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Homogeneity Analysis of Multiairport System Based on Airport Attributed Network Representation Learning

Transactions of Nanjing University of Aeronautics and Astronautics 2021年第4期38卷 616-624页

作者： LIU Caihua CAI Rui FENG Xia XU Tao College of Computer Science and Technology Civil Aviation University of ChinaTianjin 300300P.R.China Key Laboratory of Intelligent Application Technology for Civil Aviation Passenger Service Beijing101318P.R.China

The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative *** research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport *** order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is ***,the route network of a multi-airport system with attribute information is *** there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport ***,the airport attributes and the airport network vector are represented *** airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is *** calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport *** experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.

关键词： air transportation multi-airport system homogeneity analysis network representation learning airport attribute network

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Stochastic Dominant Cognitive Experience Guided Particle Swarm Optimization

Stochastic Dominant Cognitive Experience Guided Particle Swa...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Han-Yang Pan Qiang Yang Ming Li En Zhang Yuan-Yuan Ma Tao Li Dong Liu Jun Zhang College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China Department of Electrical and Electronic Engineering Hanyang University Ansan Korea

This paper proposes a stochastic dominant cognitive experience-guided learning framework for particle swarm optimization (SDCEGPSO) to enhance its search ability in complex environment. Specifically, different from classical PSOs, SDCEGPSO randomly selects dominant cognitive experiences to guide the learning of particles. To this end, the cognitive experiences of all particles, namely their personal best positions, are sorted from the best to the worst. Then, each particle randomly chooses a personal best position better than its own to learn. For the cognitive experience selection, this paper designs three selection methods, namely the random selection, the roulette wheel selection, and the tournament selection. With this learning framework, particles have diverse guiding exemplars to learn from and thus high search diversity is expectedly maintained. Experiments conducted on the 50-D and 100-D CEC2014 problem suite have verified the effectiveness of SDCEGPSO. Compared with the classical global PSO (GPSO) and local PSO (LPSO), SDCEGPSO with the three selection schemes achieve significantly better performance. Besides, among the three selection schemes, the binary tournament selection is the most effective one to help SDCEGPSO solve optimization problems.

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Characteristics, Classification and Challenges in Searching for CEMP Stars

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Research in Astronomy and Astrophysics 2025年第5期 184-193页

作者： Lefeng He Xiangru Li Xiaoming Kong A-Li Luo Haifeng Yang School of Computer Science South China Normal University School of Mechanical Electrical&Information EngineeringShandong University Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Key Lab of Optical Astronomy National Astronomical ObservatoriesChinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Technology Taiyuan University of Science and Technology

The study of carbon-enhanced metal-poor (CEMP) stars is of great signi?cance for understanding the chemical evolution of the early universe and stellar *** stars are characterized by carbon overabundance and are classi?ed into several subclasses based on the abundance patterns of neutron-capture elements,including CEMP-s,CEMP-no,CEMP-r,and CEMP-r/*** subclasses provide important insights into the formation of the?rst stars,early stellar nucleosynthesis,and supernova ***,one of the major challenges in CEMP star research is the relatively small sample size of identi?ed stars,which limits statistical analyses and hinders a comprehensive understanding of their ***,a series of large-scale spectroscopic survey projects have been launched and developed in recent years,providing unprecedented opportunities and technical challenges for the search and study of CEMP *** this end,this paper draws on the progress and future prospects of existing methods in constructing large CEMP data sets and offers an in-depth discussion from a technical standpoint,focusing on the strengths and *** addition,we review recent advancements in the identi?cation of CEMP stars,emphasizing the growing role of machine learning in processing and analyzing the increasingly large data sets generated by modern astronomical *** to traditional spectral analysis methods,machine learning offers greater ef?ciency in handling complex data,automatic extraction of stellar parameters,and improved prediction *** these advancements,the research faces persistent challenges,including the scarcity of labeled samples,limitations imposed by low-resolution spectra,and the lack of interpretability in machine learning *** address these issues,the paper proposes potential solutions and future research directions aimed at advancing the study of CEMP stars and enhancing our understanding of their role in the chemical evolution of t

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Leveraging proficiency and preference for online Karaoke recommendation

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Frontiers of computer Science 2020年第2期14卷 273-290页

作者： Ming HE Hao GUO Guangyi LV Le WU Yong GE Enhong CHEN Haiping MA Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China School of Computer and Information Hefei University of TechnologyHefei 230026China Eller College of Management The University of ArizonaArizona 85721-0108USA iFlyTek Research Hefei 230026China

Recently,many online Karaoke(KTV)platforms have been released,where music lovers sing songs on these *** the meantime,the system automatically evaluates user proficiency according to their singing *** approximate songs to users can initialize singers5 participation and improve users,loyalty to these ***,this is not an easy task due to the unique characteristics of these ***,since users may be not achieving high scores evaluated by the system on their favorite songs,how to balance user preferences with user proficiency on singing for song recommendation is still ***,the sparsity of the user-song interaction behavior may greatly impact the recommendation *** solve the above two challenges,in this paper,we propose an informationfused song recommendation model by considering the unique characteristics of the singing ***,we first devise a pseudo-rating matrix by combing users’singing behavior and the system evaluations,thus users'preferences and proficiency are *** we mitigate the data sparsity problem by fusing users*and songs'rich information in the matrix factorization process of the pseudo-rating ***,extensive experimental results on a real-world dataset show the effectiveness of our proposed model.

关键词： KTV matrix factorization recommendation system

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Training of Spiking Neural Network joint Curriculum Learning Strategy

arXiv

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arXiv 2023年

作者： Tang, Lingling Chu, Jielei Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL. Copyright © 2023, The Authors. All rights reserved.

关键词： Curricula

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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Short-term Load Forecasting based on Kalman Filter and Nonlinear Autoregressive Neural Network

Short-term Load Forecasting based on Kalman Filter and Nonli...

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第33届中国控制与决策会议

作者： Zhang Liang Zheng Chengyuan Zhao Zhengang Zhang Dacheng Faculty of Information Engineering and Automations Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology

Power load forecasting is significant to research for power supply and strategy departments of electric power *** affects the distribution and the demand for electrical energy in different *** load forecasting methods mainly include regression analysis,time series analysis,and other statistical methods combined with machine *** this paper,the authors analyzed the load data of a region in Yunnan Province,established a load calculation model by combining environmental factors,week types,special events,and daily load,and obtained the best load estimation data through the Kalman filtering ***,the authors analyzed the statistical characteristics of the load series and established the Nonlinear Autoregressive Neural Network(NARnn) based on the Kalman filtered *** following two weeks' load data was predicted by shifting the timeline keeping the total load data *** average daily mean absolute precentage error(MAPE) for the two weeks is 7.69%.The MAPE between two-week forecast data and actual load is 8.05%.

关键词： Short-term Load Forecasting Time Series Kalman Filter Nonlinear Autoregressive Neural Network

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Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

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Plant Phenomics 2023年第4期5卷 746-756页

作者： Junfeng Chen Yun Wang Peng Di Yulong Wu Shi Qiu Zongyou Lv Yuqi Qiao Yajing Li Jingfu Tan Weixu Chen Ma Yu Ping Wei Ying Xiao Wansheng Chen The SATCM Key Laboratory for New Resources&Quality Evaluation of Chinese Medicine Institute of Chinese Materia MedicaShanghai University of Traditional Chinese MedicineShanghai 201203China School of Medicine Shanghai UniversityShanghai 200444China State Local Joint Engineering Research Center of Ginseng Breeding and Application Jilin Agricultural UniversityChangchun 130118China School of Computer Science Sichuan Normal UniversityChengdu 610066China Shangyao Huayu(Linyi)Traditional Chinese Resources Co. Ltd.Linyi 276000China School of Life Science and Engineering Southwest University of Science and TechnologyMianyang 621010SichuanChina Sichuan Academy of Traditional Chinese Medicine Chengdu 610041China Department of Pharmacy Changzheng HospitalSecond Military Medical UniversityShanghai 200003China

Plant phenomics aims to perform high-throughput,rapid,and accurate measurement of plant traits,facilitating the identification of desirable traits and optimal genotypes for crop *** miltiorrhiza(Danshen)roots possess remarkable therapeutic effect on cardiovascular diseases,with huge market *** great advances have been made in metabolic studies of the bioactive metabolites,investigation for *** roots on other physiological aspects is ***,we developed a framework that utilizes image feature extraction software for in-depth phenotyping of *** *** employing multiple software programs,*** roots were described from 3 aspects:agronomic traits,anatomy traits,and root system *** K-means clustering based on the diameter ranges of each root branch,all roots were categorized into 3 groups,with primary root-associated key *** a proof of concept,we examined the phenotypic components in a series of randomly collected *** roots,demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation(R^(2)=0.8312),which was sufficient for subsequently estimating the production of bioactive metabolites without content *** study provides an important approach for further grading of medicinal materials and breeding practices.

关键词： Root utilize anatomy

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QBMK: quantum-based matching kernels for un-attributed graphs 24

QBMK: quantum-based matching kernels for un-attributed graph...

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Proceedings of the 41st International Conference on Machine Learning

作者： Lu Bai Lixin Cui Ming Li Yue Wang Edwin R. Hancock School of Artificial Intelligence Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China Zhejiang Institute of Optoelectronics Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science University of York York UK

In this work, we develop a new Quantum-based Matching Kernel (QBMK) for unattributed graphs, by computing the kernel-based similarity between the quantum Shannon entropies of aligned vertices through the Continuous-time Quantum Walk (CTQW). The theoretical analysis reveals that the proposed QBMK kernel not only addresses the shortcoming of neglecting the structural correspondence information between graphs arising in existing R-convolution graph kernels, but also overcomes the problem of neglecting the structural differences between pairs of aligned vertices arising in existing vertex-based matching kernels. Moreover, the proposed QBMK kernel can simultaneously capture both global and local structural characteristics through the quantum Shannon entropies. Experimental evaluations on standard graph datasets demonstrate that the proposed QBMK kernel is able to outperform state-of-the-art graph kernels and graph deep learning approaches.

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A New Image Encryption Strategy Based on Arnold Transformation and Logistic Map

A New Image Encryption Strategy Based on Arnold Transformati...

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作者： Xiyu Sun Zhong Chen College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

Aiming at the problem of digital image encryption, an image encryption algorithm is proposed. The algorithm is divided into preparation stage and permutation diffusion stage. In the preparation stage, Arnold algorithm is improved and combined with Logistic chaotic map to generate the necessary sequence for the next stage. In the permutation diffusion stage, the final encrypted image is obtained by permutation, positive XOR operation, shift operation, S-box substitution and reverse XOR operation. The experimental results show that the algorithm can effectively resist the common attacks. For the 128 × 128 Lena gray image, the resistance performance number of pixel change rate(NPCR) is 99.6090, unified average changing intensity(UACI) is 33.4316. In addition, the time consumption of the algorithm is about 0.0055 s, and the experiments show that the algorithm has high security and real-time performance.

关键词： Image encryption Arnold transformation Chaotic system

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