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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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PLIP: Language-Image Pre-training for Person Representation Learning

arXiv

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arXiv 2023年

作者： Zuo, Jialong Hong, Jiahao Zhang, Feng Yu, Changqian Zhou, Hanyu Gao, Changxin Sang, Nong Wang, Jingdong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Skywork AI Department of Computer Vision Baidu Inc China

Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image;and 3) Identity-based vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings. © 2023, CC BY-NC-SA.

关键词： Large datasets

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Code Refactoring for Software Reusability: An Experimental Study

Code Refactoring for Software Reusability: An Experimental S...

引用

Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah Almogahed Hairulnizam Mahdin Mazidah Mat Rejab Abdulwadood Alawadhi Samera Obaid Barraood Manal Othman Omar Al-Jamili Abdulwahab Ali Almazroi Shazlyn Milleana Shaharudin Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok Malaysia College of Computers and Information Technology Hadhramout University Hadhramout Yemen Computer Science Department College of Applied Science Taiz University Taiz Yemen Faculty of Engineering and Computing University of Science and Technology Yemen Department of Information Technology College of Computing and Information Technology at Khulais University of Jeddah Jeddah Saudi Arabia Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Refactoring plays a critical role in enhancing the quality of software systems, enabling them to accommodate growing user bases, feature sets, and performance demands. Software reusability is an essential aspect of software quality. It is crucial in modern software development as it offers several benefits, including increased productivity, improved code quality, and cost-effectiveness. However, there is a lack of studies investigating how specific refactoring techniques affect reusability. Therefore, this study aims to explore the effects of code refactoring on software reusability. This study was conducted in four stages: selecting refactoring techniques, selecting a large dataset, selecting software metrics to measure the reusability, and applying the refactoring techniques. The five most common refactoring techniques (Extract Interface, Encapsulate Field, Extract Class, Inline Class, and Inline Method) used in practices were selected and applied in the jEdit dataset. The refactoring techniques were carried out 338 times through five experiments in the jEdit dataset. The finding reveals that Extract Interface, Encapsulate Field, and Extract Class significantly improve the code reusability while Inline Class and Inline Method impair it. Developers and software architects can use this knowledge to make well-informed judgments about refactoring techniques and develop more robust and reusable software systems.

关键词： Productivity Industries Codes Software quality Software systems Maintenance Software measurement

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Federated Learning and Convolutional Neural Networks for Intrusion Detection Systems

Federated Learning and Convolutional Neural Networks for Int...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Malika Abid Mohammed Kamel Benkaddour Mohamed Benouis Computer Science Department Faculty of Sciences and Technology Artificial Intelligence and Information Technology Laboratory (LINATI) University of Kasdi Merbah Ouargla Algeria Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Recently, there has been a growing focus on network traffic threats due to the proliferation of mobile devices and the expanding Internet of Things (IoT). The centralised anomaly detection approach implies sharing the user’s data with a cloud server, which mostly exposes the user to the data breach issue. Given the above issues, this paper proposes a federated learning intrusion detection system (FedIDS) to improve the performance of intrusion detection while protecting user privacy. Motivated by recent exploits into deep learning for intrusion detection, we propose a convolutional neural network (CNN) model to learn and train the record data collected from independent and non-independent identically distributed (Non-IID, IID)data. This work conducts experiments on the NSL-KDD Data set for network intrusion detection. Experimental results show that FedIDS has achieved a competitive advantage over state-of-the-art centralised machine learning methods.

关键词： Performance evaluation Analytical models Privacy Federated learning Intrusion detection Telecommunication traffic Convolutional neural networks

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Kepler-aSI: Semantic Annotation for Tabular Data

Kepler-aSI: Semantic Annotation for Tabular Data

引用

2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2024

作者： Baazouzi, Wiem Kachroudi, Marouen Faiz, Sami RIADI Laboratory National School of Computer Science University of Manouba Manouba2010 Tunisia ESPRIT School of Engineering El Ghazala 2080 Tunisia University of Tunis El Manar Faculty of Sciences of Tunis LR11ES14 Tunis2092 Tunisia University of Tunis El Manar National School of Engineers of Tunis Laboratory of Remote Sensing and Spatial Reference Information Systems 99/UR/11-11 Tunis2092 Tunisia

The identification of semantic concepts in tabular data is crucial for numerous applications, including data integration, cleaning, retrieval, feature engineering, and model development in machine learning. Recently, various studies have introduced methods using supervised learning or heuristic models to annotate semantic types. However, these approaches have limitations, making it difficult for them to generalize to a wide range of concepts or examples. Additionally, many neural network-based methods struggle with scalability, and the majority of the existing techniques do not perform well with numerical data. We present Kepler-aSI, a column-to-concept mapping technique that employs a maximum likelihood estimation approach through sets. This method effectively leverages large amounts of publicly available table data, even in the presence of some noise. We showcase the effectiveness of Kepler-aSI in the Semtab2024 challenge. © 2024 Copyright for this paper by its authors.

关键词： Self-supervised learning

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Fog Computing in the Age of Big Healthcare Data: Powering the Medical Internet of Things 1st

Fog Computing in the Age of Big Healthcare Data: Powering th...

引用

1st International Conference of Advanced Computing and Informatics, ICACIN 2020

作者： Khaloufi, Hayat Abouelmehdi, Karim Beni-Hssane, Abederrahim Department of Computer Science LAROSERI Laboratory Faculty of Science El Jadida Chouaib Doukkali University El Jadida Morocco Laboratory of Applied Mathematics and Information System Faculty Polydisciplinary of Nador Mohamed First University Nador Morocco

ISBN: (纸本)9789811560477

Today’s global marketplace for Internet of Things (IoT) remains fiercely competitive within the healthcare sector. This can be attributed to many exciting achievements. It is expected to have a positive influence on all aspects of healthcare such as reducing prevalence and incidence of chronic conditions, improving the patient treatment and lowering in per capita healthcare costs, resulting from the forecasted increases in the adoption of remote monitoring devices. These smart devices along with wearables and Internet-connected sensors are expected to generate a great amount of data every second. However, the present model used to handle these unprecedented data named as cloud computing becomes inadequate. The solution must be seen in fog computing. The strong need of this model was mainly driven by a growing demand for real-time analysis and low latency. Motivated from these facts, this paper defines fog computing for IoT-driven smart healthcare while comparing it with cloud computing. It also presents an architecture of fog-based healthcare system containing three layers: edge, fog, and cloud. Finally, this paper highlights opportunities and top challenges facing smart healthcare organizations in the coming years. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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IDFusion: An Infrared and Visible Image Fusion Network for Illuminating Darkness

IDFusion: An Infrared and Visible Image Fusion Network for I...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Guohua Lv Xiyan Wang Zhonghe Wei Jinyong Cheng Guangxiao Ma Hanju Bao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Comuter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Lianxin Intelligent Technology (Shandong) Co. Ltd Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The purpose of infrared and visible image fusion is to combine the background information of the visible images and the thermal target information of the infrared images. Current fusion methods often neglect the challenges of low-light conditions. In the night scene, the existing methods fail to capture texture information from the visible images that is hidden in the darkness, producing suboptimal fusion results that can hinder subsequent visual applications. Therefore, we propose a method for infrared and visible image fusion under night scenes, termed as IDFusion. Specifically, our method is divided into three parts, first, a dense auto-encoder is designed to obtain more useful features from the source images. Then we design a brightness enhancement network that removes visible degraded illumination maps to obtain brightness-enhanced features. Finally, a texture fusion network is designed so that the infrared features and enhanced visible features avoid texture loss during the fusion process. Experimental results show that our network can obtain better fusion results, outperforming state-of-the-art methods in terms of subjective visual effects and quantitative metrics.

关键词： Measurement Federated learning Brightness Lighting Visual effects Image fusion

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Short-Term Load Forecasting with TDCI: Temporal Decomposition and Channel Independence 7

Short-Term Load Forecasting with TDCI: Temporal Decompositio...

引用

7th International Conference on Electrical, Mechanical and computer Engineering, ICEMCE 2023

作者： Li, Wenxue Cheng, Chi Xu, Peihua Liu, Renfeng Zhang, Yun Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Prov. Wuhan430205 China Wuhan Institute of Technology Hubei Provincial Key Laboratory of Intelligent Robot Hubei Prov. Wuhan430205 China Hubei Meteorological Service Center Wuhan430079 China School of Educational Information Technology Central China Normal University Faculty of Artificial Intelligence Education Wuhan430079 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China

ISBN: (纸本)9798350382877

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models. © 2023 IEEE.

关键词： Electric power plant loads

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Volume Hiding Conjunctive Searchable Symmetric Encryption over Cloud

Volume Hiding Conjunctive Searchable Symmetric Encryption ov...

引用

International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Qinghua Deng Lanxiang Chen Yizhao Zhu Yi Mu Faculty of Data Science City University of Macau Macau Macau China School of Computer and Information Engineering Hanshan Normal University Chaozhou China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (数字)9798331506896

ISBN: (纸本)9798331506902

Symmetric searchable encryption (SSE) is crucial for promoting cloud storage. It enables users to encrypt private data and outsource it to untrusted servers while retaining query capabilities. To prevent the cloud from obtaining users’ private data, researchers have proposed numerous schemes to protect users' privacy. However, the issue of volume hiding has received less attention. Recent studies have demonstrated that volume leakage can be exploited to infer query *** mitigate the aforementioned leakage problem, we introduce an efficient volume-hiding SSE scheme supporting conjunctive queries, denoted as VHXT. The primary strategy for implementing volume hiding comprises two aspects: first, employing a bitmap-based inverted index to obscure the query response length of the least frequent keyword, and second, introducing laconic PSI for efficient membership testing. The laconic PSI enables our scheme to achieve approximately optimal overhead. Moreover, security analysis and performance evaluation demonstrate that VHXT effectively prevents volume leakage with minimal overhead.

关键词： Performance evaluation Cloud computing Data privacy Protocols Knowledge discovery Encryption Servers Indexes Distributed computing Testing

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GeoDTR+: Toward Generic Cross-View Geolocalization via Geometric Disentanglement

arXiv

引用

arXiv 2023年

作者： Zhang, Xiaohan Li, Xingyu Sultani, Waqas Chen, Chen Wshah, Safwan Department of Computer Science and Vermont Complex Systems Center University of Vermont Burlington United States Lin Gang Laboratory Shanghai China Intelligent Machine Lab Information Technology University Pakistan Center for Research in Computer Vision University of Central Florida United States

Cross-View Geo-Localization (CVGL) estimates the location of a ground image by matching it to a geo-tagged aerial image in a database. Recent works achieve outstanding progress on CVGL benchmarks. However, existing methods still suffer from poor performance in cross-area evaluation, in which the training and testing data are captured from completely distinct areas. We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details. Our preliminary work [1] introduced a Geometric Layout Extractor (GLE) to capture the geometric layout from input features. However, the previous GLE does not fully exploit information in the input feature. In this work, we propose GeoDTR+ with an enhanced GLE module that better models the correlations among visual features. To fully explore the LS techniques from our preliminary work, we further propose Contrastive Hard Samples Generation (CHSG) to facilitate model training. Extensive experiments show that GeoDTR+ achieves state-of-the-art (SOTA) results in cross-area evaluation on CVUSA [2], CVACT [3], and VIGOR [4] by a large margin (16.44%, 22.71%, and 13.66% without polar transformation) while keeping the same-area performance comparable to existing SOTA. Moreover, we provide detailed analyses of GeoDTR+. Our code will be available at https://***/vail-uvm/geodtr_plus. Copyright © 2023, The Authors. All rights reserved.

关键词： Antenna grounds

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