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arXiv 2022年

作者： Zhang, Wuqi Wei, Lili Cheung, Shing-Chi Liu, Yepang Li, Shuqing Liu, Lu Lyu, Michael R. The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Electrical and Computer Engineering McGill University MontrealQC Canada The Department of Computer Science and Engineering The Research Institute of Trustworthy Autonoumous Systems Southern University of Science and Technology Guangdong Shenzhen China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Front-running attacks have been a major concern on the blockchain. Attackers launch front-running attacks by inserting additional transactions before upcoming victim transactions to manipulate victim transaction executions and make profits. Recent studies have shown that front-running attacks are prevalent on the Ethereum blockchain and have caused millions of US dollars loss. It is the vulnerabilities in smart contracts, which are blockchain programs invoked by transactions, that enable the front-running attack opportunities. Although techniques to detect front-running vulnerabilities have been proposed, their performance on real-world vulnerable contracts is unclear. There is no large-scale benchmark based on real attacks to evaluate their capabilities. We make four contributions in this paper. First, we design an effective algorithm to mine real-world attacks in the blockchain history. The evaluation shows that our mining algorithm is more effective and comprehensive, achieving higher recall in finding real attacks than the previous study. Second, we propose an automated and scalable vulnerability localization approach to localize code snippets in smart contracts that enable front-running attacks. The evaluation also shows that our localization approaches are effective in achieving higher precision in pinpointing vulnerabilities compared to the baseline technique. Third, we build a benchmark consisting of 513 real-world attacks with vulnerable code labeled in 235 distinct smart contracts, which is useful to help understand the nature of front-running attacks, vulnerabilities in smart contracts, and evaluate vulnerability detection techniques. Last but not least, we conduct an empirical evaluation of seven state-of-the-art vulnerability detection techniques on our benchmark. The evaluation experiment reveals the inadequacy of existing techniques in detecting front-running vulnerabilities, with a low recall of ≤ 6.04%. Our further analysis identifies four common li

关键词： Smart contract

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Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

arXiv

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arXiv 2023年

作者： Liu, Yuhan Wang, Pengyu Lee, Chang-Hun Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance. © 2023, CC BY-NC-SA.

关键词： Maneuverability

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A Dual Power Grid Cascading Failure Model for the Vulnerability Analysis

arXiv

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arXiv 2024年

作者： Zhou, Tianxin Li, Xiang Lu, Haibing Department of Computer Science & Engineering Santa Clara University Santa Clara United States Dept. of Information Systems & Analytics Santa Clara University Santa Clara United States

Considering the attacks against the power grid, one of the most effective approaches could be the attack to the transmission lines that leads to large cascading failures. Hence, the problem of locating the most critical or vulnerable transmission lines for a Power Grid Cascading Failure (PGCF) has drawn much attention from the research society. There exists many deterministic solutions and stochastic approximation algorithms aiming to analyze the power grid vulnerability. However, it has been challenging to reveal the correlations between the transmission lines to identify the critical ones. In this paper, we propose a novel approach of learning such correlations via attention mechanism inspired by the Transformer based models that were initially designated to learn the correlation of words in sentences. Multiple modifications and adjustments are proposed to support the attention mechanism producing an informative correlation matrix, the Attention Matrix. With the Attention Ranking algorithm, we are able to identify the most critical lines. The proposed Dual PGCF model provide a novel and effective analysis to improve the power grid resilience against cascading failure, which is proved by extensive experiment results. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart power grids

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Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances

arXiv

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arXiv 2024年

作者： Zhang, Hanlei Xu, Hua Long, Fei Wang, Xin Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China School of Information Science and Engineering Hebei University of Science and Technology China Technology Development Co. Ltd Nanchang330036 China

Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training to establish well-initialized representations for subsequent clustering. An innovative strategy is proposed to dynamically select high-quality samples as guidance for representation learning, gauged by the density of each sample’s nearest neighbors. Besides, it is equipped to automatically determine the optimal value for the top-K parameter in each cluster to refine sample selection. Finally, both high- and low-quality samples are used to learn representations conducive to effective clustering. We build baselines on benchmark multimodal intent and dialogue act datasets. UMC shows remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain. The complete code and data are available at https://***/thuiar/UMC. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Exploring transfer learning for Deep NLP systems on rarely annotated languages

arXiv

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arXiv 2024年

作者： Yadav, Dipendra Strauß, Tobias Yordanova, Kristina University of Rostock Department of Computer Science and Electrical Engineering Institute of Visual & Analytic Computing Mobile Multimedia Information Systems Albert-Einstein-Str. 22 Rostock18059 Germany

Natural language processing has seen exponential growth since deep learning has been used for various tasks. Deep learning has replaced the old rule-based methods by providing better performance on almost all the tasks in NLP, as deep learning basically looks into the hidden patterns and structures in the data which were sometimes not detected and at other-times overlooked by the rule-based systems. The research and development in NLP have been however focused on some handful languages of the world given their financial importance and a huge number of speakers. This has left out most of the other languages unexplored. The other reason for the lack of research in these languages is the unavailability of properly annotated and the required amount of datasets which is very important for training deep learning neural networks. The situation looks grim. When we look at the world language tree, thankfully we see that most of the unexplored languages have other well-explored languages nearby which basically means that both the languages are kind of similar. So if we could somehow adapt research and development from one language to another, it could be really helpful for both the languages. The goal of this thesis work is to explore transfer learning for the task of Part-of-speech(POS) tagging between Hindi and Nepali languages which are part of the Indo-Aryan language family with very high similarity. We have tried to explore the possibility of jointly training a model for the task of POS tagging in both Hindi and Nepali language and see if it helps in improving the performance of the model. We also try to explore if multitask learning in the Hindi language can be helpful for the task of POS tagging with auxiliary tasks of the gender tagging and singularity/plurality tagging. The deep learning architecture used for this work is BLSTM-CNN-CRF. The model is trained with monolingual word embeddings, vector mapped embeddings, and also with jointly trained Hindi-Nepali word emb

关键词： Embeddings

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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

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29th European systems, Software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Conformal Prediction for STL Runtime Verification

Conformal Prediction for STL Runtime Verification

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Annual Allerton Conference on Communication, Control, and Computing

作者： Lars Lindemann Xin Qin Jyotirmoy V. Deshmukh George J. Pappas Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA

We are interested in predicting failures of learning-enabled systems during their operation. Particularly, we consider stochastic learning-enabled systems and signal temporal logic specifications, and we want to calculate the probability that the current system trajectory will violate the specification. The paper presents two predictive runtime verification algorithms that predict future system states from the current observed system trajectory. As these predictions may not be accurate, we construct prediction regions that quantify prediction uncertainty by using conformal prediction, a statistical tool for uncertainty quantification. Our first algorithm directly constructs a prediction region for the satisfaction measure of the specification so that we can predict specification violations with a desired confidence. The second algorithm constructs prediction regions for future system states first, and uses these to obtain a prediction region for the satisfaction measure. To the best of our knowledge, these are the first formal guarantees for a predictive runtime verification algorithm that applies to widely used trajectory predictors such as RNNs and LSTMs, while being computationally simple and making no assumptions on the underlying distribution. We present numerical experiments of an F-16 aircraft and a self-driving car.

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MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation

MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segme...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Jintao Cheng Xingming Chen Jinxin Liang Xiaoyu Tang Xieyuanli Chen Dachuan Li Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China School of Electronic and Information Engineering South China Normal University Foshan China College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Effectively summarizing dense 3D point cloud data and extracting motion information of moving objects (moving object segmentation, MOS) is crucial to autonomous driving and robotics applications. How to effectively utilize motion and semantic features and avoid information loss during 3D-to-2D projection is still a key challenge. In this paper, we propose a novel multi-view MOS model (MV-MOS) by fusing motion-semantic features from different 2D representations of point clouds. To effectively exploit complementary information, the motion branches of the proposed model combines motion features from both bird's eye view (BEV) and range view (RV) representations. In addition, a semantic branch is introduced to provide supplementary semantic features of moving objects. Finally, a Mamba module is utilized to fuse the semantic features with motion features and provide effective guidance for the motion branches. We validated the effectiveness of the proposed multi-branch fusion MOS framework via comprehensive experiments, and our proposed model outperforms existing state-of-the-art models on the SemanticKITTI benchmark. The implementation codes are available at https://***/Chengjt1999/MV-MOS.

关键词： Point cloud compression Solid modeling Three-dimensional displays Computational modeling Biological system modeling Semantics Neural networks Object segmentation Robots Autonomous vehicles

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Causal Effect Estimation from Observational and Interventional Data Through MatrixWeighted Linear Estimators

arXiv

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arXiv 2023年

作者： Kladny, Klaus-Rudolf Von Kügelgen, Julius Schölkopf, Bernhard Muehlebach, Michael Department of Computer Science Eth Zürich Switzerland Max Planck Institute for Intelligent Systems Tübingen Germany Department of Engineering University of Cambridge United Kingdom

We study causal effect estimation from a mixture of observational and interventional data in a confounded linear regression model with multivariate treatments. We show that the statistical efficiency in terms of expected squared error can be improved by combining estimators arising from both the observational and interventional setting. To this end, we derive methods based on matrix weighted linear estimators and prove that our methods are asymptotically unbiased in the infinite sample limit. This is an important improvement compared to the pooled estimator using the union of interventional and observational data, for which the bias only vanishes if the ratio of observational to interventional data tends to zero. Studies on synthetic data confirm our theoretical findings. In settings where confounding is substantial and the ratio of observational to interventional data is large, our estimators outperform a Stein-type estimator and various other baselines. © 2023, CC BY.

关键词： Regression analysis

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