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17th International Conference on Provable and Practical Security, ProvSec 2023

作者： Shi, Wenhao Han, Jiang Jiang, Haodong Ma, Zhi Henan Key Laboratory of Network Cryptography Technology Zhengzhou China TCA of State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031455124

Plaintext-checking (PC) oracle-based side-channel-attacks (SCAs) is a major class of key recovery chosen-ciphertext SCAs on lattice-based key encapsulation mechanisms (KEMs). In practice, the PC oracle derived in SCAs is usually imperfect. In this paper, we propose an improved SCA for lattice-based KEMs with lower sample complexity (i.e. the required number of traces) when the PC oracle is imperfect. The basic idea of our method is to roughly recover the secret key and detect the erroneous position in the secret key, later correct the problematic entries with various known attacks for LWE (e.g., primal attack). We experimentally verified our method on Kyber512, and the reduction of sample complexity is significant. For example, it can reduce the sample complexity by 54.06 % when the accuracy is 0.890, for higher accuracy 0.950, the reduction of sample complexity is 35.81 %. We also estimate the concrete security of Kyber512 after the side-channel-attack for different accuracy levels, including the primal attack, dual attack, decoding approach and the hybrid attack. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Side channel attack

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Enhancing Multi-modal Regular Expression Synthesis via Large Language Models and Semantic Manipulations of Sub-expressions 10th

Enhancing Multi-modal Regular Expression Synthesis via Larg...

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10th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2024

作者： Tang, Zipan Yan, Yixuan Li, Rongchen Dong, Hanze Chen, Haiming Gao, Hongyu Beijing University of Technology Beijing China and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819606016

Real-world regular expressions (regexes) are widely used in practice. Since regexes are difficult to comprehend and write, automatically synthesizing regexes has been an important research problem. However, current techniques face challenges like weak capability in generalization and severely limited support of extended features of regexes. In this paper, we address these challenges via Large Language Models (LLMs) together with semantic manipulations of sub-expressions, and propose PowerSyn, a framework for regex synthesis based on both natural language descriptions and examples and supports extended features. Specifically, we design the prompt suitable for synthesizing regexes with LLMs and present novel algorithms to manipulate sub-expressions, which consider the matching relation, and are guided by examples. The evaluation results demonstrate the significant efficacy of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices 38

DapperFL: Domain Adaptive Federated Learning with Model Fusi...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jia, Yongzhe Zhang, Xuyun Hu, Hongsheng Choo, Kim-Kwang Raymond Qi, Lianyong Xu, Xiaolong Beheshti, Amin Dou, Wanchun State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas San Antonio United States College of Computer Science and Technology China University of Petroleum [East China China School of Computer and Software Nanjing University of Information Science and Technology China

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL. © 2024 Neural information processing systems foundation. All rights reserved.

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Stabilizing Zero-Shot Prediction: A Novel Antidote to Forgetting in Continual Vision-Language Tasks 38

Stabilizing Zero-Shot Prediction: A Novel Antidote to Forget...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gao, Zijian Zhang, Xingxing Xu, Kele Mao, Xinjun Wang, Huaimin School of Computer National University of Defense Technology Changsha410000 China State Key Laboratory of Complex & Critical Software Environment Changsha410000 China School of Computer Science Tsinghua University Beijing100049 China

Continual learning (CL) empowers pre-trained vision-language (VL) models to efficiently adapt to a sequence of downstream tasks. However, these models often encounter challenges in retaining previously acquired skills due to parameter shifts and limited access to historical data. In response, recent efforts focus on devising specific frameworks and various replay strategies, striving for a typical learning-forgetting trade-off. Surprisingly, both our empirical research and theoretical analysis demonstrate that the stability of the model in consecutive zero-shot predictions serves as a reliable indicator of its anti-forgetting capabilities for previously learned tasks. Motivated by these insights, we develop a novel replay-free CL method named ZAF (Zero-shot Antidote to Forgetting), which preserves acquired knowledge through a zero-shot stability regularization applied to wild data in a plug- and-play manner. To enhance efficiency in adapting to new tasks and seamlessly access historical models, we introduce a parameter-efficient EMA-LoRA neural architecture based on the Exponential Moving Average (EMA). ZAF utilizes new data for low-rank adaptation (LoRA), complemented by a zero-shot antidote on wild data, effectively decoupling learning from forgetting. Our extensive experiments demonstrate ZAF's superior performance and robustness in pre-trained models across various continual VL concept learning tasks, achieving leads of up to 3.70%, 4.82%, and 4.38%, along with at least a 10x acceleration in training speed on three benchmarks, respectively. Additionally, our zero-shot antidote significantly reduces forgetting in existing models by at least 6.37%. Our code is available at https://***/Zi-Jian-Gao/Stabilizing-Zero-Shot-Prediction-ZAF. © 2024 Neural information processing systems foundation. All rights reserved.

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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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Research on Visual Slam technology for UAV Positioning and Navigation 21

Research on Visual Slam Technology for UAV Positioning and N...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Wang, Zhou Zhihao, Liu Ying, Tian Yujun, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China College of Computer and Artificial Intelligence Software College Huaihua University Huaihua China

ISBN: (纸本)9798331519254

UAV (Unmanned Aerial Vehicle) navigation can be considered as the process of robots that determine how to successfully and quickly reach the target location. Specifically, in order to complete the scheduled task successfully, the UAV must accurately understand its status, such as position, navigation speed, heading, as well as the starting point and target position. Moreover, it mainly depends on the current environment and location. This article generally focuses on the research on visual SLAM (Simultaneous localization and mapping) for UAV positioning and navigation. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in software Stress Testing 35

Auto-PIP: Real-time Identification of Critical Performance I...

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35th IEEE International Symposium on software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Xiong, Xiao Li, Mengyao Sun, Yongqian Zhao, Yongxin Chen, Xia Deng, Bowen Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area"(SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research. © 2024 IEEE.

关键词： software testing

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Research on Prediction of Medical & IT Sectors in A-shares Based on Random Forest Algorithm 5

Research on Prediction of Medical & IT Sectors in A-shares B...

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5th International Conference on computers, Information Processing and Advanced Education, CIPAE 2024

作者： Chen, Kunrui Yang, Lei Du, Shipei Zhang, Longqing Chen, Yungui Tian, Liwei School of Computer Science Guangdong University of Science and Technology Dongguan China Guangdong University of Science and Technology Dongguan Key Laboratory Industrial Software for Intelligent Manufacturing Dongguan China Guangdong University of Science and Technology Dongguan China School of Computer Science Guangdong University of Science and Technology Dongguan Key Laboratory of Kunpeng Computing Dongguan China Guangdong University of Science and Technology Dongguan Key Laboratory of Kunpeng Computing Dongguan China

ISBN: (纸本)9798331527662

This research is set against the backdrop of China's Shanghai Stock Exchange A-share market, selecting three representative stocks from each of two distinct sectors as the research objects. Leveraging the "orange" software, we employed the Random Forest model to predict the closing prices of stocks belonging to these two sectors. The findings indicate that, with respect to these two diverse sectors, the Random Forest model yielded no discernible difference in prediction outcomes. © 2024 IEEE.

关键词： Prediction models

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A Survey on Evolutionary Computation Based Drug Discovery

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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Boosting MLPs on Graphs via Distillation in Resource-Constrained Environment

Boosting MLPs on Graphs via Distillation in Resource-Constra...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yang, Zhihui Qu, Zhihao Jia, Ninghui Hu, Shihong Ye, Baoliu Zeng, Deze Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Nanjing University National Key Laboratory for Novel Software Technology Nanjing China School of Computer Science China University of Geosciences Nanjing China

ISBN: (纸本)9781665410205

Graph Neural Networks (GNNs) have emerged as a powerful technique across various applications, due to their effective message-passing mechanism. However, their deployment is constrained by limited computational resources, energy concerns, and low-latency processing requirements. While existing works employ logit-based knowledge distillation from GNNs to guide Multilayer Perceptrons (MLPs) training, these methods may lead to reduced accuracy and compromised robustness. These drawbacks arise from two primary factors: the insufficient exploitation of the rich information embedded within the graph structures and the inherent susceptibility of MLPs to noisy data. To tackle these issues, we propose a Mixed Multi-order Knowledge Distillation (MMKD) method, which combines the GNN's logits with hidden layer information through the multi-order distillation to improve the accuracy of the MLP. Moreover, we employ both raw data and perturbed data as input, enhancing the density of knowledge extraction as well as the MLPs' generalization. Extensive experiments across seven benchmark datasets verify the superior performance of our approach in terms of effectiveness and robustness. In comparison with the baseline, our approach achieves an accuracy improvement of up to 8.68% in typical GNN tasks. © 2024 IEEE.

关键词： Graph neural networks

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