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IEEE International Conference on data Mining (ICDM)

作者： Yi-Hong Lu Chang-Dong Wang Pei-Yuan Lai Jian-Huang Lai School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China South China Technology Commercialization Center Guangzhou China

With the rapid growth of online platforms and the abundance of available information, personalized recommender systems have become essential for assisting users in discovering relevant and interesting content. Among the various methods, knowledge-aware recommendation model has achieved notable success by leveraging the rich semantic information encoded in knowledge graphs. However, it overlooks the fact that users’ historical click sequences can better reflect their preferences within a period of time, thus imposing certain limitations on the recommendation performance. On the other hand, the application of pre-trained language models in recommender systems has demonstrated increasingly significant potential, as they can capture sequential patterns and dependencies within users’ historical click sequences and effectively capture contextual information in user-item interactions. To this end, we propose a hybrid recommendation model that leverages Pre-training in the collaborative Knowledge graph Attention neTwork (PKAT), to extract both the high-order connectivity information in collaborative knowledge graphs and the contextual information in users’ historical click sequences captured by Bidirectional Encoder Representations from Transformers (BERT). The collaborative knowledge graph attention network enables the model to effectively capture the intricate relationships between users, items, and knowledge entities, thus enhancing the representation learning process. Furthermore, what sets PKAT apart from other state-of-the-art knowledge-aware recommendation methods is the incorporation of the BERT language model. This integration allows PKAT to capture the contextual sequence information of user behavior, enabling it to generate more accurate and personalized recommendations. Extensive experiments are conducted on multiple benchmark datasets. And the results demonstrate that our PKAT model outperforms several state-of-the-art baselines.

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Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

arXiv

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arXiv 2022年

作者： Fan, Zhaoxin Song, Zhenbo Xu, Jian Wang, Zhicheng Wu, Kejian Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China Nreal China

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Incomplete Multi-View Clustering via Multi-Level Contrastive Learning

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IEEE Transactions on Knowledge and data engineering 2025年

作者： Yin, Jun Wang, Pei Sun, Shiliang Zheng, Zhonglong Shanghai Maritime University College of Information Engineering Shanghai201306 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science Shanghai200062 China Zhejiang Normal University School of Computer Science Zhejiang Jinhua321004 China

Although significant progress has been made in multi-view learning over the past few decades, it remains challenging, especially in the context of incomplete multi-view clustering, where modeling complex correlations among different views and handling missing data are key difficulties. In this paper, we propose a novel incomplete multi-view clustering network to address the aforementioned issue, named Incomplete Multi-view Clustering via Multi-level Contrastive Learning (IMC-MCL). Specifically, the proposed model aims to minimize the conditional entropy between views to recover missing data by dual prediction strategy. Moreover, the approach learns multi-level features, including latent, high-level and semantic features, with the goal of satisfying both reconstruction and consistency objectives in distinct feature spaces. Specifically, latent features are utilized to accomplish the reconstruction objective, while high-level features and semantic labels are employed to achieve the two consistency goals through contrastive learning. This framework enables the exploration of shared semantics within high-level features and achieves clustering assignment using semantic features. Extensive experiments have shown that the proposed approach outperforms other state-of-the-art incomplete multi-view clustering methods on seven challenging datasets. © 1989-2012 IEEE.

关键词： Sorting

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Efficient Wideband Beamforming with Null Broadening Using Fusion Convolutional Block

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Li, Chenglong Feng, Liwen Gao, Yajie Shi, Baozhu Du, Ruiyan Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing 066004 China Northeastern University School of Computer Science and Engineering Shenyang 110819 China Hebei University College of Mathematics and Information Science Baoding 071002 China

To meet the development requirements of bandwidth and efficiency in future 6G wireless communications, an efficient wideband beamforming (WB) algorithm is proposed based on fusion convolutional block neural network (FCBNN) in this paper, named as WB-FCBNN algorithm. The presented algorithm can significantly enhance the real-time performance while obtaining a superior output signal to interference plus noise ratio (SINR) of WB. Firstly, the algorithm utilizes null broadening algorithm with interference plus noise covariance matrix (INCM) reconstruction to generate labels, avoiding the influence of interference direction of arrival (DOA) estimation errors. Then, a novel FCBNN framework is constructed to enhance the real-time performance and the output SINR. Specifically, a fusion convolutional block (FCB) is introduced into the FCBNN framework, which can fuse the asymmetric convolution and the standard convolution into one convolution. In this way, the network parameters will be reduced, thus the computational complexity of the network can be reduced. Furthermore, the FCBNN model can be trained via the label. Finally, the well-trained FCBNN model can quickly predict the near-optimal weight vector of WB. Simulation results demonstrate that the presented algorithm can effectively increase the real-time performance of WB, and still has a satisfactory SINR in the environment of high SNR and low snapshot. © 2017 IEEE.

关键词： Array signal processing deep learning wideband beamforming

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Cloud-Powered Healthcare Appointment Optimization with Reinforcement Learning for Efficiency

Cloud-Powered Healthcare Appointment Optimization with Reinf...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： S. Mohan Kumar John Benito Jesudasan Peter S. Kolangiammal Azath Mubarakali S. Karthik S. Sujatha Professor and Director Research and Innovations CMR University Bangalore India Cyber | Data Science & Engineering Capital One Services LLC Richmond Virginia USA Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Improving efficiency and patient satisfaction through better appointment scheduling is a problem for healthcare systems across the globe. To improve healthcare appointment scheduling procedures, this research proposes a new method that makes use of cloud computing and reinforcement learning (RL) algorithms. to maximize healthcare provider efficiency while minimizing patient wait times, resource usage, and operational costs by dynamically learning and adapting scheduling rules using RL, taking use of the scalability and computing capacity of cloud infrastructure. To train RL agents, create a simulation environment that mimics real-world healthcare conditions. Our findings show that RL-based scheduling strategies are more efficient at scheduling appointments than conventional techniques, and we prove it via rigorous testing and assessment. Additionally, we demonstrate how our strategy can handle dynamic healthcare contexts with different patient numbers and resource restrictions, demonstrating its flexibility and resilience. The results show that RL approaches run by the cloud can change healthcare appointment scheduling for the better, leading to healthcare systems that are more responsive and flexible. Utilizing the complementary strengths of cloud computing and RL, our method provides a data-driven, scalable solution to the hard problem of healthcare schedule optimization, which improves both patient care and operational efficiency

关键词： Cloud computing Technological innovation Schedules Job shop scheduling Processor scheduling Medical services Optimal scheduling Reinforcement learning Dynamic scheduling Testing

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A Switch Architecture for Time-Triggered Transmission with Best-Effort Delivery

arXiv

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arXiv 2023年

作者： Li, Zonghui Zhu, Wenlin Shin, Kang G. Wan, Hai Song, Xiaoyu Yang, Dong Ai, Bo The Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2121 United States The Software School Tsinghua University Beijing100084 China The Department of Electrical and Computer Engineering Portland State University PortlandOR United States The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

In Time-Triggered (TT) or time-sensitive networks, the transmission of a TT frame is required to be scheduled at a precise time instant for industrial distributed real-time control systems. Other (or best-effort (BE)) frames are forwarded in a BE manner. Under this scheduling strategy, the transmission of a TT frame must wait until its scheduled instant even if it could have been transmitted sooner. On the other hand, BE frames are transmitted whenever possible but may miss deadlines or may even be dropped due to congestion. As a result, TT transmission and BE delivery are incompatible with each other. To remedy this incompatibility, we propose a synergistic switch architecture (SWA) for TT transmission with BE delivery to dynamically improve the end-to-end (e2e) latency of TT frames by opportunistically exploiting BE delivery. Given a TT frame, the SWA generates and transmits a cloned copy with BE delivery. The first frame arriving at the receiver device is delivered with a configured jitter and the other copy ignored. So, the SWA achieves shorter latency and controllable jitter, the best of both worlds. We have implemented SWA using FPGAs in an industry-strength TT switches and used four test scenarios to demonstrate SWA's improvements of e2e latency and controllable jitter over the state-of-the-art TT transmission scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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A linearization for stable and fast geographically weighted Poisson regression

arXiv

引用

arXiv 2023年

作者： Murakami, Daisuke Tsutsumida, Narumasa Yoshida, Takahiro Nakaya, Tomoki Lu, Binbin Harris, Paul Department of Data Statistical Science Institute of Statistical Mathematics Tachikawa Japan Department of Information and Computer Sciences Graduate School of Science and Engineering Saitama University Saitama Japan Department of Urban Engineering School of Engineering The University of Tokyo Tokyo Japan Department of Frontier Science for Advanced Environment The Graduate School of Environmental Studies Tohoku University Sendai Japan School of Remote Sensing and Information Engineering Wuhan University Wuhan China Net Zero and Resilient Farming Rothamsted Research North Wyke United Kingdom

Although geographically weighted Poisson regression (GWPR) is a popular regression for spatially indexed count data, its development is relatively limited compared to that found for linear geographically weighted regression (GWR), where many extensions (e.g., multiscale GWR, scalable GWR) have been proposed. The weak development of GWPR can be attributed to the computational cost and identification problem in the underpinning Poisson regression model. This study proposes linearized GWPR (L-GWPR) by introducing a log-linear approximation into the GWPR model to overcome these bottlenecks. Because the L-GWPR model is identical to the Gaussian GWR model, it is free from the identification problem, easily implemented, computationally efficient, and offers similar potential for extension. Specifically, L-GWPR does not require a double-loop algorithm, which makes GWPR slow for large samples. Furthermore, we extended L-GWPR by introducing ridge regularization to enhance its stability (regularized L-GWPR). The results of the Monte Carlo experiments confirmed that regularized L-GWPR estimates local coefficients accurately and computationally efficiently. Finally, we compared GWPR and regularized L-GWPR through a crime analysis in Tokyo. Copyright © 2023, The Authors. All rights reserved.

关键词： Linearization

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The economics of global personality diversity

arXiv

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arXiv 2025年

作者： McCarthy, Paul X. Gong, Xian Coetzer, Marieth Rizoiu, Marian-Andrei Kern, Margaret L. Johnson, John A. Holden, Richard Braesemann, Fabian The Data Science Institute University of Technology SydneyNSW Australia School of Computer Science and Engineering UNSW Sydney NSW Australia Faculty of Engineering and Information Technology University of Technology Sydney Australia Oxford Internet Institute University of Oxford Oxford United Kingdom Einstein Center Digital Future Berlin Germany Melbourne Graduate School of Education The University of Melbourne ParkvilleVIC Australia Penn State University Pennsylvania United States

This study explores the relationship between personality diversity and national economic performance, introducing the Global Personality Diversity Index (Ψ-GPDI) as a novel metric. Leveraging a dataset of 760,242 individuals across 135 countries, we quantify within-country diversity based on the Big Five personality traits. Our findings reveal that personality diversity accounts for 19.9% of the variance in GDP per capita and provides an additional 2.8% explanatory power beyond institutional quality and immigration, underscoring its unique contribution to economic vitality. Through multi-factor analysis, we demonstrate how personality diversity complements existing economic frameworks, offering actionable insights for policymakers seeking to enhance innovation, productivity, and resilience. This research positions psychological diversity as a critical yet under explored factor in driving economic growth, bridging the fields of psychology and economics. © 2025, CC BY.

关键词： Inflation

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An IoT and Random Forest-DT-Based Early Enhanced Forest Fire Detection in Large and Small Targets

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Manjula Prabakaran Omesh Wadhwani V. Pandiaraj Kaneez Zainab Neerav Nishant Justin Z Department of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science & Engineering B.N. College of Engineering and Technology Lucknow India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India Department of Science and Humanities (English) RMK College of Engineering and Technology Thiruvalluvar India

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth’s climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LBP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach.

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A cross Transformer for image denoising

arXiv

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arXiv 2023年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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