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Understanding the Bad Development Practices of Android Custom Permissions in the Wild

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Xiaohan Yu, Zhiyuan Li, Xinghua Zhang, Cen Sun, Cong Zhang, Ning Deng, Robert H. Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi'an710071 China Ministry of Education Engineering Research Center of Big data Security China Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States Nanyang Technological University 639798 Singapore Singapore Management University 188065 Singapore

Android system provides application developers with the ability to define custom permissions, which serve to regulate the sharing of resources and functionalities with other applications. However, developers' improper development practices can render the permission mechanism ineffective, facilitating easy exploitation by attackers. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, and even component hijacking. To automatically identify these patterns of bad practices, we devised PERMEAGRE, a static analysis tool. Employing PERMEAGRE, we performed a large-scale analysis of 83,085 applications obtained from seven major app markets, aiming to detect instances of BPCP issues. The results revealed that more than 26% of the analyzed apps contained at least one issue, and a significant number of apps had garnered millions of downloads. Our analysis delved into the underlying causes of these issues. Consequently, this analysis sheds light on the potential threat landscape associated with bad practices in custom permissions, emphasizing the urgent requirement for effective mitigation strategies. © 2004-2012 IEEE.

关键词： Android custom permission empirical study static analysis

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Titania@ITO-Grown Nanoflakes: A Promising Heterocatalyst for 4-Nitrophenol Reduction and Synergetic Profile Against Bacterial Growth

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Applied Organometallic Chemistry 2025年第6期39卷

作者： Pato, Abdul Hameed Chandio, Imran Ali Javaid, Saba Channa, Iftikhar Ahmed Balouch, Aamna Sharmmari, Basheer A. Pervez, Tasneem Al-Jahwar, Farooq K. Liu, Wen Ibrahim, Talib K. Chandio, Ali Dad College of Chemistry State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical Technology Beijing China Department of Physics NED University of Engineering and Technology Karachi Pakistan Advanced Materials and Sustainable Environments Research GroupDepartment of Metallurgical Engineering NED University of Engineering and Technology Sindh Karachi Pakistan National Centre of Excellence in Analytical Chemistry University of Sindh Sindh Jamshoro Pakistan Advanced Materials Technology Institute King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Department of Industrial and Mechanical Engineering Sultan Qaboos University Muscat Oman Department of Petroleum Engineering College of Engineering Knowledge University Erbil Iraq

The proposed study was undertaken to design a facile, straightforward recoverable Titania@ITO-grown nanocatalyst, owning ironic catalytic sites synthesized via a liquid phase deposition protocol. The size, shape, charge, and morphological characteristics of the synthesized functional nanocatalyst were determined via zeta seizer, XRD, zeta potential, and SEM, critically disclosing ⁓5–6 nm, anatase crystalline nature, −44.0 mV, and flakes or sheet-like morphology, respectively. The reuse of the nanocatalyst particles was evaluated for their catalytic and antimicrobial efficiency. The obtained data revealed that the titania nanocatalyst is promising for ultrafast reduction of organic pollutants (4-nitrophenol) with 99% reduction efficiency, utilizing only 120 μg within 60 s following microwave-assisted protocols. Moreover, phenomenal antibacterial activity was disclosed by the titania nanocatalyst against both gram-positive and gram-negative bacteria. The anticipated nanocatalyst is advantageous due to its ultrafine and economical design, a significant amount of reduction capabilities in a short space of time, along with extreme recyclability, with a negligible loss in reduction abilities. The nanocatalyst provided here is more valuable and useful than the disclosed methods, symbolizing an exceptional commercial icon. © 2025 John Wiley & Sons Ltd.

关键词： Listeria

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Outage-Aware Online Prediction Control for Securing UAV-Aided Communication

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IEEE Transactions on Vehicular Technology 2025年

作者： Sheng, Zhichao Fu, Huabo Huang, Zhiyu Nasir, Ali A. Wu, Qingqing Zeng, Dan Shanghai University Shanghai Institute for Advanced Communication and Data Science Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai200444 China Department of Electrical Engineering and Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Shanghai Jiao Tong University Department of Electronic Engineering Shanghai201210 China

Considering an unmanned aerial vehicle (UAV)-aided multi-user system with multiple mobile eavesdroppers, this paper aims to develop an outage-aware online control strategy to counter the imperfect information of mobile user equipments (UEs) and mobile eavesdroppers. To perform online control effectively, a novel online prediction model is proposed, which models the trajectory of UEs as a stochastic Gauss-Markov mobility model and the trajectory of eavesdroppers as a worst-case model based on the current imperfect location information. The online control problem is formulated as a new multi-stage optimization framework, in which we perform online control by solving per-slot prediction problems and a resource allocation problem for the initial point. In the per-slot prediction problem, we jointly optimize the predictive trajectory and transmit power for the upcoming time slot (TS) by maximizing the minimum achievable secrecy rate (ASR) among all UEs. For resource allocation at the initial TS, we optimize transmit power by maximizing the minimum ASR among all UEs. The per-slot prediction problems and the resources allocation problem are all non-convex problems hence they are intractable in terms of computational complexity. To address them, we propose an efficient inner-approximation methods, by applying successive convex approximation (SCA) methods and S-procedure as well as Bernstein -type inequality. Numerical results demonstrate the superiority of our proposed online control scheme over the conventional benchmark schemes which include existing offline schemes and online scheme without outage based prediction as well as shortest-path navigation scheme. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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CMCache: An Adaptive Cross-Level data Placement Method for Multi-Level Cache

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Zeng, Zhaoyang Tan, Yujuan Ma, Zhulin Li, Jiali Zhao, Sanle Liu, Duo Chen, Xianzhang Ren, Ao Chongqing University College of Computer Science Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing Universityof Posts and Telecommunications College of Software Engineering China Chongqing University College of Big Data and Software Engineering Chongqing China

Multi-level cache systems enhance I/O performance by optimizing data placement across various cache levels from a global perspective. However, existing methods often struggle to place data at the optimal cache level promptly due to their reliance on historical access patterns and inflexible placement strategies. These methods face two main challenges: (1) For already cached data with sufficient access history, existing approaches only optimize movement between adjacent cache levels, potentially delaying data arrival at its globally optimal cache level and leading to unnecessary bandwidth consumption and increased latency. (2) For newly entered data without access history, current methods cannot accurately predict their future hotness and simply place them at a fixed cache level (i.e. first or last level), overlooking future accesses of new data and potentially resulting in high cache miss rates or cache pollution. To address these issues, we propose CMCache, an adaptive cross-level data placement method for multi-level cache. CMCache applies distinct placement strategies for cached and new data to reach the optimal level timely, considering their different characteristics. It also logically divides cache space into two sections to manage cached and new data separately, dynamically adjusting section sizes based on access patterns. This approach significantly improves data placement efficiency, achieving up to an 89% reduction in miss rates and a 79% decrease in average response times compared to existing methods. © 2025 IEEE.

关键词： Cache memory

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UGCM-LU: A Unified Stream and Batch Graph Computing Model with Local Update for Community Detection 19th

UGCM-LU: A Unified Stream and Batch Graph Computing Model w...

引用

19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Li, Hong Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Unified stream and batch computing (USBC) aims to incorporate stream and batch computation into a unified framework, thereby enabling the development of a one-stop solution for stream and batch data processing and enhancing the generalization of the framework. However, research on unified graph computing models (UGCMs) faces several challenges. First, existing UGCMs need to consider all graph information in the cache during the incremental update phase, thus leading to decreased execution efficiency. Second, existing UGCMs use fixed bytes to store nodes without considering the actual space occupied by nodes resulting in wasted memory when dealing with large graphs. This paper proposes a UGCM with Local Updates for community detection (UGCM-LU). We first implement a local update strategy to consider partial information of the graph to achieve incremental updates. Secondly, we also designed a byte-compression-based module to store graph data according to the space occupied by nodes. The experimental results show the effectiveness and efficiency of the model in real-world and artificial networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lutetium alloys

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High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wei, Yuxuan Wang, Zehan Guo, Tian Liu, Hao Shen, Liquan Yuan, Hui Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Yantai University School of Computer and Control Engineering Yantai264005 China Shanghai University Shanghai Institute for Advanced Communication and Data Science Shanghai200072 China

Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applications such as virtual reality and immersive communication. However, due to the huge data volume and unstructured geometry, efficient compression of point clouds is very crucial. The Moving Picture Expert Group is establishing a geometry-based point cloud compression (G-PCC) standard for both static and dynamic point clouds in recent years. Although lossy compression of G-PCC can achieve a very high compression ratio, the reconstruction quality is relatively low, especially at low bitrates. To mitigate this problem, we propose a high efficiency Wiener filter that can be integrated into the encoder and decoder pipeline of G-PCC to improve the reconstruction quality as well as the rate-distortion performance for dynamic point clouds. Specifically, we first propose a basic Wiener filter, and then improve it by introducing coefficients inheritance and variance-based point classification for the Luma component. Besides, to reduce the complexity of the nearest neighbor search during the application of the Wiener filter, we also propose a Morton code-based fast nearest neighbor search algorithm for efficient calculation of filter coefficients. Experimental results demonstrate that the proposed method can achieve average Bjøntegaard delta rates of -6.1%, -7.3%, and -8.0% for Luma, Chroma Cb, and Chroma Cr components, respectively, under the condition of lossless-geometry-lossy-attributes configuration compared to the latest G-PCC encoding platform (i.e., geometry-based solid content test model version 7.0 release candidate 2) by consuming affordable computational complexity. © 2025 IEEE. All rights reserved.

关键词： Wiener filtering

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Community-Aware Heterogeneous Graph Contrastive Learning 19th

Community-Aware Heterogeneous Graph Contrastive Learning

引用

19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Li, Xinying Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Recently, heterogeneous graph contrastive learning, which can mine supervision signals from the data, has attracted widespread attention. However, most existing methods employ random data augmentation strategies to construct contrastive views, which may destroy the semantic information in heterogeneous graphs. Moreover, they often select positive and negative samples based solely on node-level proximity and overlook hard samples that are difficult to distinguish from anchors. To solve the above problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning model called CAHGCL. In particular, we design an adaptive data augmentation strategy to construct views, including feature augmentation and topology augmentation. To improve the quality of samples, we propose a dynamic sample weighting strategy based on node similarity and community information, capable of identifying both hard positive samples and hard negative samples. Finally, we introduce community-level contrast to improve community cohesion. Extensive experiments and analyses demonstrate that CAHGCL consistently outperforms state-of-the-art baselines on three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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keyword-Oriented Multimodal Modeling for Euphemism Identification

arXiv

引用

arXiv 2025年

作者： Hu, Yuxue Li, Junsong Chen, Meixuan Su, Dongyu Wang, Tongguan Sha, Ying College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China

Euphemism identification deciphers the true meaning of euphemisms, such as linking "weed" (euphemism) to "marijuana" (target keyword) in illicit texts, aiding content moderation and combating underground markets. While existing methods are primarily text-based, the rise of social media highlights the need for multimodal analysis, incorporating text, images, and audio. However, the lack of multimodal datasets for euphemisms limits further research. To address this, we regard euphemisms and their corresponding target keywords as keywords and first introduce a keyword-oriented multimodal corpus of euphemisms (KOM-Euph), involving three datasets (Drug, Weapon, and Sexuality), including text, images, and speech. We further propose a keyword-oriented multimodal euphemism identification method (KOM-EI), which uses cross-modal feature alignment and dynamic fusion modules to explicitly utilize the visual and audio features of the keywords for efficient euphemism identification. Extensive experiments demonstrate that KOM-EI outperforms state-of-the-art models and large language models, and show the importance of our multimodal datasets 1 Copyright © 2025, The Authors. All rights reserved.

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Rapid Non-Destructive Detection of Formaldehyde Addition in Shrimp Based on Deep Learning-Assisted Portable Raman Spectroscopy

SSRN

引用

SSRN 2025年

作者： Wei, Chencheng Zhang, Jiheng Li, Gaozheng Zhong, Yi Ye, Zhaoting Wang, Handong Li, Kezhi Wu, Yuanzi Wu, Yuezhong Sun, Qi Luo, Heng Weng, Zuquan College of Biological Science and Engineering College of Biological Science and Engineering Fuzhou University Fujian Fuzhou350108 China College of Computer and Data Science Fuzhou University Fujian Fuzhou350108 China Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety Fuzhou University Fujian Fuzhou350108 China

Formaldehyde (FA), a known carcinogen, is occasionally used illegally as a preservative in seafood. Traditional detection methods for FA residues in seafood often fail to meet the practical needs for nondestructive detection. In this study, a new approach was developed by combining a portable Raman spectrometer with the InceptionTime deep learning model. Model were trained by Raman spectral data collected from the surface of shrimps. The model achieved an accuracy of 85.17% and 84.40% for detection limits of 100 mg/kg and 5 mg/kg, respectively. Weight visualization indicated that the model particularly focused on peaks associated with amino acids with specific Raman shift and astaxanthin-binding proteins. Additionally, two amino acid metabolites, tomoncaic and spinacine, were identified as direct indicators of FA addition and could serve as complementary biomarkers for FA detection. This study highlights the potential of integrating portable spectroscopy with deep learning for nondestructive FA detection in seafood. © 2025, The Authors. All rights reserved.

关键词： Shellfish

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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