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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91-100 订阅

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A machine learning Approach for Points of Interest Extraction and Event Classification 20th

A Machine Learning Approach for Points of Interest Extractio...

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

作者： Dias, Pedro Ferreira, Flora Guimaraes, Pedro M. F. Wojtak, Weronika Erlhagen, Wolfram Monteiro, Sergio Sousa, Emanuel Bicho, Estela Univ Minho Dept Ind Elect Braga Portugal Univ Minho Ctr ALGORITMI Braga Portugal Univ Minho Res Ctr Math Braga Portugal CCG ZGDV Inst Ctr Comp Graph Guimaraes Portugal

ISBN: (纸本)9783031632259;9783031632235;9783031632228

This paper presents a novel approach that utilizes machine learning techniques, specifically clustering algorithms and artificial neural networks, to improve the prediction and understanding of human routines in urban mobility contexts. Our method focuses on the identification and categorization of Points of Interest (POIs) from travel data, facilitating the accurate prediction of user routines for intelligent transportation systems. By integrating a clustering phase that groups individual stop points into POIs, followed by a correction mechanism through user interaction, we address the limitations of existing methods in adapting to dynamic mobility patterns and the contextual ambiguity of GPS coordinates. Subsequently, a classification phase employs a feed-forward neural network to assign incoming travel events to the identified POIs. This dual-phase approach not only improves the precision of routine predictions but also enhances the adaptability of the system to changes in mobility behavior over time. The incorporation of a cognitive module, based on Dynamic Neural Fields (DNF), further allows for personalized predictions regarding the timing, duration, and nature of trips. Validated with datasets from the Portuguese city of Braga, our results demonstrate the effectiveness of this methodology in providing actionable insights for the development of cognitive solutions for the project ***'s innovative vehicle "BEN". By emphasizing user involvement and algorithmic transparency, our work contributes to the advancement of smart transportation technologies in urban environments.

关键词： Clustering algorithms artificial Neural Networks Points of Interest (POIs)

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Deep Reinforcement learning to Assist Command and Control 4

Deep Reinforcement Learning to Assist Command and Control

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

作者： Park, Song Jun Vindiola, Manuel M. Logie, Anne C. Narayanan, Priya Davies, Jared DEVCOM Army Res Lab Aberdeen Proving Ground MD 21005 USA Cole Engn Serv Inc Orlando FL USA

ISBN: (纸本)9781510651036;9781510651029

Multi-domain operations drastically increase the scale and speed required to generate, evaluate, and disseminate command and control (C2) directives. In this work we evaluate the effectiveness of using reinforcement learning (RL) within an Army C2 system to design an artificial intelligence (AI) agent that accelerates the commander and staff's decision making process. Leveraging RL's superior ability to explore and exploit produces novel strategies that widen a commander's decision space without increasing cognitive burden. Integrating RL into an efficient course of action war-gaming simulator and training hundreds of thousands of simulated battles using the DoD supercomputing resources generated an AI that produces acceptable strategic actions during a simulated operation. Moreover, this approach played an unexpected but significant role in strengthening the underlying wargame simulation engine by discovering and exploiting weaknesses in its design. This highlights a future role for the use of RL to test and improve DoD systems during their development.

关键词： deep reinforcement learning command and control COA simulation engine

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learning Instrumental Variable from Data Fusion for Treatment Effect Estimation 37

Learning Instrumental Variable from Data Fusion for Treatmen...

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37th AAAI conference on artificial intelligence (AAAI) / 35th conference on Innovative applications of artificial intelligence / 13th Symposium on Educational Advances in artificial intelligence

作者： Wu, Anpeng Kuang, Kun Xiong, Ruoxuan Zhu, Minqing Liu, Yuxuan Li, Bo Liu, Furui Wang, Zhihua Wu, Fei Zhejiang Univ Dept Comp Sci & Technol Hangzhou Peoples R China Emory Univ Dept Quantitat Theory & Methods Atlanta GA USA Tsinghua Univ Sch Econ & Management Beijing Peoples R China Huawei Noahs Ark Iab Montreal PQ Canada Shanghai AI Lab Shanghai Peoples R China Zhejiang Univ Shanghai Inst Adv Study Hangzhou Peoples R China

ISBN: (纸本)9781577358800

The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism). Due to possibly omitted source labels and unmeasured confounders, traditional methods cannot estimate individual treatment assignment probability and infer treatment effect effectively. Therefore, we propose to reconstruct the source label and model it as a Group Instrumental Variable (Giv) to implement iv-based Regression for treatment effect estimation. In this paper, we conceptualize this line of thought and develop a unified framework (Meta-EM) to (1) map the raw data into a representation space to construct Linear Mixed Models for the assigned treatment variable;(2) estimate the distribution differences and model the Giv for the different treatment assignment mechanisms;and (3) adopt an alternating training strategy to iteratively optimize the representations and the joint distribution to model Giv for iv regression. Empirical results demonstrate the advantages of our Meta-EM compared with state-of-the-art methods. The project page with the code and the Supplementary materials is available at https://***/causal-machine-learning-lab/meta-em.

关键词： Data fusion

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Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable artificial intelligence 5

Exploiting Large Neuroimaging Datasets to Create Connectome-...

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

作者： Johnson, Erik C. Robinson, Brian S. Vallabha, Gautam K. Joyce, Justin Matelsky, Jordan K. Norman-Tenazas, Raphael Western, Isaac Villafane-Delgado, Marisel Cervantes, Martha Robinette, Michael S. Reddy, Arun V. Kitchell, Lindsey Rivlin, Patricia K. Reilly, Elizabeth P. Drenkow, Nathan Roos, Matthew J. Wang, I-Jeng Wester, Brock A. Gray-Roncal, William R. Hoffmann, Joan A. Johns Hopkins Univ Res & Exploratory Dev Dept Appl Phys Lab Laurel MD 20723 USA

ISBN: (数字)9781510661936

ISBN: (纸本)9781510661929;9781510661936

Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We have pursued multiple neuroscience-inspired AI efforts which may overcome these data inefficiencies, power inefficiencies, and lack of generalization. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches. We have pursued different approaches within this pipeline structure, including data-driven discovery in biological networks, augmenting existing computational neuroscience models, investigating the biological structure which enables behaviors, and modifying existing machine learning architectures with insight from biological structure. First, as a demonstration of data-driven discovery, the team has developed a technique for discovery of repeated subcircuits, or motifs. These were incorporated into a neural architecture search approach to evolve network architectures. Second, we have conducted analysis of the heading direction circuit in the fruit fly, which performs fusion of visual and angular velocity features, to explore augmenting existing computational models with new insight. Our team discovered a novel pattern of connectivity, implemented a new model, and demonstrated sensor fusion on a robotic platform. Third, the team analyzed circuitry for memory formation in the fruit fly connectome, enabling the design of a novel generative replay approach. This replay approach resulted in an over 20% accuracy improvement in an incremental class learning scenario, and also demonstrated the ability to utilize large neuroscience datasets to analyze the neural connectivity underlying behavior. Finally, the team has begun analysis of connectivity in mammalian cortex to explore potential improvements to transformer networks. These constraints in

关键词： machine learning continual learning SWaP Computational Neuroscience Connectomics

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A Methodology for Flattening the Command and Control Problem Space 4

A Methodology for Flattening the Command and Control Problem...

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

作者： Chiu, Carmen Delanovic, Adis Platts, Jill Loy, Alexa Beckus, Andre US Air Force Res Lab Informat Directorate Rome NY 13441 USA

ISBN: (纸本)9781510651036;9781510651029

Modeling Command and Control (C2) problems is notoriously complex across operating environments, systems, and missions. We seek to systematically decompose the complexities of C2, and present the foundational problems in a form that is accessible to the larger research community. Our first objective is to "flatten" core C2 problems by formulating and developing a surrogate environment that can foster new insights and evaluation. Using this framework, the second objective is to host a public challenge to acquire a diversity of solutions from outside the DoD community.

关键词： Problem Decomposition Multi-agent Systems Competitions machine learning artificial intelligence Reinforcement learning OpenAI Gym

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An AI-based framework for remote sensing supporting multi-domain operations 4

An AI-based framework for remote sensing supporting multi-do...

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

作者： Bennett, Kelly W. Robertson, James Army Res Lab US Army Combat Capabil Dev Command Sensors & Electron Devices Directorate Adelphi MD 20783 USA Clearhaven Technol LLC Severna Pk MD 21146 USA

ISBN: (纸本)9781510651036;9781510651029

In today's battlefield, military operations are conducted in all domains including air, land, maritime, space, and cyberspace in addition to the electromagnetic spectrum, and the information environment. The data, network connections, sensors, computer systems and other information technology infrastructures utilized must be protected as adversaries are competing for dominance across all domains. Cyberspace attacks can cause significant disruption to a nation's power grid and other critical infrastructure components resulting in collateral damage and even loss of life. With the continued shortage in Cybersecurity professionals, the government, universities and companies look to artificial intelligence machine learning (AI/ML) techniques to fill the gap. AI applications have been successfully applied to Department of defense (DoD) to detect and mitigate cyber-attacks. Moreover, cybercriminals, state actors and other adversaries have used AI to plan and launch cyber-attacks. The Army Research Laboratory is working to employ AI services using a novel framework that proactively detects and responds to attacks on a remote sensor network. The components of the framework include AI/ML for detecting malware and intrusion detection and prevention (IDP), cloud AI tools for sensor and system monitoring, log monitoring for detection of user behavior anomalies and sensor resiliency characteristics, and a visual mapping application to display sensor and target locations supporting geographically and social distancing of observers and parties of interest. Notifications are part of the framework to ensure system administrators are made aware of possible threats in near real-time. This paper provides a detailed description of components of this framework and the initial use-case for a small distributed remote sensing network application.

关键词： IoT Sensors artificial intelligence machine learning Intrusion Detection Multi-Domain Operations

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Evaluating the efficacy of different adversarial training strategies 5

Evaluating the efficacy of different adversarial training st...

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

作者： Sengphanith, Roger Marez, Diego Berk, Jane Parameswaran, Shibin Naval Informat Warfare Ctr Pacific 53560 Hull St San Diego CA 92152 USA Alutiiq 4247 Ponderosa Ave San Diego CA 92123 USA

ISBN: (数字)9781510661936

ISBN: (纸本)9781510661929;9781510661936

Adversarial training (AT) is considered the most effective strategy to defend a machine learning model against adversarial attacks. There are many different methods to perform AT, but the underlying principle is the same, namely, augment the training data with adversarial examples. In this work, we investigate the efficacy of four different adversarial example generation strategies on AT of a given classification model. The four methods represent different categories of attack and data. Specifically, two of the adversarial generation algorithms perform attacks in the pixel domain, while others operate in the latent space of the data. On the other hand, two of the methods generate adversarial data samples designed to be near the model decision boundaries, while the other two generate generic adversarial examples (not necessarily at the boundary). The adversarial examples from these methods are used to adversarially train models on MNIST and CIFAR10. In the absence of a good metric to measure robustness of a model, capturing the effect of AT using a single metric can be a challenge. Hence, we opt to evaluate the robustness improvements resulting of the adversarially trained model using a variety of empirical metrics introduced in the literature that measure local Lipshitz value of a network (CLEVER), smoothness of decision boundaries, robustness to adversarial perturbations and defense transferability.

关键词： adversarial training robustness metrics pixel domain attacks latent space attacks boundary smoothness boundary attacks generalizable ensemble adversarial training defense transferability

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RIN: Towards a Semantic Rigorous Interpretable artificial Immune System for Intrusion Detection 4th

RIN: Towards a Semantic Rigorous Interpretable Artificial Im...

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4th International conference on Soft Computing and its Engineering applications (icSoftComp)

作者： Zhou, Qianru Li, Rongzhen Xu, Lei Fu, Anmin Yang, Jian Gray, Alasdair J. G. McLaughlin, Stephen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland Heriot Watt Univ Dept Comp Sci Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9783031276088;9783031276095

The Internet is the most complex machine humankind has ever built, and how to defense it from intrusions is even more complex. With the ever increasing of new intrusions, intrusion detection task rely on artificial intelligence more and more. Interpretability and transparency of the machine learning model is the foundation of trust in AI-driven intrusion detection results. Current interpretation artificial intelligence technologies in intrusion detection are heuristic, which is neither accurate nor sufficient. This paper proposed a rigorous interpretable artificial intelligence driven intrusion detection approach, based on formal logic calculations. Details of rigorous interpretation calculation process for a decision tree model is presented. Prime implicant explanation for benign traffic flow are given in detail as rule for negative selection of the cyber immune system. Experiments are carried out in real-life traffic.

关键词： Interpretable machine learning Explainable artificial intelligence Prime implicant artificial immune system Intrusion detection

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2023 International conference on artificial intelligence and applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023 - Proceeding

2023 International Conference on Artificial Intelligence and...

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2023 International conference on artificial intelligence and applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

ISBN: (纸本)9781665456272

The proceedings contain 82 papers. The topics discussed include: hybrid model for email spam prediction using random forest for feature extraction;social engineering defender (***): human emotion factor based classification and defense against social engineering attacks;a survey paper on precision agriculture based intelligent system for plant leaf disease identification;digital twin of a musculoskeletal system;detecting copy move image forgery using a deep learning model: a review;fabric fault and extra thread detection using convolutional neural network;performance evaluation of classifiers for ECG signal analysis;software requirement classification using machine learning algorithms;a review on diagnosis of lung cancer and lung nodules in histopathological images using deep convolutional neural network;video prediction using recurrent neural network;a sustainable social media smart model for the deployment of learning skills in universities;use of artificial intelligence to avoid errors in referring a football match;and extracting and exploiting the behavior business process graph through transition-centric event-log data.

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A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images 12

A Distributed Inference System for Detecting Task-wise Singl...

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12th International Winter conference on Brain-Computer Interface (BCI)

作者： Kim, Sung-Jin Kwak, Heon-Gyu Han, Hyeon-Taek Lee, Dae-Hyeok Jeong, Ji-Hoon Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Brain & Cognit Engn Seoul South Korea Chungbuk Natl Univ Dept Comp Sci Cheongju South Korea

ISBN: (纸本)9798350309430

Brain-computer interface (BCI) has garnered the significant attention for their potential in various applications, with event-related potential (ERP) performing a considerable role in BCI systems. This paper introduces a novel Distributed Inference System tailored for detecting task-wise single-trial ERPs in a stream of satellite images. Unlike traditional methodologies that employ a single model for target detection, our system utilizes multiple models, each optimized for specific tasks, ensuring enhanced performance across varying image transition times and target onset times. Our experiments, conducted on four participants, employed two paradigms: the Normal paradigm and an AI paradigm with bounding boxes. Results indicate that our proposed system outperforms the conventional methods in both paradigms, achieving the highest F-beta scores. Furthermore, including bounding boxes in the AI paradigm significantly improved target recognition. This study underscores the potential of our Distributed Inference System in advancing the field of ERP detection in satellite image streams.

关键词： event-related potential target detection deep learning electroencephalogram satellite images

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