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Towards a Generalized SDK for a Programmable Drones-as-a-Service

Towards a Generalized SDK for a Programmable Drones-as-a-Ser...

引用

ieee international conference on High Performance computing Workshops (HiPCW)

作者： Suman Raj Rajdeep Singh Kautuk Astu Yogesh Simmhan Department of Computational and Data Sciences Indian Institute of Science Bangalore India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

The proliferation of Unmanned Aerial Vehicles (UAVs) in spatio-temporal applications has given rise to the need for Drone-as-a-Service (DaaS) platforms that support diverse tasks, ranging from urban air mobility to precision agriculture. However, existing DaaS platforms are often tightly-coupled to hardware-specific frameworks that lack programmability and flexibility for custom applications. For instance, using UAVs to help a Visually Impaired Person (VIP) navigate requires a suite of applications, such as obstacle avoidance, route planning, and real-time tracking. In this poster, we propose Ocularone SDK (O-SDK) that integrates on our prior Ocularone stack for VIP applications with Aerostack2, which generalizes the hardware abstraction. O-SDK offers an analytics layer as a novel contribution to enable developers to easily integrate Deep Neural Network (DNN)-based models, and interface swarms of UAVs for complex tasks like navigating the visually impaired. Our evaluations show that the minimal overheads of using O-SDK for real-time experiments using Tello drones and Jetson Orin Nano. This opens the potential to develop a domain-neutral, programmable and customizable DaaS Software Development Kit (SDK) in future.

关键词： Precision agriculture Navigation High performance computing conferences Real-time systems Hardware Distance measurement Planning Software development management Drones

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Algae Recognition and Usage Prediction Using Deep Convolutional Neural Network

Algae Recognition and Usage Prediction Using Deep Convolutio...

引用

Distributed computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： Shambbavi S Deeksha Holla Harish H Pooja M Maharani Lakshmi Ammanni College for Women Autonomous Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Algae species are essential to many environmental processes, such as the sequestration of carbon, the creation of oxygen, and the cycling of nutrients. Precise identification and forecasting of algae species are crucial for comprehending the dynamics of ecosystems, evaluating the quality of water, and creating sustainable solutions like the manufacturing of biofuel. However, conventional techniques for classifying algae are frequently labor-intensive and prone to human mistake. In response, a unique method that uses deep convolutional neural networks (CNNs) to automate the procedure presented in this study. Through the process of selecting and organizing a wide range of algal picture datasets and the application of data augmentation techniques, an attempt to improve the model's capacity to generalize to other species and environmental circumstances. With the help of the deep CNN architecture, an attempt to accurately identify and forecast species from photographs of algae by extracting complex patterns and traits. Through extensive experimentation, this method performs better than traditional methods, underscoring its potential to transform algae-related research and applications in environmental science and other fields.

关键词： Deep learning Training Biological system modeling Algae Training data Transforms Predictive models

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Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforcement Learning

Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforc...

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Junghoon Park Jiook Cha Samuel Yen-Chi Chen Shinjae Yoo Huan-Hsin Tseng Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Psychology Seoul National University Department of Brain and Cognitive Sciences Seoul National University Graduate School of Data Science Seoul National University Wells Fargo Computational Science Initiative Brookhaven National Laboratory

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In the realm of artificial intelligence, deep rein-forcement learning (RL) agents struggle with generalizability and require substantial computational resources, unlike humans who easily adapt and generalize across tasks. To address these challenges, we introduce Quantum Rainbow, a hybrid algorithm that leverages the neural mechanisms of human decision-making and the efficiency of quantum computing. Quantum Rainbow combines variational quantum circuits with the Rainbow Deep Q-Network (DQN) model to create a novel approach in rein-forcement learning that integrates quantum principles into deep learning paradigms. We evaluate our model using behavioral experiments through the Iowa Gambling Task and 4-Armed Bandit Task. Our investigations reveal a significant relationship between the architecture of quantum circuits and the performance of quantum RL agents. Specifically, using causal discovery methods, we demonstrate the critical role of quantum entanglement in enhancing model performance. These findings not only show promising results but also pave the way for future explorations into optimizing quantum circuit architectures for reinforcement learning applications. This study underscores the potential of quantum-enhanced algorithms to achieve “quantum advantage” by addressing fundamental limitations in conventional deep RL methods.

关键词： Analytical models Quantum computing Quantum algorithm Quantum advantage Quantum entanglement Computational modeling Reinforcement learning Quantum state Integrated circuit modeling Quantum circuit

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Disentangled Conditional Variational Autoencoder for Unsupervised Anomaly Detection

Disentangled Conditional Variational Autoencoder for Unsuper...

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ieee international conference on Big data

作者： Asif Ahmed Neloy Maxime Turgeon Department of Computing Studies and Information Systems Douglas College New Westminster Canada Department of Statistics University of Manitoba Winnipeg Canada

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Generative models have recently become an effective approach for anomaly detection by leveraging auto-encoders to model high-dimensional data and identify anomalies based on reconstruction quality. However, a primary challenge in unsupervised anomaly detection (UAD) lies in learning meaningful, disentangled features without losing essential information. In this paper, we introduce a novel generative architecture that combines the frameworks of β-VAE, Conditional Variational Auto-encoder (CVAE), and the principle of total correlation (TC) to enhance feature disentanglement and retain critical information. Our approach improves the separation of latent features, optimizes TC loss more effectively, and enhances the detection of anomalies in complex, high-dimensional datasets such as image data. Through extensive qualitative and quantitative evaluations in benchmark datasets, we demonstrate that our method not only achieves strong performance in anomaly detection but also captures interpretable, disentangled representations, highlighting the importance of feature disentanglement in advancing UAD.

关键词： Measurement Source separation Correlation Image synthesis High dimensional data Biological system modeling Feature extraction Anomaly detection Image reconstruction Biomedical imaging

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Cloud-Driven AI: Enhancing Machine Learning Performance Through Distributed computing

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Kunal Dixit Himani Parul Bhardwaj Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. This research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application’s efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. This framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5–12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. These results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today’s AI applications while opening the door to new advancements in ML throughout different domains.

关键词： Training Machine learning algorithms Protocols Scalability Government Partitioning algorithms Synchronization Resource management Distributed computing Random forests

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An Analysis of Research data Storage Systems

An Analysis of Research Data Storage Systems

引用

ieee international conference on e-Science and Grid computing

作者： Jake Carroll David Abramson Bronis R. de Supinski University of Queensland Australia Livermore Computing LLNL California USA

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

The scientific protocols, experiments and instruments that generate data are an integral part of the research lifecycle. Consequently, almost every scientific research institution requires a Research data Storage System (RDSS). However, RDSS implementations vary significantly due to factors that include cost, geography, workloads, policy, risk tolerance and available technical skills. A RDSS may be on premises, in the public cloud or a mixture of both. Previously we identified 10 key high level features of a RDSS and defined an abstract high-level Research data Reference Architecture (RDRA). Together, these features enable data fabrics, creating repeatable and consistent structures for low friction, highly efficient data movement and near real time data analysis for decision making and scientific workflows. We build on this earlier work in this paper to present a new Research data Implementation Architecture (RDIA) that meets the RDRA and can guide implementations without locking in any specific product or service. This paper presents and compares six significant RDSSs and shows how they meet both the RDIA and therefore the RDRA. We identify a new structure – the Research data Storage System Aggregator (RDSS-A) that describes clusters of RDSSs, and survey five such instances. Finally, we provide a real-world validation of the RDIA by documenting the technical specification of a real RDSS. The new work both clarifies a complex landscape, and aids groups building new systems or adopting existing systems.

关键词： Surveys Protocols Buffer storage Architecture Instruments Soft sensors Buildings

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Zephyr: A High-Performance Framework for Graph Attention Networks on Heterogeneous data

Zephyr: A High-Performance Framework for Graph Attention Net...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Wenxiu Ding Muzhi Liu Yuxuan Cai Mingxin Chen Zheng Yan Mingjun Wang School of Cyber Engineering Xidian University Xi’an China School of Computer Science and Technology Xidian University Xi’an China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The Graph Attention Network (GAT), based on the foundational principles of Graph Neural Network (GNN), represents a significant advancement in the accurate representation and in-depth understanding of complex data structures. However, GAT can’t proficiently handle heterogeneous data, which negatively affects model performance. Existing solutions incur substantial computational overhead when addressing these challenges, compromising efficiency and impeding practical deployment. In this paper, we propose Zephyr to enable GAT to efficiently process heterogeneous data with high performance. In Zephyr, we introduce Dynamic Multi-matrix Design and Spatial Multi-activation Design to eliminate the effects of finiteness of matrix representation capability and difference in heterogeneous spaces. Remarkably, Zephyr shows superior capability in processing heterogeneous data while requiring minimal storage space. Through comprehensive simulations and comparisons with existing schemes, Zephyr demonstrates a substantial performance improvement, increasing accuracy by over 8% on heterogeneous datasets and enhancing fitting speed by approximately 20%. Moreover, with heterogeneous data, it maintains memory overhead below 1.05x and curtails time overhead to 70%.

关键词： data privacy Accuracy Computational modeling Fitting data structures data processing Graph neural networks data models Computational efficiency Security

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DI-GAE: A Dynamic and Resource-Efficient Attack Detection Framework with Incremental Learning and Graph Autoencoders

DI-GAE: A Dynamic and Resource-Efficient Attack Detection Fr...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Mengmi Tan Jianyi Liu Ru Zhang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

data-driven deep learning methods have made significant strides in attack detection, with provenance graph analysis based on audit logs emerging as a key approach for identifying complex threats. However, the inherent "redundancy" and "dynamic variability" in audit logs pose two major challenges: redundant data leads to inefficient use of computational resources during model training, and shifts in data distribution over time can degrade detection performance. To address these challenges, we propose DI-GAE, a framework that is both resource-efficient and dynamically adaptive. We initially train a graph autoencoder on benign data. As data distribution changes, Behavior Pattern Scores (BPS) are applied at the time-window level to probe new behavioral patterns while filtering out redundant benign data, allowing for dynamic updates through incremental learning. Additionally, we integrate data replay and Elastic Weight Consolidation (EWC) strategies to ensure the model retains existing patterns while learning new ones. Experimental results demonstrate that DI-GAE not only conserves resources and adapts dynamically but also achieves detection performance comparable to state-of-the-art frameworks. Furthermore, we provide a detailed analysis of the model’s components, illustrating its ability to learn new patterns and maintain existing knowledge.

关键词： Training Adaptation models Incremental learning Computational modeling Autoencoders Redundancy Training data Dynamic scheduling data models Threat assessment

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***: Vertical and Horizontal Learning for Request Scheduling in Edge-Cloud Systems

***: Vertical and Horizontal Learning for Request Scheduling...

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ieee international conference on Communications (ICC)

作者： Xiaoyun Shi Yunfeng Zhao Chao Qiu Xiaofei Wang Xiuhua Li Wenyu Wang College of Intelligence and Computing Tianjin University Tianjin China School of Big Data & Software Engineering Chongqing University Chongqing China Paiou Cloud Computing (Shanghai) Co. Ltd. Shanghai China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

With the rapid development of Internet of Things (loT) device performance, edge-cloud systems are generating vast volumes of complex data. Meanwhile, the distributed and multi-layer structure of edge-cloud systems pose significant challenges to scheduling decision-making and convergence of the algorithm. In this paper, from a vertical and horizontal perspective of edge-cloud systems, we propose a deep reinforcement learning (DRL) algorithm called CrossLearning. We apply a curiosity-driven multi-agent learning method horizontally to accelerate the convergence speed of the algorithm. We introduce an inter-layer decision refinement mechanism vertically to address the challenge of inaccurate decision-making. We also refine the service types and levels to efficiently match the various application needs of users in the big data era. Finally, we implement a prototype system on a network hardware system and conduct experiments using real datasets. The evaluation shows that, in comparison to baseline methods, CrossLearning demonstrates significant im-provements in terms of time efficiency and load balance, with a notable enhancement in algorithm convergence speed.

关键词： Performance evaluation Learning systems System performance Decision making Prototypes Big data Scheduling

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StarFlow: fine-grained execution of workflows in Hybrid Cloud HPC for data stream applications 23

StarFlow: fine-grained execution of workflows in Hybrid Clou...

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Proceedings of the ieee/ACM 16th international conference on Utility and Cloud computing

作者： Luca Ferrucci Marco Danelutto Patrizio Dazzi University of Pisa Pisa IT

ISBN: (纸本)9798400702341

Nowadays we are witnessing the diffusion of the Function-as-a-service paradigm, that has emerged due to its ability to deploy fine-grained units of computation on heterogeneous resources on-demand, reducing costs and improving resource utilization. In this work, we discuss our objective: extending the runtime support of a structured parallel programming framework, FastFlow, to empower it with the mechanisms needed to exploit FaaS functionalities. To achieve this objective, we also integrates workflows as a way to specify and describe, at a very high level of abstraction, the topology and the requirements of complex parallel data stream applications.

关键词： function-as-a-service

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