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The Web Application Test Based on Page Coverage Criteria

Journal of Donghua University(English Edition) 2008年第3期25卷 291-296页

作者：蔡立志童维勤杨根兴 School of Computer Engineering and Science Shanghai University The Key Lab of Software Testing and Evaluating

software testing coverage criteria play an important role in the whole testing *** current coverage criteria for web applications are based on program or *** are not suitable for black-box test or intuitional to *** paper defines a kind of test criteria based on page coverage sequences only navigated by web application,including Page_Single,Page_Post,Page_Pre,Page_Seq2,Page_*** test criteria based on page coverage sequences made by interactions between web application and browser are being under consideration after *** order to avoid ambiguity of natural language,these coverage criteria are depicted using Z formal *** empirical result shows that the criteria complement traditional coverage and fault detection capability criteria.

关键词： web application coverage criteria browser interaction Z formal language

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M-DETR: Multi-scale DETR for Optical Music Recognition

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期249卷

作者： Luo, Fei Dai, Yifan Fuentes, Joel Ding, Weichao Zhang, Xueqin East China Univ Sci & Technol Shanghai Peoples R China Univ Bio Bio Dept Comp Sci & Informat Technol Chillan 3780000 Chile Shanghai Key Lab Comp Software Evaluating Testing Shanghai Peoples R China

Optical Music Recognition (OMR) is an important way to digitize score images and has broad application prospects in fields such as the storage of music documents, music education and digital creation. As a new paradigm for object detection, DETR (detection transformer) has the ability to associate contextual information, which can be exploited to resolve the OMR task. However, the original DETR does not fit OMR well due to its high computational complexity and numerous parameters. To address the DETR defects and improve the recognition accuracy of OMR, we propose a novel multi-scale DETR (M-DETR) with a multi-scale feature fusion mechanism and improved attention mechanisms. First, a new multi-scale feature fusion mechanism is designed to let the backbone network of M-DETR get rich multi-scale information. Then, a key-region attention mechanism is incorporated based on the character that the key information is concentrated on a score image. Finally, the pre-context attention mechanism is introduced to make better use of the contextual association between recognition notes in music scores. Experiment results show that M-DETR achieves recognition accuracy of 90.6% for 7 typical small-sized notes, which is better than Faster R-CNN and YOLO v5, and the improvement rate is 10.02% compared to the original DETR algorithm. The results indicate that M-DETR is an effective way for the OMR task, which also provides a new solution for the detection of small-sized objects with contextual association.

关键词： OMR DETR Feature fusion Attention mechanism

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AN RFID INDOOR POSITIONING SYSTEM BY USING PARTICLE SWARM OPTIMIZATION-BASED ARTIFICIAL NEURAL NETWORK 5

AN RFID INDOOR POSITIONING SYSTEM BY USING PARTICLE SWARM OP...

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5th International Conference on Audio, Language and Image Processing (ICALIP)

作者： Wang, Changzhi Shi, Zhicai Wu, Fei Zhang, Juan Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Shanghai Key Lab Comp Software Evaluating Testing Shanghai 200235 Peoples R China

ISBN: (纸本)9781509006540

Indoor Location information service (ILS) has been the hot topics of research in recent years. However, localization cost and positioning accuracy is still a challenge for indoor positioning system (IPS). RFID positioning technology is low cost but high positioning accuracy which is usually used for an IPS. In this study, a RFID indoor positioning algorithm is proposed, which is based on the Particle Swarm Optimization Artificial Neural Network (PSO-ANN). The algorithm uses PSO to optimize the weight and threshold of ANN network, and establish an accurate classification model that can learn the relationship between the Received Signal Strength Indication (RSSI) and tag position. In addition, in order to reduce the impact of the environmental factors on the position estimation effectively, the Gaussian Filter is adopted to process the RSSI information. The experimental result demonstrates that the proposed algorithm has better performance than other artificial neural network.

关键词： Radio frequency identification Particle swarm optimization artificial neural network indoor positioning

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An Efficient Method to Measure Robustness of ReLU-Based Classifiers via Search Space Pruning

An Efficient Method to Measure Robustness of ReLU-Based Clas...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Xinping Chen, Liangyu Wang, Tong Chen, Mingang Zhang, Min East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China Shanghai Dev Ctr Comp Software Technol Lab Comp Software Testing & Evaluating Shanghai Peoples R China Beijing Inst Syst Engn Beijing Peoples R China

ISBN: (纸本)9780738133669

Deep Neural Networks (DNNs) have achieved high accuracy on image classification. However, a small disturbance to an input may fool the networks to misclassify the label, which can cause a series of security and social problems. Thus, the robustness of DNNs must be ensured, particularly to those safety-critical systems. In this paper, we focus on the problem of measuring the robustness of ReLU-based DNNs, which can be equivalently formulated to solve a Mixed Integer Linear Programming problem (MILP). The complexity of solving MILP is directly related to the number of integer variables. We propose an efficient method for robustness measurement and verification by pruning the search space of MILP problems. Particularly, we design a greedy algorithm based on linear programming (LP) to determine the reasonable boundary. Then the search space is pruned by setting the boundary to integer variables in MILP. The comparison experiments on five classifiers trained on MNIST and CIFAR-10 datasets show our method outperforms other related tools in terms of efficiency and accuracy.

关键词： Greedy algorithms Sensitivity Transforms Tools Linear programming Robustness Time measurement

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Rethinking Generalizable Face Anti-Spoofing via Hierarchical Prototype-Guided Distribution Refinement in Hyperbolic Space

Rethinking Generalizable Face Anti-Spoofing via Hierarchical...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chengyang Hu Ke-Yue Zhang Taiping Yao Shouhong Ding Lizhuang Ma Shanghai Jiao Tong University Youtu Lab Tencent Shanghai Key Laboratory of Computer Software Evaluating and Testing

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Generalizable face anti-spoofing (FAS) approaches have drawn growing attention due to their robustness for diverse presentation attacks in unseen scenarios. Most previous methods always utilize domain generalization (DG) frame-works via directly aligning diverse source samples into a common feature space. However, these methods neglect the hierarchical relations in FAS samples which may hinder the generalization ability by direct alignment. To address these issues, we propose a novel Hierarchical Prototype-guided Distribution Refinement (HPDR)framework to learn embedding in hyperbolic space, which facilitates the hierarchical relation construction. We also collaborate with prototype learning for hierarchical distribution refinement in hyperbolic space. In detail, we propose the hierarchical Prototype Learning to simultaneously guide domain alignment and improve the discriminative ability via constraining the multi-level relations between prototypes and instances in hyperbolic space. Moreover, we design a Prototype-oriented Classifier, which further considers relations between the sample and prototypes to improve the robustness of the final decision. Extensive experiments and visualizations demonstrate the effectiveness of our method against previous competitors.

关键词： Visualization Computer vision Face recognition Prototypes Robustness Faces

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Contrastive Pseudo Learning for Open-World DeepFake Attribution

Contrastive Pseudo Learning for Open-World DeepFake Attribut...

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International Conference on Computer Vision (ICCV)

作者： Zhimin Sun Shen Chen Taiping Yao Bangjie Yin Ran Yi Shouhong Ding Lizhuang Ma Shanghai Jiao Tong University Tencent YouTu Lab Shanghai Key Laboratory of Computer Software Testing & Evaluating

The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken essential steps on GAN-generated faces, more threatening attacks related to identity swapping or expression transferring are still overlooked. And the forgery traces hidden in unknown attacks from the open-world unlabeled faces still remain under-explored. To push the related frontier research, we introduce a new benchmark called Open-World DeepFake Attribution (OW-DFA), which aims to evaluate attribution performance against various types of fake faces under open-world scenarios. Meanwhile, we propose a novel framework named Contrastive Pseudo Learning (CPL) for the OW-DFA task through 1) introducing a Global-Local Voting module to guide the feature alignment of forged faces with different manipulated regions, 2) designing a Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused by similar methods in unlabeled set. In addition, we extend the CPL framework with a multi-stage paradigm that leverages pre-train technique and iterative learning to further enhance traceability performance. Extensive experiments verify the superiority of our proposed method on the OW-DFA and also demonstrate the interpretability of deepfake attribution task and its impact on improving the security of deepfake detection area.

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Instance-Aware Domain Generalization for Face Anti-Spoofing

Instance-Aware Domain Generalization for Face Anti-Spoofing

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Qianyu Zhou Ke-Yue Zhang Taiping Yao Xuequan Lu Ran Yi Shouhong Ding Lizhuang Ma Shanghai Jiao Tong University Shanghai Key Laboratory of Computer Software Evaluating and Testing Youtu Lab Tencent Deakin University

Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-grained and subjective, which cannot reflect real domain distributions accurately. Besides, such domain-aware methods focus on domain-level alignment, which is not fine-grained enough to ensure that learned representations are insensitive to domain styles. To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels. Specifically, Instance-Aware Domain Generalization framework is proposed to learn the generalizable feature by weakening the features' sensitivity to instance-specific styles. Concretely, we propose Asymmetric Instance Adaptive Whitening to adaptively eliminate the style-sensitive feature correlation, boosting the generalization. Moreover, Dynamic Kernel Generator and Categorical Style Assembly are proposed to first extract the instance-specific features and then generate the style-diversified features with large style shifts, respectively, further facilitating the learning of style-insensitive features. Extensive experiments and analysis demonstrate the superiority of our method over state-of-the-art competitors. Code will be publicly available at this link.

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Contrastive Pseudo Learning for Open-World DeepFake Attribution

arXiv

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arXiv 2023年

作者： Sun, Zhimin Chen, Shen Yao, Taiping Yin, Bangjie Yi, Ran Ding, Shouhong Ma, Lizhuang Shanghai Jiao Tong University China Tencent YouTu Lab China Shanghai Key Laboratory of Computer Software Testing & Evaluating China

关键词： Iterative methods

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MULTI-SIGNAL FUSION OF SOCIAL DIFFUSION GRAPH WITH BI-DIRECTIONAL SEMANTIC CONSISTENCY 49

MULTI-SIGNAL FUSION OF SOCIAL DIFFUSION GRAPH WITH BI-DIRECT...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Li, Huacheng Xia, Chunhe Wang, Tianbo Lin, Wanshuang Jiang, Changnan Chen, Chen Zhao, Yuan Beihang Univ Lab Beijing Network Technol Beijing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China Guangxi Normal Univ Guangxi Collaborat Innovat Ctr Multi Source Infor Guilin Peoples R China Key Lab Comp Software Evaluating & Testing Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Devising diffusion graph to learn user representations is a crucial step in studying information propagation prediction. However, previous works mainly focused on structural and temporal features. To better incorporate content features, we introduce the Backward Decomposition and Forward Preservation mechanisms. The former involves decomposing content features for initializing node signals in the diffusion graph, thus fusing user features with content features. The latter aims to maintain node features generated by graph encoder consistent with the original content features. A series of experiments demonstrate that our model outperforms state-of-the-art models, and both mechanisms significantly enhance the prediction performance. Furthermore, our methods enables the features generated by the diffusion graph to more effectively incorporate features from various semantic spaces, whether encoded by language models or generated by graph embedding algorithms.

关键词： Heterogeneous Diffusion Graph Graph Representation Learning Social Network Analysis

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Instance-Aware Domain Generalization for Face Anti-Spoofing

arXiv

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arXiv 2023年

作者： Zhou, Qianyu Zhang, Ke-Yue Yao, Taiping Lu, Xuequan Yi, Ran Ding, Shouhong Ma, Lizhuang Shanghai Jiao Tong University China Youtu Lab Tencent China Shanghai Key Laboratory of Computer Software Evaluating and Testing China Deakin University Australia

关键词： Machine learning

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