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Modeling contextual goals and detecting context conflicts from BDD-based user stories

International Journal of computers and Applications 2024年第12期46卷 1057-1068页

作者： Zheng, Liwei Wang, Yan Cui, Zhanqi Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

Behavior-Driven Development (BDD) user stories are widely used in agile methods for capturing user requirements and acceptance criteria due to their simplicity and clarity. However, the concise structure of BDD-based user stories prevents capturing contextual information and connections between them, which help stakeholders understand requirements and uncover potential issues. The contextual goal models (CGMs) can provide explicit relationships between goals and contexts, but manually constructing models is effort-intensive. This paper proposes an automated approach called BUS2C to model contextual goals and detect context conflicts from BDD-based user stories. The aim is to improve requirements analysis by systematically eliciting goals and dependencies. BUS2C approach involves: (1) mapping BDD-based user story elements to CGMs, and (2) merging related models based on similarity. The context conflict detection algorithm checks if contexts associated with different goals are compatible using natural language metrics. We evaluated BUS2C approach through three experiments. First, we showed the effectiveness in merging scattered CGMs with common features. Second, we demonstrated the capability to construct CGMs from BDD-based user stories quickly, assisting modelers. Our merging method also produced models closer to manually built ones versus without merging. Third, we validated that the context conflict detection can successfully identify inconsistencies in small datasets, enhancing requirements quality. Further validation on larger datasets is needed. In conclusion, this work contributes automated techniques to systematically model and analyze contextual goals from BDD-based user stories. By providing unified goal understanding and detecting issues like conflicts, BUS2C approach aims to improve requirements analysis and support adaptive systems development. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： BDD-based user stories contextual goal model context conflict detection

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Study on Electromagnetic Performance of Permanent Magnet Rotor and Dual Stator Starter Generator for Electric Vehicle Range Extender

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Progress In Electromagnetics Research B 2024年 106卷 39-55页

作者： Gao, Mingling Yu, Zhenhai Jiao, Wenjie Hu, Wenjing Geng, Huihui Liu, Yixin Liu, Shiqiang Liu, Yishuo School of Computer Science and Technology Shandong University of Technology Zibo255000 China School of Transportation and Vehicle Engineering Shandong University of Technology Zibo255000 China

The flywheel-type dual-stator permanent magnet starter generator combines engine flywheel and starter generator rotor into a single unit, which has the advantages of high efficiency, high power density, and compact structure. This paper proposes a new type of dual-stator permanent magnet starter generator topology in which the two stators are concentric and share the same permanent magnet rotor. Equivalent magnetic circuit modeling of the inner stator’s magnetic field, outer stator’s magnetic field, and synthetic magnetic field using the equivalent magnetic circuit method list the system of flux equations and solve the main magnetic flux, leakage flux, and leakage coefficient, and the results show that the equivalent magnetic circuit method has smaller error and higher accuracy than the finite element method. The harmonic electric potential of the starter generator is modeled and analyzed. The permanent magnet rotor and inner and outer stator structures are optimized to obtain the optimal parameters, and the prototype is manufactured and tested. The optimized starter generator no-load induced electromotive force fundamental amplitude is improved. The induced electromotive force harmonic distortion rate is reduced, and the output performance of the whole generator is significantly improved. © (2024), Electromagnetics Academy. All rights reserved.

关键词： Stators

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Quantifying Bytes:Understanding Practical Value of Data Assets in Federated Learning

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Tsinghua science and technology 2025年第1期30卷 135-147页

作者： Minghao Yao Saiyu Qi Zhen Tian Qian Li Yong Han Haihong Li Yong Qi School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China School of Cyber Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China

The data asset is emerging as a crucial component in both industrial and commercial *** valuable knowledge from the data benefits decision-making and ***,the usage of data assets raises tension between sensitive information protection and value *** an emerging machine learning paradigm,Federated Learning(FL)allows multiple clients to jointly train a global model based on their data without revealing *** approach harnesses the power of multiple data assets while ensuring their *** the benefits,it relies on a central server to manage the training process and lacks quantification of the quality of data assets,which raises privacy and fairness *** this work,we present a novel framework that combines Federated Learning and Blockchain by Shapley value(FLBS)to achieve a good trade-off between privacy and ***,we introduce blockchain in each training round to elect aggregation and evaluation nodes for training,enabling decentralization and contribution-aware incentive distribution,with these nodes functionally separated and able to supervise each *** experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.

关键词： Federated Learning(FL) blockchain fairness

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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A Frog-Shaped UWB MIMO Antenna Design for 5G

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Progress in Electromagnetics Research C 2025年 151卷 101-112页

作者： Yao, Shanhua Yang, Tianchu Qiu, Xiaorong Li, Xiang School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China School of Computer Science Huainan Normal University Huainan232001 China

In this paper, a frog-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna is proposed for 5G applications in the n77, n78, n79, and 6 GHz bands with a compact antenna structure of 31 × 55 × 1.5 mm3. The designed antenna consists of two frog-shaped monopole antennas and a floor from which a part has been removed, and the operating bandwidths range from 3.05 to 13.38 GHz, which meets the design criteria for UWB. The T-shaped floor with two T-shaped slots impedes the flow of coupling currents and improves the isolation of the antenna. This results in an isolation of less than −17 dB over the entire operating bandwidth and less than −20 dB in the 5G band. In addition, the envelope correlation coefficient (ECC) is less than 0.007, the diversity gain (DG) more than 9.96, the total active reflection coefficient (TARC) less than −30 dB, and the channel capacity loss (CCL) less than 0.34 bit/s/Hz. The measured and simulated results agree with each other, demonstrating the antenna’s potential application in 5G communication systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Bandwidth

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Sparse Sampling and Fusion of Multi-Fidelity Aerodynamic Heating Data 17

Sparse Sampling and Fusion of Multi-Fidelity Aerodynamic Hea...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Nie, Yutang Yang, Chunming Wei, Mingzhi Jiang, Xianwei Ma, Xin School of Computer Science and Technology Southwest University of Science and Technology China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9798331507398

To reduce the acquisition cost of aerodynamic heating data for high-speed aircraft by leveraging diverse data sources, this paper proposes a multi-source data fusion method based on sparse sampling and multi-fidelity neural network. First, a large volume of low-fidelity data is generated using low-cost engineering calculation methods along the flight trajectory, and sparse sampling is performed according to the data variation trends. Then, CFD numerical simulation is conducted on the sampled flight parameters to obtain a limited set of high-fidelity data. Finally, high-fidelity data across the flight trajectory is estimated by combining high-fidelity and low-fidelity data through multi-fidelity neural network. The effectiveness of the fusion is verified using heat flux data at the nose tip of a certain aircraft under the simulated flight trajectory. Results indicate that the proposed fusion approach can achieve a prediction error of less than 4% while using only 5% of the CFD data points from the original trajectory, significantly reducing the CFD simulation workload and demonstrating the method's potential for engineering applications. © 2024 IEEE.

关键词： Flight paths

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A comprehensive framework for decoding salty taste information from electroencephalography signals:distinguishing brain reactions to saltiness of comparable intensity

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Food science and Human Wellness 2025年第4期14卷 1538-1545页

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering Northeast Electric Power UniversityJilin 132012China Department of Computer Science the University of GeorgiaAthens 30602USA School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China

The perception of salty taste is crucial for individuals to make healthy food ***,the brain electrophysiological signals underlying salty taste perception have been poorly *** this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response *** this method to the EEG processing workflow can form a complete data processing *** results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the ***,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.

关键词： Saltiness Salty perception Electroencephalography Stimulus paradigm

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Emotion-Based Music Recommendation System Integrating Facial Expression Recognition and Lyrics Sentiment Analysis

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IEEE Access 2025年 13卷 87740-87752页

作者： Phaneendra Bottu, V.S.G.S. Ragavan, K. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632007 India

Facial Expression Recognition (FER) has created widespread interest due to its potential uses in personalized technology and mental health, notably in systems that recommend music based on emotion. These systems can improve adaptable user interfaces and support music therapy. While prior research has explored algorithms for FER and their effectiveness in identifying emotions, existing solutions often lack optimal accuracy in pairing emotion recognition with music recommendations, particularly in real-world contexts with diverse user preferences. This study introduces a robust Convolutional Neural Network (CNN)-based model specifically designed for accurate FER, complemented by an innovative music recommendation system. The model was trained and evaluated using the publicly available FER-2013 dataset, which comprises 35,887 labelled facial images, along with a curated music dataset of 1,000 songs annotated with emotions. This approach incorporates a distinctive emotion-based song classification pipeline that utilizes a pre-built text emotion classification model to categorize song lyrics into seven emotional categories: anger, fear, disgust, neutral, surprise, sadness, and happiness. Experimental results demonstrate that our model achieved a FER accuracy of 91.78% and various methodologies discussed for emotion-based music categorization, significantly outperforming baseline methods. These findings offer a scalable framework for personalized and emotionally resonant music experiences, representing a viable solution for applications in tailored entertainment and music therapy. © 2013 IEEE.

关键词： Emotion Recognition

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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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