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arXiv 2021年

作者： Zhao, Yang Ma, Yanbo Chen, Yuan Jia, Wei Wang, Ronggang Liu, Xiaoping The Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School 2199 Lishui Road Shenzhen518055 China Peng Cheng Laboratory Shenzhen518000 China

Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality. Although the high-definition reconstruction technology for early videos has made great progress in recent years, related research on deinterlacing is still lacking. Traditional methods mainly focus on simple interlacing mechanism, and cannot deal with the complex artifacts in real-world early videos. Recent interlaced video reconstruction deep deinterlacing models only focus on single frame, while neglecting important temporal information. Therefore, this paper proposes a multiframe deinterlacing network joint enhancement network for early interlaced videos that consists of three modules, i.e., spatial vertical interpolation module, temporal alignment and fusion module, and final refinement module. The proposed method can effectively remove the complex artifacts in early videos by using temporal redundancy of multi-fields. Experimental results demonstrate that the proposed method can recover high quality results for both synthetic dataset and real-world early interlaced videos. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

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Cloud detection from visual band of satellite image based on variance of fractal dimension

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Journal of Systems engineering and Electronics 2019年第3期30卷 485-491页

作者： TIAN Pingfang GUANG Qiang LIU Xing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-Time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

关键词： cloud detection visual image satellite image variance of local fractal dimension (VLFD)

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The Influence of Using a Static Diastolic Geometry in ECG Imaging

The Influence of Using a Static Diastolic Geometry in ECG Im...

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2019 Computing in Cardiology, CinC 2019

作者： Stoks, Job Cluitmans, Matthijs J.M. Peeters, Ralf Volders, Paul G.A. Department of Cardiology Maastricht University Medical Centre Maastricht Netherlands Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands BIOMED Hasselt University Hasselt Belgium

ISBN: (纸本)9781728169361

One of the common choices when performing electrocardiographic imaging (ECGI) is that the cardiac geometry is in a static, diastolic state. To test the influence of this approximation, we compared epicardial potential maps and isochrones during systolic and diastolic geometries in four patients. Zero-th order Tikhonov regularization was used to reconstruct ventricular epicardial potentials. A spatiotemporal estimation method was then used to determine the activation and recovery times from the reconstructed epicardial electrograms. Activation times (AT), recovery times (RT) and electrogram correlation coefficients (CC) were compared for both geometries. Furthermore, CC and differences in AT/ RT were correlated against the linear movement and a substitute for rotational movement. Poor correlation was found between linear/rotational movement and reconstruction differences. Overall, agreement between epicardial potential maps and isochrones of both geometries was high when assessed quantitatively, but regional differences might occur for qualitative interpretation. These differences mostly occurred in areas of flat T-waves. This novel, more accurate quantification of the influence of assuming a diastolic geometry in ECGI may further help in interpreting ECGI measurements. © 2019 Creative Commons.

关键词： Geometry

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StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder

arXiv

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arXiv 2022年

作者： Chatterjee, Soumick Sciarra, Alessandro Dünnwald, Max Tummala, Pavan Agrawal, Shubham Kumar Jauhari, Aishwarya Kalra, Aman Oeltze-Jafra, Steffen Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642±0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859±0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ±0.135 and 0.783 ±0.111, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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Q&A driven conversational recommendation integrating multiple interest modeling

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Neurocomputing 2025年 647卷

作者： Haiping Zhu Yuqi Sun Shuwei Che Yan Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an 710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an 710049 China

The conversational recommendation aims to provide users with recommendations in an interaction form of “System Ask-User Respond”. Existing studies rarely consider combining users’ multi-interest at the attribute level for preference modeling, and conduct inefficient conversational recommendation strategy learning, which affects the recommendation performance and conversation performance. To this end, we proposed a Q-A driven conversational recommendation method integrating multiple interest modeling. Specifically, we integrate the user’s positive and negative feedback to model a session dynamic graph, then use the signed graph convolutional network for graph representation learning, and we model multiple interest sequences based on the attention mechanism, then obtain the user interest state representation by fusing of sequence representations to solve the problem of insufficient user preference representation. Besides, we proposed a personalized decision space optimization method to narrow the range of action candidates and train the model with a multi-agent reinforcement learning method integrating hierarchical decision debiasing to solve the problem of poor conversational recommendation strategy learning effect. Experimental results on three public datasets, LastFM, Yelp, and Book, show that compared with existing conversational recommendation methods, our method demonstrates consistent performance improvement across all datasets. In addition, the results of ablation experiments verify the effectiveness of each component in our method.

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Balanced Tree Partitioning with Succinct Logic

Balanced Tree Partitioning with Succinct Logic

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IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Shaojing Sheng Peng Zhou Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China School of Compuster Science and Technology Anhui University Heifei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As a widely used data structure, graphs are good at characterizing data with internal associations, such as social and biological data. Tree structured data are special and are widely used in many real-world applications, such as organizational structure analysis and genealogical knowledge graph reasoning. For example, in kinship knowledge graph analysis, when a genealogical tree is particularly large (more than 25 levels and 45,000 nodes), it is a great challenge to partition this large tree into a specified number of subtrees with succinct logic and a balanced number of nodes. Therefore, in this paper, we propose the TPA (tree partitioning algorithm) algorithm to achieve a balanced and succinct logic partition of large-scale tree structured data. TPA first extracts all related nodes from a massive graph database and then constructs the convergent subgraph into a complete tree with a specified root node. Specifically, several virtual nodes are supplemented for generation-skipping connected nodes to achieve correct node numbering and partitioning. Finally, a graph partitioning algorithm is executed on the complete tree to obtain a specified number of subtrees with succinct logic and balanced node scales. Experiments conducted on four real-world datasets verify the effectiveness of our TPA algorithm.

关键词： Partitioning algorithms data visualization Convergence data analysis Distributed databases Big data

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Acute MI Detection Derived From ECG Parameters Distribution

Acute MI Detection Derived From ECG Parameters Distribution

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Computers in Cardiology (CinC)

作者： Alfonso Aranda Joël Karel Pietro Bonizzi Ralf Peeters Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (数字)9781728169361

ISBN: (纸本)9781728159423

Several studies in the past have evaluated the use of different ECG-based features to diagnose acute myocardial infarction (AMI). This was generally done by looking at how well a feature reflects differences between baseline (no AMI) and AMI situations. This approach tends to overlook the progress of AMI and to underestimate false positives when implemented into a continuous monitoring setting and therefore appears inadequate for it. This has hindered the adoption of those methods in the clinical practice. In this research, we present a novel set of parameters for the dynamic assessment of AMI condition. Those parameters are obtained by analyzing the changes over time in the distribution properties of ECG-based features.

关键词： Standards Electrocardiography Myocardium databases Lead Market research Monitoring

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data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

TechRxiv

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TechRxiv 2021年

作者： Zhang, Zhao Wei, Yanyan Zhang, Haijun Yang, Yi Yan, Shuicheng Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Shenzhen China Centre for Artificial Intelligence University of Technology Sydney SydneyNSW Australia Department of Electrical and Computer Engineering National University of Singapore Singapore Hefei University of Technology China

Single Image Deraining task aims at recovering the rain-free background from an image degraded by rain streaks and rain accumulation. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods obtained significant improvement over traditional ones. Current SID methods usually focus on improving the deraining performance by proposing different kinds of deraining networks, while neglecting the interpretation of the solving process. As a result, the generalization ability may still be limited in real-world scenarios, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (e.g., object detection). To explore these issues, we in this paper re-examine the three important factors (i.e., data, rain model and network architecture) for the SID problem, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We also study the relationship of the three factors from a new perspective of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. We further discuss the current mainstream data-driven SID methods from five aspects, i.e., training strategy, network pipeline, domain knowledge, data preprocessing, and objective function, and some useful conclusions are summarized by statistics. Besides, we profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets through extensive experiments to reveal the effectiveness of SID data. Finally, with the comprehensive review and in-depth analysis, we draw some valuable conclusions and suggestions for future research. © 2021, CC BY.

关键词： Object detection

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Joint Resource Management for Energy-efficient UAV-Assisted SWIPT-MEC: A Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Chen, Yue Kang, Hui Li, Jiahui Sun, Geng Wang, Boxiong Wang, Jiacheng Liang, Cong Liang, Shuang Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore China Mobile Communications Group Jilin Co. Ltd China Northeast Normal University School of Information Science and Technology Changchun130117 China

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms. © 2014 IEEE.

关键词： Resource allocation

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Machine learning approach to algorithm selection for exact computation of Treewidth

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Algorithms 2019年第10期12卷

作者： Slavchev, Borislav Masliankova, Evelina Kelk, Steven Department of Data Science and Knowledge Engineering Maastricht University LK Maastricht6211 Netherlands

We present an algorithm selection framework based on machine learning for the exact computation of treewidth, an intensively studied graph parameter that is NP-hard to compute. Specifically, we analyse the comparative performance of three state-of-the-art exact treewidth algorithms on a wide array of graphs and use this information to predict which of the algorithms, on a graph by graph basis, will compute the treewidth the quickest. Experimental results show that the proposed meta-algorithm outperforms existing methods on benchmark instances on all three performance metrics we use: in a nutshell, it computes treewidth faster than any single algorithm in isolation. We analyse our results to derive insights about graph feature importance and the strengths and weaknesses of the algorithms we used. Our results are further evidence of the advantages to be gained by strategically blending machine learning and combinatorial optimisation approaches within a hybrid algorithmic framework. The machine learning model we use is intentionally simple to emphasise that speedup can already be obtained without having to engage in the full complexities of machine learning engineering. We reflect on how future work could extend this simple but effective, proof-of-concept by deploying more sophisticated machine learning models. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Machine learning

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