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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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Evaluation of Hallucination and Robustness for Large Language Models

Evaluation of Hallucination and Robustness for Large Languag...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Rui Hu Junhao Zhong Minjie Ding Zeyu Ma Mingang Chen Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai AI Industry Association Shanghai China

As large language models (LLMs) rapidly advance, rigorous testing and evaluation of these models grows increasingly crucial. To address this need, we have developed three types of questions: Chinese contextual, English contextual, and language context-independent. testing in both Chinese and English probes the LLMs' hallucination tendencies. We investigate the impact of language on hallucinations from two perspectives: the type of language used in the input prompt and the cultural context underlying the prompt's content. Additionally, 52 multi-domain single-choice questions from C-EVAL are presented in original and randomized order to assess robustness to perturbations. Among the five LLMs, the tests demonstrate GPT -4 has the strongest anti-hallucination and robustness capabilities, answering with greater accuracy, consistency, and reliability. ChatGLM ranks second and outperforms GPT -4 on Chinese context-dependent questions. Emergent testing phenomena are analyzed from the user's perspective. Hallucinated responses are categorized and potential causal factors leading to hallucination and fragility are examined. Based on these findings, viable avenues for improvement are proposed.

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InsightCube: Accelerating Interactive Insight Discovery in Exploratory Data Analysis

InsightCube: Accelerating Interactive Insight Discovery in E...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Hanbing Zhang Yinan Jing Wei Zhang Zhenying He Kai Zhang X. Sean Wang Zhenqiang Chen Changchun Zhang School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Transwarp Technology(Shanghai) Co. Ltd Shanghai China

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Exploratory data analysis (EDA) is usually human-in-the-loop and time tedious since identifying insights that show interesting characteristics and trends in data is laborious. Insight discovery helps users gain structured knowledge when understanding the dataset by categorizing interesting characteristics and trends into commonnesses and exceptions. However, existing insight discovery methods cannot interact with users when discovering insights. More importantly, the efficiency of insight discovery for these methods is low. In this paper, we propose a framework that supports and accelerates interactive insight discovery. We first divide the dataset into multiple subsets, and precompute the pattern snippets that include the distribution characteristics of subsets. Considering efficiency, we only pre-compute for subsets that cannot be further split. Then we design a novel cube structure, called InsightCube, to store precomputed pattern snippets. In order to accelerate insight discovery, we reuse the precomputed pattern snippets stored in InsightCube to infer the pattern of subsets that cannot be split. Furthermore, during insight discovery, we propose pruning strategies to accelerate the pattern inference for subsets that can be split. Experimental results on real-world datasets show that InsightCube improves the insight discovery efficiency by up to $2.5 \times$ compared to the state-of-the-art insight discovery method.

关键词： Data analysis Big Data Market research Human in the loop Optimization

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Object Oriented BDD and Executable Human-Language Module Specification 26

Object Oriented BDD and Executable Human-Language Module Spe...

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26th ACIS International Winter Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Lee, Eric Gong, Jiayu Cao, Qinghong Intelligent Technology Co. Ltd. Shanghai China Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China System Operation Department Shanghai Stock Exchange Shanghai China

ISBN: (纸本)9798350345865

This paper presents an approach to software development which uses a generative AI Model as compiler to translate human language requirements into high-level programming language. We propose an executable human-language module specification and a tool to support it, which has been used successfully for human-language UI test automation. We anticipate further development of this approach to enable complex software to be programmed in human language, allowing for more intuitive and efficient software development. © 2023 IEEE.

关键词： Specifications

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An Approach to Optimize Symbolic Execution in Ethereum Smart Contracts

An Approach to Optimize Symbolic Execution in Ethereum Smart...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Yi Liu Lizhi Cai Chenchen Chai Yuan Ma School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

As the application of smart contracts in blockchain technology becomes increasingly widespread, their security issues have emerged as a focal point of both research and practice. Although symbolic execution technology has been extensively applied for the detection of security vulnerabilities in smart contracts, its limitations in terms of execution efficiency and code coverage remain significant. In response to these challenges, this study introduces an innovative method named HoneyToken-DetectorRev, which optimizes traditional symbolic execution through program slicing technology, aiming to enhance the efficiency and effectiveness of smart contract security detection. By selecting ten smart contracts from the Ethereum platform, which vary in business logic and complexity, we conducted an empirical analysis of HoneyToken-DetectorRev. The experimental results demonstrate that this method significantly improves the efficiency of smart contract detection without sacrificing precision. This research not only provides an effective technical approach for the security detection of smart contracts but also offers theoretical and practical guidance for further enhancing the security of blockchain applications.

关键词： Codes Smart contracts software quality Inspection Boosting Complexity theory software reliability Security Logic Business

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Estimating Mechanical Vibration Period Using Smartphones

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Journal of Donghua University(English Edition) 2021年第4期38卷 323-332页

作者： WANG Jiacheng CHANG Shan School of Computer Science and Technology Donghua UniversityShanghai 201620China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai 201112China

Driven by a wide range of real-world applications,significant efforts have recently been made to explore facile vibration *** vibration inspection systems are normally sensed via accelerometers,laser displacement sensors or velocimeters,and most of them are neither non-intrusive nor *** paper presents a novel solution based on acoustic waves of commercial mobile phones to inspect mechanical *** core observation is that the Doppler effect occurs when acoustic waves pass through a vibrating *** study leverages this opportunity to build a bridge between the Doppler frequency excursion and the vibrating frequency of *** solution of difference operation of the reassignment vector is used to make time-frequency domain images more ***,by processing time-frequency images,the system further accomplishes two reconstruction approaches to find out the energy concentration of acoustic signals respectively based on ridges and *** and real-life applications are employed to show the effectiveness and practicability of the proposed *** prototype system can inspect the vibration period with a relative error of 0.08%.Furthermore,this paper studies two practical cases in life to associate our measurement solution with the requirements of daily life.

关键词： vibration sensing Doppler effect density clustering ridge extraction

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Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

Multi-MedVit: a deep learning approach for the diagnosis of ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Cai, Yunjie Zheng, Zeqi Nie, Shanling Guo, Yue Zhang, Ruijie Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China University of Southampton School of Electronics and Computer Science United Kingdom

ISBN: (纸本)9781665468190

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study's 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly. © 2022 IEEE.

关键词： COVID-19

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CFC: a Cascade Forest approach to discover Cancer driver genes using multi-omics data

CFC: a Cascade Forest approach to discover Cancer driver gen...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhang, Lei Yang, Yijing Wang, Zhe Li, Dongdong Liu, Jingping Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China University of Illinois Urbana-Champaign Department of Computer Science ChampaignIL United States

ISBN: (纸本)9781665468190

With the development of next-generation sequencing technology, massive genomic data has been generated, primarily encouraging research on cancer driver genes. Many bioinformatics methods were proposed to identify driver genes. However, the results of driver gene identification a mong these methods show considerable differences. It is still challenging to obtain a comprehensive catalog of cancer drivers. Although current methods have greatly promoted the development of driver genes, few methods can integrate the identification results of existing methods. To solve such problems in cancer driver genes research, we proposed a cascade forest model to discover cancer driver genes(CFC) that can integrate multi-omics data and annotation scores from different cancer driver gene identification algorithms. The proposed method got precise results for 33 cancer types and Pan-cancer. The CFC framework identified 275 driver genes in Pan-cancer, of which 179 were included in the Gold standard. The identified genes were enriched i n t he principal cancer signaling pathways. © 2022 IEEE.

关键词： Genes

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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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CRCS: Learning Synergistic Cascade Correlation for Microscopic Cascade Prediction 19

CRCS: Learning Synergistic Cascade Correlation for Microscop...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Li, Huacheng Xia, Chunhe Wang, Tianbo Zhao, Haopeng Beihang University Key Laboratory of Beijing Network Technology Beijing China Key Laboratory of Beijing Network Technology Beijing China Beihang University School of Cyber Science and Technology Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9798350346558

Information diffusion prediction is the basis of many fundamental tasks, such as social recommendation and community detection. Currently, most researchers infer user correlation based on cascade records to predict future infected users. However, they only consider whether the infected users at each location are correctly predicted, ignoring the semantic differences between users who participate in the retweets of similar content and other users. This naturally leads to learning biased user features. In this paper, we compute semantic relevance between two different cascades (e.g., cm and cn) based on user coincidence and develop the cascade correlation graph (CCG) to learn user features. Afterward, we devise a position-independent sequence similarity loss (SSL) function combined with an auxiliary predictor to optimize the user encoder. The above two methods make users participating in the relevant cascades more relevant than others. Experiments show that the two methods are effective, and the combination of them can improve performance more significantly. © 2022 IEEE.

关键词： Forecasting

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