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Intention-sensitive Preference Learning Network for Personalized Session-based Recommendation

ACM Transactions on Recommender Systems 1000年

作者： Qingbo Zhang Xiaochun Yang Hao Chen Bin Wang Xiangmin Zhou Computer Science and Engineering Northeastern University Shenyang China Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China School of Computing Technologies RMIT University Melbourne Australia

Nowadays, research on session-based recommender systems (SRSs) is one of the hot spots in the recommendation domain. Existing methods make recommendations based on the user’s current intention (also called short-term preference) during a session, often overlooking the specific preferences associated with these intentions. In reality, users usually exhibit diverse preferences for different intentions, and even for the same intention, individual preferences can vary significantly between users. As users interact with items throughout a session, their intentions can shift accordingly. To enhance recommendation quality, it is crucial not only to consider the user’s intentions but also to dynamically learn their varying preferences as these intentions change. In this paper, we propose a novel Intention-sensitive Preference Learning Network (IPLN) including three main modules: intention recognizer, preference detector, and prediction layer. Specifically, the intention recognizer infers the user’s underlying intention within his/her current session by analyzing complex relationships among items. Based on the acquired intention, the preference detector learns the intention-specific preference by selectively integrating latent features from items in the user’s historical sessions. Besides, the user’s general preference is utilized to refine the obtained preference to reduce the potential noise carried from historical records. Ultimately, the fine-tuned preference and intention collaborate to instruct the next-item recommendation in the prediction layer. To prove the effectiveness of the proposed IPLN, we perform extensive experiments on two real-world datasets. The experiment results demonstrate the superiority of IPLN compared with other state-of-the-art models.

关键词： session-based recommender systems personalized next-item recommendation user intention intention-specific preference

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Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

arXiv

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arXiv 2023年

作者： Qiao, Yuanyuan Niu, Yong Han, Zhu Mao, Shiwen He, Ruisi Wang, Ning Zhong, Zhangdui Ai, Bo The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this paper, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

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CMRxRecon2024: A Multi-Modality, Multi-View K-Space dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Wang, Fanwen Qin, Chen Lyu, Jun Ouyang, Cheng Wang, Shuo Li, Yan Yu, Mengyao Zhang, Haoyu Guo, Kunyuan Shi, Zhang Li, Qirong Xu, Ziqiang Zhang, Yajing Li, Hao Hua, Sha Chen, Binghua Sun, Longyu Sun, Mengting Li, Qin Chu, Ying-Hua Bai, Wenjia Qin, Jing Zhuang, Xiahai Prieto, Claudia Young, Alistair Markl, Michael Wang, He Wu, Lianming Yang, Guang Qu, Xiaobo Wang, Chengyan Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Bioengineering and Imperial-X Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom Department of Electrical and Electronic Engineering & Imperial-X Imperial College London London United Kingdom Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computing Department of Brain Sciences Imperial College London London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China GE Healthcare Beijing China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Siemens Healthineers Ltd. Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong School of Data Science Fudan University Shanghai China School of Engineering Pontificia Universidad Católica de Chile Santiago Chile School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Millenium Institute for Intelligent Healthcare Engineering Santiago Chile Department of Radiology Feinberg School of Medicine Northwestern University Chicago Unit

The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each o... 详细信息

关键词： Adversarial machine learning

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Comparison of UAV-based multispectral sensors for detection of Solenopsis invicta Nests

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IOP Conference Series: Earth and Environmental science 2020年第1期569卷

作者： Yuejun Song Feng Chen Kaitao Liao Jiangxi Institute of Soil and Water Conservation Nanchang 330029 China Jiangxi Key Laboratory of Soil Erosion and Prevention Nanchang 330029 China College of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen 361024 China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Xiamen 361005 China

The invasive red imported fire ant (Solenopsis invicta Buren) (Hymenoptera: Formicidae) has been continuing to expand its range in China, resulting in adverse ecological impacts to where it has invaded. As such, the reliable detection of Solenopsis invicta Buren nests is needed to effectively manage the species. In this paper, the spectral features of the Solenopsis invicta Buren nests were demonstrated using spectra samples obtained by ASD FieldSpec 4 during field surveys in Shanggao County, Jiangxi Province. Furthermore, channel reflectance estimations for three unmanned aerial vehicle (UAV)-based multispectral sensors were simulated correspondingly. Based on the channel reflectance from individual sensors, the detection of the Solenopsis invicta Buren nests was investigated. Generally, between-class differences in reflectance were relatively significant in both Green channel and Red channel, which suggests the importance of these two channels for the detection of Solenopsis invicta Buren nest. Minor between-sensor differences observed in both Green channel and Red channel suggest that detection results obtained from UAV-based multispectral observations could be comparable among the three sensors, considering only the spectral features. There are significant differences in reflectance between the soil of Solenopsis invicta Buren nests and ordinary soil over the longwave region of near infrared (NIR). It suggests the spectral measurement over the longwave region of NIR could be more useful in distinguishing the soil of Solenopsis invicta Buren nests from ordinary soil. Nevertheless, the multispectral sensors provided with channels covering visible and the shortwave region of NIR, possibly do not meet requirements completely for application. An effective method for detecting Solenopsis invicta Buren nests using UAV-based multispectral sensors is currently being evaluated.

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Corrigendum: “Provably-secure electronic cash based on certicateless partially-blind signatures”

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Electronic Commerce Research and Applications 2013年第1期12卷 60-60页

作者： Lei Zhang Futai Zhang Bo Qin Shubo Liu East China Normal University Software Engineering Institute Shanghai Key Laboratory of Trustworthy Computing Shanghai China Universitat Rovira i Virgili Department of Computer Engineering and Mathematics UNESCO Chair in Data Privacy Tarragona Catalonia Spain Nanjing Normal University School of Computer Science and Technology China Xi’an University of Technology School of Science Department of Maths China Wuhan University School of Computer Science Wuhan China

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RETRACTED ARTICLE: An enhanced pairing-based authentication scheme for smart grid communications

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Journal of Ambient Intelligence and Humanized computing 2021年第1期15卷 165-165页

作者： Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Tian, Yuan Al-Nabhan, Najla Abdulrahman College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Computer Engineering Nanjing Institute of Technology Nanjing China Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Kingdom of Saudi Arabia

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-Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

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Fuel 2023年第Part1期332卷

作者： Hai, Tao Hikmat Hama Aziz, Kosar Zhou, Jincheng Dhahad, Hayder A. Sharma, Kamal Fahad Almojil, Sattam Ibrahim Almohana, Abdulaziz Fahmi Alali, Abdulrhman Ismail Kh, Teeba Mehrez, Sadok Abdelrahman, Anas School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun 558000 China nstitute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Uttar Pradesh Mathura 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belvédère Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China

In this research, we try to investigate a solar-geothermal energy system. This system includes three turbines for power production, a PEM electrolyzer for hydrogen production, and a thermoelectric for generating electricity from excess heat. In addition, the seawater will be passed through the osmotic cycle to gain fresh water. The required power for this osmotic cycle will be obtained through the energy produced by the main turbines. The generated load, hydrogen production flow rate, purified water flow rate, and heating consumption are assessed in this study. The results showed that this system can produce 3.8 megawatts of electricity as well as 8 g per second of hydrogen fuel at the operating point. Also, the energy efficiency of this system is estimated to be 19%. Afterward, machine learning methods are used to optimize designing parameters, and the optimum operating point in terms of useful power and stored fuel flow rate is obtained by a genetic algorithm. The optimum operating point of this energy system has a useful power output of 4.099 megawatts and a hydrogen flow rate of 29 g per second. In the end, the distribution of the design parameters is displayed for points of the beam curve. © 2022

关键词： Biofuel Electrolyzer Fuel production Hydrogen fuel Optimization Solar energy

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Application of informatics in cancer research and clinical practice: Opportunities and challenges

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Cancer Innovation 2022年第1期1卷 80-91页

作者： Na Hong Gang Sun Xiuran Zuo Meng Chen Li Liu Jiani Wang Xiaobin Feng Wenzhao Shi Mengchun Gong Pengcheng Ma Department of Medical Sciences Digital Health China Technologies Co. Ltd. Beijing China Xinjiang Cancer Center Key Laboratory of Oncology of Xinjiang Uyghur Autonomous Region The Affiliated Cancer Hospital of Xinjiang Medical University Ürümqi China Department of Information Central Hospital of Wuhan Tongji Medical College Huazhong University of Science and Technology Wuhan China National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Big Data Center Nanfang Hospital Southern Medical University Guangzhou China Hepato-Pancreato-Biliary Center Beijing Tsinghua Changgung Hospital School of Clinical Medicine Tsinghua University Beijing China Institute of Health Management Southern Medical University Guangzhou China Mengchun Gong Institute of Health Management Southern Medical University Guangzhou 510515 China.

Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high-throughput omics data mining, machine-learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes, and focus on how informatics can provide opportunities for cancer research and practices. Finally, we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices. It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics-specific insights.

关键词： artificial intelligence application cancer informatics machine learning

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Machine Learning and Physics-Driven Modelling and Simulation of Multiphase Systems

SSRN

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SSRN 2024年

作者： Basha, Nausheen Arcucci, Rossella Angeli, Panagiota Anastasiou, Charitos Abadie, Thomas Casas, César Quilodrán Chen, Jianhua Cheng, Sibo Chagot, Loïc Galvanin, Federico Heaney, Claire E. Hossein, Fria Hu, Jinwei Kovalchuk, Nina Kalli, Maria Kahouadji, Lyes Kerhouant, Morgan Lavino, Alessio Liang, Fuyue Nathanael, Konstantia Magri, Luca Lettieri, Paola Materazzi, Massimiliano Erigo, Matteo Pico, Paula Pain, Christopher C. Shams, Mosayeb Simmons, Mark Traverso, Tullio Valdes, Juan Pablo Wolffs, Zef Zhu, Kewei Zhuang, Yilin Matar, Omar K. Department of Chemical Engineering Imperial College London United Kingdom Department of Earth Science & Engineering Imperial College London United Kingdom Department of Chemical Engineering University College London United Kingdom School of Chemical Engineering University of Birmingham United Kingdom Data Science Institute Department of Computing Imperial College London United Kingdom State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing China Centre for AI-Physics Modelling Imperial-X White City Campus Imperial College London United Kingdom The Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Department of Aeronautics Imperial College London United Kingdom Institute of Physics University of Amsterdam Science Park Amsterdam904 Netherlands Nikhef Science Park Amsterdam105 Netherlands Department of Computer Science University of York United Kingdom

We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems), which is at the intersection of multi-physics and machine learning, aiming to enhance predictive capabilities in complex multiphase flow systems across diverse length and time scales. Our contributions encompass a variety of approaches, including the Design of Experiments for nanoparticle synthesis optimisation, Generalised Latent Assimilation models for drop coalescence prediction, Bayesian regularised artificial neural networks, eXtreme Gradient Boosting for microdroplet formation prediction, and a sub-sampling based adversarial neural network for predicting slug flow behaviour in two-phase pipe flows. Additionally, we introduce a generalised latent assimilation technique, Long Short-Term Memory networks for sequence forecasting mixing performance in stirred and static mixers, active learning via Bayesian optimisation to recover coalescence model parameters for high current density electrolysers, Gaussian process regression for drop size distribution predictions for sprays, and acoustic emission signal inversion using gradient boosting machines to characterise particle size distribution in fluidised beds. We also offer perspectives on the development of a shape optimisation framework that leverages the use of a multi-fidelity multiphase emulator. The results presented have applications in chemical synthesis, microfluidics, product manufacturing, and green hydrogen generation. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

引用

arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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