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Clause-level Relationship-aware Math Word Problems Solver

Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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BjuIR:A multi-omics database with various tools for accelerating functional genomics research in Brassica juncea

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Plant Communications 2024年第8期5卷 96-99页

作者： Linna Zhang Jinyuan Xiao Congyuan Liang Yifan Chen Changchun Yu Xinle Zhao Jiawei Li Mingli Yan Qian Yang Hao Chen Zhongsong Liu Zhengjie Wan Zhiquan Yang Qing-Yong Yang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops College of InformaticsHuazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data College of InformaticsHuazhong Agricultural UniversityWuhan 430070China National Key Laboratory of Crop Genetic Improvement Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070China Yazhouwan National Laboratory Sanya 572025China National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhan 430070China Crop Research Institute Hunan Academy of Agricultural SciencesChangsha 410125China College of Agronomy Hunan Agricultural UniversityChangsha 410128China

Dear Editor,Advances in high-throughput omics technologies,along with methodologies for integrating multi-omics datasets,have substantially enhanced the efficiency of identifying candidate genes in breeding(Gusev et al.,2018;Gupta et al.,2019).However,this process is often complex and *** address this challenge,databases that integrate extensive data and enable convenient and efficient functional genomics studies are being developed(Ma et al.,2021;Yang et al.,2023).

关键词： functional integrating convenient

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On the parameterized complexity of minimum/maximum degree vertex deletion on several special graphs

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Frontiers of Computer science 2023年第4期17卷 97-107页

作者： Jia LI Wenjun LI Yongjie YANG Xueying YANG School of Information Engineering Hunan Industry PolytechnicChangsha 410036China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410015China Chair of Economic Theory Saarland UniversitySaarbrücken 66123Germany

In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the distinguished vertex has the unique minimum *** maximum degree vertex deletion problem is defined analogously but here we want the distinguished vertex to have the unique maximum *** is known that both problems areΨ-hard and fixed-parameter intractable with respect to some natural *** this paper,we study the(parameterized)complexity of these two problems restricted to split graphs,p-degenerate graphs,and planar *** study provides a comprehensive complexity landscape of the two problems restricted to these special graphs.

关键词： minimum degree maximum degree vertex deletion split graphs planar graphs parameterized complexity

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Blockchain-Enhanced Federated Learning for Secure and Intelligent Consumer Electronics : An Overview

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IEEE Consumer Electronics Magazine 2025年

作者： Zhu, Chenlu Wang, Shuilong Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao He, Yuanyuan Wu, Celimuge Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China The University of Electro-Communications China

With the rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT), consumer electronics have become essential in daily life. However, these devices generate and store vast amounts of personal data, posing significant security threats. Federated learning, a distributed machine learning method, protects user privacy by training models collaboratively without sharing raw data. However, it still faces numerous challenges. To further enhance data security, the integration of blockchain technology is crucial. Blockchain's decentralized and immutable characteristics enable secure data sharing and privacy protection in consumer electronics. This paper analyzes the application of federated learning in consumer electronics, examines how blockchain can enhance federated learning, and discusses future research directions. The primary contributions of this paper include a detailed application of blockchain-enhanced federated learning in consumer electronics, intended to serve as a reference for future research. © 2012 IEEE.

关键词： Federated learning

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A Unified Framework Integrating Knowledge and data for Collaborative Root Cause Identification 5

A Unified Framework Integrating Knowledge and Data for Colla...

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5th International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Yu, Jiefei Cao, Zicheng He, Siyi Gu, Zuyi Xu, Yingcheng Zhong, Kai Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui China School of Big Data and Statistics Anhui University Anhui China

ISBN: (纸本)9798331529192

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller. © 2024 IEEE.

关键词： Knowledge graph

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Customized scheduling for shared bus with deadlines

Customized scheduling for shared bus with deadlines

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作者： Jin, Yong Xu, Jia Xu, Lijie Liu, Linfeng Xiao, Fu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science & Engineering Changshu Institute of Technology Jiangsu Changshu China

Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S-MBSA for the shared bus with strong endurance and E-MBSA for the large-scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S-MBSA and E-MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue. © 2022 John Wiley & Sons Ltd.

关键词： Scheduling

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Liquid metal assistant self-propagating high-temperature synthesis of S-containing high-entropy MAX-phase materials

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材料科学技术（英文版） 2025年第6期209卷 1-8页

作者： Donglong Bai Qiang Wang Bin Deng Yang Li Ao Huang Zitong Cheng Yun Zhao Jing Li Wei Yao Jianguang Xu School of Materials and Energy Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai 201209China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional MaterialsShanghai 201209China School of Materials Science and Engineering Yancheng Institute of TechnologyYancheng 224000China School of Materials and Energy Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai 201209China

Due to their high-entropy effects,the high-entropy(HE)MAX-phase materials improve the comprehen-sive performance of MAX phases,opening up more possibilities for practical engineering ***,it is still challenging to obtain S-containing high-entropy MAX phases because of the high volatilization behavior of sulfur,suffering from issues such as high reaction temperature and long re-action time of traditional synthesis *** paper proposes a novel process named as liquid metal assistant self-propagating high-temperature synthesis(LMA-SHS)for efficient synthesis of high-purity S-containing high-entropy MAX-phase ***-melting-point metal(Sn or In)has been introduced into the raw mixture and melted into a liquid phase during the early stage of the SHS *** serv-ing as a"binder"between transition metal atoms of the M-site due to the negative mixing enthalpy,this liquid phase can accelerate mass and heat transfer during the SHS process,ensuring a uniform solid solution of each element and realizing the synthesis of high-purity(TiNbVZr)2SC in an extremely short *** synthesis method for high-entropy MAX-phase materials developed in this study,i.e.,LMA-SHS,showing very short reaction time,low energy consumption,high yield,and low cost,has the promise to be a general energy-and resource-efficient route towards high-purity HE materials.

关键词： High-entropy S-containing MAX phases Liquid metal LMA-SHS

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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Online Streaming Features Causal Discovery Algorithm Based on Partial Rank Correlation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 197-208页

作者： Yang, Jing Jiang, Liufeng Shen, Anbo Wang, Aiguo Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of Electronic and Information Engineering Foshan University Foshan528011 China

Aimedat the problem of dynamic causal discovery in the era of artificial intelligence, this article combines partial rank correlation coefficients and streaming features in the field of Bayesian network structure learning and proposes a new online streaming feature causal discovery algorithm based on partial rank correlation named the partial rank casual discovery streaming feature based algorithm. This algorithm is not only suitable for Bayesian causal structure learning in dynamic feature spaces generated by sequential streams of features but can also effectively process multivariate linear Gaussian and nonlinear non-Gaussian data. We present three main contributions. First, for arbitrarily distributed datasets, which can be generated by the additive noise model, we proved that the partial rank correlation coefficient can be used as the criterion for the conditional independence test and explored the distribution of corresponding statistics. Second, the PCSDSF algorithm redefined the relevance based on partial rank statistic prospects and then redefined conditional dependence or independence. This method can significantly reduce the number of conditional independence tests and achieves a good time performance. Finally, theoretical analysis and several experiments proved the reliability of the algorithm. A simulation showed that on average, the PCSDSF algorithm outperforms existing algorithms in terms of both the accuracy and time performance. © 2022 IEEE.

关键词： Additive noise

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Learning to Unlearn for Bayesian Personalized Ranking via Influence Function

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Chinese Journal of Electronics 2025年第3期34卷 990-1001页

作者： Chen Jundong Zhang Honglei Li Haoxuan Li Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing 100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing 100044 China School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 China Center for Data Science Peking University Beijing 100871 China

Learning recommender models from vast amounts of behavioral data has become a mainstream paradigm in recent information systems. Conversely, with privacy awareness grown, there has been increasing attention to the removal of sensitive or outlier data from well-trained recommendation models (known as recommendation unlearning). However, current unlearning methods primarily focus on fully/partially retraining the entire model. Despite considerable performance, it inevitably introduces significant efficiency bottlenecks, which is impractical for latency-sensitive streaming services. While recent efforts exploit efficient unlearning in point-wise recommender tasks, these approaches overlook the partial order relationships between items, resulting in suboptimal performance in both recommendation and unlearning capabilities. In light of this, we explore learning to unlearn for Bayesian personalized ranking via influence function, which relies on a pair-wise ranking loss to model user preferences and item characteristics, making unlearning more challenging than in point-wise settings. Specifically, we propose an influence function-guided unlearning framework tailored for pair-wise ranking models to efficiently perform unlearning requests, which involves unlearning partial order relationships while handling negative samples appropriately during the unlearning process. Besides, we prove that our proposed method can theoretically match the performance of retraining counterparts. Finally, we conduct extensive experiments to validate the effectiveness and efficiency of our model.

关键词： Recommendation unlearning Influence function Bayesian personalized ranking The right to be forgotten

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