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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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Comprehensive Semantic Repair of Obsolete GUI Test Scripts for Mobile Applications 24

Comprehensive Semantic Repair of Obsolete GUI Test Scripts f...

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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Cao, Shaoheng Pan, Minxue Pei, Yu Yang, Wenhua Zhang, Tian Wang, Linzhang Li, Xuandong Nanjing University State Key Lab for Novel Software Technology Nanjing China The Hong Kong Polytechnic University Department of Computing Hong Kong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798400702174

Graphical User Interface (GUI) testing is one of the primary approaches for testing mobile apps. Test scripts serve as the main carrier of GUI testing, yet they are prone to obsolescence when the GUIs change with the apps' evolution. Existing repair approaches based on GUI layouts or images prove effective when the GUI changes between the base and updated versions are minor, however, they may struggle with substantial changes. In this paper, a novel approach named COSER is introduced as a solution to re-pairing broken scripts, which is capable of addressing larger GUI changes compared to existing methods. COSER incorporates both external semantic information from the GUI elements and internal semantic information from the source code to provide a unique and comprehensive solution. The efficacy of COSER was demonstrated through experiments conducted on 20 Android apps, resulting in superior performance when compared to the state-of-the-art tools METER and GUIDER. In addition, a tool that implements the COSER approach is available for practical use and future research. © 2024 ACM.

关键词： Graphical user interfaces

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Context-aware API recommendation using tensor factorization

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science China(Information sciences) 2023年第2期66卷 78-93页

作者： Yu ZHOU Chen CHEN Yongchao WANG Tingting HAN Taolue CHEN College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University Birkbeck University of London

An activity constantly engaged by most programmers in coding is to search for appropriate application programming interfaces(APIs). Contextual information is widely recognized to play a crucial role in effective API recommendation, but it is largely overlooked in practice. In this paper, we propose contextaware API recommendation using tensor factorization(CARTF), a novel API recommendation approach in considering programmers' working context. To this end, we use tensors to explicitly represent the queryAPI-context triadic relation. When a new query is made, CARTF harnesses word embeddings to retrieve similar user queries, based on which a third-order tensor is constructed. CARTF then applies non-negative tensor factorization to complete missing values in the tensor and the Smith-Waterman algorithm to identify the most matched context. Finally, the ranking of the candidate APIs can be derived based on which API sequences are recommended. Our evaluation confirms the effectiveness of CARTF for class-level and method-level API recommendations, outperforming state-of-the-art baseline approaches against a number of performance metrics, including SuccessRate, Precision, and Recall.

关键词： API recommendation tensor factorization context awareness word embedding intelligent software development

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GaitCycFormer: Leveraging Gait Cycles and Transformers for Gait Emotion Recognition 39

GaitCycFormer: Leveraging Gait Cycles and Transformers for G...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zeng, Qingyang Shang, Lin State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)157735897X

Gait Emotion Recognition (GER) is an emerging task within Human Emotion Recognition. Skeleton-based GER requires discriminative spatial and temporal features. However, current methods primarily focus on capturing spatial topology information but fail to effectively learn temporal features from long-distance frames. Moreover, these methods are mostly sensitive to the order of sampled sequences, resulting in significant accuracy drops when sequences are randomly sampled. In order to obtain a more robust and comprehensive spatial-temporal representation of gait, we introduce the Graph-Transformer architecture into GER for the first time, proposing a novel framework named GaitCycFormer. Specifically, we designed a Cycle Position Encoding (CPE) based on the gait cycle, which explicitly segments any gait sequence into more manageable periodic units, to enhance temporal feature modeling. Additionally, we incorporate a bi-level Transformer, consisting of an Intra-cycle Transformer and an Inter-cycle Transformer to capture local and global temporal information within each gait cycle and between gait cycles respectively. Experiments demonstrate that our GaitCycFormer achieves state-of-the-art performance on popular datasets, and proves to be more reliable and robust. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Robust Activity Recognition Based on Human Skeleton for Video Surveillance 9

Robust Activity Recognition Based on Human Skeleton for Vide...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhang, Xiang Xie, Lei Bu, Yanling Lin, Zhenjie Wang, Liming Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China China Southern Power Grid Digital Platform Technology Company China

ISBN: (纸本)9798350319804

Activity recognition is an important task in video analysis, which can be used in accident monitoring and other daily applications. Traditional activity recognition methods are mainly based on the pixel-level analysis of 2D images. However, they achieve poor robustness in various complex environments, and are vulnerable to the perspective distortion caused by the fixed camera view. To address these challenges, we propose a robust skeleton-based human activity recognition method using a fixed monocular surveillance camera. We encode human skeleton with more critical motion information like pairwise distances between keypoints to capture high-level motion modality. Besides, we normalize skeleton data to eliminate the defects of 2D frames, such as the impact of distance on skeleton scale. Furthermore, we propose a skeleton calibration method based on perspective transformation to adapt our method to the deployment environment of surveillance cameras, especially different downward pitch angles. Experimental results show the recognition accuracy of our system reaches 91 percent with a frame rate of 10 FPS. © 2023 IEEE.

关键词： Activity recognition Human skeleton Surveillance video

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Nearly Optimal Regret for Decentralized Online Convex Optimization 37

Nearly Optimal Regret for Decentralized Online Convex Optimi...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Wan, Yuanyu Wei, Tong Song, Mingli Zhang, Lijun The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China Institute of Blockchain and Data Security Hangzhou China School of Computer Science and Engineering Southeast University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using_only local computations and communications. Previous studies have established O(n5/4ρ-1/2 √T) and O(n3/2ρ-1 log T) regret bounds for convex and strongly convex functions respectively, where n is the number of local learners, ρ -1/4 √T) and Õ(nρ-1/2 log T). The primary technique is to design an online accelerated gossip strategy that enjoys a faster average consensus among local learners. Furthermore, by carefully exploiting the spectral properties of a specific network topology, we enhance the lower bounds for convex and strongly convex functions to Ω(nρ-14 √T) and Ω(nρ-1/2), respectively. These lower bounds suggest that our algorithms are nearly optimal in terms of T, n, and ρ. © 2024 Y. Wan, T. Wei, M. Song & L. Zhang.

关键词： Optimization algorithms

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Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

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On the Noise Robustness of In-Context Learning for Text Generation 38

On the Noise Robustness of In-Context Learning for Text Gene...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gao, Hongfu Zhang, Feipeng Jiang, Wenyu Shu, Jun Zheng, Feng Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology China School of Economics and Finance Xi'an Jiaotong University China National Key Laboratory for Novel Software Technology Nanjing University China School of Mathematics and Statistics Xi'an Jiaotong University China Department of Computer Science and Engineering Southern University of Science and Technology China

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking. © 2024 Neural information processing systems foundation. All rights reserved.

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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

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18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

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A quantum federated learning framework for classical clients

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science China(Physics,Mechanics & Astronomy) 2024年第5期67卷 1-10页

作者： Yanqi Song Yusen Wu Shengyao Wu Dandan Li Qiaoyan Wen Sujuan Qin Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 00876China Department of Physics The University of Western AustraliaPerthWA 6009Australia School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China

Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local ***,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing *** raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this *** each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of ***,the server constructs a classical representation of the QML model and transmits it to the *** clients encode their local data onto observables and use this classical representation to calculate local *** local gradients are then utilized to update the parameters of the QML *** evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST *** framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.

关键词： client server quantum

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