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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Min, Qingkai Qin, Libo Teng, Zhiyang Liu, Xiao Zhang, Yue School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China School of Computer Science and Technology Beijing Institute of Technology China

ISBN: (纸本)9780999241165

Dialogue state modules are a useful component in a task-oriented dialogue system. Traditional methods find dialogue states by manually labeling training corpora, upon which neural models are trained. However, the labeling process can be costly, slow, error-prone, and more importantly, cannot cover the vast range of domains in real-world dialogues for customer service. We propose the task of dialogue state induction, building two neural latent variable models that mine dialogue states automatically from unlabeled customer service dialogue records. Results show that the models can effectively find meaningful dialogue states. In addition, equipped with induced dialogue states, a state-of-the-art dialogue system gives better performance compared with not using a dialogue state module. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Speech processing

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Predicting Li Transport Activation Energy with Graph Convolutional Neural Network 3rd

Predicting Li Transport Activation Energy with Graph Convol...

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3rd BenchCouncil International Symposium on Intelligent computers, Algorithms, and Applications, IC 2023

作者： Shi, Siqi Lin, Hailong Wu, Linhan Yang, Zhengwei Avdeev, Maxim Liu, Yue State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China Materials Genome Institute Shanghai University Shanghai200444 China Australian Nuclear Science and Technology Organisation Sydney2232 Australia School of Chemistry The University of Sydney Sydney2006 Australia

ISBN: (纸本)9789819700646

Exploring activation energy in ionic transport is one of the critical pathways to discovering high-performance inorganic solid electrolytes (ISEs). Although traditional machine learning methods have achieved relatively accurate activation energy predictions, they suffer from issues such as complex descriptor construction and poor generalization. Graph neural network (GNN) has gained widespread usage in accurate material property prediction due to their ability to uncover structure-property relationships latent in materials data in an end-to-end way. However, current graph representation methods and corresponding GNN models have not been widely applied to the ion transport properties of materials. Here, we introduce the interstitial network graph representation method, and design a GNN model to predict activation energy in Li-containing compounds. As a result, the dynamic ion migration process is characterized, enabling the GNN to automatically capture the inherent mechanisms of ion transport. Performance tests demonstrate that interstitial network representation method achieves high prediction accuracy, with a 10% improvement in prediction accuracy compared with the crystal structure representation method. The developed model can be used to screen and design ISEs and in general providing new ideas for applying machine learning in materials science. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Activation energy

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Super-Robust Nonadiabatic Holonomic Quantum Computation in coherence-protected Superconducting Circuits

arXiv

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arXiv 2024年

作者： Wang, Yuan-Sheng Su, Zhaofeng Chen, Xiaosong Yung, Man-Hong School of Systems Science Beijing Normal University Beijing100875 China School of Electronics and Information Engineering Suzhou Vocational University Suzhou215104 China School of Computer Science and Technology University of Science and Technology of China Hefei230094 China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Hefei230026 China Institute for Advanced Study in Physics School of Physics Zhejiang University Hangzhou310058 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The schmeme of nonadiabatic holonomic quantum computation (NHQC) offers an error-resistant method for implementing quantum gates, capable of mitigating certain errors. However, the conventional NHQC schemes often entail longer operations concerning standard gate operations, making them more vulnerable to the effects of quantum decoherence. In this research, we propose an implementation of the Super-Robust NHQC scheme within the Decoherence-Free Subspace (DFS). SR-NHQC has demonstrated robustness against Global Control Errors (GCEs). By utilizing capacitance-coupled transmon qubits within a DFS, our approach enables universal gate operations on a scalable two-dimensional square lattice of superconducting qubits. Numerical simulations demonstrate the practicality of SR-NHQC in DFS, showcasing its superiority in mitigating GCEs and decoherence effects compared to conventional NHQC schemes. Our work presents a promising strategy for advancing the reliability of quantum computation in real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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New Decision-Making Technique Based on Hurwicz Criteria for Fuzzy Ranking

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computers, Materials & Continua 2022年第12期73卷 4595-4609页

作者： Deepak Sukheja Javaid Ahmad Shah G.Madhu K.Sandeep Kautish Fahad A.Alghamdi Ibrahim.S.Yahia El-Sayed M.El-Kenawy Ali Wagdy Mohamed Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia Department of Mathematics Dr.APJ Abdul Kalam University Indore(M.P)India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia LBEF Campus Kathmandu44600Nepal Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Laboratory of Nano-Smart Materials for Science and Technology(LNSMST) Department of PhysicsFaculty of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Nanoscience Laboratory for Environmental and Biomedical Applications(NLEBA) Semiconductor Lab.Department of PhysicsFaculty of EducationAin Shams UniversityRoxyCairo 11757Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Sciences Engineering The American University in CairoEgypt

Efficient decision-making remains an open challenge in the research community,and many researchers are working to improve accuracy through the use of various computational *** this case,the fuzzification and defuzzification processes can be very *** is an effective process to get a single number from the output of a fuzzy *** defuzzification as a center point of this research paper,to analyze and understand the effect of different types of vehicles according to their *** this paper,the multi-criteria decision-making(MCDM)process under uncertainty and defuzzification is discussed by using the center of the area(COA)or ***,to find the best solution,Hurwicz criteria are used on the defuzzified ***-making technique is proposed using Hurwicz criteria for triangular and trapezoidal fuzzy *** proposed technique considers all types of decision makers’perspectives such as optimistic,neutral,and pessimistic which is crucial in solving decisionmaking problems.A simple case study is used to demonstrate and discuss the Centroid Method and Hurwicz Criteria for measuring risk attitudes among *** significance of the proposed defuzzification method is demonstrated by comparing it to previous defuzzification procedures with its application.

关键词： Defuzzification decision-making fuzzy numbers Hurwicz multicriteria decision-making ranking order

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Model Ownership Protection for Healthcare Consumer Electronics in Federated Edge Learning

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IEEE Transactions on Consumer Electronics 2024年

作者： Wang, Rong Liang, Junchuan Feng, Chaosheng Chakraborty, Chinmay Alqahtani, Fayez Yu, Keping Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu610068 China Birla Institute of Technology Mesra India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

With the rising demand for intelligent services and privacy protection in consumer artificial intelligence (AI), federated edge learning has emerged as a beacon for privacy-preserving distributed machine learning. This non-centralized approach offers distinct data privacy advantages, making it especially suitable for applications with strict privacy policies, such as consumer-centric healthcare systems. However, consumer electronics/devices in federated edge learning might unlawfully violate model ownership by duplicating or distributing models. Another concern is that a central server, if compromised, can become a conduit for model leakage, whether for profit motives or due to external intrusions, thus exacerbating the model ownership violation. To our knowledge, few works have focused on model ownership protection for federated edge learning in the consumer-centric healthcare domain. In this paper, we introduce a federated model ownership protection (FedMOP) approach. FedMOP is a model ownership safeguard and a formidable fortress built upon two core innovations. First, FedMOP can swiftly embed and replace watermarks during local training, allowing for the accurate identification and subsequent flagging of deceitful consumer devices. Second, FedMOP ensures that the server only accesses encrypted versions of the federated model, keeping the model a secret. Extensive evaluations demonstrate the robustness and effectiveness of FedMOP. We hope that FedMOP can light the way for surmounting similar ethical challenges in the broader realm of consumer-edge AI. © 1975-2011 IEEE.

关键词： ethical AI Federated edge learning healthcare consumer electronicss model ownership protection

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Construction of Scientific Talents Training Evaluation System Based on Improved Forest Random Algorithm

Construction of Scientific Talents Training Evaluation Syste...

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Education, Network and Information technology (ICENIT), International Conference on

作者： Wei Ma Yongjun Qi School of Computer Science & Engineering Guangzhou Institute of Science and Technology Guangzhou Guangdong China Faculty of Megadate and Computing Guangdong Baiyun University Guangzhou Guangdong China

ISBN: (纸本)9781665463089

The cultivation of scientific talents is an urgent need to achieve national prosperity, national rejuvenation and people's happiness. At present, it has received extensive attention and thinking from all walks of life, and has become the mainstream value orientation for the purpose of talent cultivation in higher education in my country. It fully reflects the needs of the times and social responsibility to crack this issue. This paper first uses the demand dimension model to analyze the differences between the perspectives of teachers and students in talent training in colleges and universities, which is conducive to the unification of training models and structures. This paper establishes a random forest classification architecture based on the C4.5 algorithm, calculates the information gain rate of different statistical features, selects the statistical feature with prominent information gain rate as the root node to construct multiple decision trees, and composes the generated multiple trees into a random forest. And adopt the simple majority voting mechanism to obtain the classification results. The cultivation of scientific talents is a social system project, involving many complex historical reasons and social factors. It is necessary to pay attention to the institutional mechanisms and cultural traditions at the macro level of the country and society, and to analyze the university spirit and training mode at the meso level of colleges and universities. The final results of the research show that when the maximum tree depth on the dataset is 3dm, the accuracy rates of ID3 algorithm, RF algorithm and DPRF algorithm are 84.56%, 80.66% and 86.89%, respectively, and the improved forest random DPRF algorithm has the highest accuracy. It has certain feasibility in the construction of scientific personnel training evaluation system.

关键词： Training Radio frequency Analytical models Technological innovation Forestry Social factors Personnel

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Polar-Net: A Clinical-Friendly Model for Alzheimer’s Disease Detection in OCTA Images

arXiv

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arXiv 2023年

作者： Liu, Shouyue Hao, Jinkui Xu, Yanwu Fu, Huazhu Guo, Xinyu Liu, Jiang Zheng, Yalin Liu, Yonghuai Zhang, Jiong Zhao, Yitian Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Cixi Biomedical Research Institute Wenzhou Medical University China School of Future Technology South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Department of Computer Science Southern University of Science and Technology China Department of Eye and Vision Science University of Liverpool United Kingdom Department of Computer Science Edge Hill University United Kingdom

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer’s disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. In this work, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model’s decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Addressing Non-stationarity with Stochastic Trend in the Context of Limited Time Series Data: An Experimental Survey

Research Square

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Research Square 2025年

作者： Batoure Bamana, Apollinaire Sokdou Bila Lamou, Yannick Fotsa-Mbogne, David Jaures Shafiee Kamalabad, Mahdi Department of Computer Engineering University Institute of Technology The University of Ngaoundere Cameroon Department of Robotic and Industrial Computing School of Chemical Engineering and Mineral Industries The University of Ngaoundere Cameroon Department of Mathematics and Computer Science Faculty of Science The University of Ngaoundere Cameroon Laboratory of Mathematics Computer Science and Applications Department of Mathematics and Computer Science National Higher School of Agro-Industrial Sciences The University of Ngaoundéré Cameroon Laboratory of Mathematics and Computer Science Department of Mathematics Statistics and Computer Science Faculty of Science The University of Bertoua Cameroon Department of Methodology and Statistics Faculty of Social and Behavioural Sciences Utrecht University Utrecht Netherlands Centre for Complex Systems Studies Utrecht University Utrecht Netherlands

Stationarity in time series is a key property for practical data analysis, inferences, and predictions particularly in biosciences. Stationarity can be either deterministic or stochastic. If a time series data is not stationary, it can be rendered stationary through detrending or differentiation techniques. Differentiation can be performed in either an integer or fractional form. This paper investigates non-stationarity in time series data, focusing specifically on stochastic trends characterised by a unit root. It hypothesises that fractional differentiation can enhance forecast accuracy for datasets with limited volume. For experiments, 24 series corresponding to weekly Malaria and Typhoid Fever from Adamawa Region (Cameroon) are analysed and forecasted. Each series comprises 156 observations covering January 2021 to December 2023. After collecting, checking and classifying the data, four forecasting models, Auto Regressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Long Short-Term Memory (LSTM), and the proposed Fractional Differencing and LSTM (FD-LSTM), are implemented. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2) metrics, which inform further analysis and recommendations. Results reveal that ARFIMA outperformed the ARIMA by 93% in training and 100% in testing. Similarly, FD-LSTM achieved a 100% improvement over LSTM in training and testing. These findings underscore the value of achieving stationarity through fractional differentiation, enhancing model performance and providing a robust framework for forecasting in datasets with limited observations. © 2025, CC BY.

关键词： Time series

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Smart Well Networks for Adaptive Groundwater Management Integrating Cloud computing and Wireless Sensor Technologies

Smart Well Networks for Adaptive Groundwater Management Inte...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： V G Sivakumar S. Vimaladevi R. Thenmozhi P. Ensiba S. Sujatha M. Nalini Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad Telangana India School of Commerce Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Electronics and Communication Engineering Sri Indu College of Engineering and Technology Hyderabad India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Business Systems S.A. Engineering College (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

This research presents a new method for managing groundwater that combines cloud computing with wireless sensor technology to create what are called “Smart Well Networks” (SWNs). Innovative solutions are needed for sustainable management of groundwater, which is a major worldwide concern due to pollution and depletion. The use of wireless sensors placed within wells allows SWNs to gather data in real-time, allowing for the continuous monitoring of groundwater levels, quality, and other relevant characteristics. For the purposes of storing, analyzing, and making decisions, this data is sent to a cloud computing platform. With the help of cloud-based technology, stakeholders may quickly react to changing groundwater conditions by implementing adaptive management plans. Highlighting its ability to improve groundwater management practices via the provision of actionable insights generated from comprehensive and up-to-date data, the study delves into the design, features, and advantages of SWNs. Evidence from models and case studies shows that SWNs may optimize the use of groundwater resources while providing their sustainability in the long run. Modern groundwater management presents difficult issues; SWNs provide a potential solution via their creative integration of technology.

关键词： Wireless communication Cloud computing Wireless sensor networks Groundwater Real-time systems Water pollution Stakeholders Sustainable development Water resources Remote sensing

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Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrieval

arXiv

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arXiv 2023年

作者： Wang, Yabing Wang, Shuhui Luo, Hao Dong, Jianfeng Wang, Fan Han, Meng Wang, Xun Wang, Meng College of Computer Science and Technology Zhejiang Gongshang University Hangzhou310035 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Alibaba Group Hangzhou310052 China the Binjiang Institute of Zhejiang University Hangzhou310027 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Current research on cross-modal retrieval is mostly English-oriented, as the availability of a large number of English-oriented human-labeled vision-language corpora. In order to break the limit of non-English labeled data, cross-lingual cross-modal retrieval (CCR) has attracted increasing attention. Most CCR methods construct pseudo-parallel vision-language corpora via Machine Translation (MT) to achieve cross-lingual transfer. However, the translated sentences from MT are generally imperfect in describing the corresponding visual contents. Improperly assuming the pseudo-parallel data are correctly correlated will make the networks overfit to the noisy correspondence. Therefore, we propose Dual-view Curricular Optimal Transport (DCOT) to learn with noisy correspondence in CCR. In particular, we quantify the confidence of the sample pair correlation with optimal transport theory from both the cross-lingual and cross-modal views, and design dual-view curriculum learning to dynamically model the transportation costs according to the learning stage of the two views. Extensive experiments are conducted on two multilingual image-text datasets and one video-text dataset, and the results demonstrate the effectiveness and robustness of the proposed method. Besides, our proposed method also shows a good expansibility to cross-lingual image-text baselines and a decent generalization on out-of-domain data. Fi Copyright © 2023, The Authors. All rights reserved.

关键词： Translation (languages)

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