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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jing, Fengrui Ding, Hongzhen Song, Tao Qingdao Institute of Software College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350368741

Seeking effective numerical approximations for partial differential equations (PDEs) is a major challenge in modern science and technology. Recently, AI-inspired data-driven solvers, such as neural operators, have achieved great success in quickly PDE solving. However, in the design of neural operators, the processing of frequency domain information is crucial, while the importance of low-frequency information is excessively overlooked. In order to reduce low-frequency error in PDE solving, we propose the multi scale spectral mixture neural operator (MSSMNO) architecture and design a residual learning structure that transfers residuals in multi-scale cycle by combining the frequency domain learning patterns of the FNO method. Meanwhile, we designed interpolation operator and restriction operator to effectively transmit and reconstruct high-frequency information in MSSMNO. Experimentally, MSSMNO achieves state-of-the-art and yields a relative error reduction of 21.9% averaged on four classical benchmarks. © 2025 IEEE.

关键词： neural operator PDE

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Provably secure authentication protocol for traffic exchanges in unmanned aerial vehicles

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High-Confidence Computing 2023年第4期3卷 60-72页

作者： Vincent Omollo Nyangaresi Department of Computer Science&Software Engineering Jaramogi Oginga Odinga University of Science&TechnologyKenya

Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled *** addition,they have been deployed in civilian critical infrastructure *** this environment,real-time and massive data is exchanged between the aerial vehicles and the ground control *** on the mission of these aerial vehicles,some of the collected and transmitted data is sensitive and ***,many security protocols have been presented to offer privacy and security ***,majority of these schemes fail to consider attack vectors such as side-channeling,de-synchronization and known secret session temporary information *** last attack can be launched upon adversarial physical capture of these *** addition,some of these protocols deploy computationally intensive asymmetric cryptographic primitives that result in high *** this paper,an authentication protocol based on lightweight quadratic residues and hash functions is *** formal security analysis is executed using the widely deployed random oracle *** addition,informal security analysis is carried out to show its robustness under the Dolev–Yao(DY)and Canetti–Krawczyk(CK)threat *** terms of operational efficiency,it is shown to have relatively lower execution time,communication costs,and incurs the least storage costs among other related ***,the proposed protocol provides a 25%improvement in supported security and privacy features and a 6.52%reduction in storage *** overall,the proposed methodology offers strong security and privacy protection at lower execution time,storage and communication overheads.

关键词： Authentication Drones Quadratic residues Privacy Security Side-channeling UAV

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E±cient Construction of Practical Python Call Graphs with Entity Knowledge Base

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International Journal of software Engineering and Knowledge Engineering 2024年第7期34卷 999-1024页

作者： Cao, Yulu Chen, Lin Chen, Zhifei Zhong, Jiacheng Zhang, Xiaowei Wang, Linzhang State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

Call graphs facilitate various tasks in software engineering. However, for the dynamic language Python, the complex language features and external library dependencies pose enormous challenges for building the call graphs of real projects. Some program analysis techniques used for call graph construction in other languages are impractical for Python. In this paper, we present STAR, a practical technique for the construction of Python static call graphs. We reformulate call graph construction as an entity identification task. STAR leverages inter-module summary and cross-project dependencies to construct a fine-grained entity knowledge base to identify the possible nodes and edges of the call graph in the code, and then construct the call graph. Our evaluation of three benchmarks shows that (1) STAR improves recall in three benchmarks compared to three baseline tools. Especially, STAR improves the recall of reachable nodes and reachable edges compared with the state-of-the-art tool by 11.3% and 9.8%, respectively;(2) STAR achieves comparable performance as three baseline tools in execution time and memory usage and is more efficient in large projects;(3) STAR can be effectively used for the task of detecting vulnerability propagation with real-world cases. We expect our results will attract more exploration of practical methods and improve the application of Python call graphs. © World Scientific Publishing Company.

关键词： Python

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Analysis for Microservice Architecture Application Quality Model and Testing Method 26

Analysis for Microservice Architecture Application Quality M...

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

作者： Gong, Jiayu Cai, Lizhi Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai China

ISBN: (纸本)9798350345865

In recent years, microservice architectures have benefited from low coupling and high cohesion characteristics, allowing flexible on-demand deployment of complex applications and simplifying the difficulty of developing and maintaining large software systems. However, there is limited research on quality models and testing methods for microservice architectures, which leads to the problem that microservice architecture applications are difficult to test and evaluate. This paper analyzes the main problems when microservice architecture applications face quality measurement and software testing, further improves and refines the quality model of microservice architecture applications on the basis of the existing ISO software product quality model, and introduces testing methods for microservice architecture applications, which can provide reference for the construction of microservice architecture applications. © 2023 IEEE.

关键词： software testing

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Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic Science and technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China College of Computer Science&Software Engineering Shenzhen UniversityShenzhen518060China School of Computer Science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network Multi-head attention mechanism Spatio-temporal dependency Traffic flow prediction

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Improving Fully Non-Autoregressive Translation with Pre-Trained Language Models 24

Improving Fully Non-Autoregressive Translation with Pre-Trai...

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3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Fu, Zijian Wang, Shuheng School of Computer and Software Nanyang Institute of Technology Henan Nanyang China

ISBN: (纸本)9798400712692

In recent years, Non-Autoregressive Translation (NAT) has received lots of attention because of its outstanding decoding speed. However, there is a certain gap between the NAT model and its autoregressive comparator. It, how to effectively narrow this gap, remains to be further investigated. In this work, we use BERT to improve the performance of NAT model. Specifically, we add a CTC model on the top of BERT for extracting semantic and learning alignment. In order to test the effect of the model, we conducted the experiments on three public translation datasets. The experimental results show that our model can significantly outperforms existing non-autoregressive baselines. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Neural machine translation

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RoSense: Refining LOS Signal Phase for Robust RFID Sensing via Spinning Antenna

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Performance Evaluation Review 2023年第4期50卷 53-55页

作者： Zhu, Yinan Duan, Chunhui Ding, Xuan Tsinghua University School of Software and BNRist Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China

RFID sensing leveraging backscatter signal features (e.g., phase shift) from tags has gained increasing popularity in numerous applications, but also suffers from negative impacts of environmental multipaths. Past works to address it rely on extra customized devices, labor-intensive offine training or frequency channel hopping, all of which are nonubiquitous or ineffective for real-life adoption. This paper presents RoSense, a universal method to alleviate multipath reflections' impacts by spinning the reader antenna, thus enabling more robust RFID sensing. Besides, RoSense requires no RF devices or offline training, and operates in a nonintrusive manner. The key insight of RoSense is to exploit two properties of line-of-sight (LOS) signal when spinning the antenna, i.e., the linearity of phase changes and stability of received signal strength to attenuate the non-linear and non-monotonic effect of multipath signals and refine the phase shift of LOS signal. We have implemented a prototype of RoSense with COTS devices and studied two cases for evaluation: material identification and object localization. Experimental results show that RoSense can improve the material identification accuracy by 16.22% and reduce the mean localization error by 39.93%, greatly outperforming the state-of-the-art solutions. © 2023 Copyright is held by the owner/author(s).

关键词： Mobile antennas

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Blockchain-based IVPPA Scheme for Pseudonym Privacy Protection in Internet of Vehicles

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1260-1277页

作者： Wang, Hui Zhang, Fangyu Shen, Zihao Liu, Peiqian Liu, Kun School of Software Henan Polytechnic University Jiaozuo454000 China School of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China

Existing Vehicular Ad-hoc Networks (VANETs), while enabling vehicles to communicate with each other, share data, and connect to external networks, also face a large number of data security challenges, such as data leakage and hijacking, cyber-attacks, and malware. For the security and privacy of vehicle user data in Internet of Vehicles (IoV), this paper proposes a blockchain-based IVPPA scheme for pseudonym privacy protection in IoV using a blockchain to assist the security of VANETs authentication and key certificate protocols. Firstly, Elliptic Curve Cryptography (ECC) and secure hash functions are introduced in the message signing and authentication scheme technique. Second, the hash operation is performed on the vehicle certificate and the certificate hash value and the certificate are stored on the blockchain, and the mutual authentication between the vehicle and the roadside unit (RSU) is realized by querying the hash value on the blockchain, which improves the effectiveness of the mutual authentication between the entities. Finally, the improved Merkle Patricia Trie (MMPT) is used to store and manage the pseudonyms assigned to the vehicle efficiently, thus avoiding the tracking of the vehicle path and the leakage of the vehicle information. Simulation experiments show that this scheme has significantly lower average latency in computing overhead and byte size in communication overhead than the comparison scheme, in which the communication overhead is only 232 bytes, which is about 50% of the highest overhead of the compared schemes, and has better authentication performance and stronger feasibility in the context of vehicle networking. © 2024, J. Network Intell. All rights reserved.

关键词： Blockchain

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A Coarse-to-Fine Prototype Learning Approach for Multi-Label Few-Shot Intent Detection

A Coarse-to-Fine Prototype Learning Approach for Multi-Label...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Xiaotong Li, Xinyi Zhang, Feng Wei, Zhiyi Liu, Junfeng Liu, Han School of Software Technology Dalian University of Technology China School of Computer Science Peking University China

ISBN: (纸本)9798891761681

Few-shot intent detection is a challenging task, particularly in scenarios involving multiple labels and diverse domains. This paper presents a novel prototype learning approach that combines the label synset augmentation and the coarse-to-fine prototype distillation for multi-label few-shot intent detection. To tackle the data scarcity issue and the lack of information for unseen domains, we propose to enhance the representations of utterances with label synset augmentation and refine the prototypes by distilling the coarse domain knowledge from a universal teacher model. To solve the multilingual intent detection in real-world dialogue systems, we fine-tune a cross-lingual teacher model to make our method fast adapt to different languages and re-annotate two non-English task-oriented dialogue datasets CrossWOZ and JMultiWOZ in multi-label form. Experimental results on one English and two non-English datasets demonstrate that our approach significantly outperforms existing methods in terms of accuracy and generalization across different domains. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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WSN Data Fusion Algorithm Based on Radial Cluster and Elephant Swarm Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2137-2150页

作者： Liu, Jia-Wei An, Wei-Peng School of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China School of Software Henan Polytechnic University Jiaozuo454000 China

To minimize the propagation of redundant data in wireless sensor networks, conserve energy, and extend network lifespan, we propose an algorithm (R-IEHOBP) that combines radial clustering and an elephant swarm neural network. Initially, we suggest employing the radial clustering routing protocol to address the issue of uneven energy consumption in nodes caused by the majority of current data fusion algorithms based on the LEACH clustering routing protocol. In the radial clustering protocol, we first divide candidate cluster heads into clusters of different sizes, and then we screen the cluster heads based on the number of required cluster head nodes in the sub-cluster, the remaining energy of the candidate nodes, distance from the center of gravity, and the number of nearby one-hop nodes. Secondly, we improve the elephant swarm optimization algorithm by introducing chaotic sequences, elite strategies, adversarial learning, and guided substitution principles. Next, we use the elephant group algorithm to select the initial matrices of weights and thresholds for the neural network to avoid the impact of unsuitable initial parameters on the final convergence speed and output accuracy. Addressing issues in single-stage neural network data fusion, such as incomplete data processing and large errors in the fused data, this paper proposes using a second-stage neural network to fuse the initial data received by the cluster head node and output it to the aggregation node. Finally, we establish a WSN data fusion model based on the radial clustering routing protocol by combining the radial clustering structure of wireless sensor networks with the neural network structure. Simulation experiment results demonstrate that the algorithm exhibits better redundant data rejection ability compared to similar algorithms, with lower energy consumption and higher data fusion accuracy. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Spatio-temporal data

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