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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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Ontologies in Design: How Imagining a Tree Reveals Possibilities and Assumptions in Large Language Models 25

Ontologies in Design: How Imagining a Tree Reveals Possibili...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Nava Haghighi Sunny Yu James A. Landay Daniela Rosner Computer Science Stanford University Stanford California USA Symbolic Systems Program Stanford University Stanford California USA Computer Science Department Stanford University Stanford California USA Human Centered Design & Engineering University of Washington Seattle Washington USA

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A Study on the Effectiveness of Interactive systems Using Electromyography

A Study on the Effectiveness of Interactive Systems Using El...

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IEEE/SICE International Symposium on System Integration

作者： Shinnosuke Hanawa Yihsin Ho Iciro Kobayashi Eri Sato-Shimokawara Hiroki Shibata Takenori Obo Graduate School of Engineering Takushoku University Tokyo Japan Faculty of Engineering Takushoku University Tokyo Japan Department of Computer Science Tokyo Metropolitan University Tokyo Japan Department of Mechanical Systems Engineering Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

The paper presented an intuitive control system using electromyography (EMG) data that is obtained from the Myo gesture control armband. The aim of this study is to enable users to control multiple devices with a single EMG device in an intuitive way. The presented system shows the ability of EMG-based gestures to control Phillips Hue that allow user to control light bulb by simple hand movements. Moreover, the authors also developed new functionalities, which is according to users’ preferences, to register gestures. These functions aim to improve the usability and enhance the naturalness of operations. Additionally, unique gestures, which less common in everyday life, is defined to reduce misrecognition when switching between different devices. Furthermore, to achieve reliable and accurate recognition of multiple gestures, the Support Vector Machine (SVM) models is considered to be a machine learning method for training processed EMG data. The experiment results demonstrate significant improvements in user experience and practical applicability in various interactive scenarios.

关键词： Support vector machines Training Interactive systems Switches Machine learning System integration Electromyography User experience Robustness Usability

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Alignment-Free Estimation of Read to Genome Distances and Its Applications 29th

Alignment-Free Estimation of Read to Genome Distances and...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Şapcı, Ali Osman Berk Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering UC San Diego San DiegoCA92093 United States

ISBN: (纸本)9783031902512

Searching reads from unknown origins in a reference database and finding evolutionarily similar genomes is central to many applications. Quantifying the similarity by estimating the distance between each read and matching references could further help downstream analyses such as taxonomic characterization or even placing the read on a reference phylogeny. Such distances can be computed using alignment. Since alignment becomes impractical for ultra-large reference databases, many use k-mer-based tools for downstream tasks, but these methods do not compute distances. We introduce krepp to compute the distance between a read and a genome using a maximum likelihood framework built around k-mers. We further introduce algorithms to place reads on a reference tree. Empirical evaluations show that krepp accurately estimates distances, scales well with large databases, and can place short reads coming from any part of the genome on a reference phylogeny. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Average nucleotide identity Metagenomics Phylogenetic placement

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program Calgary AB Canada University of Calgary Department of Radiology Calgary AB Canada University of Calgary Hotchkiss Brain Institute Calgary AB Canada University of Calgary Alberta Children's Hospital Research Institute Calgary AB Canada University of Calgary Department of Computer Science Calgary AB Canada University of Calgary Departments of Pediatrics and Community Health Sciences Calgary AB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： causal artificial intelligence causality deep learning dimensionality reduction generative models normalizing flows

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Wind speed forecasting approach using conformal prediction and feature importance selection

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International Journal of Electrical Power and Energy systems 2025年 168卷

作者： Zuege, Cesar Vinicius Stefenon, Stefano Frizzo Yamaguchi, Cristina Keiko Mariani, Viviana Cocco Gonzalez, Gabriel Villarrubia Coelho, Leandro dos Santos Graduate Program in Electrical Engineering Federal University of Parana PR Curitiba Brazil Postgraduate Program in Productive Systems in Association with UNIPLAC UNC UNESC and UNIVILLE SC Brazil Department of Electrical Engineering Federal University of Parana PR Curitiba Brazil Graduate Program in Mechanical Engineering Federal University of Parana PR Curitiba Brazil Expert Systems and Applications Lab Faculty of Science University of Salamanca Salamanca37008 Spain

Wind energy is a rising renewable energy source that plays an important role in the transition to a more sustainable energy system. Variation in wind power generation is one of the main challenges facing this energy source. Wind forecasting approaches are essential for planning and operating wind farms, but are complex due to the dynamic nature of the wind and the influence of local factors. This paper evaluates short-term forecasting of time series with a measure of uncertainty associated with wind speeds. The proposed method considers the conformal prediction approach and, based on Shapley values, uses optimal selection of features given their importance. Furthermore, a Bayesian Optimization with Tree-structured Parzen Estimators (BO-TPE) will be used to tune the hyperparameters of the models. The results showed that using Variational Mode Decomposition (VMD) allied with Singular Spectrum Analysis (SSA) to feed into a conformal prediction model improved the performance of the model. Taking into account a Beutenberg data set, Germany, the best model was Light Gradient Boosting Machine (LGBM)-VMD-SSA without partial fit, resulting in a root mean squared error (RMSE) criterion of 0.25031, coverage measure of 94.4%, and width measure of 1.008. When considering a dataset from Limoeiro, Brazil, the best model was also LGBM-VMD-SSA without partial fit, resulting in an RMSE of 0.21597, a coverage of 90.3%, and a width of 0.678. SHapley Additive exPlanations (SHAP) bring explainability to the model results. The models proposed in this study, hypertuned by BO-TPE with interpretable results based on SHAP, can be useful in predicting wind speed and power generation. © 2025 The Authors

关键词： Windmill

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Towards Gradient Equalization and Feature Diversification for Long-Tailed Multi-Label Image Recognition

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IEEE Transactions on Multimedia 2025年

作者： Chen, Zhao-Min Cui, Quan Zhang, Xiaoqin Deng, Ruoxi Xia, Chaoqun Lu, Shijian Nanjing University National Key Laboratory for Novel Software Technology Nanjing210093 China Waseda University Graduate School of Information Production and Systems Fukuoka169-8050 Japan Wenzhou University College of Computer Science and Artificial Intelligence Wenzhou325060 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Multi-label image recognition with convolutional neural networks has achieved remarkable progress in the past few years. However, most existing multi-label image recognition methods suffer from the long-tailed data distribution problem, i.e., head categories occupy most training samples, while tailed classes have few samples. This work firstly studies the influence of long-tailed data distribution on existing multi-label image recognition methods. Based on this, two crucial issues of the existing methods are identified: 1) severe gradient imbalance between head and tailed categories, even though re-balancing strategies are adopted;2) the lack of diversity of tail category training samples. To tackle the first issue, this paper proposes a group sampling strategy to create group- wise balanced data distribution. Meanwhile, a dynamic gradient balancing loss is proposed to equalize the gradient for all categories. To tackle the second issue, this paper proposes a diversity enhancement module to fuse the information across all categories, preventing the network from overfitting tail classes. Furthermore, it also balances the gradient, promoting the discriminability of learned classifiers. Our method significantly outperforms the baseline method and achieves competitive performance with state-of-the-art methods on VOC-LT and COCO-LT datasets. Extensive ablation studies are conducted to verify the effectiveness of the essential proposals. © 1999-2012 IEEE.

关键词： Labeled data

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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Separability Membrane: 3D Active Contour for Point Cloud Surface Reconstruction

arXiv

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arXiv 2025年

作者： Pratamasunu, Gulpi Qorik Oktagalu Hao, Guoqing Fukui, Kazuhiro Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba305-0085 Japan Department of Integrated Information Technology Aoyama Gakuin University Kanagawa Sagamihara252-5258 Japan

This paper proposes Separability Membrane, a robust 3D active contour for extracting a surface from 3D point cloud object. Our approach defines the surface of a 3D object as the boundary that maximizes the separability of point features, such as intensity, color, or local density, between its inner and outer regions based on Fisher’s ratio. Separability Membrane identifies the exact surface of a 3D object by maximizing class separability while controlling the rigidity of the 3D surface model with an adaptive B-spline surface that adjusts its properties based on the local and global separability. A key advantage of our method is its ability to accurately reconstruct surface boundaries even when they are ambiguous due to noise or outliers, without requiring any training data or conversion to volumetric representation. Evaluations on a synthetic 3D point cloud dataset and the 3DNet dataset demonstrate the membrane’s effectiveness and robustness under diverse conditions. © 2025, CC BY.

关键词： 3D reconstruction

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LEARNING DISCRIMINATIVE FEATURES FROM SPECTROGRAMS USING CENTER LOSS FOR SPEECH EMOTION RECOGNITION

arXiv

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arXiv 2025年

作者： Dai, Dongyang Wu, Zhiyong Li, Runnan Wu, Xixin Jia, Jia Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Graduate School at Shenzhen Tsinghua University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong

Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition is difficult, as emotions are ambiguous. We propose a novel approach to learn discriminative features from variable length spectrograms for emotion recognition by cooperating softmax cross-entropy loss and center loss together. The softmax cross-entropy loss enables features from different emotion categories separable, and center loss efficiently pulls the features belonging to the same emotion category to their center. By combining the two losses together, the discriminative power will be highly enhanced, which leads to network learning more effective features for emotion recognition. As demonstrated by the experimental results, after introducing center loss, both the unweighted accuracy and weighted accuracy are improved by over 3% on Mel-spectrogram input, and more than 4% on Short Time Fourier Transform spectrogram input. Copyright © 2025, The Authors. All rights reserved.

关键词： Spectrographs

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