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arXiv 2024年

作者： Hu, Yongxiang Wang, Xuan Wang, Yingchuan Zhang, Yu Guo, Shiyu Chen, Chaoyi Wang, Xin Zhou, Yangfan Meituan Shanghai China School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

The Graphical User Interface (GUI) is how users interact with mobile apps. To ensure it functions properly, testing engineers have to make sure it functions as intended, based on test requirements that are typically written in natural language. While widely adopted manual testing and script-based methods are effective, they demand substantial effort due to the vast number of GUI pages and rapid iterations in modern mobile apps. This paper introduces AUITestAgent, the first automatic, natural language-driven GUI testing tool for mobile apps, capable of fully automating the entire process of GUI interaction and function verification. Since test requirements typically contain interaction commands and verification oracles. AUITestAgent can extract GUI interactions from test requirements via dynamically organized agents. Then, AUITestAgent employs a multi-dimensional data extraction strategy to retrieve data relevant to the test requirements from the interaction trace and perform verification. Experiments on customized benchmarks 1 demonstrate that AUITestAgent outperforms existing tools in the quality of generated GUI interactions and achieved the accuracy of verifications of 94%. Moreover, field deployment in Meituan has shown AUITestAgent’s practical usability, with it detecting 4 new functional bugs during 10 regression tests in two months. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphical user interfaces

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation 9th

Improving Transferability Reversible Adversarial Examples Ba...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819959679

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding (RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example (RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper, we optimize the data hiding structure and combine data augmentation technology, which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Weight Perturbation as Defense against Adversarial Word Substitutions

Weight Perturbation as Defense against Adversarial Word Subs...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Xu, Jianhan Li, Linyang Zhang, Jiping Zheng, Xiaoqing Chang, Kai-Wei Hsieh, Cho-Jui Huang, Xuanjing School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing China Department of Computer Science University of California Los Angeles United States

The existence and pervasiveness of textual adversarial examples have raised serious concerns to security-critical applications. Many methods have been developed to defend against adversarial attacks for neural natural language processing (NLP) models. Adversarial training is one of the most successful defense methods by adding some random or intentional perturbations to the original input texts and making the models robust to the perturbed examples. In this study, we explore the feasibility of improving the adversarial robustness of NLP models by performing perturbations in the parameter space rather than the input feature space. The weight perturbation helps to find a better solution (i.e., the values of weights) that minimizes the adversarial loss among other feasible solutions. We found that the weight perturbation can significantly improve the robustness of NLP models when it is combined with the perturbation in the input embedding space, yielding the highest accuracy on both clean and adversarial examples across different datasets. © 2022 Association for Computational Linguistics.

关键词： Natural language processing systems

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DCSN: A Flexible and Efficient Lightweight Network for Dense Cell Segmentation

DCSN: A Flexible and Efficient Lightweight Network for Dense...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Futai Pan Fei Wang Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Fudan University Shanghai China

Cells serve as the fundamental building blocks of living organisms and necessitate the utilization of diverse microscopic imaging techniques for observation. Nevertheless, there exist numerous limitations within phase-contrast microscopy imaging technology, leading to indistinct imaging in a considerable number of acquired cell images. Furthermore, the wide range of cell shapes and sizes, often densely distributed, poses challenges for research in the field of cell image analysis. We propose Dense Cell Segmentation Network (DCSN), an anchor-based single-stage instance segmentation method, to address the challenge of dense detection and instance segmentation in high-density cell images. While methods based on transformers have emerged as prominent approaches in natural image detection and segmentation, they are limited in their applicability to cell image analysis due to the scarcity of cell image datasets. Consequently, we aim to introduce a simple yet effective instance segmentation method to further address the challenge of cell image segmentation. DCSN is a lightweight multi-task model, combining object detection and semantic segmentation to achieve accurate instance segmentation. We validate our approach on LIVECell, a publicly available annotated cell dataset, and demonstrate its efficacy. By combining the advantages of anchor-based methods and single-stage methods, DCSN achieves a favorable balance between speed and performance. In comparison to the anchor-based two-stage model Mask R-CNN, DCSN offers a streamlined process and faster inference speed. Compared to the anchor-free single-stage method, CenterMask, our model demonstrates superior segmentation performance and generalization capabilities. Moreover,DCSN is a general end-to-end model that can be applied to various domains beyond cell segmentation tasks.

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scGAT: A Cell-Type Annotation Framework for Single-Cell Transcriptomics Using Graph Attention Network and Meta Learning

scGAT: A Cell-Type Annotation Framework for Single-Cell Tran...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Jiayi Dong Ming Li Fei Wang Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Fudan University Shanghai China

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a valuable tool for classifying cell types across various species, tissues, and environmental conditions, thereby advancing the field of life sciences. Automatic cell annotation plays a crucial role in bioinformatics, addressing the challenges associated with time-consuming and labor-intensive manual cell annotation. Although numerous cell annotation algorithms exist, their performance is often constrained by the quality of reference data and inherent technical noise in biological experiments. To address these challenges, we propose the integration of a heterogeneous graph neural network into our cell annotation model, named scGAT. This integration leverages the intricate relationships between expressed genes and cells while disregarding unexpressed genes. Additionally, we introduce a novel approach that utilizes prototype cells to mitigate technical noise. However, the use of prototype cells may lead to limitations in sample size, prompting us to employ meta-learning techniques to determine the model's initial parameters. To assess the effectiveness of scGAT in cell annotation tasks, we conducted comparative analyses using real datasets against other models. The experimental results clearly demonstrate that scGAT significantly outperforms alternative methods.

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Automated Clinical Summary Generation via Integrating Structured and Unstructured Data 12th

Automated Clinical Summary Generation via Integrating Struct...

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12th CCF Conference on BigData, BigData 2024

作者： Fu, Jiaojiao Yang, Bowen Guo, Yi Zhou, Yangfan Wang, Xin School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Lab. of Intelligent Information Processing Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9789819610235

Automatically generating clinical texts can significantly reduce the time physicians spend on clinical data recording, which is particularly important for developing countries where physicians are extremely busy due to a severe shortage. This work automatically generates discharge summaries as a case to explore the methods and feasibilities of automatic clinical text summarization. Existing work typically uses either structured or unstructured data alone to generate discharge summaries. However, the content generated often has issues such as being overly verbose, lacking focus, or omitting significant information, especially key indicators and medications. This work innovatively proposes a data integration-based clinical text generation approach, using content generated from unstructured clinical data as the basis and supplementing it with text generated from structured clinical data. This study utilizes advanced natural language processing algorithms and models to create clinical texts. It addresses the challenges of lacking datasets suitable for fine-tuning pre-trained models and combining the advantages rather than the disadvantages of both types to produce discharge summaries. Experimental results show that the structured supplementation approach can effectively improve the generation of clinical texts. This work demonstrates that clinical texts generated using existing natural language processing technologies still do not meet the demands of medical practice, pointing out the need to develop further text generation technologies tailored to the characteristics of clinical data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data assimilation

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GROD: Joint Inference of Gene Regulatory Networks and Data Imputation in Single-Cell RNA Sequencing with Temporal Consideration

GROD: Joint Inference of Gene Regulatory Networks and Data I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiayi Dong Fei Wang Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Fudan University Shanghai China

Since single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular dynamics, the construction of gene relationships based on dynamic information has attracted much attention. However, the sparsity and dropout events inherent in scRNA-seq data present challenges for downstream analysis like Gene Regulatory Network (GRN) inference. Existing data imputation methods have yielded good results beneficial for cell clustering, but it does not adequately consider gene expression dynamics. To address this, we introduce GROD, a novel deep learning approach that simultaneously infers GRN and imputes scRNA-seq data from a temporal perspective. GROD consists of three key components: an encoder, a graph learner, and a decoder. Experimental results demonstrate that GROD outperforms existing methods in both GRN inference and data imputation tasks, providing superior accuracy in capturing gene regulatory relationships and accurately imputing missing values. By integrating these two tasks, GROD enables more accurate downstream analysis and facilitates deeper insights into cellular dynamics.

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A Search Cryptographically Significant S-Boxes with Improved DPA Resistance Based on Genetic Algorithm 8th

A Search Cryptographically Significant S-Boxes with Improve...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Qiu, Tingxiu Wang, Qichun School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing210023 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

ISBN: (纸本)9783031067631

Finding an S-box that is resistant against DPA attacks is an important research topic in cryptography. Until now, the notion of the transparency order including transparency order (TO) and modified transparency order (MTO) has received considerable attention and is one of the best metrics to measure the resistance of S-boxes against DPA attacks. Recently, Li et al. revisited the concepts of TO and MTO. They spotted a flaw that is overlooked and provided a revised definition (revised transparency order, RTO). There are few works on evolving cryptographically strong S-boxes with good RTO values. In fact, generating high nonlinear balanced S-boxes with low differential uniformity and improved DPA resistance is an extremely challenging problem. In this paper, we propose to use the genetic algorithm to search cryptographically significant S-boxes. Particularly, we generate an 8 × 8 balanced S-box, with algebraic degree of 7, nonlinearity of 112 and differential uniformity of 4, absolute indicator 32, RTO 7.5222, SNR 8.8869 (the RTO of the S-box of AES is 7.5402 and SNR is 9.6000), which improves the resistance to DPA attack. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Exploring Object-Centered External Knowledge for Fine-Grained Video Paragraph Captioning

Exploring Object-Centered External Knowledge for Fine-Graine...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guorui Yu Yimin Hu Yiqian Xu Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University School of Information Management and Engineering Shanghai University of Finance and Economics School of Information Technology Deakin University

Video paragraph captioning task aims to generate a detailed, fluent and relevant paragraph for a given video. Prior studies often focus on isolating visual objects (potential main components in a sentence) from the overall video content. They rarely explore the latent semantic relations between objects and high-level video concepts, resulting in dull or even incorrect descriptions. To create fine-grained and contextually relevant paragraph captions, we propose a novel framework that constructs a concept graph from a commonsense knowledge base and infers richer semantic meaning from the visual objects. Moreover, we employ a Vision-Guided Concept Selection Network that incorporates an under-sentence supervision mechanism to align the external knowledge with the visual information. Through extensive experiments on ActivityNet captions and YouCook2, the effectiveness of our method is demonstrated compared to state-of-the-art methods.

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AgeTrans: Estimating the Age of Faces Using Global Features 4

AgeTrans: Estimating the Age of Faces Using Global Features

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4th International Symposium on computer Engineering and intelligent Communications, ISCEIC 2023

作者： Nguyen, Van Anh School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing No. 220 Handan Road Shanghai200433 China Viettel Aerospace Institute Viettel Group Thach That Hanoi13100 Viet Nam

ISBN: (纸本)9798350306323

To meet many different needs in practice, the estimation of the age via face images is of great interest and this is also one of the important tasks of the machine vision field. Therefore, there are currently many methods that can solve this task, typically methods using Convolution Neural Network (CNN). However, the structures using CNN method can only extract local features of the face and not showing much of the relationship between facial parts. Therefore, we propose a solution called AgeTrans that combines Transformer method and CNN method to create an 'end to end' model with better efficiency. Specifically, taking advantage of the Attention mechanism of Transformer method to extract the global feature of the face and using CNN method to extract the local feature, then logically combining the two types of features and giving the result of the task. From the experimental results, our model is more efficient and competitive enough on the UTKFace dataset and at the same time, confirms that it is possible to use global features to estimate the age of the face. © 2023 IEEE.

关键词： computer vision

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