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共 636 条记录，以下是51-60 订阅

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FORTUNATE: Decrypting and Classifying Malware by Variable Length Instruction Sequences 13

FORTUNATE: Decrypting and Classifying Malware by Variable Le...

引用

13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Andrade, Cesar A. B. Rocha Filho, Geraldo P. Meneguette, Rodolfo I. Maranhao, Joao Paulo A. Sant'ana, Ricardo Duarte, Julio Cesar Serrano, Andre Luiz M. Goncalves, Vinicius P. University of Brasília Electrical Engineering Dept. Brasília Brazil State University of Southwest Bahia Exact and Technological Sciences Dept. Vitória da Conquista Brazil Institute of Mathematics and Computer Science Universityof São Paulo São Carlos Brazil Brazilian Army Systems Development Center Brasília Brazil Military Institute of Engineering Defense Engineering Dept. Rio de Janeiro Brazil Military Institute of Engineering Computing Engineering Dept. Rio de Janeiro Brazil University of Brasília Production Engineering Dept. Brasília Brazil

ISBN: (纸本)9798350376562

After identifying an artifact as malware, the additional challenge arises of correctly classifying it into a specific family. This raises the challenge of classifying malware amid growing complexity and the increasing volume of cyber threats. Therefore, we introduce FORTUNATE, a framework that employs variable-length instruction sequences to classify malware more efficiently and accurately, focusing on real and active malware. By introducing effective methods for extracting opcodes and their representation in the smallest possible vectors, FORTUNATE surpasses the limitations of previous approaches, enabling the correct identification of malware in their respective families, while avoiding redundancies in the data. The results achieved with FORTUNATE demonstrate advances in both precision and computational efficiency, consolidating it as a significant contribution to the field of cybersecurity. © 2024 IEEE.

关键词： Long short-term memory

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WebRTC-based Resource Offloading in Smart Home Environments

WebRTC-based Resource Offloading in Smart Home Environments

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Jeong, Hunseop Lee, Taehyung Eom, Young Ik Sungkyunkwan University Dept. of Digital Media and Communications Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665441544

Web platforms face new demands for emerging applications, which use machine learning models such as pose recognition or object detection. These models require significant computing powers in processing enormous inputs such as images or audios for machine learning computation. These demands are also being generated in smart home appliances based on web platforms. Unfortunately, smart home appliances do not generally have built-in input devices, such as cameras or microphones, due to privacy issues and have limited performance compared to mobile devices. This paper proposes a WebRTC-based resource offloading system for web applications, which allows smart home appliances to use resources of nearby mobile devices as if the resources are their own. We implemented the proposed system, performed experiments on the resource offloading framework, and evaluated the performance using five computation-intensive web applications, which use a machine learning model. Our system was able to run machine learning models, through resource offloading to mobile devices, on smart home appliances without an attached camera, and achieved up to 1.5x speedup, compared to local execution with a camera. © 2022 IEEE.

关键词： Domestic appliances

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Innovative Approaches in Education: A Systematic Literature Review on Computer-Supported Collaborative Activities and digital Inclusion for Youth and Adult Education

Innovative Approaches in Education: A Systematic Literature ...

引用

Frontiers in Education (FIE) Conference

作者： Flávio Lopes da Silva Anarosa Alves Franco Brandão Dept of Computing Engineering and Digital Systems Universidade de São Paulo São Paulo Brazil

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research full paper presents a systematic review exploring methods and pedagogical strategies utilized for digitally including adult education students through computer-assisted collaborative activities. Youth and adult education faces significant disparities along the globe, with low enrollment rates in many countries as highlighted in the UNESCO's $5^{th}$ Global Report. Whenever we consider the $4^{th}$ United Nations' Sustainable Development Goal for 2030, education aims for inclusion and lifelong learning opportunities. With the aim of investigating the literature about existing computer-assisted collaborative methods and strategies for digital inclusion of youth and adults, we conduct a systematic literature review on the theme. Findings reveal a scarcity of works tailored to adult education, especially in developing nations, even with the inclusion of Brazilian and South African knowledge repositories to broaden the scope. Collaboration emerges as a prevalent strategy, often alongside other methodologies. Notably, mobile devices are widely utilized due to their accessibility, and social networks and virtual learning environments play significant roles. However, the integration of computational thinking and artificial intelligence for digital inclusion is still in its infancy. Overall, digitally including adult learners is crucial for fostering inclusive education and enhancing opportunities for marginalized populations.

关键词： Electronic learning Systematics Social networking (online) Education Collaboration Mobile handsets digital divide Sustainable development Faces Systematic literature review

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Investigation on Relative Pose Regression for Multi-Camera Setups with Neural Radiance Fields 8

Investigation on Relative Pose Regression for Multi-Camera S...

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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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Multi-sensor Area Deployment Optimization Method Based on Improved Cuckoo Search Algorithm

Multi-sensor Area Deployment Optimization Method Based on Im...

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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Wang, Yanjun Zhang, Guoping Wuhan Digital Engineering Institute Dept. of Systems Engineering Wuhan China

ISBN: (纸本)9780738146577

Aiming at the problem that the detection range of sensors in a specific area on the sea cannot meet the coverage requirements, a multi-sensor area deployment detection blind compensation model is proposed. On the basis of the sensor detection model, the sensor area coverage model is established with the coverage area as an indicator;the sensor movement cost model is established according to the movement of non-fixed sensors (the sensor is deployed on a mobile platform);Draw lessons from the theory of electric charges being forced in the electric field the cuckoo algorithm (CS) is improved according to the detection model, and a multi-sensor area deployment detection blind compensation method based on the improved cuckoo search algorithm (R-CS) is proposed. Comparing the R-CS algorithm with the basic CS algorithm, the simulation results show that the improved algorithm has improved the convergence speed and accuracy. © 2021 IEEE.

关键词： Electric fields

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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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Unmanned Aircraft Trajectory Prediction Based on Isolated Forest and LSTM Algorithm

Unmanned Aircraft Trajectory Prediction Based on Isolated Fo...

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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Wang, Yizhi Wu, Hanbao Liu, Hao Zhang, Shanqiang Wuhan Digital Engineering Institute Dept. of Systems Engineering Wuhan China

ISBN: (纸本)9780738146577

Nowadays, the technology of unmanned aircraft trajectory prediction has been more mature. But one of the reasons that affect the accuracy of the prediction curve is the inevitable systematic error of the radar itself. In addition, outliers and random oscillations generated in the process of data acquisition and transmission will also interfere with the prediction results. In order to solve this problem, we use Isolated Forest algorithm for data cleaning, and design a parallel LSTM system to deal with the inherent system error of radar. Experiments show that the performance of this method is improved compared with the mainstream methods. © 2021 IEEE.

关键词： Radar

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Track correlation uncertainty evaluation for Multi-sensor Data Fusion System and its application

Track correlation uncertainty evaluation for Multi-sensor Da...

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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Chen, Shiyou Liu, Hao Xiao, Hou Wuhan Digital Engineering Institute Dept. Systems Engineering Wuhan China

ISBN: (纸本)9780738146577

An evaluation method of track correlation uncertainty was put forward in this paper. Firstly, the measure of track correlation uncertainty was defined by Shannon entropy measure and Hartley measure of information uncertainty. Then, an evaluation model for track correlation uncertainty was proposed. Lastly, the usage of online evaluation of track correlation uncertainty in data fusion system was briefly illustrated. Simulation results show that the track correlation uncertainty can be quantitatively evaluated and can be used to improve the level of automation for data fusion system. © 2021 IEEE.

关键词： Sensor data fusion

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Fine-grained recognition based on improved EfficientNet model

Fine-grained recognition based on improved EfficientNet mode...

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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Shao, Jiang Wu, Hanbao Liu, Hao Wuhan Digital Engineering Institute Dept. of Systems Engineering Wuhan China

ISBN: (纸本)9780738146577

Due to subtle differences between fine-grained images, ordinary CNN models cannot extract key features well when directly applied to fine-grained image classification, leading low recognition accuracy. To solve this problem, based on the EfficientNet-B3 model proposed by Google, we add a spatial attention module before the GAP (Global average pooling) layer and use transfer learning to build the improved recognition model taking CUB-200-2011 datasets as the validation object. The test results show that the recognition accuracy of our improved model is about 84.5%, which is 2.9% higher than the original network. Moreover, compared with the VGG16 model and the Resnet50 model, our improved model has lower complexity and better recognition accuracy. © 2021 IEEE.

关键词： Computer vision

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