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

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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos 38

Retrospective for the Dynamic Sensorium Competition for pred...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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artificial intelligence in Cybersecurity: Enhancing Automated defense Mechanisms to Combat Sophisticated Cyber Threats and Guarantee Digital Resilience

Artificial Intelligence in Cybersecurity: Enhancing Automate...

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Global conference for Advancement in Technology (GCAT)

作者： V. S. Prakash P. Murugesan P. Poongothai Shivakumar B N P. Vijayakumar P. J. Shriidhar Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam Department of Mathematics CMR Institute of Technology Bengaluru Department of Computer Applications Kongu Engineering College Perundurai Erode Department of CSE Vinayaka Mission’s Kirupananda Variyar Engineering College VMRF(DU) Salem

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity approaches, with a growing emphasis on leveraging artificial intelligence (AI) to bolster automated defense mechanisms. This research delves into the integration of AI technologies within cybersecurity frameworks, exploring their potential to enhance digital resilience against sophisticated cyber threats. By examining the current landscape of cyber attacks and the limitations of traditional defense strategies, the study underscores the need for intelligent, adaptive systems capable of learning from vast datasets and responding in real-time to emerging threats. The research focuses on the development and evaluation of AI-driven defense mechanisms, including machine learning algorithms for anomaly detection, predictive analytics for threat forecasting, and natural language processing for cyber threat intelligence. Through a series of controlled experiments and case studies, the effectiveness of these AI-enhanced systems is assessed in terms of their accuracy, speed, and adaptability to new attack vectors. Findings indicate that AI-augmented cybersecurity solutions can significantly improve the detection and mitigation of advanced persistent threats, zero-day exploits, and other complex cyber attacks. Moreover, the research highlights the importance of ethical considerations and the responsible use of AI in cybersecurity to prevent the misuse of powerful AI tools and ensure privacy and fairness. The study concludes with recommendations for the strategic implementation of AI in cybersecurity, emphasizing the need for a holistic approach that combines human expertise with AI capabilities to achieve robust digital resilience. By fostering a collaborative ecosystem between cybersecurity professionals, AI developers, and policymakers, this research aims to contribute to the ongoing effort to safeguard digital infrastructure against the ever-evolving landscape of cyber threats.

关键词： Ethics Prevention and mitigation Ecosystems Natural language processing Threat assessment Real-time systems artificial intelligence Computer security Cyberattack Resilience

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Federated Clustering defense: Mitigating Data Exposure in Collaborative learning

Federated Clustering Defense: Mitigating Data Exposure in Co...

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2025 International conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Radhakrishnan, Anuja Krishna, Sathya Bama Sathyabhama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331507541

The development of intelligent industrial applications is being driven globally by advancements in the next generation of Internet of Things (IoT) technologies. At the same time, artificial intelligence (AI) technologies like machine learning and deep learning are becoming more widely used. Conventional machine learning models primarily rely on large data sets. However, the vast data sets generated by network-edge devices are expensive, inefficient to acquire and handle, and they pose major privacy hazards. Federated learning (FL) is a novel paradigm for statistical model training on distributed edge networks that allows data to take part in the training of federated models without being localized. Traditional machine learning issues with low data usage, data privacy, and information security brought on by data isolation can be resolved with this method. We propose a novel Federated Clustering with Differential Privacy (FedCDP) algorithm designed to efficiently model user behavior in an unsupervised manner. This algorithm focuses on optimizing computation and storage requirements while minimizing data leakage through robust differential privacy mechanisms. © 2025 IEEE.

关键词： Deep learning

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MCNN-Based Model for Rotating Equipment Fault Diagnosis

MCNN-Based Model for Rotating Equipment Fault Diagnosis

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2024 International conference on Guidance, Navigation and Control

作者： Xu, Zhenyi Wu, Qilai Wei, Xiaolong Liu, Binkun Guo, Yanming Kang, Yu Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Peoples R China Univ Sci & Technol China Anhui Prov Key Lab Intelligent Low Carbon Informa Hefei 230027 Peoples R China China Railway Engn Equipment Grp Co Ltd Zhengzhou 450000 Peoples R China Natl Univ Def Technol Changsha 410073 Peoples R China

ISBN: (纸本)9789819622429;9789819622405;9789819622399

Rotating machinery and equipment have a wide range of applications in industry, and they play an extremely important role in industrial production, therefore, fault diagnosis of rotating machinery and equipment is indispensable in industrial manufacturing systems. Numerous fault diagnosis techniques leveraging machine learning and deep learning have emerged, yielding promising results. The paper introduces a novel fault diagnosis approach, integrating a hybrid expert model with convolutional neural networks. Experiments show that this method has good results in diagnosing rotating equipment faults.

关键词： 1D-CNN mixture of experts fault diagnosis

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Recent Advances in Adversarial machine learning: Status, Challenges and Perspectives

Recent Advances in Adversarial Machine Learning: Status, Cha...

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

作者： Rawal, Atul Rawat, Danda B. Sadler, Brian Howard Univ Ctr Excellence AI ML CoE AI ML Dept Elect Engn & Comp Sci Washington DC 20059 USA US Army Res Lab Adelphi MD USA

ISBN: (数字)9781510643307

ISBN: (纸本)9781510643307

The recent advances in machine learning (ML) and artificial intelligence (AI) have resulted in widespread application of data-driven learning algorithms. Rapid growth of AI/ML and their penetration within a plethora of civilian and military applications, while successful, has also opened new vulnerabilities. It is now clear that ML algorithms for AI systems are viable targets for malicious attacks. Therefore, there is a pressing need for better understanding of adversarial attacks against ML models, in order to secure them against such malicious attacks. In this paper, we present a survey of adversarial machine learning and some associated countermeasures. We also present a taxonomy of ML/AI system attacks that follow the same properties and characteristics, allowing them to be linked with different defensive approaches. A taxonomy is given for both attack and defense, and attacks proposed in the literature are categorized according to our taxonomy.

关键词： Adversarial machine learning machine learning Cybersecurity Attack Classification Attack Taxonomy

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Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games 35

Bandit Linear Optimization for Sequential Decision Making an...

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35th AAAI conference on artificial intelligence / 33rd conference on Innovative applications of artificial intelligence / 11th Symposium on Educational Advances in artificial intelligence

作者： Farina, Gabriele Schmucker, Robin Sandholm, Tuomas Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Strateg Machine Inc Pittsburgh PA USA Strategy Robot Inc Pittsburgh PA USA Optimized Markets Inc Pittsburgh PA USA

ISBN: (纸本)9781577358664

Tree-form sequential decision making (TFSDM) extends classical one-shot decision making by modeling tree-form interactions between an agent and a potentially adversarial environment. It captures the online decision-making problems that each player faces in an extensive-form game, as well as Markov decision processes and partially-observable Markov decision processes where the agent conditions on observed history. Over the past decade, there has been considerable effort into designing online optimization methods for TFSDM. Virtually all of that work has been in the full feedback setting, where the agent has access to counterfactuals, that is, information on what would have happened had the agent chosen a different action at any decision node. Little is known about the bandit setting, where that assumption is reversed (no counterfactual information is available), despite this latter setting being well understood for almost 20 years in one-shot decision making. In this paper, we give the first algorithm for the bandit linear optimization problem for TFSDM that offers both (i) linear-time iterations (in the size of the decision tree) and (ii) O (root T) cumulative regret in expectation compared to any fixed strategy, at all times T. This is made possible by new results that we derive, which may have independent uses as well: 1) geometry of the dilated entropy regularizer, 2) autocorrelation matrix of the natural sampling scheme for sequence-form strategies, 3) construction of an unbiased estimator for linear losses for sequence-form strategies, and 4) a refined regret analysis for mirror descent when using the dilated entropy regularizer.

关键词： Markov processes

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Transfer learning and Domain Adaptation in Hyperspectral Image Processing: an Overview 2

Transfer Learning and Domain Adaptation in Hyperspectral Ima...

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2nd International conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Pokale, Komal Chaudhri, Shiv Nath Department of Artificial Intelligence and Data Science Maharashtra India Department of Computer Science and Design Maharashtra India

ISBN: (纸本)9798331505745

Fine-tuning a pre-trained model is a type of transfer learning: a pre-trained model that works on a large dataset is transferred onto a particular task or a smaller dataset, such as a hyperspectral image. The efficacy of such a technique is most visible in hyperspectral imaging since the data is usually too high-dimensional with a very scarce number of available annotated datasets. By using the pre-trained model, fine-tuning enables spatial and spectral features to be captured with lower computational overheads than fully training models. Domain adaptation approaches, including feature alignment and adversarial training, are also adapted to overcome limitations caused by the domain shift phenomenon due to the variability of the sensor, environmental changes, and temporal changes. Hyperspectral imaging is applied in diverse fields, including agriculture, environmental monitoring, mineral exploration, defense, medical diagnostics, and food quality assessment. Such applications are unique in the sense that HSI can integrate spatial and spectral information to achieve detailed material analysis. Advanced learning techniques will enhance the effectiveness of HSI models in real-world scenarios. © 2025 IEEE.

关键词： Photointerpretation

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Differentially Private Synthetic Adversarial Network Traffic Generation Based on Tabular Diffusion Processes

Differentially Private Synthetic Adversarial Network Traffic...

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7th International conference on artificial intelligence in Information and Communication, ICAIIC 2025

作者： Kang, Minjae Cho, Gunhee Yun, Sungju Lee, Yeonjoon Hanyang University Major in Bio-Artificial Intelligence Department of Computer Science and Engineering Ansan15588 Korea Republic of Hanyang University Department of Intelligent Information Convergence Engineering Ansan15588 Korea Republic of College of Computing Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9798331507022

IDS is becoming increasingly important as networks become more vulnerable to a variety of attacks. However, traditional ML and DL-based IDS have limited accuracy and false positive rates due to the lack of open adversarial network datasets and vendors' concerns regarding potential data leakage during the training process. To address these challenges, VAE and GAN-based network packet generation models have emerged, but they still face issues with low data fidelity. In this context, TabDDPM is a suitable option for generating network packets, as it surpasses VAE and GAN in accurately capturing data distribution characteristics and effectively learning the inherent structure of network packet formats. Despite its potential, research applying TabDDPM to network packet generation has not yet been explored. In this paper, we propose and evaluate DP-NetDDPM, a novel framework that enables adversarial network traffic generation while preserving both fidelity and privacy guarantees. Specifically, we examine the use of diffusion to generate adversarial network packet datasets and compare its performance with baseline models. Our framework focuses on three key criteria: data fidelity, adaptability for machine learning applications, and data privacy. The results show that DP-NetDDPM surpasses traditional models in both fidelity and adaptability, achieving a notable 72% improvement in fidelity and a 45% enhancement in adaptability over baselines. © 2025 IEEE.

关键词： Interdiffusion (solids)

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Simulated SAR for ATR pre-training 3

Simulated SAR for ATR pre-training

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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： Willis, Christopher J. BAE Syst Appl Intelligence Labs Chelmsford CM2 8HN Essex England

ISBN: (数字)9781510645851

ISBN: (纸本)9781510645851;9781510645844

Deep learning has revolutionized the performance of many computer vision systems in recent years. In particular deep convolutional neural networks have demonstrated ground-breaking performance in object classification from imagery. However, these techniques typically require sizeable volumes of training data in order to derive the large number of parameters that these types of approaches must learn. In many situations sufficient volumes of imagery for the object types of interest are unavailable. One solution to this problem is to use an initial training set which has properties similar to those of the objects of interest, but is available with a large number of labelled examples. These can be used for initial network training and the resulting partially-learned solution may subsequently be tuned using a smaller sample of the actual target objects. This type of approach, transfer learning, has shown considerable success in conventional imaging domains. Unfortunately, for Synthetic Aperture Radar imaging sensors, large volumes of labelled training samples of any type are hard to come by. The challenge is exacerbated when variations in imaging geometry and sensor configuration are taken into account. This paper examines the use of simulated SAR imagery in pre-training a deep neural network. The simulated imagery is generated using a straightforward process which has the capability to generate sufficient volumes of training exemplars in a modest amount of time. The samples generated are used to train a deep neural network which is then retrained using a comparatively small volume of MSTAR SAR imagery. The value of such a pre-training process is assessed using techniques to explain model performance by visualization. The assessment highlights some interesting aspects of the MSTAR SAR image set with regard to bias.

关键词： Synthetic Aperture Radar SAR Automatic Target Detection Recognition & Identification ATDRI image synthesis image simulation target modelling machine learning performance assessment MSTAR

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Proposed V-Model for Verification, Validation, and Safety Activities for artificial intelligence

Proposed V-Model for Verification, Validation, and Safety Ac...

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Assured Autonomy (ICAA), International conference on

作者： Benjamin Schumeg Franklin Marotta Benjamin Werner Quality Engineering & System Assurance Combat Capabilities Development Command Armaments Center Picatinny Arsenal NJ USA Aberdeen Test Center Army Test and Evaluation Command Aberdeen Proving Ground MD USA

The Department of defense strives to continuously develop and acquire systems that utilize novel technologies and methods for implementing new and complex mission requirements. One of the identified technologies with high impact and benefit to the Warfighter is the integration of artificial intelligence (AI) and machine learning (ML). Current AI models and methods have added layers of complexity to achieving a satisfactory level of verification and validation (V&V), possibly resulting in elevated risks with fewer mitigations. Regardless of the type of applications for AI technology within the DoD, the technology implementation must be verified, validated, and ultimately any residual risks accepted. This paper looks to introduce a V-model concept for artificial intelligence and machine learning, to include an outline of proposed activities that the development, assurance, and evaluation communities can follow. By following this proposed assessment, these organizations can increase their understanding and knowledge of the system, mitigating risk and helping to achieve justified confidence.

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