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Asia-Pacific Conference on Software Engineering

作者： Guang Yang Yanlin Zhou Xiang Chen Chi Yu School of Information Science and Technology Nantong University China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China

ISBN: (纸本)9781665437851

Pseudo-code written by natural language is helpful for novice developers' program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo.

关键词： Codes Semantics Natural languages Writing Feature extraction Transformers Generators

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Multi-Strategy Bus Deviation-Driven Layer Assignment Algorithm

Multi-Strategy Bus Deviation-Driven Layer Assignment Algorit...

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Electronics Design Automation (ISEDA), International Symposium of

作者： Yantao Yu Zepeng Li Jiarui Chen Xing Huang Genggeng Liu Ning Xu College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China School of Computer Science Northwestern Polytechnical University Xian China School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

In modern very large-scale integration (VLSI) design, the solution quality of the bus routing is a crucial factor that determines the timing and power of circuit, and finally affects the performance and yield of chips. Taking bus deviation as the main optimization objective, an effective multi-strategy bus deviation-driven layer assignment algorithm is proposed to solve the timing-matching problem of bus routing. First, a net priority determination method that integrates multiple features is presented to determine the layer assignment order, thus obtaining a routing sequence which can weigh the wirelength and bus deviation well. Second, an effective single net layer assignment algorithm is proposed to assign each net based on dynamic programming, thus reducing the number of vias. Third, a layer shifting strategy based on the bus lookup table is designed to effectively balance total wirelength and bus deviation by sacrificing a certain number of vias. Experimental results, compared to existing work, show that the proposed algorithm can achieve significant optimization on the bus deviation and total wirelength, and finally obtain the best results in terms of the bus deviation, which is the most important optimization objective for bus routing.

关键词： Design automation Heuristic algorithms Very large scale integration Routing Timing Pins Dynamic programming

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Blockchain and Trusted Reputation Assessment-Based Incentive Mechanism for Healthcare Services

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SSRN 2023年

作者： Liu, Yanhua Liu, Zhihuang Zhang, Qiu Su, Jinshu Li, Xiaoyan College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer National University of Defense Technology Changsha410073 China

Blockchain-based healthcare IoT technology research promotes increased security for smart healthcare services such as real-time monitoring and remote disease diagnosis. In a healthcare IoT service system, it remains a challenging problem to explore effective incentive mechanisms throughout the healthcare service process and feedback to facilitate the efficiency of the blockchain consensus process. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and stores the assessment results credibly on the blockchain. Then, we propose an improved PBFT algorithm based on the reputation assessment to enhance blockchain consensus efficiency. Finally, BtRaI designs a token reward and punishment mechanism to motivate all parties to participate in the blockchain honestly, inhibit potential misbehavior, and encourage the healthcare system to provide better service quality. Theoretical analysis and experimental evaluation demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services, improves blockchain node failure tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 seconds in a 100-node consortium chain. The reputation assessment and token incentive mechanism of BtRaI have a realistic differentiation granularity and change curve, making it suitable for dynamic and complex healthcare service scenarios. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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A Novel DDoS Detection Model for SDN Using Single-Class Cluster Oversampling and Weighted Ensemble Method

A Novel DDoS Detection Model for SDN Using Single-Class Clus...

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International Conference on network Protocols

作者： Hao Zhang Shuqi Wu Jie Pan Zhi Wang Xiaolong Sun College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The centralized control plane characteristic of Software Defined networking (SDN) makes it a prime target for Distributed Denial of Service (DDoS) attacks. A significant issue in network traffic data is the severe imbalance between background traffic and various types of DDoS attack traffic. To address this challenge, we propose a novel DDoS detection model based on single-class cluster oversampling and weighted ensemble method. Initially, we design a multi-strategy feature selection using variance inflation factor and recursive feature elimination. Next, we construct a single-class cluster model based on the spatial distribution of samples, and create minority class samples independently within each cluster. Finally, detection results are determined using the weight vector of each base classifier in ensemble learning. The proposed DDoS detection model's performance is validated through simulations of various DDoS attack scenarios in an SDN environment. Experimental results indicate that the proposed strategy exhibits superior performance, showing improvements in precision, recall, and F1 score compared to state-of-the-art techniques.

关键词： Interpolation Protocols Telecommunication traffic Denial-of-service attack Feature extraction Vectors Data models Ensemble learning Computer crime Software defined networking

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Energy-Efficient Task Allocation for VFI-Based Real-Time Multi-core Systems

Energy-Efficient Task Allocation for VFI-Based Real-Time Mul...

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International Conference on information Science and Cloud computing Companion (ISCC-C)

作者： Xiaodong Wu Yuzhu Zeng Jianjun Han College of Mathematics and Computer Science Quanzhou Normal University Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian China College of Computer Science and Technology Huazhong University of Science and Technology Wuhan Hubei China

Chip Multiprocessor (CMP) has become computing engine for a wide spectrum of applications due to its higher throughput and better energy efficiency. The problem of optimal task-to-core allocation with the minimum energy consumption has been proven to be NP-hard. In order to solve the energy-efficient real-time task mapping in the voltage frequency islands (VFI) based multicore system, we propose a heuristics EEGA (Energy-Efficient and Genetic Algorithm) to address the problem. During the iteration process of the algorithm, the energy consumption of the processor can be gradually optimized by the selection, crossover and mutation operators. Experimental results show that when compared with other energy-efficient mapping algorithms, our proposed approach can gain better performance with regard to the energy efficiency and schedulability ratio.

关键词： Biological cells Real-time systems Multicore processing Resource management Energy efficiency Frequency synchronization Sociology

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Research on network Intrusion Detection Based on Support Vector Machine Optimized with Grasshopper Optimization Algorithm

Research on Network Intrusion Detection Based on Support Vec...

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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Zhiwei Ye Yiheng Sun Shuang Sun Sikai Zhan Han Yu Quanfeng Yao Key Laboratory of Intelligent Computing and Information Processing Fujian Province University School of computer Science Hubei university of Technology Wuhan China Wuhan FiberHome Technical Services Co. Ltd.

As one of the most important parts of network security, more significance is attached to intrusion detection system (IDS). Numerous techniques including support vector machine (SVM) have been applied to the intrusion detection. However, many methods are utilized to improve the original SVM whose performance is markedly depended on its kernel parameters. Evolutionary algorithms such as genetic algorithm (GA) and particle swarm algorithm (PSO) are also employed to search better kernel parameters while the traditional optimization methods are vulnerable to fall into local minima with slow speed of convergence. In order to improve the precision of SVM in intrusion detection, the support vector machine based on grasshopper optimization algorithm (GOA-SVM) is proposed in the paper. To verify the practicality of the proposed method, several contrast experiments have been carried out using tool of Matlab. The experimental results finally demonstrates the superior performance of the proposed method on intrusion detection.

关键词： Support vector machines Optimization Intrusion detection Kernel Genetic algorithms Particle swarm optimization Artificial neural networks

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Multi-Scale Structure-Guided Graph Generation for Multi-View Semi-Supervised Classification

SSRN

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SSRN 2024年

作者： Wu, Yilin Chen, Zhaoliang Zou, Ying Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Department of Computer Science Hong Kong Baptist University Hong Kong

Graph convolutional network has emerged as a focal point in machine learning because of its robust graph processing capability. Most existing graph convolutional network-based approaches are designed for single-view data, yet in many practical scenarios, data is represented through multiple views. Although the ability of graph convolutional network is undeniable, the quality of graph directly affects its performance. Moreover, the process of graph generation with traditional nearest neighbor in traditional multi-view learning actually amplifies the influence of different information on performance across these views. To tackle the aforementioned challenges, this paper proposes a multi-scale graph generation deep learning framework, called multi-scale semi-supervised graph generation based multi-view classification, consisting of two modules: edge sampling and path sampling. The former aims to generate an adjacency graph by selecting edges based on the maximum likelihood among graphs from different views. Meanwhile, the latter seeks to construct adjacency graphs according to the characteristics of paths within the graphs. Extensive experimental results robustly demonstrate the superior performance of our proposed framework, compared to other state-of-the-art multi-view semi-supervised approaches. © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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A news image captioning approach based on multimodal pointer-generator network

A news image captioning approach based on multimodal pointer...

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作者： Chen, Jingqiang Zhuge, Hai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Network Engineering Guangzhou University Guangzhou China Department of Computer Science School of Engineering and Applied Science Aston University Birmingham United Kingdom Key Laboratory of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer-generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer-generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word-image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on the DailyMail test dataset and the out-of-domain BBC test dataset show that the proposed model outperforms the original pointer-generator network, the generic image captioning method, the extractive news image captioning method, and the LDA-based method according BLEU, METEOR, and ROUGL-L evaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model. © 2020 John Wiley & Sons, Ltd.

关键词： Statistical tests

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Exploring Multi-loss Function Based Attention Framework for Vehicle Re-Identification

Exploring Multi-loss Function Based Attention Framework for ...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Wei Liu Yi Zhang Zhiming Luo Shaozi Li East China Jiaotong University Jiangxi China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yi Chang China Postdoc Center of Information and Communication Engineering Xiamen University China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan China Cognitive Science Department Xiamen University Fujian China

Vehicle Re-identification(Re-ID) refers to the task of matching a specific vehicle across multiple non-overlapping cameras. Precise vehicle Re-ID suffers from two main challenges: (1) different vehicle identities with a similar appearance of the same type have a subtle inter-instance discrepancy, (2) one vehicle usually has large intra-instance differences due to viewpoint and illumination variations. To address these challenges, in this paper, we proposed an Attention-based Framework for Vehicle Re-ID named AFVR, which combines the benefits of both the cross-entropy loss function and the triplet loss function. Experimental results on two popular vehicle Re-ID benchmarks demonstrate the effectiveness of our proposed method, compared with the state of the arts.

关键词： Feature extraction Cameras Training Measurement Lighting Visualization Benchmark testing

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intelligent ultrasonic distance measurer system with temperature compensation algorithm

Intelligent ultrasonic distance measurer system with tempera...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Wu, Yuwen Yang, Qiang Tang, Kai Zou, Jianhang Zhuo, Jianming Feng, Naixing Cai, Yijun Xiamen University of Technology Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen361024 China Xiamen Wish Information Technology Co. Ltd Xiamen361006 China Xiamen Blue Ocean Technology Co. Ltd Xiamen361022 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665452366

With regard to distance measurement in daily life, the traditional way is to measure the distance between objects through rulers. Nowadays, ultrasonic ranging, laser ranging, radar ranging, sonar ranging and other technologies have enriched the way of distance measurement. At the same time, it is possible to choose a suitable solution according to different actual environments. This paper design an intelligent ultrasonic ranging system through the research of ultrasonic characteristics and the application of existing ultrasonic ranging. The paper introduces the design ideas and process of the intelligent ultrasonic ranging system. Through the design of independent buttons, the normal mode ranging and the high-precision mode ranging are generated to achieve the division of the measurement range and accuracy. When the distance between the measured object and the intelligent ultrasonic ranging system is less than the preset value or exceeds the range of this mode, the buzzer will sound an alarm, so that the system can be used in more fields. The system detects the current ambient temperature in real time, so that the distance measurement can be compensated and modified by the temperature compensation algorithm in the system to achieve self-adaptation and reduce measurement errors. © 2022 IEEE.

关键词： Range finding

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