introduces a novel permutation-based feature importance algorithm aiming at elucidating the contribution of each feature in enhancing fairness on predictions. Unlike existing explainability methods concentrating on explaining feature contribution to predictive performance, our proposed method uniquely bridges the gap in understanding how each feature contributes to fairness. This advancement is pivotal, given sepsis's significant mortality rate and its role in one-third of hospital deaths. Our method not only aids in identifying and mitigating biases within the predictive model but also fosters trust among healthcare stakeholders by improving the transparency and fairness of model predictions, thereby contributing to more equitable and trustworthy healthcare delivery.

关键词： Fairness Explainable artificial intelligence MIMIC-iv Sepsis mortality prediction

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Bandwidth constrained cooperative object detection in images 4

Bandwidth constrained cooperative object detection in images

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conference on artificial intelligence and machine learning in defense applications iv

作者： Marez, Diego Nans, Lena Borden, Samuel Naval Informat Warfare Ctr 53560 Hull St San Diego CA 92152 USA

ISBN: (纸本)9781510655560

Cooperative autonomous systems, such as swarms, multi-camera systems, or fleets of self-driving vehicles, can better understand a given scene by sharing multiple modalities and varying viewpoints. This superposition of data adds robustness and redundancy to a system typically burdened with obstructions and unrecognizable, distant objects. Collaborative perception is a key component of cooperative autonomous systems where modalities can include camera sensors, LiDAR, RADAR, and depth images. Meanwhile, the amount of useful information that can be shared between agents in a cooperative system is constrained by current communication technologies (e.g. bandwidth limitations). Recent developments in learned compression can enable the training of end-to-end cooperative systems using deep learning with compressed communication in the pipeline. We explore the use of a deep learning object detector in a cooperative setting with a learned compression model facilitating communication between agents. To test our algorithm, this research will focus on object detection in the image domain as a proxy for one of the modalities used by collaborative systems.

关键词： Deep learning Data Fusion Cooperative Communication RGB Camera Object Detection

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