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Edge-aware Feature Aggregation Network for Polyp Segmentation

Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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Improved Infrared Road Object Detection Algorithm Based on Attention Mechanism in YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 673-680页

作者： Luo, Zilong Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In Currently, research in the field of infrared road object detection is primarily focused on enhancing model performance and robustness to address the challenges posed by complex real-world driving scenarios. In response to these challenges, this paper proposes an infrared road object detection algorithm based on an attention mechanism. By incorporating the CPCA module, which utilizes attention mechanisms, into the YOLOv8s model, the algorithm enhances the model’s focus on unobstructed areas and highly illuminated sections, extracting crucial feature information to improve both accuracy and robustness. Additionally, the original model’s downsampling layer is replaced with the Context Grided Network Block Downsampling (CGBD) module, which not only preserves feature edge information but also effectively handles local and contextual features, thereby enhancing the overall feature capturing capabilities of the model. To address the issue of equal aspect ratios in the model’s original loss function, the proposed algorithm adopts the superior Weighted Intersection over Union (WIoU). This not only addresses the shortcomings of the original loss function (CIoU) but also demonstrates increased sensitivity in classification tasks. Experimental results show that the improved algorithm, compared to YOLOv8s, achieves a 1.4% increase in mean average precision (mAP), along with notable improvements in precision and recall. Furthermore, when compared to mainstream model algorithms, the enhanced model significantly outperforms in infrared road object detection tasks, providing validation of its effectiveness. © (2024), (International Association of Engineers). All rights reserved.

关键词： Object detection

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New development of cognitive diagnosis models

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Frontiers of computer science 2023年第1期17卷 157-169页

作者： Yingjie LIU Tiancheng ZHANG Xuecen WANG Ge YU Tao LI School of Computer Science and Engineering Northeastern UniversityShenyang 110169China

Cognitive diagnosis is the judgment of the student’s cognitive ability, is a wide-spread concern in educational science. The cognitive diagnosis model (CDM) is an essential method to realize cognitive diagnosis measurement. This paper presents new research on the cognitive diagnosis model and introduces four individual aspects of probability-based CDM and deep learning-based CDM. These four aspects are higher-order latent trait, polytomous responses, polytomous attributes, and multilevel latent traits. The paper also sorts on the contained ideas, model structures and respective characteristics, and provides direction for developing cognitive diagnosis in the future.

关键词： higher-order latent traits polytomous responses polytomous attributes multilevel latent traits cognitive diagnosis

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Underwater Target Detection Based on Improved YOLOv7

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第4期51卷 422-429页

作者： Fu, Junshang Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan 114051 China

Underwater target detection is an important part of marine exploration. However, in complex underwater environments due to factors like light absorption and scattering, as well as variations in water quality and clarity. These challenges result in inaccurate target feature extraction, sluggish detection speeds, and insufficient robustness in the detection methods. In order to address these issues, an enhanced YOLOv7 network (YOLOv7-SPNW-D) is proposed for underwater target detection in this study. The SPD-MP module structure replaces the MP module in the neck network to capture small targets and enhance detection accuracy. A novel NWD loss function is employed to facilitate smoother extraction of small target features. This enhances feature extraction and improves network inference speed. Additionally, incorporating a small target detection module enables the providing of more comprehensive small target information within a deep feature map. This, in turn, improves the capture of small target features in complex backgrounds, and avoids feature loss and enhancing model exactness. Through ablation experiments on the URPC dataset, it is shown that the improved YOLOv7-SPNW-D algorithm performs better than the original YOLOv7 algorithm, with the mAP50 value increased to 87.0%, proving the effectiveness of this method. In conclusion, the improved YOLOv7-SPNW-D model is more suitable for underwater marine organism target detection. © (2024) International Association of Engineers.

关键词： Water quality

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Small Object Detection in Aerial Drone Imagery based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第9期51卷 1346-1354页

作者： Pan, Junyu Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In recent years, the utilization of unmanned aerial vehicles (UAVs) for aerial target detection has gained significant attention due to their high-altitude perspective and maneuverability, which offer novel opportunities and tremendous potential in this field. However, detecting targets in UAV aerial images remains highly challenging due to the presence of numerous small targets with limited feature information, as well as issues like target occlusion and complex backgrounds that severely impact detection accuracy. To address these challenges, we propose a detection model called BDC-YOLOv8 that aims to enhance accuracy for small targets while minimizing computational complexity. Specifically, we augment the YOLOv8 architecture by incorporating a dedicated detection head tailored for small targets to improve performance when encountering such objects. Additionally, we restructure the neck network of the model to better extract and fuse feature information from targets with significant scale variations. Furthermore, we introduce the concept of DynamicHead to enhance the detection head by incorporating various attention mechanisms suitable for our task ahead of the original detection head, thereby enhancing the model’s capability to detect objects of different scales and complex backgrounds. Moreover, we introduce Convolutional Block Attention Module (CBAM) to identify regions of interest in densely populated areas. Extensive experiments conducted on the VisDrone2019 dataset yield promising results where our model achieves a mean Average Precision (mAP) score of 38% and an AP50 score of 59.6%. Compared to the original YOLOV8 model, improvements are observed with increases in mAP by 2.5% and AP50 by 3.7%, respectively. Notably, our model demonstrates a significant enhancement in detecting small targets with an increase in APs evaluation metric by 4.1%. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Aerial photography

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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Robust subspace clustering via two-way manifold representation

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Multimedia Tools and Applications 2025年第16期84卷 16339-16356页

作者： Ezeora, Nnamdi Johnson Anichebe, Gregory Emeka Nzeh, Royransom Chiemela Uzo, Izuchukwu Uchenna Department of Computer Science University of Nigeria Nsukka Nigeria School of Computer Science and Communication Engineering JiangSu University JiangSu 212013 China

Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

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Task offloading mechanism based on federated reinforcement learning in mobile edge computing

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Digital Communications and Networks 2023年第2期9卷 492-504页

作者： Jie Li Zhiping Yang Xingwei Wang Yichao Xia Shijian Ni School of Computer Science and Engineering Northeastern UniversityShenyang110000China

With the arrival of 5G,latency-sensitive applications are becoming increasingly *** Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among *** improve the Quality of Service(QoS),this study focuses on computation offloading in *** consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new *** QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in *** proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing *** solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model *** the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay ***,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of *** simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.

关键词： Mobile edge computing Task offloading QoS Deep reinforcement learning Federated learning

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An Online Website Fingerprinting Defense Based on the Non-Targeted Adversarial Patch

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Tsinghua science and Technology 2023年第6期28卷 1148-1159页

作者： Xiaodan Gu Bingchen Song Wei Lan Ming Yang School of Computer Science and Engineering Southeast UniversityNanjing 211189China

Website Fingerprinting(WF)attacks can extract side channel information from encrypted traffic to form a fingerprint that identifies the victim’s destination website,even if traffic is sophisticatedly anonymized by *** offline defenses have been proposed and claimed to have achieved good ***,such work is more of a theoretical optimization study than a technology that can be applied to real-time traffic in the practical *** defenders generate optimized defense schemes only if the complete traffic traces are *** practicality and effectiveness are *** this paper,we provide an in-depth analysis of the difficulties faced in porting existing offline defenses to the online *** then the online WF defense based on the non-targeted adversarial patch is *** reduce the overhead,we use the Gradient-weighted Class Activation Mapping(Grad-CAM)algorithm to identify critical segments that have high contribution to the *** addition,we optimize the adversarial patch generation process by splitting patches and limiting the values,so that the pre-trained patches can be injected and discarded in real-time *** experiments are carried out to evaluate the effectiveness of our *** bandwidth overhead is set to 20%,the accuracies of the two state-of-the-art attacks,DF and Var-CNN,drop to 10.83%and 15.49%,***,we implement the real-time patch traffic injection based on WFPadTools framework in the online scenario,and achieve a defense accuracy of 95.50%with 12.57%time overhead.

关键词： website fingerprinting online defense adversarial patch traffic analysis

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