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MadDroid: malicious adware detection in Android using deep learning

Journal of Cyber Security technology 2024年第3期8卷 163-190页

作者： Seraj, Saeed Pavlidis, Michalis Trovati, Marcello Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom Department of Computer Science Edge Hill University Omrskirk United Kingdom

The majority of Android smartphone apps are free. When an application is used, advertisements are displayed in order to generate revenue. Adware-related advertising fraud costs billions of dollars each year. Adware is a form of advertising-supported software, that turns into malware when it automatically installs additional malware and adware on an infected device, steals user data, and exposes other vulnerabilities. Better techniques for detecting adware are needed due to the evolution of increasingly sophisticated evasive malware, particularly adware. Even though significant work has been done in the area of malware detection, the adware family has received very little attention. This paper presents a deep learning-based scheme called MadDroid to detect malicious Android adware based on static features. Moreover, this paper delivers a novel dataset that consists of malicious Adware and benign applications and an optimised Convolutional neural network (CNN) for detecting Adware infected by malware based on the permissions of the applications. The results indicate an average classification rate that is higher than previous work for individual adware family classification in terms of well-known evaluation metrics. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Android (operating system)

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition

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China Communications 2022年第2期19卷 47-61页

作者： Miao Shi Chao Wang Wei Zhao Xinshi Zhang Ye Ye Nenggang Xie Department of Mechanical Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China College of Civil Engineering and Architecture Anhui Polytechnic UniversityAnhuiWuhu 241000PR China Department of Computer Science and Technology Anhui University of TechnologyAnhuiMa’anshan 243002PR China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterAnhuiHefei230000PR China Department of Management science and Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and *** Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing *** are two parameters in VMD that have a great influence on the result of signal ***,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based *** original seasonal monitoring condition in SSA is *** feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring ***-based learning is introduced to reposition the position of the population in this *** is applied to optimize the important parameters of ***-VMD model is established to remove ocular artifacts from EEG *** have verified the effectiveness of our proposal in a public dataset compared with other *** proposed method improves the SNR of the dataset from-2.03 to 2.30.

关键词： ocular artifact variational mode decomposition squirrel search algorithm global guidance ability opposition-based learning

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Integrating virtual reality technology into architecture education: the case of architectural history courses

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Open House International 2021年第4期46卷 498-509页

作者： Ibrahim, Anwar Al-Rababah, Amneh Ibrahim Bani Baker, Qanita Department of Architecture Jordan University of Science and Technology Irbid Jordan Department of Computer Science and Software Engineering Jordan University of Science and Technology Irbid Jordan

Purpose: Virtual reality (VR) technology invaded various domains including architecture practice and education. Despite its high applications in architecture design education, VR has a high potential to be used in architectural history courses as well. This paper aims to examine the effect of using VR technology on the students’ learning abilities of history of architecture. Design/methodology/approach: The experimental approach was used. Two experiments were designed by creating virtual environments for two selected architectural examples from the Modern architecture course. The participants who were students of Modern architecture class had to complete two questionnaires, one for each example. The first one was based on Bloom’s taxonomy, and the other was prepared to test the participants’ analytical and critical skills. Besides, participants had to fill out satisfaction and ease-of-use survey on a five-step Likert scale. Findings: Participants in the VR condition achieved better grades in knowledge gain compared to those in the traditional conditions. Their analytical and critical thinking skills were improved in the VR conditions. Gender has a significant impact on analytical and critical thinking skills. Participants recorded a high level of satisfaction;however, male students were more satisfied than female students who reported concerns about the weight of the used tools and nausea symptoms. Research limitations/implications: This study informs architecture education and provides insights into the potentials of using advanced technology in architectural history education. Teaching the various history of architecture courses will be improved, shifted toward a more student-centered curriculum, and may acquire more excitement and conscious curiosity. Originality/value: Using VR in architectural education is rigorous in architectural design courses and students’ design projects’ presentations. This research expands architectural education research by examining ot

关键词： Architectural education Bloom’s taxonomy Built environment tectonics and technologies History of architecture VR technology

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Distributed Learning by Local Training ADMM 63

Distributed Learning by Local Training ADMM

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Kth Royal Institute of Technology 2School of Electrical Engineering and Computer Science and Digital Futures Stockholm Sweden Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Trieste Italy

ISBN: (纸本)9798350316339

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments. © 2024 IEEE.

关键词： Peer to peer networks

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A Trusted and Decentralized Federated Learning Framework for IoT devices in Smart City 17

A Trusted and Decentralized Federated Learning Framework for...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Wang, Sheng Chen, Chun Han, Bing Zhu, Jun Nanjing Construction Market Supervision Station Nanjing China Jiangsu Public Works Co.Ltd Nanjing China Nanjing University of Aeronautics and Astronautics Department of College of Computer Science and Technology Nanjing China City University of Hong Kong Department of Architecture and Civil Engineering Hong Kong

ISBN: (纸本)9798350351637

Smart cities, through investment in human and social capital as well as traditional and modern communications infrastructure, drive sustainable economic growth and high quality of life. IoT devices, as crucial components of smart cities, leverage the Federated Learning (FL) paradigm to contribute significantly to the advancement of smart cities by providing the value of data while safeguarding local data. However, existing federated learning frameworks for smart cities face several issues, including excessive centralization of decision-making and data, unbalanced resource allocation, and security and privacy concerns. To address these challenges, This paper aims to propose a blockchain-based federated learning framework for IoT devices in smart cities. We combine multiple security and privacy-preserving techniques such as differential privacy and Trusted Execution Environments (TEE) with federated learning to establish a framework that facilitates secure and trustworthy data exchange and FL requirements for IoT devices in smart cities. Moreover, we offload the local training computation from the limited IoT devices to the edge server and execute trust aggregation on the blockchain smart contracts. We build a prototype of our designed system and conduct experiments on diverse federated learning datasets. Experimental results demonstrate that our scheme achieves high efficiency while ensuring security and privacy. Through this work, we provide a viable solution for federated learning in smart cities, thereby advancing the sustainable development and intelligence of smart cities. © 2024 IEEE.

关键词： Federated learning

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Unmilled Rice Moisture Content Sensor for Rice Mill Factory Applications 12

Unmilled Rice Moisture Content Sensor for Rice Mill Factory ...

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12th International Electrical Engineering Congress, iEECON 2024

作者： Seewattanapon, Somporn Chudpooti, Nonchanutt Akkaraekthalin, Prayoot Rajamangala University Of Technology Suvarnabhumi Faculty Of Engineering And Architecture Nonthaburi Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Industrial Physics And Medical Instrumentation Bangkok10800 Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Departmemnt Of Electrical And Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This research presents a novel electromagnetic-based sensor for inline measurement of unmilled rice paddy moisture content in the rice mill factory. The proposed sensor has been designed based on the modified planar dipole structure at the operating frequency of 440 MHz. The unmilled rice dielectric constant properties have been studied and applied for characterizing the moisture content. The unmilled rice moisture content sensor system then has been designed consisting of the proposed sensor, a microwave signal generator, a microwave signal detector, and a microcontroller. The moisture content sensor system has been implemented and used for measuring the unmilled rice paddy moisture content in a real rice mill factory in Udon Thani province, Thailand. When compared with the commercial rice moisture content tool based on a capacitance technique, the proposed rice moisture content sensor system gives almost same results, which only about 4% difference has been found. © 2024 IEEE.

关键词： Moisture control

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A Nonlinear Convolution Network for Image Enhancement and Translation Tasks

SSRN

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SSRN 2024年

作者： Bhattacharya, Jhilik Carini, Alberto Marsi, Stefano Ramponi, Giovanni Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India Department of Architecture and Engineering University of Trieste Italy

In this paper we introduce a special nonlinear convolution block with sine and cosine transformations, channel gating and local linear filter operations, which allows improved nonlinear CNN-based modeling with small network sizes. We motivate and demonstrate significant improvement in the task performances with a major reduction in the network size, thanks to the use of such a nonlinear convolution block. We perform extensive experiments to verify the performance of the network for different image transformation tasks such as deraining, dehazing, deblurring, low to bright light and night to day transformation. For all instances the network either shows acceptable or better performances when compared to State-of-Art (SoA) techniques for each task using benchmark datasets. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Automated Discovery of CEP Applications with Evolutionary Computing 25

Automated Discovery of CEP Applications with Evolutionary Co...

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Proceedings of the 19th ACM International Conference on Distributed and Event-based Systems

作者： Giulio Appetito Eric Medvet Vincenzo Gulisano Department of Civil Engineering and Computer Science Engineering (DICII) University of Rome Tor Vergata Roma Italy Department of Engineering and Architecture University of Trieste Trieste Italy Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden

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A Lottery-based Spherical Evolution Algorithm with Elite Retention Strategy 14

A Lottery-based Spherical Evolution Algorithm with Elite Ret...

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14th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022

作者： Li, Jiayi Zhang, Zihang Lei, Zhenyu Yi, Junyan Gao, Shangce University of Toyama Faculty of Engineering Toyama930-8555 Japan Beijing University of Civil Engineering and Architecture Department of Computer Science and Technology Beijing100044 China

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

Spherical evolution (SE) is a recently proposed meta-heuristic algorithm. Its special search approach has been proved to be very effective in exploring the search space. SE is very powerful for optimization, but still has room for improvement due to some promising solutions usually fail to survive into the next generation. To alleviate this issue, we innovatively design a novel lottery-based elite retention strategy and propose a lottery elite spherical evolution algorithm (LESE). To verify the effectiveness of the proposed LESE, we experimentally compare it with the original SE algorithm and other representative meta-heuristics algorithms. We use 30 benchmark functions from the IEEE CEC2017 benchmark as the test set for our experiments. The effectiveness of LESE is demonstrated by analyzing the experimental results from perspectives of solution accuracy, convergence speed, solution distribution, and search dynamics. © 2022 IEEE.

关键词： Spheres

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