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A potential game-based outsourcing computation framework for multiple calculators

Digital Communications and Networks 2022年第5期8卷 654-663页

作者： Duo Zhang Youliang Tian Linjie Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Mathematics and Statistics Guizhou UniversityGuiyang550025China

Outsourcing computation enables a computationally weak client to outsource the computation of a function f to a more powerful but untrusted *** traditional outsourcing computation model forbids communication between players,but it has little *** on the game theory,this paper establishes an outsourcing computation model which is more in line with the actual ***,we construct a structural mapping relationship between security outsourcing computation and the optimization ***,by designing the individual potential function and the global potential function,the individual goal is consistent with the global goal to ensure the correctness of the calculation ***,in the information exchange environment between calculators,we construct a Zero-determinant strategy to ensure that the calculator chooses the strategy according to the predetermined target.

关键词： Outsourcing computation Game theory Potential function Zero-determinant strategy Information exchange

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Planning with Logical Graph-Based Language Model for Instruction Generation 27

Planning with Logical Graph-Based Language Model for Instruc...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhang, Fan Jin, Kebing Zhuo, Hankz Hankui School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9781643685489

Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture strict logic from free-form texts. In this paper, we propose a novel graph-based language model, Logical-GLM, to extract strict logic from free-form texts and then infuse into language models. Specifically, we first capture information from natural language instructions and construct logical probability graphs that generally describe domains. Next, we generate logical skeletons to guide language model training, infusing domain knowledge into language models. At last, we alternately optimize the searching policy of graphs and language models until convergence. The experimental results show that Logical-GLM is both effective and efficient compared with traditional language models, despite using smaller-scale training data and fewer parameters. Our approach can generate instructional texts with more correct logic owing to the internalized domain knowledge. Moreover, the search of logical graphs reflects the inner mechanism of the language models, which improves the interpretability of black-box models. © 2024 The Authors.

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Confidence Aware Inverse Constrained Reinforcement Learning 41

Confidence Aware Inverse Constrained Reinforcement Learning

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41st International Conference on Machine Learning, ICML 2024

作者： Subramanian, Sriram Ganapathi Liu, Guiliang Elmahgiubi, Mohammed Rezaee, Kasra Poupart, Pascal Vector Institute for Artificial Intelligence Toronto Canada School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen518172 China Huawei Technologies Canada Canada Cheriton School of Computer Science University of Waterloo Canada

In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement learning (RL) agents need these constraints to learn the correct optimal policy in these settings. The field of Inverse Constraint Reinforcement Learning (ICRL) deals with this problem and provides algorithms that aim to estimate the constraints from expert demonstrations collected offline. Practitioners prefer to know a measure of confidence in the estimated constraints, before deciding to use these constraints, which allows them to only use the constraints that satisfy a desired level of confidence. However, prior works do not allow users to provide the desired level of confidence for the inferred constraints. This work provides a principled ICRL method that can take a confidence level with a set of expert demonstrations and outputs a constraint that is at least as constraining as the true underlying constraint with the desired level of confidence. Further, unlike previous methods, this method allows a user to know if the number of expert trajectories is insufficient to learn a constraint with a desired level of confidence, and therefore collect more expert trajectories as required to simultaneously learn constraints with the desired level of confidence and a policy that achieves the desired level of performance. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Design, Mathematical Modeling, and Control of an Underactuated 3-DOF Experimental Helicopter

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IEEE Access 2024年 12卷 55568-55586页

作者： Makki, Osamah Talal Moosapour, Seyyed Sajjad Mobayen, Saleh Nobari, Jafar Heyrani Shahid Chamran University of Ahvaz Faculty of Engineering Ahvaz*** Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu Yunlin640301 Taiwan K. N. Toosi University of Technology Department of Electrical and Computer Engineering Tehran19697 Iran

This article covers the design, implementation, mathematical modelling, and control of a multivariable, underactuated, low-cost, three-degrees-of-freedom experimental helicopter system (namely a 3-DOF helicopter). The system is considered an essential benchmark for the development and evaluation of control algorithms and identification methods as it mimics real helicopters and UAVs. The system is multivariable and difficult to control due to its underactuation characteristics. In addition, it presents further challenges in modelling and control analysis in terms of high nonlinearity, coupling, uncertainty, and unmodeled dynamics. In this scene, the system is designed, developed, and operated virtually in SolidWorks using the necessary hardware and software. A new accurate multivariable coupled nonlinear mathematical model is derived and proposed. The model takes into account the complex dynamics and interactions between the system components, providing a more comprehensive representation of the real system compared to previous models. System parameters are then identified using the Sum of the Square Error (SSE) algorithm, and the mathematical model is compared to the practical results in many different scenarios via Simulink. Experimental tests confirmed the mathematical model and robust performance of the implemented experimental 3-DOF helicopter system, as well as the ability of the proposed controller to stabilize the helicopter and track different trajectories in the presence of uncertainties and cross-coupling effects. © 2013 IEEE.

关键词： Iron

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An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 113-127页

作者： Zhao, Kaixin Ren, Menghui College of Computer Science and Technology Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Manufacturing IoT Big Data Engineering Technology Research Center of Henan Province Xinxiang453003 China

In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are not just directly connected but also engage in a sophisticated feedback loop crucial for spatial encoding during navigation. We developed an innovative network model based on this feedback mechanism, linking entorhinal inputs to hippocampal spatial encoding cells through a non-negative sparse coding, using grid cells and weak spatial cells as inputs. Our findings demonstrate that the model learns and adjusts the characteristics of spatial encoding cells, successfully learning the specific spatial selectivities of place cells even with inputs from cells carrying weaker spatial information. This highlights the critical role of feedback encoding in refining spatial representations, an indispensable component of the brain’s navigation system. Moreover, experiments comparing our model with others validate our model’s exceptional ability in generating precise spatial encodings, underscoring its superiority. This research not only deepens our understanding of the brain’s navigational mechanisms but also offers a robust computational framework for future neuroscience studies, emphasizing the intricate interplay between feedback loops and spatial encoding in the brain’s navigation system. © The Institution of Engineers (India) 2024.

关键词： Brain

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Analysis of Discharge Parameters Influenced by Electrode Velocity Based on LSTM Prediction Model 16

Analysis of Discharge Parameters Influenced by Electrode Vel...

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16th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2022

作者： He, Xiusi Ruan, Fangming Yin, Lan Chen, Yanli Yang, Yaru Wang, Xiao School of Big Data and Computer Science Guizhou Normal University Guiyang China

ISBN: (纸本)9781665490672

Relationships were discussed in this work between discharge current and electrode moving speed, ionization coefficient, field strength, gas pressure, temperature, humidity and other factors. Gas flow distribution around electrode, according to Bernoulli's law in aerodynamics, was analyzed during the electrode moving toward the target. Mechanism was explained on forming of local part low vacuum in the discharge gap. Two sub-processes of gas ionization avalanche process and surface electronic emission process were used to describe small gap discharge. With Paschen's law, analysis of influence was performed on discharge current by electrode moving speed toward the target. Experiments were conducted many times based on test platform of electrostatic discharge electrode moving speed effect, while experiment data were recorded. Long Short-Term Memory network (LSTM) model was used to train and learn experimental data, predicted peak value of discharge current at different electrode moving speeds and rise time. The experimental results show that there is a strong correlation between the electrode moving speed and the peak discharge current, while there is a certain correlation between the electrode moving speed and the current rise time, but it is relatively weak. The research results maybe have some reference significance for the proposal and formulation of non-contact electrostatic discharge standards. © 2022 IEEE.

关键词： Long short-term memory

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks 38

Model LEGO: Creating Models Like Disassembling and Assemblin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Gao, Jing Ye, Jingwen Gao, Yang Wang, Xingen Feng, Zunlei Song, Mingli College of Computer Science and Technology Zhejiang University China Robotics Institute School of Computer Science Carnegie Mellon University United States Electrical and Computer Engineering National University of Singapore Singapore School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Bangsheng Technology Co. Ltd. China

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components. The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline, demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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Fixed-Time Consensus Tracking of Nonlinear Multi-Agent Systems Under DoS Attacks 63

Fixed-Time Consensus Tracking of Nonlinear Multi-Agent Syste...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Wang, Xinhe Wen, Guanghui Zheng, Wei Xing Southeast University School of Cyber Science and Engineering Nanjing211189 China Southeast University School of Mathematics Department of Systems Science Nanjing211189 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9798350316339

This paper studies the fixed-time consensus tracking problem of nonlinear multi-agent systems, where communication links are subjected to denial-of-service (DoS) attacks. The DoS attacks make the communication networks switch arbitrarily and therefore the network connectivity may be broken, which poses a serious challenge to the consensus tracking problem within the fixed-time framework. To deal with this issue, a switching-based fixed-time control strategy is proposed. Critical conditions for fixed-time consensus tracking of nonlinear multi-agent systems under DoS attacks are developed. In particular, the adverse impact of DoS attacks on the settling time can be clearly reflected by the fixed-time strategy derived in this paper. Finally, numerical examples are provided to validate the theoretical analysis. © 2024 IEEE.

关键词： Denial-of-service attack

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Multi-level Learning for Semi-supervised Multi-view Classification 11

Multi-level Learning for Semi-supervised Multi-view Classifi...

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11th International Conference on Advanced Cloud and Big data, CBD 2023

作者： Song, Na Yang, Shundong Cai, Zhiling Lin, Jian Huang, Sujia Putian University School of Mechanical Electrical and Information Engineering Putian China Hainan University School of Computer Science and Technology Haikou China Fujian Agriculture and Forestry University College of Computer and Information Science Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350345346

Multi-view semi-supervised classification primarily aims to enhance classification accuracy when dealing with limited labeled samples. Although existing methods have shown impressive performance, significant challenges still persist in efficiently propagating label information and capturing global relationship information within multi-view data. To address the aforementioned challenges, a Graph Convolutional Network (GCN)-based framework is presented, which explores the acquisition of multilevel representations, encompassing feature-level representations and the fusion of global structural associations. The state-of-the-art performance of the proposed model is unequivocally supported by a comprehensive set of empirical results. © 2023 IEEE.

关键词： computer vision

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