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Automated GUI widgets classification

Frontiers of computer science 2023年第1期17卷 233-235页

作者： Kabir Sulaiman SAID Liming NIE Yuanchang LIN Yaowen ZHENG Zuohua DING School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China School of Computer Science and Engineering Nanyang Technological UniversitySingap

1 Introduction Graphical User Interface(GUI)widgets classification entails classifying widgets into their appropriate domain-specific types(e.g.,CheckBox and EditText)[1,2].The widgets classification is essential as it supports several software engineering tasks,such as GUI design and testing[1,3].The ability to obtain better widget classification performance has become one of the keys to the success of these *** in recent years have proposed many techniques for improving widget classification performance[1,2,4].For example,Moran et al.[1]proposed a deep learning technique to classify GUI widgets into their domain-specific *** authors used the deep learning algorithm,a Convolutional Neural Network(CNN)architecture,to classify the GUI *** et al.[2]proposed combining text-based and non-text-based models to improve the overall performance of GUI widget detection while classifying the widgets with the ResNet50 model.

关键词： GUI User classify

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Deep-BERT:Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

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computer Systems science & engineering 2023年第2期44卷 1775-1791页

作者： Md.Anwar Hussen Wadud M.F.Mridha Jungpil Shin Kamruddin Nur Aloke Kumar Saha Department of Computer Science and Engineering Bangladesh University of Business and TechnologyDhakaBangladesh School of Computer Science and Engineering University of AizuAizuwakamatsuJapan Department of Computer Science American International University-BangladeshDhakaBangladesh Department of Computer Science and Engineering University of Asia PacificDhakaBangladesh

Offensive messages on social media,have recently been frequently used to harass and criticize *** recent studies,many promising algorithms have been developed to identify offensive *** algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in *** addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive *** this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass *** paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based ***,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efﬁcacy outperformed all existing offensive text classiﬁcation algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.

关键词： Offensive text classiﬁcation deep convolutional neural network(DCNN) bidirectional encoder representations from transformers(BERT) natural language processing(NLP)

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Facial Depression Estimation via Multi-Cue Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6007-6020页

作者： Wang, Xinke Xu, Jingyuan Sun, Xiao Li, Mingzheng Hu, Bin Qian, Wei Guo, Dan Wang, Meng Anhui University School of Artificial Intelligence Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230031 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China University of Science and Technology of China Institute of Advanced Technology Hefei230094 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Vision-based depression estimation is an emerging yet impactful task, whose challenge lies in predicting the severity of depression from facial videos lasting at least several minutes. Existing methods primarily focus on fusing frame-level features to create comprehensive representations. However, they often overlook two crucial aspects: 1) inter- and intra-cue correlations, and 2) variations among samples. Hence, simply characterizing sample embeddings while ignoring to mine the relation among multiple cues leads to limitations. To address this problem, we propose a novel Multi-Cue Contrastive Learning (MCCL) framework to mine the relation among multiple cues for discriminative representation. Specifically, we first introduce a novel cross-characteristic attentive interaction module to model the relationship among multiple cues from four facial features (e.g., 3D landmarks, head poses, gazes, FAUs). Then, we propose a temporal segment attentive interaction module to capture the temporal relationships within each facial feature over time intervals. Moreover, we integrate contrastive learning to leverage the variations among samples by regarding the embeddings of inter-cue and intra-cue as positive pairs while considering embeddings from other samples as negative. In this way, the proposed MCCL framework leverages the relationships among the facial features and the variations among samples to enhance the process of multi-cue mining, thereby achieving more accurate facial depression estimation. Extensive experiments on public datasets, DAIC-WOZ, CMDC, and E-DAIC, demonstrate that our model not only outperforms the advanced depression methods but that the discriminative representations of facial behaviors provide potential insights about depression. © 1991-2012 IEEE.

关键词： Face recognition

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JPEG Reversible Data Hiding via Block Sorting Optimization and Dynamic Iterative Histogram Modification

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IEEE Transactions on Multimedia 2025年 27卷 3729-3743页

作者： Li, Fengyong Wang, Qiankuan Cheng, Hang Zhang, Xinpeng Qin, Chuan Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Fuzhou University School of Mathematics and Statistics Fujian Fuzhou350108 China Fudan University School of Computer Science Shanghai200433 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

JPEG reversible data hiding (RDH) refers to covert communication technology to accurately extract secret data while also perfectly recovering the original JPEG image. With the development of cloud services, a large number of private JPEG images can be efficiently managed in cloud platforms by embedding user ID or authentication labels. Nevertheless, data embedding operations may inadvertently disrupt the encoding sequence of the original JPEG image, resulting in severe distortion of the host image when it is re-compressed to JPEG format. To address this problem, this paper proposes a new JPEG RDH scheme based on block sorting optimization and dynamic iterative histogram modification. We firstly design a block ordering optimization strategy by combining the number of zero coefficients and the quantization table values of non-zero coefficients in a DCT block. Subsequently, a dynamic iterative histogram modification scheme is proposed by considering the local features and embedding capability of histograms generated from different texture images. According to the given payloads, we introduce different parameters to control the iterations of two-dimensional histogram and then adaptively generate the optimal histogram modification mapping, which can realize low JPEG file size increments by guaranteeing most of the AC coefficients unchanged as much as possible. Numerous experiments have shown that our scheme can achieve an effective balance among embedding capacity, visual quality, file size increment, computational complexity, and outperforms the state-of-the-arts in terms of the above metrics. © 1999-2012 IEEE.

关键词： Embeddings

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Integrating machine learning and extreme value theory for estimating crash frequency-by-severity via AI-based video analytics

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Communications in Transportation Research 2024年第1期4卷 382-392页

作者： Fizza Hussain Yuefeng Li Md Mazharul Haque School of Civil&Environment Engineering Queensland University of TechnologyBrisbane4000Australia School of Computer ScienceQueensland University of TechnologyBrisbane4000Australia

Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value *** sampling techniques,namely,block maxima and peak over threshold,form the core of these *** studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical ***,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity *** address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity *** particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity ***-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of ***-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were *** non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters *** results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant ***,the modeling f

关键词： Bivariate models Traffic conflicts Crash severity Crash frequency Generalized extreme value Anomaly detection techniques

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Resource Management, Security, and Privacy Issues in Semantic Communications: A Survey

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IEEE Communications Surveys and Tutorials 2025年第3期27卷 1758-1797页

作者： Won, Dongwook Woraphonbenjakul, Geeranuch Wondmagegn, Ayalneh Bitew Tran, Anh-Tien Lee, Donghyun Lakew, Demeke Shumeye Cho, Sungrae Chung-Ang University School of Computer Science and Engineering Seoul06974 Korea Republic of Wollo University Kombolcha Institute of Technology Department of Computer Science Dessie1145 Ethiopia

Resource management, security, and privacy stand as fundamental pillars for the reliable and secure operation of efficient semantic communications (SC) system. By addressing these aspects, SC system can pave the way for efficient resource utilization, improved network efficiency, enhanced communication performance, and protection of sensitive information. In this study, we begin by presenting the background of SC and reviewing several existing studies in this field. Subsequently, we provide a comprehensive and exhaustive survey of resource management, security, and privacy in SC. We identify and highlight existing challenges and open research challenges related to resource management, security, and privacy in SC in order to spur further investigation in these areas. © 1998-2012 IEEE.

关键词： Differential privacy

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A Four Port Ultra-Wideband MIMO Antenna with Windmill Shaped Decoupling Structure and Olympic Five Ring Patch

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Progress in Electromagnetics Research C 2025年 155卷 255-264页

作者： Li, Chang Wang, Zhonggen Nie, Wenyan Yang, Ming School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China School of Mechanical and Electrical Engineering Huainan Normal University Huainan232001 China School of Electrical and Communications Engineering West Anhui University Lu’an237012 China

In this paper, a compact and highly isolated ultra-wideband (UWB) four-port multiple-input-multiple-output (MIMO) antenna design is proposed, employing coplanar waveguide (CPW) feeding technology. The antenna system is meticulously arranged on a 0.787 mm thick RO5880 substrate, occupying a footprint of 56 mm × 56 mm (1.5λ × 1.5λ). The design comprises four identically modified antenna elements orthogonally positioned. Each element consists of an Olympic ring-shaped radiating patch within a slotted frame and two fin-shaped ground planes with E-shaped slots. Additionally, a windmill-like decoupling structure formed by rotating stubs is introduced to further enhance performance. This layout not only optimizes the isolation between antenna elements but also enhances overall performance through its unique structural design, ensuring better signal stability and wider bandwidth. The antenna system achieves an impedance bandwidth ranging from 3.65 GHz to 13.95 GHz. A thorough examination was performed on various essential MIMO performance indicators, such as envelope correlation coefficient (ECC) and total active reflection coefficient (TARC). The findings reveal that the antenna exhibits outstanding performance in these aspects, with an ECC value under 0.0012 and a TARC value under −30 dB. These results underscore the considerable potential and high performance of the proposed UWB-MIMO antenna for diverse UWB applications. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Antenna feeders

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FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation 39

FAMNet: Frequency-aware Matching Network for Cross-domain Fe...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bo, Yuntian Zhu, Yazhou Li, Lunbo Zhang, Haofeng School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

Existing few-shot medical image segmentation (FSMIS) models fail to address a practical issue in medical imaging: the domain shift caused by different imaging techniques, which limits the applicability to current FSMIS tasks. To overcome this limitation, we focus on the cross-domain few-shot medical image segmentation (CD-FSMIS) task, aiming to develop a generalized model capable of adapting to a broader range of medical image segmentation scenarios with limited labeled data from the novel target domain. Inspired by the characteristics of frequency domain similarity across different domains, we propose a Frequency-aware Matching Network (FAMNet), which includes two key components: a Frequency-aware Matching (FAM) module and a Multi-Spectral Fusion (MSF) module. The FAM module tackles two problems during the meta-learning phase: 1) intra-domain variance caused by the inherent support-query bias, due to the different appearances of organs and lesions, and 2) inter-domain variance caused by different medical imaging techniques. Additionally, we design an MSF module to integrate the different frequency features decoupled by the FAM module, and further mitigate the impact of inter-domain variance on the model's segmentation performance. Combining these two modules, our FAMNet surpasses existing FSMIS models and Cross-domain Few-shot Semantic Segmentation models on three cross-domain datasets, achieving state-of-the-art performance in the CD-FSMIS task. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

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computers, Materials & Continua 2024年第5期79卷 2035-2062页

作者： Qiong Yang Li Ma Shanshan Tu Sami Ullah MuhammadWaqas Hisham Alasmary Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different *** BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security ***,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of *** decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing *** this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their ***,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security ***,we discuss the challenges posed by BGP security problems and outline prospects for future research.

关键词： BGP security blockchain prefix hijacking trust model secure routing

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Designing Specialized Two-Dimensional Graph Spectral Filters for Spatial-Temporal Graph Modeling 39

Designing Specialized Two-Dimensional Graph Spectral Filters...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Yuxin Lin, Fangru Huo, Jingyi Yan, Hui School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

Spatial-temporal graph modeling is challenging due to the diverse node interactions across spatial and temporal dimensions. Recent studies typically adopt Graph Neural Networks (GNNs) to perform node-level aggregation at different time steps, acting as a series of low-pass graph spectral filters, for node interaction modeling. However, these filters, confined to the spatial dimension, are ill-suited for processing signals of nodes with inherent spatial-temporal interdependencies. Moreover, oversimplified low-pass filtering fails to fully exploit information from diverse node interactions. To address these issues, we propose a Spatial-Temporal Spectral Graph Neural Network (STSGNN), which designs specialized two-dimensional (2-D) graph spectral filters for comprehensive spatial-temporal graph modeling. First, based on the normalized Laplacian spectrum of spatial and temporal graphs, we extend existing graph spectral theory from a uni-variate spatial dimension to a bivariate spatial-temporal dimension through a 2-D Discrete Graph Fourier Transform (2-D DGFT). Then, we leverage the bivariate Bernstein polynomial approximation, with learned basis coefficients, to design 2-D filters with specialized spectral properties for unified spatial-temporal signal filtering. Finally, the filtered signals, with refined spatial-temporal representations, are fed into well-designed pyramidal gated convolution modules to acquire multiple ranges of spatial-temporal dependencies. Experiments on traffic and meteorological prediction tasks demonstrate that STSGNN achieves state-of-the-art performance. Additionally, we visualize the 2-D filters learned from different inputs to enhance the model's interpretability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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