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作者： Ying, Zhihao Towey, Dave Bellotti, Anthony Chua, Caslon Quan Zhou, Zhi School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC Australia School of Computing and Information Technology University of Wollongong WollongongNSW Australia

Metamorphic testing (MT) is an effective software quality assurance method;it uses metamorphic relations (MRs) to examine the inputs and outputs of multiple test cases. Metamorphic exploration (ME) and metamorphic robustness testing (MRT) are advanced additions to the MT literature: ME can enable better understanding, and thus testing, of systems;MRT can test software robustness without an oracle. Metamorphic relation patterns (MRPs) are abstractions or templates for multiple concrete MRs. The performance of MT/ME/MRT relies on the quality of MRs, but the identification of MRs is often a manual task requiring some knowledge of MRPs, creative thinking and a good understanding of the system. In other words, the effective identification of MRs remains a challenge and requires further study. In this paper, we propose formal definitions to describe the relationship between multiple MRPs in order to classify them. We also identify a series of new MRPs and propose a new MT framework that can guide the identification and application of MRPs. Based on our definitions, we present the notion of MRP family trees and further propose two trees by classifying previously published MRPs and our proposed MRPs, in order to make it easier for users to find their target MRPs for reuse, reference or inference. Finally, we report on case studies using the MRPs and our framework to guide the identification of MRs for MT/ME/MRT and successfully detect multiple violations. © 2025 John Wiley & Sons Ltd.

关键词： software testing

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Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection 29

Few-shot Message-Enhanced Contrastive Learning for Graph Ano...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Fan Wang, Nan Wen, Xuezhi Gao, Meiqi Guo, Chaoqun Zhao, Xibin University of Science and Technology of China School of Computer Science and Technology Anhui China Beijing Jiaotong University School of Software Engineering Beijing China Tsinghua University School of Software Engineering Beijing China

ISBN: (纸本)9798350330717

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies. © 2023 IEEE.

关键词： Deep-GNN few-shot graph anomaly detection

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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MFTN: A Multi-scale Feature Transfer Network Based on IMatchFormer for Hyperspectral Image Super-Resolution 41

MFTN: A Multi-scale Feature Transfer Network Based on IMatch...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Shuying Ren, Mingyang Yang, Yong Wang, Xiaozheng Wei, Yingzhi School of Software Tiangong University Tianjin China School of Computer Science and Technology Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

Hyperspectral image super-resolution (HISR) aims to fuse a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to obtain a high-resolution hyperspectral image (HR-HSI). Due to some existing HISR methods ignoring the significant feature difference between LR-HSI and HR-MSI, the reconstructed HR-HSI typically exhibits spectral distortion and blurring of spatial texture. To solve this issue, we propose a multi-scale feature transfer network (MFTN) for HISR. Firstly, three multi-scale feature extractors are constructed to extract features of different scales from the input images. Then, a multi-scale feature transfer module (MFTM) consisting of three improved feature matching Transformers (IMatchFormers) is designed to learn the detail features of different scales from HR-MSI by establishing the cross-model feature correlation between LR-HSI and degraded HR-MSI. Finally, a multiscale dynamic aggregation module (MDAM) containing three spectral aware aggregation modules (SAAMs) is constructed to reconstruct the final HR-HSI by gradually aggregating features of different scales. Extensive experimental results on three commonly used datasets demonstrate that the proposed model achieves better performance compared to state- of-the-art (SOTA) methods. Copyright 2024 by the author(s)

关键词： Image resolution

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Combing Type-Aware Attention and Graph Convolutional Networks for Event Detection

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computers, Materials & Continua 2023年第1期74卷 641-654页

作者： Kun Ding Lu Xu Ming Liu Xiaoxiong Zhang Liu Liu Daojian Zeng Yuting Liu Chen Jin The Sixty-Third Research Institute National University of Defense TechnologyNanjing210007China Hunan Normal University Changsha410000China School of Computer&Software Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science University of ManchesterManchesterM139PLUK

Event detection(ED)is aimed at detecting event occurrences and categorizing *** task has been previously solved via recognition and classification of event triggers(ETs),which are defined as the phrase or word most clearly expressing event ***,current approaches require both annotated triggers as well as event types in training ***,triggers are non-essential in ED,and it is time-wasting for annotators to identify the“most clearly”word from a sentence,particularly in longer *** decrease manual effort,we evaluate event detectionwithout *** propose a novel framework that combines Type-aware Attention and Graph Convolutional Networks(TA-GCN)for event ***,the task is identified as a multi-label classification *** first encode the input sentence using a novel type-aware neural network with attention ***,a Graph Convolutional Networks(GCN)-based multilabel classification model is exploited for event *** results demonstrate the effectiveness.

关键词： Event detection information extraction type-aware attention graph convolutional networks

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A Combination Model of Periodic and Non-periodic Events for Temporal Knowledge Graph Event Prediction

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Chinese Journal of Electronics 2025年第3期34卷 952-961页

作者： School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China School of Data Science and Software Engineering Wuzhou University Wuzhou 543002 China

Temporal knowledge graph (TKG) reasoning aims to predict missing facts or future events at given timestamps and has attracted more and more attention in recent years. Existing TKG reasoning methods mainly focus on the interactions between entities and ignore the associations between events where the entities involve. In addition, the characteristics of different types of events have not been studied and exploited, which reduces the performance of event prediction. To address these problems, this paper proposes a combination model of periodic and non-periodic events (CM-PNP). Specifically, there are two basic components designed to process different types of events. The periodic component of CM-PNP learns the recurrent pattern of periodic events and encodes the temporal information in the manner of timespan to prevent the unseen timestamp issue. The non-periodic component of CM-PNP introduces extra information (e.g., entity attributes) to represent non-periodic events, and predicts this type of events according to the related historical events. A combination model of multiple sub-models that focus on encoding different parts of the event is used to improve the performance of single model. The periodic and non-periodic components are combined by a gate block. The experimental results on three real-world datasets demonstrate that CM-PNP outperforms the existing baselines.

关键词： Temporal knowledge graph Temporal knowledge graph reasoning Event prediction Embedding learning Data mining

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A Text Image Watermarking Algorithm Based on Image Enhancement

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computers, Materials & Continua 2023年第10期77卷 1183-1207页

作者： Baowei Wang Luyao Shen Junhao Zhang Zenghui Xu Neng Wang School of Computer Nanjing University of Information Science and TechnologyCollaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment TechnologyDigital Forensics Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Digital watermarking technology is adequate for copyright protection and content *** needs to be more research on the watermarking algorithm after printing and *** at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark,an anti-print scanning watermarking algorithm suitable for text images is *** algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform(DWT)-Singular Value Decomposition(SVD)to embed the *** show that the average Normalized Correlation(NC)of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group(JPEG)compression,JPEG2000 compression,and print scanning is above ***,the average NC of the watermark extracted after print scanning attacks is greater than 0.964,and the average Bit Error Ratio(BER)is 5.15%.This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.

关键词： Print-resistant scanning image enhancement DWT SVD embedding intensity

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Sentiment Analysis of Chinese Bullet Comments Based on Enhanced Deep Learning Model 5

Sentiment Analysis of Chinese Bullet Comments Based on Enhan...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Kai, Zhao Qibo, Wang Lining, Wan Linlin, Zhang Xilei, Lu Zhiwei, Wang XinJiang University School of Computer Science and Technology School of Cyberspace Security XinJiang Urumqi China XinJiang University School of Software XinJiang Urumqi China

ISBN: (纸本)9798350355925

In the Internet environment, bullet comments texts expressing emotions are an important kind of data with clear sentiment polarity, and this particular text can be used to analyse the trend of public opinion in the video media for the reference of decision makers. Aiming at today's sentiment analysis models that suffer from poor quality and uneven levels of natural language data annotation, as well as low accuracy and weak generalization in identifying the sentiment of very short texts, a sentiment analysis augmented hybrid deep learning model, RBMHSA (RoBERTa-BiGRU-Multi-Head-Self-Attention), is proposed for sentiment analysis of Chinese bullet comments texts. The data were collected from the BiliBili video library and manually annotated for integration. Accuracy and F1 value were used as the evaluation indexes, and comparative experiments were conducted on three datasets, compared to more than deep learning models. The experimental results show that RBMHSA (RoBERTa-BiGRU-Multi-Head-Self-Attention) is the most effective deep learning model for the analysis of Chinese bullet comments texts. deep learning models, the experimental results show that the RBMHSA model has higher accuracy and F1 value in identifying the polarity of bullet comments text views. © 2024 IEEE.

关键词： Contrastive Learning

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Multiple Time-Periods POI Recommendation Model Based on Distributed ELM 6

Multiple Time-Periods POI Recommendation Model Based on Dist...

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6th International Conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Zhang, Zhen Gong, Yaqi Zhao, Xiangguo Chen, Jun Ren, Jianyu Ouyang, Jintao School of Electrical Engineering Yingkou Institute of Technology Yingkou China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China School of Software Northeastern University Shenyang China

ISBN: (纸本)9798350353174

To solve the problems of low efficiency of algorithm execution and not fully considering the POI needs of users in different time-periods in point-of-interest (POI) recommendation. In this paper, propose a Multiple time-periods POI recommendation model MP-DELM based on distributed extreme learning machine (DELM). According to the user's different periods and different types of POI access requirements, the data set was processed in different periods;extract POI preference features: distance preference feature, personalized preference feature, popularity preference feature, and friend preference feature, in which for the friend preference feature, social friend feature and the trajectory feature are integrated for the first time, and a new trajectory friend similarity calculation method is proposed;in order to improve the efficiency of POI recommendation, improve DELM classifier. And the online sequential extreme learning machine (OS-ELM) is used to update training data in real time. Finally, we prove that the MP-DELM recommendation model can effectively improve the accuracy and efficiency of POI recommendation in the real datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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A novel few-shot learning approach for the classification of histological images of liver tumors

A novel few-shot learning approach for the classification of...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Bo Wang, Qirui Li, Longjiao Yang, Jinqiao Lin, Hongfei Ding, Feng Dalian University of Technology School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350337488

The liver is an internal organ that is most commonly affected by cancer metastasis. Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. Identifying the histological category of this disease is a crucial step in determining the most appropriate treatment plan. Currently, the classification of HCC primarily relies on the observation of pathologists through a visual microscope, which is undoubtedly subjective. To address this issue, the present study conducted pathological examination of liver tumors based on few-shot learning. In order to achieve automatic identification of the three categories in HCC tissue, we propose a few-shot learning based HCC histological image classification method that combines contrastive learning (CL) using different backbone architectures with several well-known machine learning methods. We evaluate 12 backbone-classifier combinations, and the best performance is achieved by combining CL using DeiT-TinyR as the backbone architecture with support vector machine (SVM), with an accuracy of 74.42% and an F1 score of 74.58% in the 3-way-10-shot setting. Experimental results demonstrate the effectiveness of this method in NB histological image classification. © 2023 IEEE.

关键词： classification contrastive learning few-shot hepatocellular carcinoma hyperspectral image

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