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Precise control of page cache for containers

Frontiers of computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Linking Adaptive Structure Induction and Neuron Filtering: A Spectral Perspective for Aspect-based Sentiment Analysis 30

Linking Adaptive Structure Induction and Neuron Filtering: A...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Niu, Hao Wang, Maoyi Xiong, Yun Yang, Biao Jia, Xing Guo, Zhonglei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9782493814104

Recently, it has been discovered that incorporating structure information (e.g., dependency trees) can improve the performance of aspect-based sentiment analysis (ABSA). The structure information is often obtained from off-the-shelf parsers, which are sub-optimal and unwieldy. Therefore, adaptively inducing task-specific structures is helpful in resolving this issue. In this work, we concentrate on adaptive graph structure induction for ABSA and explore the impact of neuron-level manipulation from a spectral perspective on structure induction. Specifically, we consider word representations from PLMs (pre-trained language models) as node features and employ a graph learning module to adaptively generate adjacency matrices, followed by graph neural networks (GNNs) to capture both node features and structural information. Meanwhile, we propose the Neuron Filtering (NeuLT), a method to conduct neuron-level manipulations on word representations in the frequency domain. We conduct extensive experiments on three public datasets to observe the impact of NeuLT on structure induction and ABSA. The results and further analysis demonstrate that performing neuron-level manipulation through NeuLT can shorten Aspects-sentiment Distance of induced structures and be beneficial to improve the performance of ABSA. The effects of our method can achieve or come close to SOTA (state-of-the-art) performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Neurons

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Unsupervised social network embedding via adaptive specific mappings

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Frontiers of computer science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

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Learning a Mixture of Conditional Gating Blocks for Visual Question Answering

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Journal of computer science & Technology 2024年第4期39卷 912-928页

作者： Qiang Sun Yan-Wei Fu Xiang-Yang Xue School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Academy for Engineering and Technology Fudan UniversityShanghai 200433China School of Data Science Fudan UniversityShanghai 200433China School of Computer Science Fudan UniversityShanghai 200433China

As a Turing test in multimedia,visual question answering(VQA)aims to answer the textual question with a given ***,the“dynamic”property of neural networks has been explored as one of the most promising ways of improving the adaptability,interpretability,and capacity of the neural network ***,despite the prevalence of dynamic convolutional neural networks,it is relatively less touched and very nontrivial to exploit dynamics in the transformers of the VQA tasks through all the stages in an end-to-end ***,due to the large computation cost of transformers,researchers are inclined to only apply transformers on the extracted high-level visual features for downstream vision and language *** this end,we introduce a question-guided dynamic layer to the transformer as it can effectively increase the model capacity and require fewer transformer layers for the VQA *** particular,we name the dynamics in the Transformer as Conditional Multi-Head Self-Attention block(cMHSA).Furthermore,our questionguided cMHSA is compatible with conditional ResNeXt block(cResNeXt).Thus a novel model mixture of conditional gating blocks(McG)is proposed for VQA,which keeps the best of the Transformer,convolutional neural network(CNN),and dynamic *** pure conditional gating CNN model and the conditional gating Transformer model can be viewed as special examples of *** quantitatively and qualitatively evaluate McG on the CLEVR and VQA-Abstract *** experiments show that McG has achieved the state-of-the-art performance on these benchmark datasets.

关键词： visual question answering Transformer dynamic network

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Automated Theorem Provers Help Improve Large Language Model Reasoning 25th

Automated Theorem Provers Help Improve Large Language Model ...

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25th Conference on Logic for Programming, Artificial Intelligence and Reasoning, LPAR 2024

作者： McGinness, Lachlan Baumgartner, Peter School of Computer Science Australian National University and Data61 CSIRO Australia Data61 CSIRO and School of Computer Science Australian National University Australia

In this paper we demonstrate how logic programming systems and Automated first-order logic Theorem Provers (ATPs) can improve the accuracy of Large Language Models (LLMs) for logical reasoning tasks where the baseline performance is given by direct LLM solutions. We first evaluate LLM reasoning on steamroller problems using the PRON-TOQA benchmark. We show how accuracy can be improved with a neuro-symbolic architecture where the LLM acts solely as a front-end for translating a given problem into a formal logic language and an automated reasoning engine is called for solving it. However, this approach critically hinges on the correctness of the LLM translation. To assess this translation correctness, we secondly define a framework of syntactic and semantic error categories. We implemented the framework and used it to identify errors that LLMs make in the benchmark domain. Based on these findings, we thirdly extended our method with capabilities for automatically correcting syntactic and semantic errors. For semantic error correction we integrate first-order logic ATPs, which is our main and novel contribution. We demonstrate that this approach reduces semantic errors significantly and further increases the accurracy of LLM logical reasoning. © 2024, EasyChair. All rights reserved.

关键词： Semantics

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Learning from Highly Sparse Spatio-temporal data 38

Learning from Highly Sparse Spatio-temporal Data

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Deng, Leyan Lian, Defu Wu, Chenwang Chen, Enhong School of Artificial Intelligence and Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China

Incomplete spatio-temporal data in the real world has spawned much research. However, existing methods often utilize iterative message-passing across temporal and spatial dimensions, resulting in substantial information loss and high computational cost. We provide a theoretical analysis revealing that such iterative models are susceptible to data and graph sparsity, causing unstable performances on different datasets. To overcome these limitations, we introduce a novel method named One-step Propagation and Confidence-based Refinement (OPCR). In the first stage, OPCR leverages inherent spatial and temporal relationships by employing a sparse attention mechanism. These modules propagate limited observations directly to the global context through one-step imputation, which is theoretically affected only by data sparsity. Following this, we assign confidence levels to the initial imputations by correlating missing data with valid data. This confidence-based propagation refines the separate spatial and temporal imputation results through spatio-temporal dependencies. We evaluate the proposed model across various downstream tasks involving highly sparse spatio-temporal data. Empirical results indicate that our model outperforms state-of-the-art imputation methods, demonstrating its effectiveness and robustness. © 2024 Neural information processing systems foundation. All rights reserved.

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Iterative Methods via Locally Evolving Set Process 38

Iterative Methods via Locally Evolving Set Process

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Baojian Sun, Yifan Harikandeh, Reza Babanezhad Guo, Xingzhi Yang, Deqing Xiao, Yanghua The School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science Stony Brook University United States Samsung SAIT AI Lab

Given the damping factor α and precision tolerance ϵ, Andersen et al. [2] introduced Approximate Personalized PageRank (APPR), the de facto local method for approximating the PPR vector, with runtime bounded by Θ(1/(αϵ)) independent of the graph size. Recently, Fountoulakis & Yang [12] asked whether faster local algorithms could be developed using Õ(1/(√αϵ)) operations. By noticing that APPR is a local variant of Gauss-Seidel, this paper explores the question of whether standard iterative solvers can be effectively localized. We propose to use the locally evolving set process, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized. Let vol(St) and γt be the running average of volume and the residual ratio of active nodes St during the process. We show vol(St)/γt ≤ 1/ϵ and prove APPR admits a new runtime bound Õ(vol(St)/(αγt)) mirroring the actual performance. Furthermore, when the geometric mean of residual reduction is Θ(√α), then there exists c ∈ (0, 2) such that the local Chebyshev method has runtime Õ(vol(St)/(√α(2 − c))) without the monotonicity assumption. Numerical results confirm the efficiency of this novel framework and show up to a hundredfold speedup over corresponding standard solvers on real-world graphs. © 2024 Neural information processing systems foundation. All rights reserved.

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AVF-Lip: High-Fidelity Talking Face Generation via Audio-Visual Fusion 7

AVF-Lip: High-Fidelity Talking Face Generation via Audio-Vis...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Wu, Mengduo Zhao, Sirui Wu, Tong Xu, Yifan Xu, Tong Chen, Enhong University of Science and Technology of China School of Computer Science and Technology Hefei China University of Science and Technology of China School of Data Science Hefei China

ISBN: (纸本)9798350350890

Talking face generation aims to create realistic facial videos with lips precisely synchronized to the input audio, finding broad applications in human-computer interaction, virtual avatars, and video conferencing. Despite significant advancements in previous works, existing methods continue to face challenges in achieving accurate lip synchronization and high visual quality. To address these challenges, we propose a talking face generation framework named AVF-Lip that supports higher resolution and accurate lip synchronization. Specifically, an audio-visual fusion module (AVFM) is first customized to improve lip synchronization performance, and this is further enhanced by introducing a dynamic and fine-grained face mask. Subsequently, to address the problem of lip distortion in high-resolution facial images, we design a set of loss functions based on face sketches to enhance the generation of the lip areas. Extensive experiments demonstrate that our AVF-Lip outperforms state-of-the-art methods in lip synchronization and delivers competitive visual quality, positioning it as a promising solution for high-resolution and high-fidelity talking face generation. Project page: https://***/ © 2024 IEEE.

关键词： Video conferencing

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CMDM: A Control Motion Diffusion Model for 2D Digital Human Motion Video Generation 7

CMDM: A Control Motion Diffusion Model for 2D Digital Human ...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Wu, Tong Zhao, Sirui Jin, Siyuan Xu, Tong Chen, Enhong School of Data Science University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798350350890

In recent years, 2D digital human motion generation (DHMG) is becoming increasingly crucial for many areas such as virtual live broadcasting and film production. Although a lot of effort has been invested in DHMG, the complexity and diversity of human movements make it difficult to generate motions for arbitrary poses, and it is challenging to avoid distortion in the generated hand regions. To address these challenges, in this paper, we propose Control Motion Diffusion Model (CMDM), a diffusion based method for generating 2D digital human motion videos with arbitrary given poses. In CMDM, a novel Pose Control Module (PCM) is customized to fully encode pose and character information, generating multi-level and multi-resolution embedding, thereby ensuring that the model can learn more precise human motion features. In addition, data augmentation and VAE fine-tuning training strategies are adopted to decouple character and pose features, improving the model's ability to generate unseen poses. Finally, comprehensive experiments have been completed to demonstrate the effectiveness of the proposed method, and the proposed CMDM has achieved significant improvement in both subjective and objective evaluations. © 2024 IEEE.

关键词： Digital elevation model

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