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Synthesizing Invariants for Polynomial Programs by Semidefinite Programming

ACM Transactions on Programming Languages and systems 2025年第1期47卷 1-35页

作者： Wu, Hao Wang, Qiuye Xue, Bai Zhan, Naijun Zhi, Lihong Yang, Zhi-Hong State Key Lab. of Computer Sciences Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Fermat Labs Huawei Inc. Dongguan China Key Lab. of System Software and State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technology School of Computer Science Peking University Beijing China School of Mathematics and Statistics Central South University Changsha China

Constraint-solving-based program invariant synthesis takes a parametric invariant template and encodes the (inductive) invariant conditions into constraints. The problem of characterizing the set of all valid parameter assignments is referred to as the strong invariant synthesis problem, while the problem of finding a concrete valid parameter assignment is called the weak invariant synthesis problem. For both problems, the challenge lies in solving or reducing the encoded constraints, which are generally non-convex and lack efficient solvers. In this article, we propose two novel algorithms for synthesizing invariants of polynomial programs using semidefinite programming (SDP): (1) The Cluster algorithm targets the strong invariant synthesis problem for polynomial invariant templates. Leveraging robust optimization techniques, it solves a series of SDP relaxations and yields a sequence of increasingly precise under-approximations of the set of valid parameter assignments. We prove the algorithm’s soundness, convergence, and weak completeness under a specific robustness assumption on templates. Moreover, the outputs can simplify the weak invariant synthesis problem. (2) The Mask algorithm addresses the weak invariant synthesis problem in scenarios where the aforementioned robustness assumption does not hold, rendering the Cluster algorithm ineffective. It identifies a specific subclass of invariant templates, termed masked templates, involving parameterized polynomial equalities and known inequalities. By applying variable substitution, the algorithm transforms constraints into an equivalent form amenable to SDP relaxations. Both algorithms have been implemented and demonstrated superior performance compared to state-of-the-art methods in our empirical evaluation. © 2025 Copyright held by the owner/author(s).

关键词： Polynomial approximation

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Laser Scribed CNTs/PEEK/TPU Composites Film for High-Performance Stretchable Electrodes

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IEEE Sensors Journal 2025年第10期25卷 16666-16674页

作者： Jiang, Caiyun Tang, Lei Hou, Jian Ding, Guqiao Sheng, Bin University of Shanghai for Science and Technology College of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instrument and System Shanghai200093 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences State Key Laboratory of Materials for Integrated Circuits Shanghai200050 China

In this article, an elastic composite material comprising carbon nanotubes (CNTs), polyether-ether-ketone (PEEK), and thermoplastic polyurethane (TPU) is presented, in which direct laser-induced graphitization was used to fabricate high-performance stretchable electrodes. The introduction of CNTs can enhance the conductivity and mechanical strength of the composites, thus improving the overall performance of the sensors. Flexible strain sensors with porous structures were prepared by appropriate encapsulation methods, which exhibited superior gauge factors (GFs) of 72, 367, and 972 in the 0%–30%, 30%–44%, and 44%–53% strain regions, respectively. Furthermore, it features a rapid response time (100 ms), excellent stability and durability, and is capable of detecting both large-scale human movement and monitoring of small physical signals. In addition, patterned laser-induced graphene interdigital electrodes (LIG-IDEs) were fabricated on composites using laser light. Highly sensitive humidity sensors were obtained using graphene oxide as the humidity-sensing material for respiratory monitoring and noncontact sensing. The thermoplastic properties of TPU allow the straight fiber to be thermoformed into highly stretchable helical electrode, which was of a helix index of 5 and with a high-quality factor (Q = 130) at 1250% strain. In conclusion, the preparation of LIG on stretchable composite films by direct laser scribing shows a promising approach for large-scale fabrication of wearable devices. © 2001-2012 IEEE.

关键词： Humidity sensors

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy system

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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Path Optimization Method Under UAV Charging Scheduling Network 24th

Path Optimization Method Under UAV Charging Scheduling Netwo...

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24th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Yang, Tingting Wang, Yiqian Zhu, Jie Chang, Shuyu Huang, Haiping Nanjing University of Posts and Telecommunications Nanjing210003 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9789819615278

Unmanned Aerial Vehicles (UAVs) have emerged as integral components in logistics systems, where their potential for efficient delivery services is being explored. However, the limited battery capacity of UAVs poses a significant challenge for long-distance delivery applications. In this paper, we investigate a hybrid problem involving UAV trajectory planning and charging scheduling for long-distance logistics systems with a charging network. In the problem under study, multiple UAVs are assigned long-distance delivery tasks. A UAV can stop at any charging station in the charging network to charge if it is in a low-energy state. An Ant Colony Optimization-based UAV Trajectory Planning and Charging Scheduling (ACO-TPCS) algorithm is proposed to minimize task completion time by optimizing UAV trajectory and charging plans. The main framework of the ACO-TPCS algorithm consists of several key components, including reachable graph construction, candidate flight path generation, pheromone matrix construction, ant generation, feasible solution generation, and pheromone update method. All these components are delicately designed. Through extensive experimentation and comparison with baseline algorithms, we demonstrate the effectiveness of the ACO-TPCS algorithm in addressing the problem under study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 1877-1894页

作者： You, Zhichao Dong, Xuewen Liu, Ximeng Gao, Sheng Wang, Yongzhi Shen, Yulong Xidian University School of Computer Science and Technology The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Fuzhou University College of Computer and Data Science Fuzhou350025 China Central University of Finance and Economics School of Information Beijing102206 China California State Polytechnic University Pomona Department of Computer Information Systems PomonaCA91768 United States Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants. © 2024 IEEE.

关键词： Data privacy

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Air traffic complexity assessment based on multi-scale spatio-temporal data 4

Air traffic complexity assessment based on multi-scale spati...

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2024 4th International Conference on Intelligent Traffic systems and Smart City, ITSSC 2024

作者： Zhou, Zhihui Chen, Haiyan Fu, Yirui Yuan, Ligang State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510686304

Accurately assessing air traffic complexity is crucial for ensuring flight safety and efficiency. In this paper, we propose an air traffic complexity assessment method based on multi-scale spatio-temporal images. We first convert numerical air traffic data collected by surveillance radar into image data, and then interpolate the spatial and temporal scales of the images to generate multiple sets of images with different spatial and temporal scales. We found the best image set based on the Circle metric learning model by evaluating the performance of 20 image sets combined from different spatiotemporal scales. On this image set, we applied multiple deep metric learning methods to assess air traffic complexity. The results show that the new dataset performs better on several assessment metrics, confirming the validity of the approach to improve air traffic complexity assessment by adjusting the data scale. This demonstrates that the accuracy of air traffic complexity assessment can be effectively improved by adjusting the data scale. It also demonstrates the importance of choosing the right scale of dataset to improve the performance of machine learning algorithms. © 2025 SPIE.

关键词： Air transportation

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A flight slots allocation method for single airport based on Q-Learning algorithm 4

A flight slots allocation method for single airport based on...

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2024 4th International Conference on Intelligent Traffic systems and Smart City, ITSSC 2024

作者： Fang, Xinyi Chen, Haiyan Qian, Yuhang Yang, Lei State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510686304

The air traffic demand surpasses the capacity of most of the busiest airports worldwide. The mismatch between airport capacity and air traffic demand leads to serious congestion and delay problems. Since airport capacity is difficult to expand in a short time, in order to reduce flight delays, we need to adjust and optimize flight slots for flight applications, and allocate flight time resources scientifically and efficiently under the premise of meeting airspace resource constraints such as airport capacity. In this paper, we proposed an airport flight slots allocation method based on the Q-Learning algorithm in reinforcement learning. Firstly, we preprocess the original data set. Data cleaning processes data with missing attribute values and removes irrelevant attributes. Through data transformation, the data of string attribute is transformed into discrete numbers, and the time attribute is processed. After that, we construct a mathematical model of single-airport flight time allocation, which is a combinatorial optimization problem and determines the optimization objectives and constraints. Then we apply the reinforcement learning algorithm Q-Learning to solve this optimization problem, and the algorithm updates the strategy through interaction with the environment to get a better solution. Finally, we take Guangzhou Baiyun International Airport as a study case to train and verify the proposed single-airport flight time allocation model. The experimental results show that, the optimized flight slots satisfy the airport capacity constraints while minimizing the adjustment of flight schedules as much as possible, and adjust the flights at some times to alleviate the congestion. © 2025 SPIE ·

关键词： Combinatorial optimization

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Optimizing Self-training Sample Selection for Euphemism Detection in Special Scenarios 24th

Optimizing Self-training Sample Selection for Euphemism Dete...

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24th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Zhang, Zhiwei Chang, Shuyu Huang, Haiping Wang, Rui Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Nanjing University of Posts and Telecommunications Nanjing210023 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China

ISBN: (纸本)9789819615476

Euphemism, which is often used to disguise true intentions or soften the tone of speech, has garnered significant attention for its detection in recent years. Current approaches to euphemism detection struggle with uncommon data, where new and rare vocabularies frequently emerge in the test set, leading to decreased model performance. To address these challenges, we propose a novel framework that combines data augmentation, self-training, and a special sample selection method. We expand the original dataset using lexical and sentence-level data augmentation techniques, enhancing the representation of rare vocabulary and allowing the model to familiarize itself with their contexts and usage characteristics. Next, we employ self-training as a semi-supervised learning approach, utilizing the augmented datasets to iteratively generate and refine pseudo-labels. During each iteration, a KNN framework is used to generate pseudo-labels by searching for the nearest neighbor texts, thus enhancing the quality of these labels. Finally, to further increase the presence of rare vocabulary in the training data, we implement a sample selection strategy based on euphemism frequency and predictive uncertainty, integrating it with the earlier steps. Experimental results demonstrate that our framework outperforms previous research on most metrics and achieves a 10% increase over the baseline model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Towards an effective method of ReDoS detection for non-backtracking engines 24

Towards an effective method of ReDoS detection for non-backt...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Weihao Su Hong Huang Rongchen Li Haiming Chen Tingjian Ge Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences and University of Chinese Academy of Sciences Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Miner School of Computer & Information Sciences University of Massachusetts Lowell

ISBN: (纸本)9781939133441

Regular expressions (regexes) are a fundamental concept across the fields of computer science. However, they can also induce the Regular expression Denial of Service (Re-DoS) attacks, which are a class of denial of service attacks, caused by super-linear worst-case matching time. Due to the severity and prevalence of ReDoS attacks, the detection of ReDoS-vulnerable regexes in software is thus vital. Although various ReDoS detection approaches have been proposed, these methods have focused mainly on backtracking regex engines, leaving the problem of ReDoS vulnerability detection on non-backtracking regex engines largely *** address the above challenges, in this paper, we first systematically analyze the major causes that could contribute to ReDoS vulnerabilities on non-backtracking regex engines. We then propose a novel type of ReDoS attack strings that builds on the concept of simple strings. Next we propose EVILSTRGEN, a tool for generating attack strings for ReDoS-vulnerable regexes on non-backtracking engines. It is based on a novel incremental determinisation algorithm with heuristic strategies to lazily find the k-simple strings without explicit construction of finite automata. We evaluate EVILSTRGEN against six state-of-the-art approaches on a broad range of publicly available datasets containing 736,535 unique regexes. The results illustrate the significant efficacy of our tool. We also apply our tool to 85 intensively-tested projects, and have identified 34 unrevealed ReDoS vulnerabilities.

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Cepstrum Lifting and Coherent Averaging-Based Instantaneous Frequency Estimation of Low-Amplitude Signal Components for Rotating Machinery

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IEEE Sensors Journal 2025年第10期25卷 17479-17490页

作者： Liu, Shaokang Liu, Shuai Wu, Yingjie Liu, Weiliang Liu, Changliang Luo, Zhihong North China Electric Power University Control and Computer Engineering Department Beijing102200 China Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China Northeast Electric Power University Automation Engineering Department Jilin132000 China Inner Mongolia University of Technology Electric Power Department Hohhot010080 China

Instantaneous frequency (IF) serves multiple critical functions in the prognostic and health management of rotating machinery. The current IF estimation methodologies primarily concentrate on enhancing estimation accuracy at the algorithmic level. However, in practical industrial applications, the target signal component is often influenced by varying operating conditions, structural resonances, and background noise, causing it to become submerged or even disappear, ultimately resulting in the failure of conventional IF estimation techniques. To address these challenges, a novel IF estimation approach incorporating signal quality enhancement is developed. First, the complementary multisource vibration signal is processed by the proposed segmented cepstrum lifting (CL) method to overcome the submersion of the resonant frequency band on the target component. Then, the processed segmented signals are fused using coherent averaging (CA) technology to enhance the amplitude of the target component. Finally, leveraging the first-order approximation capability of the short-time fractional Fourier transform (STFrFT) for nonlinear frequency modulation modes, precise IF estimation is achieved. Verified by field data, the proposed method outperforms mainstream methods in both accuracy and applicability, highlighting its industrial application prospects. © 2001-2012 IEEE.

关键词： Frequency estimation

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