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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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MA-VoxelMorph:Multi-scale attention-based VoxelMorph for nonrigid registration of thoracoabdominal CT images

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Journal of Innovative Optical Health sciences 2025年第1期18卷 135-151页

作者： Qing Huang Lei Ren Tingwei Quan Minglei Yang Hongmei Yuan Kai Cao School of Computer Science&Engineering Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei 430205P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan Hubei 430074P.R.China Beijing Wandong Medical Technology Co. Ltd.Beijing 100015P.R.China Changhai Hospital of Shanghai Shanghai 200433 P.R.China

This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization ***,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is *** deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration *** alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels *** further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding *** method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local *** proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine *** also proved to be fast in 3D image registration,using about 1.5 s,and faster than most *** and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.

关键词： Thoracoabdominal CT image registration large deformation fine structure multi-scale attention mechanism

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Tactile Sensing of Extensor Tendons and Near-Sensor Gesture Recognition for Control of Robotic Hand

Tactile Sensing of Extensor Tendons and Near-Sensor Gesture ...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Yushen Hu Zhejun Zhang Tengteng Lei Man Wong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Guangzhou HKUST Fok Ying Tung Graduate School Guangzhou China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

A flexible active-matrix piezoelectric tactile sensor array is fabricated and affixed on the back of a hand to monitor the location and deformation of the extensor-tendons associated with a hand gesture. Based on the same thin-film transistor technology, an artificial neural network (ANN) allowing real-time and near-sensor classification of sensor signals is implemented. The integration of the tactile sensor array and an ANN facilitates the development of a compact human-machine interface system for recognition of hand gestures and their mimicry by a robotic hand.

关键词： Hands Neuromorphic engineering Tactile sensors Artificial neural networks Gesture recognition Real-time systems Thin film transistors Sensors Sensor arrays Tendons

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Predicting Bank Customer Churn: An XGBoost Approach to Enhancing Customer Retention

Predicting Bank Customer Churn: An XGBoost Approach to Enhan...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Ruchika Bhuria Srinivas Aluvala Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

This study applies the XGBoost classifier to analyze the propensity of the banking’s customer churn, by analyzing the dataset containing the basic demographics, financial records, and churn label. Some of the features captured in the dataset include; Surname, Row Number, CustomerID, Credit Score, Gender, Age, Tenure, Account Balance, Credit Card, Number of Products, Geometric Mean, Active Member No., Projected Salary, Exited where Exited = 1, the customer has left the bank. The purpose of present research is to apply the predictive analytics in order to understand what underlying factors serve as indicators of customer attrition and in turn facilitate the better strategies of the relationships’ maintenance. The model scores an average accuracy of 85%, based on 2000 samples, relatively well for predicting non-churning customers (Class 0) with an accuracy of 0.90, recall of 0.93, and an F1 of 0.91. Nevertheless, low accuracy (0,65), recall (0,56), and F1-score (0,60) were received for the churned customers (class 1), which means a high level of false negatives. These results also reveal that the task of identifying churned customers is indeed difficult especially when working with imbalanced data sets. Thus, the macro average performance shows that the model is not very good at generalizing, especially in the case of the minority class. This paper suggests that there is a need for further model improvement including hyperparameters tuning and implementing ways to address the imbalance of class problem to enhance sensitivity in detecting churned customers. Improving the model’s accuracy at identifying ‘at risk’ customers will go a long way to helping refining customer retention strategies for the banking industry, thus helping cut attrition rates and increase overall satisfaction.

关键词： Industries Accuracy Sensitivity Banking Predictive models Remuneration Churn Predictive analytics Tuning Residual neural networks

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Blockchain-aided Mobile Edge Intelligence With Sparse Rewarded Computing Resource Allocation

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IEEE Transactions on Vehicular technology 2025年

作者： Niu, Wei Shi, Long Wang, Zhe Yang, Weiwei Guo, Yongan Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523000 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing210003 China

In the blockchain-aided mobile edge intelligence (B-MEI) network, base stations (BSs) each with an edge server not only execute tasks offloaded from users but also serve as blockchain nodes to ensure trustworthy data management of task execution in a decentralized manner. A challenging issue of B-MEI is that, both task execution and blockchain consensus demand significant computing resources. Insufficient resource allocation inevitably leads to resource wastage and even degrades system performance, especially in scenarios where task arrivals and wireless channels are dynamically changing. Driven by this issue, we first formulate the dynamic allocation of computing resources as a Markov Decision Process (MDP) to maximize the average transaction throughput of the B-MEI system. In particular, we find that, due to the time-consuming nature of blockchain consensus, the MDP rewards stemmed from the block generation is typically delayed and sparse. To address this issue, we propose a novel reinforcement learning algorithm, named Stable Hindsight Credit Assignment (SHCA), to effectively learn the stable credit assignment for sparse rewards across intermediate actions to optimize policy decisions. Simulation results demonstrate that the proposed algorithm improves the average transaction throughput by 78% compared with the baseline algorithm. © 1967-2012 IEEE.

关键词： Resource allocation

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Hybrid optimization driven fake news detection using reinforced transformer models

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Scientific Reports 2025年第1期15卷 1-16页

作者： M, Ganesh Karthik Faizz Ahmad, Khadri Syed Pamidimukkala, Sai Geetha Sathe, Asha Prashant G.N.V.G, Sirisha M, Sitha Ram Ch, Koteswararao Department of Computer Science and Engineering GITAM School of Technology GITAM University- Bengaluru Campus Bengaluru India Department of Computer Science and Engineering Acharya Nagarjuna University Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Engineering Army Institute of Technology Pune India Department of CSE S.R.K.R. Engineering College Bhimavaram India Department of Computer Science and Engineering SRM University - AP Andhra Pradesh Mangalagiri India Department of Information Technology Guru Ghasidas Vishwavidyalaya Bilaspur (CG) 495009 India

The large-scale production of multimodal fake news, combining text and images, presents significant detection challenges due to distribution discrepancies. Traditional detectors struggle with open-world scenarios, while Large Vision-Language Models (LVLMs) lack specificity in identifying local forgeries. Existing methods often overestimate public opinion’s impact, failing to curb misinformation at early stages. This study introduces a Modified Transformer (MT) model, fine-tuned in three stages using fabricated news articles. The model is further optimized using PSODO, a hybrid Particle Swarm Optimization and Dandelion Optimization algorithm, addressing limitations such as slow convergence and local optima entrapment. PSODO enhances search efficiency by integrating global and local search strategies. Experimental results on benchmark datasets demonstrate that the proposed approach significantly improves fake news detection accuracy. The model effectively captures distribution inconsistencies and multimodal forgery details, outperforming conventional detectors and LVLMs. This research highlights the importance of integrating transformers and hybrid optimization to develop generalized, scalable, and accurate fake news detection systems. © The Author(s) 2025.

关键词： Fake news detection Hybrid optimization Large Vision-Language models Modified transformer Public opinion analysis

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QTER: QoS-Aware Three-Dimensional Efficient and Reliable Routing for LEO Satellite Networks

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IEEE Internet of Things Journal 2025年

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Zhang, Yanfeng Yuan, Mingfeng Pan, Jianping Dongguan University of Technology School of Electrical Engineering and Intelligentization China Xi’an Jiaotong University China Xidian University School of Cyber Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore University of Toronto Institute for Aerospace Studies University of Toronto Robotics Institute Canada University of Victoria Department of Computer Science Canada

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines. © 2014 IEEE.

关键词： Resource allocation

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Classification of Alzheimer's Disease using Attention UNet-MobileNet Model

Classification of Alzheimer's Disease using Attention UNet-M...

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Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Anitha Thavasimuthu P. Jesu Jayarain Narmatha C. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Faculty of Computers and Information Technology University of Tabuk Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Alzheimer's disease (ALD) is a degenerative and incurable neurological condition that impacts many individuals globally. Early identification and precise diagnosis of ALD are essential for efficient therapy and control of the condition. This research presents a transfer learning-based method for diagnosing ALD using magnetic resonance image (MRI) data. This research introduced a deep learning (DL) model known as MobileNet-v2 to accurately classify ALD using MRI images. The classification task involves the use of MIRIAD, a dataset consisting of MRI images specifically related to ALD. The collection comprises 708 MRI pictures pertaining to ALD and its different phases. The MRI pictures undergo preprocessing and are then split into training and testing sets, with 70% allocated for training and 30% for testing. Following the preprocessing stage, the segmentation procedure is carried out with the Attention UNet architecture. The segmented images are inputted into the MobileNet-v2 model for ALD classification. The classification is conducted as a binary classification, categorizing the data as either the presence or absence of ALD. The research model's performances are evaluated based on metrics including accuracy, specificity, sensitivity, f1-scores, and precision. The test results were compared to the existing models for validation. The research model has demonstrated superior performance when compared to all other models.

关键词： Training Image segmentation Sensitivity Accuracy Magnetic resonance imaging Computational modeling Transfer learning Data models Alzheimer's disease Testing

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Irregular sleep pattern identification and analysis from social media dataset using hybrid deep learning based attention mechanism

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Data and Information Management 2025年

作者： Islam, Mohammed Jawwadul Rafi, Mohammad Fahad Al Podder, Pranto Siddika, Aysha Kabir, Moumy Khan, Euna Mehnaz Islam, Najmul Mukta, Saddam Department of Computer Science and Engineering United International University (UIU) Bangladesh LUT School of Engineering Science LUT University Finland Department of Computer and Systems Sciences Stockholm University Sweden Department of Computer Science and Engineering University of Minnesota United States Web Communication Design Information Technology University of Southern Denmark Denmark

Following an irregular bedtime routine and having different amounts of sleep each night might increase a person's risk of obesity, cardiovascular problems, high blood pressure, insulin levels, and other metabolic problems. Similarly, in recent times, social media platforms have gained popularity among users for sharing their interests, thoughts, and opinions. Through social media activities, researchers have been able to mine the text data generated on these platforms to investigate and understand users' behaviors and habits. In this paper, we examine a total of 2,468,697 tweets to identify users' irregular sleeping patterns (ISP) using psycholinguistic and contextual features from their tweets. We conduct a linguistic analysis to understand the factors influencing users' psychological behavior and word use patterns, and find a correlation with their irregular sleeping patterns. We observe that users who have irregular sleeping patterns use anger, anxiety, death, and future categories of words in their tweets largely. In contrast, users with irregular sleeping patterns tend to use positive emotions, family, and other categories of words in their tweets. Building upon our findings, we develop a hybrid prediction model that predicts users' irregular sleeping patterns from psycholinguistic features with an accuracy of 91%. We examine the application of social media data for the early identification of irregular sleep patterns and their related mental and psychological concerns while investigating design prospects for future health technologies to enhance the monitoring and support of healthy sleep behavior. © 2025

关键词： Attention mechanism Deep learning Irregular sleeping patterns Psycholinguistic features Social media analysis Tweet analysis

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E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking

arXiv

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arXiv 2025年

作者： Gao, Kejia Zhou, Liguo Liu, Mingjun Knoll, Alois Technical University of Munich Germany School of Computer Science Nanjing University of Information Science and Technology China Anhui Engineering Research Center for Intelligent Computing and Information Innovation Fuyang Normal University China

End-to-end learning has shown great potential in autonomous parking, yet the lack of publicly available datasets limits reproducibility and benchmarking. While prior work introduced a visual-based parking model and a pipeline for data generation, training and close-loop test, the dataset itself was not released. To bridge this gap, we create and open-source a high-quality dataset for end-to-end autonomous parking. Using the original model, we achieve an overall success rate of 85.16% with lower average position and orientation errors (0.24 meters and 0.34 degrees). The dataset can be found in https://***/s/16LSSfF Qar3e-s dseDt1g?pwd=wybs. © 2025, CC BY-SA.

关键词： Deep learning

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