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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221-230 订阅

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ARL Battlespace: A Platform for Developing Novel AI for Complex Adversarial Reasoning in MDO 4

ARL Battlespace: A Platform for Developing Novel AI for Comp...

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

作者： Hung, Chou P. Hare, James Zachary Rinderspacher, B. Christopher Peregrim, Walter Kase, Sue Su, Simon Raglin, Adrienne Richardson, John T. US Army US Army Combat Capabil Dev Command Res Lab Aberdeen Proving Ground MD 21005 USA US Army US Army Combat Capabil Dev Command Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA NIST 100 Bur Dr Gaithersburg MD 20899 USA

ISBN: (纸本)9781510651036;9781510651029

A barrier to developing novel AI for complex reasoning is the lack of appropriate wargaming platforms for training and evaluating AIs in a multiplayer setting combining collaborative and adversarial reasoning under uncertainty with game theory and deception. An appropriate platform has several key requirements including flexible scenario design and exploration, extensibility across all five elements of Multi-Domain Operations (MDO), and capability for human-human and human-AI collaborative reasoning and data collection, to aid development of AI reasoning and the warrior-machine-like interface. Here, we describe the ARL Battlespace testbed which fulfills the above requirements for AI development, training and evaluation. ARL Battlespace is offered as an open source software platform (https://***/USArmyResearchLab/ARL_Battlespace). We present several example scenarios implemented in ARL Battlespace that illustrate different kinds of complex reasoning for AI development. We focus on 'gap' scenarios that simulate bridgehead and crossing tactics, and we highlight how they address key platform requirements including coordinated MDO actions, game theory and deception. We describe the process of reward shaping for these scenarios that will incentivize an agent to perform command and control (C2) tasks informed by human commanders' courses of action, as well as the key challenges that arise. The intuition presented will enable AI researchers to develop agents that will provide optimal policies for complex scenarios.

关键词： artificial intelligence Human-AI Collaboration Decision-Making Adaptation Wargaming

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Discovering Novelty via Transfer learning 1

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2nd International Semantic intelligence conference (ISIC)

作者： Islam, Shafkat Bhargava, Bharat K. Purdue Univ W Lafayette IN 47907 USA

ISBN: (数字)9789811971266

ISBN: (纸本)9789811971280;9789811971266;9789811971259

Modern-day intelligent systems, i.e., autonomous vehicles, smart manufacturing, etc., rely on distinct data-driven machine learning (ML) models for performing different safety-critical operations such as pedestrian detection, chemical plant control, among others. Since the performance of ML models depends on their generalizability capability, handling out-of-distribution data during the operational phase is of paramount importance for enhancing the adaptability of artificial intelligence (AI) systems. Hence, finding the root causes of novelty is critical for minimizing its impact on the performance of AI systems. However, detection of novelties is not trivial since each AI system operates in a specific environment or agent settings, whereas small changes cause the detection mechanism to adapt to different environmental constraints. For example, detecting novelties in identical intelligent navigation systems differs if the system is deployed in two different countries, though the operation of a navigation system is indistinguishable. In this paper, to reduce the dependability of novelty detectionmechanisms on environment's or agent's attributes, we propose transfer learning-based novelty detection mechanisms for inter-domain applications. In this regard, we analyze the importance of feature transformation to enhance novelty detection systems' transferability. The proposed detection mechanisms aim at augmenting AI systems with rapid responsiveness to novel surroundings, thus, making AI systems responsible and trustworthy. We conduct multiple experiments on state-of-the-art neural networks, i.e., ResNet50, Mobile-Net, and benchmark datasets, i.e., MNIST.

关键词： Novelty detection Transfer learning Open-world AI

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Distributing data throughout a MANET in a communications denied environment: Reinforcement learning and Game Theory approaches 4

Distributing data throughout a MANET in a communications den...

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

作者： Zwillinger, Dan Sierchio, Jen Gerken, Mark Clark, Emily BAE Syst Burlington MA 01803 USA BAE Syst Lakeland FL USA

ISBN: (纸本)9781510651036;9781510651029

This paper determines optimal strategies for transmitting messages in a mobile ad-hoc network (MANET) in a communications-limited and lossy communications environment. When an agent generates or receives a message it must decide to which neighbors, and how many times, that message is to be passed. The opposing goals are to (1) propagate all messages throughout the MANET quickly and (2) to minimize the total number of messages sent. We compare two optimized decision strategies for the agents, reinforcement learning (RL) and game theory (GT) methods. For the RL framework, each node in the MANET acts as a reinforcement learning agent who must learn optimal decisions for when to send messages to whom. For the GT framework, we create a game tree where the nodes encompass message knowledge and connectivity information, and the decision branches represent sending messages to neighbors. We solve the game using a Monte-Carlo Tree Search (MCTS) variation to determine the probability that a message is sent to a neighbor. Performance is assessed in terms of the total number of messages sent and the length of time for a given percentage of messages to reach a given percentage of nodes. Experiments with MANETs of varying size and connectivity are considered, and the RL and GT performance and training speed are compared. The decision strategies are domain agnostic and may be applied to ground, air, surface, sub-surface, or satellite networks.

关键词： game theory reinforcement learning communications lossy Hirsch index MANET MCTS drones

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Spike-Driven YOLO: Ultra Low-Power Object Detection with Neuromorphic Computing

Spike-Driven YOLO: Ultra Low-Power Object Detection with Neu...

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2024 IEEE High Performance Extreme Computing conference, HPEC 2024

作者： Barnell, Mark Raymond, Courtney Loomis, Lisa Vidal, Francesca Brown, Daniel Isereau, Darrek Air Force Research Laboratory RomeNY United States SRC Inc. Defense Systems & Solutions North SyracuseNY United States

ISBN: (纸本)9798350387131

The latest Intel neuromorphic processor, Loihi 2, provides a breakthrough in artificial intelligence (AI) for computing at the edge, where sensor information is collected. The computing architecture does this by leveraging computations at the transistor level in a fashion analogous to the human brain's biological neural networks (vs. a Von Neumann compute architecture). The Loihi 2 high performance, small form factor, and low-power consumption make it well-suited for a wide-range of real-time, deep learning applications such as target classification, object detection, and more. Our technical approach and findings support extreme computing needs for the internet of things (IoT) and various ground and airborne platforms' applications. The recently released Loihi 2 ecosystem and the thorough research study completed on this effort were combined to accelerate development, optimization, and demonstration of a new concept of operation for machine learning at the edge. This 2024 research included training and testing Spike-driven YOLO models on data from various sensors. Our concept uses representative sensor data to detect and classify targets of interest through a combination of image processing techniques and machine learning. Importantly, our technical approach allowed us to rapidly train and evaluate the performance of several models for benchmarking against current state-of-the-art algorithms - w/mean average precision > 93% in some cases. The use of Intel's latest Lava framework demonstrates the art-of-the-possible in edge computing by demonstrating capabilities on several sensor platforms with wide extensibility to other domains that can use this neuromorphic-computing hardware. In summary, this research included the use of new computing frameworks, processing algorithms, and a unique concept of operation. © 2024 IEEE.

关键词： Digital storage

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Bridging the gap from Academia to Application: Rapid Algorithm Design & Deployment for artificial intelligence (RADD-AI) 3

Bridging the gap from Academia to Application: Rapid Algorit...

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conference on artificial intelligence and machine learning in defense applications III Held as Part of SPIE Security+Defence conference

作者： Haley, Joshua Tucker, Jonathan Kessler, Brandon Fish, Trisha Elbit Syst Amer Adv Capabil Ctr 12600 Challenger Pkwy Orlando FL 32826 USA

ISBN: (纸本)9781510645851;9781510645844

machine learning (ML) and artificial intelligence (AI) have led to an increase in automation potential within defense applications such as border protection, compound security, and surveillance applications. Recent academic advances in deep learning aided computer vision have yielded impressive results on object detection, and recognition, necessary capabilities to increase automation in defense applications. These advances are often open-sourced, enabling the opportunistic integration of state-of-the-art (SOTA) algorithms into real systems. However, these academic achievements do not translate easily to engineered systems. Academics often are looking at a single capability with metrics such as accuracy or F1 score without consideration of system-level performance and how these algorithms must integrate or what level of computational performance is required. An engineered system is developed as a system of algorithms that must work in conjunction with each other with deployment constraints. This paper describes a system, called Rapid Algorithm Design & Deployment for artificial intelligence (RADD-AI (TM)), developed to enable the rapid development of systems of algorithms incorporating these advances in a modular fashion using networked Application Programming Interfaces (APIs). The inherent modularity mitigates the assumption of monolithic integration within a single ecosystem that creates vendor lock. This monolith assumption does not account for the reality that frameworks are usually targeted toward different types of problems and learning vs inference capabilities. RADD-AI makes no such assumption. If a different framework solves subsets of the system more eloquently, they can be integrated into the larger pipeline. RADD-AI enables the integration of state-of-the-art ML into deployed systems while also supporting the necessary ML engineering tasks, such as transfer learning, to operationalize academic achievements. To motivate how RADD-AI enables applications o

关键词： Applied machine learning Deep learning Surveillance Border Protection

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Adversarial Attack and defense on Natural Language Processing in Deep learning: A Survey and Perspective 4th

Adversarial Attack and Defense on Natural Language Processin...

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4th International conference on machine learning for Cyber Security, ML4CS 2022

作者： Dong, Huoyuan Dong, Jialiang Yuan, Shuai Guan, Zhitao School of Control and Computer Engineering North China Electric Power University Beijing102206 China Brock University St. CatharinesON Canada

ISBN: (纸本)9783031200953

Natural language processing (NLP) presently has become a new paradigm and enables a variety of applications such as text classification, information retrieval, and natural language generation by leveraging deep learning techniques. However, recent studies have shown that the NLP models based on deep neural network are susceptible to maliciously designed adversarial examples. Therefore, the main challenges lie in improving the robustness and ensuring the security of the system. In this paper, we first introduce common NLP tasks and quality measures on adversarial example. Next, we present a comprehensive review on literature in terms of both adversarial attack and defense methods, based upon the granularity and type. Our work is also the first of its kind to provide a brief overview of the adversarial examples on Chinese texts as a result of the language difference. Finally, we summarize this study by providing directions for future research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Training data selection for event classification in a highly variable environment 4

Training data selection for event classification in a highly...

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

作者： Iyer, Anand Flynn, Garrison Parikh, Nidhi Archer, Daniel Karnowski, Thomas Maceira, Monica Marcillo, Omar Nicholson, Andrew Ray, Will Wetherington, Randall Willis, Michael Los Alamos Natl Lab POB 1663 Los Alamos NM 87545 USA Oak Ridge Natl Lab 1 Bethel Valley Rd Oak Ridge TN 37830 USA

ISBN: (纸本)9781510651036;9781510651029

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

关键词： Jaccard dynamic time warping k-means similitude supervised learning unsupervised learning

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Autonomous Driving Model defense Study on Hijacking Adversarial Attack 31st

Autonomous Driving Model Defense Study on Hijacking Adversar...

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31st International conference on artificial Neural Networks (ICANN)

作者： Shibly, Kabid Hassan Hossain, Md Delwar Inoue, Hiroyuki Taenaka, Yuzo Kadobayashi, Youki Nara Inst Sci & Technol Div Informat Sci Ikoma Nara Japan Kyoto Sangyo Univ Fac Informat Sci & Engn Kyoto Japan

ISBN: (纸本)9783031159374;9783031159367

Modern Connected and Autonomous Vehicle (CAV) has several innovative embedded devices aimed at enhancing driving comfort. However, the most advanced embedded equipment did not address security features at the time of development;as a result, it enlarged the attack surfaces of CAVs. Consequently, an attacker can easily compromise the CAV system even with limited hacking capabilities. In case the CAV system is compromised, that can raise significant concerns about safe driving and human safety. We know that the CAV driving model is mainly based on artificial intelligence and deep learning algorithms. Despite deep learning methods being successfully used in Connected and Autonomous Vehicles, slight perturbations of the driving model can be crucial and mislead the driving system. Henceforth, a robust defense mechanism is imperative for safe driving as a countermeasure. This research proposed an effective defense mechanism based on an autoencoder. We study FGSM and AdvGAN for hijacking adversarial attack injection, and simultaneously we use the Nvidia Dave-2 Driving model for our experimentation. Our experiment results demonstrate that the proposed solution outperformed compared with the recent and most typically used defensive strategies;it achieves 93.8% and 91.2% defense success rate in theWhitebox setup and 71.3% and 63.1% defense success rate in the Blackbox setup by FGSM and AdvGAN, respectively.

关键词： Connected and Autonomous Vehicle Hijacking Adversarial Attack defense study Automotive security

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StreamAI: Dealing with Challenges of Continual learning Systems for Serving AI in Production 23

StreamAI: Dealing with Challenges of Continual Learning Syst...

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IEEE/ACM 45th International conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Barry, Mariam Bifet, Albert Billy, Jean-Luc BNP Paribas Innovat Lab IT Grp Paris France Inst Polytech Paris LTCI Palaiseau France Univ Waikato AI Inst Hamilton New Zealand Freelance Analyt & Syst Architecture Data & IT Consultant Puteaux La Defense France

ISBN: (纸本)9798350300376

How to build, deploy, update & maintain dynamic models which continuously learn from streaming data? This paper covers the industrialization aspects of these questions in production systems. In today's fast-changing environments, organizations are faced with the crucial challenge of predictive analytics in online fashion from big data and deploying artificial intelligence models at scale. applications include cyber-security, cloud infrastructure, social networks and financial markets. Online learning models that learn continuously and adapt to the potentially evolving data distributions have demonstrated efficiency for big data stream learning. However, the challenges of deploying and maintaining such models in production (serving) have stalled their adoption. In this paper, we first categorize key challenges faced by the R&D, MLOps and governance teams for deploying automated and self-training AI models in production. Next, we highlight the challenges related to stream-based online machine-learning systems. Finally, we propose StreamAI, a technology-agnostic architecture to deal with the MLOps journey (learning, serving, maintenance) of online models in production. We conclude with open research questions for AI, MLOps and software engineering to bridge the gaps between industry needs and research-oriented development.

关键词： StreamAI AI Challenges Production Serving Online learning MLOps Streaming data Industry Banking

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Federated learning for Distributed Spectrum Sensing in NextG Communication Networks 4

Federated Learning for Distributed Spectrum Sensing in NextG...

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

作者： Shi, Yi Sagduyu, Yalin E. Erpek, Tugba Virginia Tech Commonwealth Cyber Initiat Arlington VA 22203 USA Virginia Tech Natl Secur Inst Arlington VA USA

ISBN: (纸本)9781510651036;9781510651029

NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault diagnostics, user equipment identification, and authentication. For that purpose, a network of wireless sensors is needed to monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network accordingly to classify signals. To improve the accuracy, individual sensors may exchange sensing data or sensor results with each other or with a fusion center (such as in cooperative spectrum sensing). In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a deep neural network for signal identification. In distributed federated learning, each sensor broadcasts its trained model to its neighbors, collects the deep neural network models from its neighbors, and aggregates them to initialize its own model for the next round of training. Without exchanging any spectrum data, this process is repeated over time such that a common deep neural network is built across the network while preserving the privacy associated with signals collected at different locations. Signal classification accuracy and convergence time are evaluated for different network topologies (including line, star, ring, grid, and random networks) and packet loss events. In addition, the reduction of communication overhead and energy consumption is considered with random participation of sensors in model updates. The results show the feasibility of extending cooperative spectrum sensing over a general multi-hop wireless network through federated learning and indicate the robustness of federated learning to wireless network effects, thereby sustaining high accuracy with low communication overhead and energy consumpti

关键词： Federated learning spectrum monitoring cooperative spectrum sensing signal classification distributed algorithm wireless network

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