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8th International ISPRS Workshop LowCost 3D - Sensors, Algorithms, Applications

作者： Kullmann, Timo Hübner, Patrick Wirth, Tristan Kuijper, Arjan Iwaszczuk, Dorota Technical University of Darmstadt Dept. of Civil and Environmental Engineering Sciences Remote Sensing and Image Analysis Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Mathematical and Applied Visual Computing Darmstadt Germany Fraunhofer IGD Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Interactive Graphics Systems Darmstadt Germany

Neural Radiance Fields (NeRFs) are a novel approach that is being intensively investigated in 3D scene reconstruction and similar fields to overcome challenges of conventional methods. In this paper, we address the problem of estimating missing camera poses in a six degrees of freedom setting, pushing the capabilities of NeRFs to address scenarios where only the primary camera's pose is known. Specifically, we focus on dual-camera setups with this constraint. Our core contribution is a novel pose correction model that operates alongside an unmodified NeRF model, for which we have chosen Nerfacto in the Nerfstudio framework. The pose correction model learns the necessary relative translation and rotation adjustments for the secondary camera solely through the NeRF loss. This allows us to integrate our correction model directly into the Nerfacto training pipeline without altering the core functionality. Through extensive experiments on different camera configurations in a synthetic scene, we rigorously evaluate our model's performance across diverse scenarios, pushing it to its limits. Our findings reveal that our model can effectively learn pose correction parameters within a constrained range, with increased sensitivity to larger translations and particular challenges in rotation corrections. This research highlights the potential of NeRFs for machine learning-driven 3D reconstruction on dual- and multi-camera platforms, expanding the applicability of NeRFs to more complex, real-world setups despite the inherent challenges. Copyright: © 2024 Timo Kullmann et al.

关键词： 3D reconstruction

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Complex-Valued DNN for Broadband Dielectric Characterization of Dispersive Lossy Materials 1

Complex-Valued DNN for Broadband Dielectric Characterization...

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1st IEEE Latin American Conference on Antennas and Propagation, LACAP 2024

作者： Bandara, Nuwan Gugliermino, Martina Lumia, Mauro Virone, Giuseppe Vipiana, Francesca Rodriguez-Duarte, David Orlando Singapore Management University School of Computing and Information Systems Singapore Politecnico di Torino Dept. of Electronics and Telecommunications Torino Italy Inst. of Electronics and Information and Telecommunications Engineering National Research Council Torino Italy

ISBN: (纸本)9798331527587

This paper presents a broadband dielectric characterization method based on a Complex-Valued Deep Neural Network (CVNN) that allows the retrieval of permittivity and conductivity of dispersive lossy materials using ad-hoc setups. To validate the method, we numerically tested it employing a partially filled custom-made double-ridge waveguide setup, working from 0.95 to 4.2GHz. Moreover, we include a feature importance analysis using agnostic explainable-AI (XAI) techniques. The results demonstrate the flexibility and the retrieval capabilities of the method, as well as the advantages and drawbacks in comparison with traditional techniques. We publicly release the dataset and codes to support further research. © 2024 IEEE.

关键词： Ridge waveguides

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Impact of Artificial Intelligence on Decision-Making in Information systems

Impact of Artificial Intelligence on Decision-Making in Info...

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Advanced Research in computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models Information systems

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Hypergraphic representation for adaptive quantum circuits

Hypergraphic representation for adaptive quantum circuits

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Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Waldemir Cambiucci Regina Melo Silveira Wilson Vicente Ruggiero Dept. of Computing Engineering and Digital Systems University of São Paulo São Paulo Brasil

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Adaptive quantum circuits enhance flexibility and efficiency over traditional static circuits by dynamically adjusting their structure and parameters in real-time based on intermediate measurement outcomes. This paper introduces a novel hypergraph representation for adaptive quantum circuits, where groups of gates are considered as participants of hyperedges. By incorporating these gate groups into hyperedges, we create an extended hypergraph that includes constraints usable during the partitioning process. This approach guides the partitioning to maintain groups of ports associated with classical operations, ensuring that the resulting partitions prioritize qubits involved in the same sections of classical operations inherent to the adaptive approach. We present a new hypergraph partitioning algorithm based as an extension of Fiduccia-Mattheyses heuristic, to support hypergraphs created from adaptive quantum circuits. Comparative analysis between static and adaptive methods demonstrates the effectiveness of the proposed hypergraph techniques for adaptive circuits. Experimental results using benchmark quantum circuits validate our theoretical insights, showing improvements in circuit representation for partitioning heuristics. These findings highlight the practical benefits of hypergraph representation in adaptive quantum computing.

关键词： Adaptive systems Atmospheric measurements Heuristic algorithms Qubit Logic gates Benchmark testing Particle measurements Real-time systems Partitioning algorithms Quantum circuit

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Hypergraphic representation for adaptive quantum circuits

arXiv

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arXiv 2025年

作者： Cambiucci, Waldemir Silveira, Regina Melo Ruggiero, Wilson Vicente Dept. of Computing Engineering and Digital Systems University of São Paulo São Paulo Brazil

关键词： Quantum electronics

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Impact of Artificial Intelligence on Decision-Making in Information systems 5

Impact of Artificial Intelligence on Decision-Making in Info...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Rajasinghe, Rwmr Dharani Faculty of Computing Nsbm Green University Dept. of Software Engineering & Information Systems Homagama Sri Lanka

ISBN: (纸本)9798331530983

关键词： Risk perception

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge computing Environments 29

TPTO: A Transformer-PPO based Task Offloading Solution for E...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Gholipour, Niloofar De Assuncao, Marcos Dias Agarwal, Pranav Gascon-Samson, Julien Buyya, Rajkumar Univ. of Quebec Dept. of Software Engineering and IT École de Technologie Supérieure Montreal Canada The Univ. of Melbourne CLOUDS Lab School of Computing and Information Systems Australia

ISBN: (纸本)9798350330717

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network's edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a Deep Reinforcement Learning (DRL) approach called TPTO, which leverages Transformer Networks and Proximal Policy Optimization (PPO) to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach. © 2023 IEEE.

关键词： Edge computing reinforcement learning task offloading Transformers

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QCRMut: Quantum Circuit Random Mutant generator tool

arXiv

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arXiv 2024年

作者： Gil, Sinhué García Díaz, Luis Llana Jarabo, José Ignacio Requeno Dept. of Information Systems and Computing Faculty of Computer Engineering Complutense University of Madrid Madrid Spain

Quantum computing has been on the rise in recent years, evidenced by a surge in publications on quantum software engineering and testing. Progress in quantum hardware has also been notable, with the introduction of impressive systems like Condor boasting 1121 qubits, and IBM Quantum System Two, which employs three 133-qubit Heron processors. As this technology edges closer to practical application, ensuring the efficacy of our software becomes imperative. Mutation testing, a well-established technique in classical computing, emerges as a valuable approach in this context. In our paper, we aim to introduce QCRMut, a mutation tool tailored for quantum programs, leveraging the inherent Quantum Circuit structure. We propose a randomised approach compared to previous works with exhaustive creation processes and the capability for marking immutable positions within the circuit. These features facilitate the preservation of program structure, which is crucial for future applications such as metamorphic testing. © 2024, CC BY-NC-SA.

关键词： Application programs

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Taxonomy of Proactive Detection Methods of Drunk Driving for Enhancing Traffic Safety 17

Taxonomy of Proactive Detection Methods of Drunk Driving for...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Swiley, Richard Preston Alsulieman, Razan Sherif, Ahmed Elsersy, Mohamed Khalil, Kasem University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States Higher Colleges of Technology Computer and Information Systems Dept. AlAin Abu Dhabi United Arab Emirates University of Mississippi Electrical and Computer Engineering Department MS United States

ISBN: (纸本)9798331530471

Drunk driving continues to be a substantial public health issue, leading to a multitude of accidents and deaths on a global scale. Conventional techniques for identifying drunk driving, such as breathalyzers and field sobriety tests, tend to be reactive rather than proactive. This paper surveys the existing methods for detecting drunk driving to enhance traffic safety. The technologies studied can proactively detect impaired driving behaviors by combining machine learning (ML) approaches with real-time data processing. The study outlines various classifications based on measured metrics and driving behavior. © 2024 IEEE.

关键词： Artificial Intelligence Drunk Driving Detection Traffic Safety

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An Ising Machine-Based Hybrid Optimization Method Using Constraint Conversion and Correction

An Ising Machine-Based Hybrid Optimization Method Using Cons...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Kinya Iwata Masashi Tawada Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University Green Computing Systems Research Organization Waseda University

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper proposes an Ising machine-based hy-brid optimization method to solve combinatorial optimization problems, which consists of an approximate formulation process, an annealing process with an Ising machine, and a correction process. The approximate formulation and correction aim to improve the performance of the Ising machine. Computational experiments were conducted on the multi-dimensional knapsack problem (MKP) using an Ising machine. We compare the results with those of the proposed method and the conventional method and demonstrate the effectiveness of the proposed approach.

关键词： Annealing Optimization methods Consumer electronics

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