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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151-160 订阅

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Hypernetworks Build Implicit Neural Representations of Sounds

Hypernetworks Build Implicit Neural Representations of Sound...

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5th International Workshop on learning with Imbalanced Domains - Theory and applications / European conference on machine learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Szatkowski, Filip Piczak, Karol J. Spurek, Przemyslaw Tabor, Jacek Trzcinski, Tomasz Warsaw Univ Technol Warsaw Poland Jagiellonian Univ Fac Math & Comp Sci Krakow Poland IDEAS NCBR Warsaw Poland Tooploox Wroclaw Poland UES Ltd Krakow Poland

ISBN: (纸本)9783031434204;9783031434211

Implicit Neural Representations (INRs) are nowadays used to represent multimedia signals across various real-life applications, including image super-resolution, image compression, or 3D rendering. Existing methods that leverage INRs are predominantly focused on visual data, as their application to other modalities, such as audio, is nontrivial due to the inductive biases present in architectural attributes of image-based INR models. To address this limitation, we introduce HyperSound, the first meta-learning approach to produce INRs for audio samples that leverages hypernetworks to generalize beyond samples observed in training. Our approach reconstructs audio samples with quality comparable to other state-of-the-art models and provides a viable alternative to contemporary sound representations used in deep neural networks for audio processing, such as spectrograms. Our code is publicly available at https://***/WUT-AI/hypersound.

关键词： machine learning Hypernetworks Audio Processing Implicit Neural Representations

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Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments 2nd

Explaining AI Decisions: Towards Achieving Human-Centered Ex...

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2nd World conference on Explainable artificial intelligence (xAI)

作者： Shajalal, Md Boden, Alexander Stevens, Gunnar Du, Delong Kern, Dean-Robin Fraunhofer Inst Appl Informat Technol FIT St Augustin Germany Univ Siegen Siegen Germany Bonn Rhein Sieg Univ Appl Sci St Augustin Germany

ISBN: (纸本)9783031638022;9783031638039

Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models' decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.

关键词： Explainable AI (XAI) Human-Centered XAI Demand Forecasting machine learning Smart Home

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LFENav: LLM-Based Frontiers Exploration for Visual Semantic Navigation 20th

LFENav: LLM-Based Frontiers Exploration for Visual Semantic ...

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

作者： Shi, Yuhong Liu, Jianyi Zheng, Xinhu Natl Key Lab Human Machine Hybrid Augmented Intel Xian 710049 Shaanxi Peoples R China Natl Engn Res Ctr Visual Informat & Applicat Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China Hong Kong Univ Sci & Technol Guangzhou Intelligent Transportat Thrust Guangzhou Peoples R China

ISBN: (纸本)9783031632259;9783031632235;9783031632228

Robot navigation in an unknown environment is a challenge task, due to the lack of spatial awareness and semantic understanding of the environment. Previous works mostly rely on learning-based approaches, which need large amount of training data and lack of generalization ability. The emergence of Large Language Models (LLMs) provides a new way for semantic understanding. This paper proposes a method of LLM-based Frontiers Exploration for visual semantic Navigation (LFENav), which leverages the rich semantic prior knowledge of LLMs to find next subgoals with the input natural language instruction. Firstly, the semantic map is incrementally constructed and the frontiers are redefine from the observed RGB-D images. A prompt mechanism is designed to embody the Chain-of-Thought (CoT) merit of LLMs. We use geometric costs to compensate the information gap of LLMs in understanding the spatial layout of scenes. Based above, a novel exploration policy is designed by integrating LLM scores and geometric costs to select better frontiers worthy of exploring. Experiments on Habitat-Matterport 3D dataset shows that the success rate of this method is up to 0.638, which is the best performance compared with the existing methods.

关键词： visual sematic navigation frontier exploration large language models

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Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement learning

Eigensubspace of Temporal-Difference Dynamics and How It Imp...

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作者： He, Qiang Zhou, Tianyi Fang, Meng Maghsudi, Setareh Univ Tubingen Tubingen Germany Univ Maryland College Pk MD 20742 USA Univ Liverpool Liverpool Merseyside England

ISBN: (纸本)9783031434204;9783031434211

We propose a novel value approximation method, namely "Eigensubspace Regularized Critic (ERC)" for deep reinforcement learning (RL). ERC is motivated by an analysis of the dynamics of Q-value approximation error in the Temporal-Difference (TD) method, which follows a path defined by the 1-eigensubspace of the transition kernel associated with the Markov Decision Process (MDP). It reveals a fundamental property of TD learning that has remained unused in previous deep RL approaches. In ERC, we propose a regularizer that guides the approximation error tending towards the 1-eigensubspace, resulting in a more efficient and stable path of value approximation. Moreover, we theoretically prove the convergence of the ERC method. Besides, theoretical analysis and experiments demonstrate that ERC effectively reduces the variance of value functions. Among 26 tasks in the DMControl benchmark, ERC outperforms state-of-the-art methods for 20. Besides, it shows significant advantages in Q-value approximation and variance reduction. Our code is available at https://***/view/erc-ecml23/.

关键词： Reinforcement learning

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Study of Fidelity of Synthetic Imagery for artificial intelligence (AI) and machine learning (ML) Problems 4

Study of Fidelity of Synthetic Imagery for Artificial Intell...

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

作者： Young, S. Susan Agarwal, Sanjeev Reynolds, Joseph P. You, Suya US Army DEVCOM Army Res Lab Adelphi MD 20783 USA US Army DEVCOM C5ISR Ctr Ft Belvoir VA 22060 USA

ISBN: (纸本)9781510651036;9781510651029

Many studies show that using synthetic data or mixed synthetic and real data might improve machine learning (ML) performance but it is difficult to draw generalizable conclusions. A contribution to this problem is the fact that the synthetic data from most vendors are improperly filtered and contain aliased (wrapped-around) high frequency information which they should not possess. Most vendors use spatial-domain, low-pass FIR filters to generate synthetic images at various ranges. Unfortunately, these FIR filters aim for interpolation with a desired frequency domain cutoff and spatial spacing (general non-integer scale factor and/or decimation). Hence, instead of a sharp cutoff at the desired low-band, they produce aliased data. This erroneous information in synthetic imagery could actually mislead an ML algorithm. In addition, most synthetic images do not account for a camera's MTF (Modulation Transfer Function). A Fourier-based filtering can easily incorporate any MTF in the frequency domain based on Rayleigh resolution theory, properties of a camera lens and digital image properties. The spectral properties of images which are acquired with real sensors are studied and compared them with synthetic images from several vendors. We have also developed a metric that exhibits that the camera system's MTF shows the same spectral property for real images at different ranges. The metric can help us to determine if a synthetic image generation engine violates this property and, hence, produces erroneous information.

关键词： Synthetic Imagery artificial intelligence machine learning

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Exploring Bias and Fairness in artificial intelligence and machine learning Algorithms 4

Exploring Bias and Fairness in Artificial Intelligence and M...

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

作者： Khakurel, Utsab Abdelmoumin, Ghada Bajracharya, Aakriti Rawat, Danda B. Howard Univ Washington DC 20059 USA

ISBN: (纸本)9781510651036;9781510651029

machine learning algorithms are being widely used in different fields such as image recognition, speech recognition, traffic and weather prediction, recommendations, spam filtering, self-driving cars, stock market prediction, medical diagnosis, and more. The ability of machines to feed in years of data and predict the outcome has helped humans in unimaginable ways. machine learning has automated half of human work requiring very little human intervention and saving time and energy. However, sometimes individuals end up paying the price and falling victim to the unfair and biased outcome of machine learning algorithms. machines learn through data what they are provided with, but the data that machines learn from does not come free from human biases. Human biases based on race, sex, ethnicity, skin color, and other sensitive attributes are reflected in the dataset which, when fed to the machine, results in a similar biased prediction. The years of data represent the bias that has been present in society, and the machine learning model simply mimics the pattern. There has been constant research and experiments being done on how to prevent these biases from reflecting on the prediction. In this paper, we will investigate if there is any bias present in the benchmark Statlog "Australian Credit Approval" dataset and take necessary measures to mitigate the bias present in the data. The paper shows how the AIF360 tool can identify and mitigate bias in the data and eventually in the learning algorithms.

关键词： machine learning bias detection bias minimization predictive models equitable AI

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Intelligent Cyber defense: Leveraging LLMs for Real-Time Threat Detection and Analysis 21st

Intelligent Cyber Defense: Leveraging LLMs for Real-Time Thr...

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21st European, Mediterranean, and Middle Eastern conference on Information Systems

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Evripidou, Salomi Neapolis Univ Pafos Dept Comp Sci Intelligent Syst Lab Pafos Cyprus Cyprus Univ Technol Elect Engn & Comp Engn & Informat Limassol Cyprus Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Kyiv Ukraine

ISBN: (纸本)9783031813214;9783031813221

This paper reviews the application of Large Language Models (LLMs) in addressing the growing sophistication of cybersecurity threats. By evaluating numerous studies, we delineate the role of LLMs in improving the detection, analysis, and response to cyber threats in real-time. Our findings demonstrate that LLMs significantly enhance the efficiency and accuracy of cybersecurity measures compared to traditional models. However, their deployment in cybersecurity frameworks involves critical considerations around ethics, privacy, and security. This review provides a succinct overview of LLMs' current capabilities and practical implementations within the cybersecurity landscape, identifies research gaps, and suggests future research directions. The insights presented here highlight the vital contribution of LLMs to the advancement of cybersecurity solutions and adapting to evolving threat environments.

关键词： Large Language Models (LLMs) Real-Time Threat Detection Cybersecurity artificial intelligence in Security machine learning applications Pattern Recognition in Cybersecurity Natural Language Processing (NLP) AI-Powered Security Systems Cyber Threat Analysis Advanced Persistent Threat (APT)

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ACM KDD AI4Cyber/MLHat: Workshop on AI-enabled Cybersecurity Analytics and Deployable defense 22

ACM KDD AI4Cyber/MLHat: Workshop on AI-enabled Cybersecurity...

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28th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KKD)

作者： Samtani, Sagar Wang, Gang Ahmadzadeh, Ali Ciptadi, Arridhana Yang, Shanchieh Chen, Hsinchun Indiana Univ Bloomington IN 47401 USA Univ Illinois Urbana IL USA Blue Hexagon Sunnyvale CA USA TruEra Redwood City CA USA RIT Henrietta NY USA Univ Arizona Tucson AZ USA

ISBN: (纸本)9781450393850

Federal funding agencies and industry entities are seeking innovative approaches to address the ever-growing cybersecurity crisis. Increasingly, numerous cybersecurity thought leaders are indicating that artificial intelligence (AI)-enabled analytics can help tackle key cybersecurity tasks and deploy defenses. This half-day workshop, co-located with ACM KDD, sought to attain significant research contributions to various aspects of AI-enabled analytics for cybersecurity applications and deployable defense solutions from academics and practitioners. This workshop was a joint workshop of the 2021 AI-enabled Cybersecurity Analytics and 2021 International Workshop on Deployable machine learning for Security defense. As such, we developed an interdisciplinary Program Committee with significant experience in various aspects of AI, cybersecurity, and/or deployable defense.

关键词： Cybersecurity artificial intelligence analytics deployable defense machine learning

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Emergent Behaviors in Multi-Agent Target Acquisition 4

Emergent Behaviors in Multi-Agent Target Acquisition

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

作者： Sharma, Piyush K. Zaroukian, Erin Asher, Derrik E. Howell, Bryson DEVCOM Army Res Lab Adelphi MD 20783 USA Univ Tennessee Knoxville TN 37996 USA

ISBN: (纸本)9781510651036;9781510651029

Only limited studies and superficial evaluations are available on agents' behaviors and roles within a Multi-Agent System (MAS). We simulate a MAS using Reinforcement learning (RL) in a pursuit-evasion (a.k.a. predatorprey pursuit) game, which shares task goals with target acquisition, and we create different adversarial scenarios by replacing RL-trained pursuers' policies with two distinct (non-RL) analytical strategies. Using heatmaps of agents' positions (state-space variable) over time, we are able to categorize an RL-trained evader's behaviors. The novelty of our approach entails the creation of an influential feature set that reveals underlying data regularities, which allow us to classify an agent's behavior. This classification may aid in catching the (enemy) targets by enabling us to identify and predict their behaviors, and when extended to pursuers, this approach towards identifying teammates' behavior may allow agents to coordinate more effectively.

关键词： ISTAR AI MARL MAS machine learning Reinforcement learning Predator-Prey PursuitEvasion Emergent Behavior Coordination intelligence Surveillance Target Acquisition Reconnaissance

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A machine learning Approach for State-of-Charge Estimation of Li-ion batteries 4

A Machine Learning Approach for State-of-Charge Estimation o...

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

作者： Youssef, Heba Yahia Alkhaja, Latifa A. Almazrouei, Hajar Humaid Nassif, Ali Bou Ghenai, Chaouki AlShabi, Mohammad Univ Sharjah Coll Engn Dept Elect Engn Sharjah U Arab Emirates Univ Sharjah Coll Comp & Informat Comp Engn Dept Sharjah U Arab Emirates Univ Sharjah Coll Engn Dept Sustainable & Renewable Energy Engn Sharjah U Arab Emirates Univ Sharjah Coll Engn Dept Mech & Nucl Engn Sharjah U Arab Emirates

ISBN: (纸本)9781510651036;9781510651029

The state-of-charge (SOC) of Li-ion batteries is an important parameter for regulating and ensuring the safety of batteries, especially for Electric Vehicle applications, where accurate SOC estimation is important for remaining driving range prediction. The SOC is conventionally obtained through different indirect measurement methods that are either impractical or inaccurate. Data-driven approaches for SOC estimation are becoming more popular as an alternative to the conventional estimation methods due to their accuracy and low complexity. In this work, we apply 4 machine learning algorithms: Multiple Linear Regression (MLR), Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forest (RF) to predict the SOC using voltage, current, and temperature measurements from mixed driving cycles datasets, with 50000 instances. Out of the 4 models, the Random forest model performed the best with an MAE of only 0.82%.

关键词： State-of-charge Estimation machine learning Li-ion Battery SVR RF MLR MLP

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