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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Deepwalk-aware graph convolutional networks

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science China(Information sciences) 2022年第5期65卷 81-95页

作者： Taisong JIN Huaqiang DAI Liujuan CAO Baochang ZHANG Feiyue HUANG Yue GAO Rongrong JI Media Analytics and Computing Lab Department of Computer Science and Technology School of InformaticsXiamen University Media Analytics and Computing Lab Department of Artificial Intelligence School of InformaticsXiamen University School of Automation Science and Electrical Engineering Beihang University Tencent Youtu Lab School of Software Tsinghua University

Graph convolutional networks(GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph *** fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer,and Pubmed citation networks.

关键词： graph convolutional networks global information fusion node classification

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A Hybrid Machine Learning Model for Predicting Customer Churn in the Telecommunication Industry 11th

A Hybrid Machine Learning Model for Predicting Customer Chur...

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11th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020

作者： Odusami, Modupe Abayomi-Alli, Olusola Misra, Sanjay Abayomi-Alli, Adebayo Sharma, Mayank Mohan Department of Electrical and Information Engineering Covenant University Ota Nigeria Department of Software Engineering Kaunas University of Technology Kaunas Lithuania Department of Computer Science Federal University of Agriculture Abeokuta Abeokuta Nigeria Zillow Inc San Francisco United States

ISBN: (纸本)9783030736026

The churn rate, being a major issue in the telecommunication industry today as it describes the degree at which customers desert a company and discard its services in one way or another. Over the past years, various changes evolved in the telecommunications industry like liberalization of the market, giving rise to serious competition and hence the evolution of new technologies and opportunities which directly and indirectly affect customer choice and preference for the services of a specific telecommunications company. In this study, a generalized K_LoRD hybrid model for predicting customer churn in the telecommunication industry was developed using K Nearest Neighbor, Logistic Regression, Random Forest and Decision Tree. A publicly available dataset from a telecom company which has a record of customer information and their churn was collected. The K_LoRD hybrid model is evaluated based on the following performance metrics: Accuracy and Receiver Operating Curve. The results showed that the hybrid model efficiently predicts customer churn with 91.85% prediction accuracy and 95.9% Area Under Curve. The experiments have shown that our hybrid prediction model is superior to ordinary K nearest Neighbor, Logistic Regression, Random Forest and Decision Trees. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Lei Chen, Peng Zhang, Cong Pan, Cheng Long, Tao Xu, Weizhen Cui, Laizhong Liu, Jiangchuan Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518060 China Guangdong Shenzhen518000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to minimize transmission and computation overhead for mobile terminals. To improve viewport prediction accuracy, we utilize both spatial information through a saliency prediction model and temporal information through a modified LSTM model. Different computations introduced by the neural network models are distributed across the network to keep the computation light on mobile devices. To better adapt to the content dynamics in live streaming, we employ the model-agnostic meta-learning (MAML) method for video saliency prediction. The learned saliency prediction model with optimized initialization via offline meta-training can be fast fine-tuned online using a few samples. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into a typical 360-degree video live streaming system by formulating and solving the bitrate adaptation problem. Extensive experiment results demonstrate that our approach achieves real-time prediction for live video streaming and surpasses existing methods in prediction accuracy on mobile terminals, which, together with our bitrate adaptation algorithm, significantly improves the streaming QoE from various aspects. Compared to baseline methods, MFVP achieves a 4.7–28.7% improvement in accuracy and demonstrates faster adaptability to dynamic content changes, enabling rapid fine-tuning and adjustment. When integrated into a streaming syste

关键词： Deep neural networks

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Feasibility Study of a DC House Connected to a Conventional AC Distribution Network

Feasibility Study of a DC House Connected to a Conventional ...

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Compatibility in Power Electronics (CPE)

作者： Hossein Nourollahi Hokmabad Noman Shabir Victor Astapov Eduard Petlenkov Oleksandr Husev Juri Belikov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia FinEST Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798350318265

ISBN: (纸本)9798350318272

Leveraging advancements in power electronics, the adoption of Direct Current (dc) technology in net-Zero Energy Buildings (nZEBs) is seen as a promising approach to boost energy efficiency. Emerging dc technology aims to reduce power losses by eliminating unnecessary conversions between dc and Alternating Current (ac). This paper thoroughly assesses the effectiveness of dc and hybrid dc (partial dc topologies in comparison to conventional ac nZEBs. The hybrid solution integrates ac and dc networks and loads within the building, while the dc solution benefits from a purely dc internal electricity distribution network and exclusively dc loads. The study involves analyzing annual load profile data from 16 neighboring houses in Estonia, simulating their transition to nZEBs with dc, hybrid dc, and pure dctopologies. The study examines power conversion losses, the operational periods of power converters in relation to their maximum power capacity, and energy exchanges with the utility grid. Additionally, it explores the potential for creating energy communities based on the consumption patterns of these houses. The results indicate that pure dc nZEBs might not be as efficient as initially thought, especially when renewable resources are limited.

关键词： Measurement Renewable energy sources Power engineering Network topology Net zero Buildings Hybrid power systems

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Machine Learning in E-health: A Comprehensive Survey of Anxiety

Machine Learning in E-health: A Comprehensive Survey of Anxi...

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Teelhawod, Bibi Nushrina Akhtar, Faijan Heyat, Md Belal Bin Tripathi, Pragati Mehrotra, Rajat Asfaw, Ashamo Betelihem Shorman, Omar Al Masadeh, Mahmoud University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan China Shenzhen University IoT Research Center College of Computer Science and Software Engineering Guangdong Shenzhen China Gautam Buddha University Department of Electrical Engineering Greater Noida India Gl Bajaj Institute of Technology and Management Aktu UP India Northwestern Polytechnical University School of Software Shaanxi Xian China Najran University College of Engineering and A1 Shrouk Trading Company Najran Saudi Arabia Yarmouk University Computer Engineering Department Irbid Jordan

ISBN: (纸本)9781665416566

Anxiety is the sixth major disorder that arises due to sleeplessness, inspiration, energy, hunger loss and desperate ideas. This study aims to define the extent of the research carried on anxiety with machine learning (ML) and to generate a database. We highlight serious problems for additional research obligations and communications. This paper designs a perception-based image of ML We have investigated deep insights of material to re-claim the current understanding of anxiety with ML from engineering detection to clinical treatment. We found 18 research articles and 28 closest keywords in the Web of science (WoS) database. In addition, we extracted the information including objective, subject, major, scoring technique, classifier, and performance measure from previously published 18 articles. This study has discovered the research gap and future direction, so far focused on anxiety with ML. In addition, it also delivers objectives for the upcoming work to be competent in a significant and scientific manner. © 2021 IEEE.

关键词： Machine learning

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WHAT IS YOUR METRIC TELLING YOU? EVALUATING CLASSIFIER CALIBRATION UNDER CONTEXT-SPECIFIC DEFINITIONS OF RELIABILITY

arXiv

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arXiv 2022年

作者： Kirchenbauer, John Oaks, Jacob Heim, Eric Department of Computer Science University of Maryland College Park United States Software Engineering Institute Carnegie Mellon University United States

Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observation that modern neural network classifiers are poorly calibrated. Much of this focus has been towards the goal of learning classifiers such that their output with largest magnitude (the "predicted class") is calibrated. However, this narrow interpretation of classifier outputs does not adequately capture the variety of practical use cases in which classifiers can aid in decision making. In this work, we argue that more expressive metrics must be developed that accurately measure calibration error for the specific context in which a classifier will be deployed. To this end, we derive a number of different metrics using a generalization of Expected Calibration Error (ECE) that measure calibration error under different definitions of reliability. We then provide an extensive empirical evaluation of commonly used neural network architectures and calibration techniques with respect to these metrics. We find that: 1) definitions of ECE that focus solely on the predicted class fail to accurately measure calibration error under a selection of practically useful definitions of reliability and 2) many common calibration techniques fail to improve calibration performance uniformly across ECE metrics derived from these diverse definitions of reliability. © 2022, CC BY-NC-SA.

关键词： Calibration

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CS-Net: Contribution-based Sampling Network for Point Cloud Simplification

arXiv

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arXiv 2025年

作者： Guo, Tian Chen, Chen Yuan, Hui Mao, Xiaolong Hamzaoui, Raouf Hou, Junhui School of Control Science and Engineering Shandong University Ji’nan250061 China School of Software Shandong University Ji’nan250061 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computer Science City University of Hong Kong Hong Kong

Point cloud sampling plays a crucial role in reducing computation costs and storage requirements for various vision tasks. Traditional sampling methods, such as farthest point sampling, lack task-specific information and, as a result, cannot guarantee optimal performance in specific applications. Learning-based methods train a network to sample the point cloud for the targeted downstream task. However, they do not guarantee that the sampled points are the most relevant ones. Moreover, they may result in duplicate sampled points, which requires completion of the sampled point cloud through post-processing techniques. To address these limitations, we propose a contribution-based sampling network (CS-Net), where the sampling operation is formulated as a Top-k operation. To ensure that the network can be trained in an end-to-end way using gradient descent algorithms, we use a differentiable approximation to the Top-k operation via entropy regularization of an optimal transport problem. Our network consists of a feature embedding module, a cascade attention module, and a contribution scoring module. The feature embedding module includes a specifically designed spatial pooling layer to reduce parameters while preserving important features. The cascade attention module combines the outputs of three skip connected offset attention layers to emphasize the attractive features and suppress less important ones. The contribution scoring module generates a contribution score for each point and guides the sampling process to prioritize the most important ones. Experiments on the ModelNet40 and PU147 showed that CS-Net achieved state-of-the-art performance in two semantic-based downstream tasks (classification and registration) and two reconstruction-based tasks (compression and surface reconstruction). CS-Net also achieved high average precision for objection detection on the KITTI LiDAR point cloud dataset, demonstrating its effectiveness in three-dimensional object detection. Co

关键词： Importance sampling

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Intrusion detection system for malicious traffic using evolutionary search algorithm

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Recent Advances in computer science and Communications 2021年第5期14卷 1381-1389页

作者： Al-Saqqa, Samar Al-Fayoumi, Mustafa Qasaimeh, Malik Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Science King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Department of Software Engineering King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan

Introduction: Intrusion detection systems play a key role in system security by identifying potential attacks and giving appropriate responses. As new attacks are always emerging, intrusion detection systems must adapt to these attacks, and more work is continuously needed to develop and propose new methods and techniques that can improve efficient and effective adaptive intrusion systems. Feature selection is one of the challenging areas that need more work because of its importance and impact on the performance of intrusion detection systems. This paper applies an evolutionary search algorithm in feature subset selection for intrusion detection systems. Methods: The evolutionary search algorithm for the feature subset selection is applied and two clas-sifiers are used, Naïve Bayes and decision tree J48, to evaluate system performance before and after features selection. NSL-KDD dataset and its subsets are used in all evaluation experiments. Results: The results show that feature selection using the evolutionary search algorithm enhances the intrusion detection system with respect to detection accuracy and detection of unknown attacks. Furthermore, time performance is achieved by reducing training time, which is reflected positively in overall system performance. Discussion: The evolutionary search applied to select IDS algorithm features can be developed by modifying and enhancing mutation and crossover operators and applying new enhanced techniques in the selection process, which can give better results and enhance the performance of intrusion detection for rare and complicated attacks. Conclusion: The evolutionary search algorithm is applied to find the best subset of features for the intrusion detection system. In conclusion, it is a promising approach to be used as a feature selection method for intrusion detection. The results showed better performance for the intrusion detection system in terms of accuracy and detection rate. © 2021 Bentham science Publisher

关键词： Feature Selection

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Assessment of user-interaction strategies for neurosurgical data navigation and annotation in virtual reality

arXiv

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arXiv 2022年

作者： Hellum, Owen Kersten-Oertel, Marta Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada PERFORM Centre Concordia University MontrealQC Canada

While virtual-reality (VR) has shown great promise in radiological tasks, effective user-interaction strategies that can improve efficiency and ergonomics are still under-explored and systematic evaluations of VR interaction techniques in the context of complex anatomical models are rare. Therefore, our study aims to identify the most effective interaction techniques for two common neurosurgical planning tasks in VR (point annotation and note-taking) from the state-of-the-arts, and propose a novel technique for efficient sub-volume selection necessary in neuroanatomical navigation. We assessed seven user-interaction methods with multiple input modalities (gaze, head motion, controller, and voice) for point placement and note-taking in the context of annotating brain aneurysms for cerebrovascular surgery. Furthermore, we proposed and evaluated a novel technique, called magnified selection diorama (Maserama) for easy navigation and selection of complex 3D anatomies in VR. Both quantitative and semi-quantitative (i.e., NASA Task Load Index) metrics were employed through user studies to reveal the performance of each interaction scheme in terms of accuracy, efficiency, and usability. Our evaluations demonstrated that controller-based interaction is preferred over eye-tracking-based methods for point placement while voice recording and virtual keyboard typing are better than freehand writing for note-taking. Furthermore, our new Maserama sub-volume selection technique was proven to be highly efficient and easy-to-use. Our study is the first to provide a systematic assessment of existing and new VR interaction schemes for neurosurgical data navigation and annotation. It offers valuable insights and tools to guide the design of future VR systems for radiological and surgical applications. © 2022, CC BY-NC-ND.

关键词： Neurosurgery

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