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22nd International Conference on Advanced computing and Communication, ADCOM 2016

作者： Jivrajani, Aarti Raghu, Dhanya Apoorva, K.H. Phalachandra, H.L. Sitaram, Dinkar Center for Cloud Computing and Big Data PES University India

ISBN: (纸本)9781538623893

With the emergence of large, heterogeneous, shared computing clusters, their efficient use by mixed distributed workloads and tenants remains an important challenge. This paper focuses on scheduling of batches of similar jobs on the cluster of machines with similar characteristics to fully utilize the resources at the node and reduce the energy consumption of the datacenter. We use a novel algorithm to find the best fit machine in that cluster. We perform hierarchical clustering to identify common groups of jobs and common groups of machines, which is a more efficient way of clustering than the conventional k-means clustering. To estimate the energy saving, we analyze a recent Google release of scheduler request and utilization data across a large (12500+) general-purpose compute cluster over 29 days. We offer a statistical profile of the data, with several interesting discoveries regarding batching similar tasks in a heterogeneous dataset, CPU and memory consumptions, task durations, and others. © 2016 IEEE.

关键词： Scheduling

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Predicting the Risk of Incident Cardiovascular Disease Using Retinal Vessel Caliber Measured by Artificial Intelligence in a Chinese Community-Based Population

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SSRN 2025年

作者： Luo, Xingyu Yang, Guangzhengao Fan, Fangfang Jiang, Yimeng Jia, Jia Zhao, Jie Chen, Zifan Yan, Shenshen She, Haicheng Zheng, Bo Zhang, Li Zhang, Yan Department of Cardiology Peking University First Hospital Beijing China Institute of Cardiovascular Disease Peking University First Hospital Beijing China Center for Data Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Changsha Institute for Computing and Digital Economy Beijing China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Science Key Lab Beijing China

Background: The predictive value of retinal vessel caliber, measured through artificial intelligence (AI), for incident cardiovascular disease (CVD) risk remains to be explored. Our study aimed to investigate the association between AI-measured retinal vessel caliber and the risk of incident CVD in a Chinese community-based population. Methods: We conducted a prospective cohort study on subjects from a Beijing community in China who were free of prevalent stroke or myocardial infarction and underwent retinal photography at baseline. By deep-learning algorithm, retinal vessel caliber measurements, including central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), and arteriolar-to-venular ratio (AVR), were obtained from 0.5 to 1 disc diameter from the optic disc margin. The primary endpoint was an incident major adverse cardiovascular event (MACE), defined as a composite of first non-fatal stroke, first non-fatal acute myocardial infarction (AMI), and cardiovascular death. The secondary endpoints included first stroke, first AMI, and cardiovascular death. Findings: A total of 3,585 participants were included. The mean age was 56.49 ± 8.94 years, and 2335 (65.1%) were female. Prevalence of prior hypertension and diabetes was 1594 (44.5%) and 676 (18.9%), respectively. The mean values for CRAE, CRVE, and AVR were 190.83 ± 29.92 μm, 278.26 ± 36.34 μm, and 0.69 ± 0.08, respectively. During the follow-up (median, 7.53 years), there were 293 cases of incident MACE (8.2%), 226 strokes (6.3%), 57 AMIs (1.6%), and 44 cardiovascular deaths (1.2%). The Kaplan–Meier curves showed significant differences in cumulative hazards of the primary endpoint among tertiles of CRAE, CRVE, and AVR. In Cox proportional-hazards models adjusting for covariates, CRAE was negatively associated with the risk of incident MACE (per 10 μm increase, hazard ratio [HR] = 0.95, 95% confidence interval [CI]: 0.92-0.99, P = 0.009) and the risk of incident stroke (per 10 μm

关键词： Risk assessment

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Restoring Missing Slices of Serial Section Electron Microscopy Using Diffusion Models 2nd

Restoring Missing Slices of Serial Section Electron Microsco...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Yang, Hongyu Zhang, Ruobing Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China University of Science and Technology of China No. 96 Jinzhai Road Anhui Baohe District Hefei230022 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

ISBN: (纸本)9789819619030

Serial section electron microscopy (ssEM), an advanced three-dimensional imaging technique, has played a crucial role in studying neuronal connections and microstructures of the brain. However, imperfect sample preparation and image acquisition randomly cause degradation of ssEM images, where Missing Slices (MS) is the most significant one. The previous deep learning methods such as interpolation model using spatially-adaptive convolutions have been proven to outperform conventional approaches, but those methods fail to recover some high-frequency information of missing slices. In this work, we propose a novel approach leveraging Denoising Diffusion Probabilistic Models (DDPM) for MS restoration in ssEM images. Our method improves the U-Net backbone with asymmetric and symmetric 3D convolutions, adapting to the anisotropic characteristic of ssEM images. Then, we introduce the Adaptive and Learnable Reconstruction module (ALR) and First and Last slices Attention Block (FLAB) to extract the features of slices effectively. Moreover, we employ a diffusion correction method, reducing training-testing data discrepancies. Experiment results demonstrate the effectiveness of our method in generating realistic missing slices, surpassing previous methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Restoration

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Parallel implementation of w-projection wide-field imaging

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Science Bulletin 2019年第9期64卷 586-594页

作者： Baoqiang Lao Tao An Ang Yu Wenhui Zhang Junyi Wang Quan Guo Shaoguang Guo Xiaocong Wu Shanghai Astronomical Observatory Key Laboratory of Radio AstronomyChinese Academy of Sciences Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology

w-Projection is a wide-field imaging technique that is widely used in radio synthesis arrays. Processing the wide-field big data generated by the future Square Kilometre Array(SKA) will require significant updates to current methods to significantly reduce the time consumed on data processing. data loading and gridding are found to be two major time-consuming tasks in w-projection. In this paper, we investigate two parallel methods of accelerating w-projection processing on multiple nodes: the hybrid Message Passing Interface(MPI) and Open Multi-Processing(OpenMP) method based on multicore Central Processing Units(CPUs) and the hybrid MPI and Compute Unified Device Architecture(CUDA)method based on Graphics Processing Units(GPUs). Both methods are successfully employed and operated in various computational environments, confirming their robustness. The experimental results show that the total runtime of both MPI + OpenMP and MPI + CUDA methods is significantly shorter than that of single-thread processing. MPI + CUDA generally shows faster performance when running on multiple nodes than MPI + OpenMP, especially on large numbers of nodes. The single-precision GPU-based processing yields faster computation than the double-precision processing; while the single-and doubleprecision CPU-based processing shows consistent computational performance. The gridding time remarkably increases when the support size of the convolution kernel is larger than 8 and the image size is larger than 2,048 pixels. The present research offers useful guidance for developing SKA imaging pipelines.

关键词： Radio synthesis arrays Square kilometre array Wide field imaging Parallelization w-Projection

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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Science China(Information Sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN 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 Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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Characteristic Decomposition: From Regular Sets to Normal Sets

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Journal of Systems Science & Complexity 2019年第1期32卷 37-46页

作者： MOU Chenqi WANG Dongming LMIB–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University LMIB–SKLSDE–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Centre National de la Recherche Scientifique

In this paper it is shown how to transform a regular triangular set into a normal triangular set by computing the W-characteristic set of their saturated ideal and an algorithm is proposed for decomposing any polynomial set into ?nitely many strong characteristic pairs, each of which is formed with the reduced lexicographic Gr?bner basis and the normal W-characteristic set of a characterizable ideal.

关键词： Characteristic decomposition characteristic pair normal set regular set W-characteristic set

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DeepFake Detection Using Deep Learning 1st

DeepFake Detection Using Deep Learning

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1st International Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Rehman, Mariam Rasool, Mehran Safder, Sadaf Government College University Faisalabad Pakistan National Center in Big Data and Cloud Computing University of Agriculture Faisalabad Pakistan

ISBN: (纸本)9789819979684

A deepfake is a computer-generated video or image where one person's face is replaced with another face of a person which uses a generative adversarial network (GAN) to create and alter images that are practically impossible for humans to distinguish from authentic ones. The development of GAN technology has led to significant improvements in image generation. This progress has made it difficult for humans to differentiate between generated images and raised concerns about their misuse and also creating security threats for the society. Deepfake image detection is an essential challenge because the current methods often face inaccuracy and time-consuming issues. The research work employed GAN discriminators to address these issues. The experiment used the CelebA dataset, which contained diverse celebrity images. Moreover, the proposed model consists of components i.e., generator and discriminator. The generator creates fake data from the training images, while the discriminator distinguishes real from the fake images. The result shows that the proposed approach is out-performing with an accuracy of 95.8% in identifying deep fake images, even in the presence of various manipulations. The proposed model is helpful for enhancing trust and security in the society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Generative adversarial networks

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JGC-IAGCL: Fusing joint graph convolution and intent-aware graph contrastive learning for explainable recommendation

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Information Fusion 2025年 123卷

作者： Yang, Zhi Lin, Chuan Qin, Yongbin Huang, Ruizhang Chen, Yanping Qin, Jiwei State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang Guizhou550025 China College of Information Science and Engineering Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

Graph contrastive learning (GCL) enhances recommendation accuracy by leveraging self-supervised features to refine node representations from large-scale unlabeled data. Traditional GCL-based recommendation models typically construct contrastive views via graph augmentation (e.g., stochastic node/edge dropout) or embedding-space perturbation, aiming to maximize representation consistency. However, these methods struggle to effectively model and interpret user preferences and consumption intents, limiting explainability and recommendation performance. To address these challenges, we propose JGC-IAGCL (Joint Graph Convolution and Intent-Aware Graph Contrastive Learning), an explainable recommendation framework. JGC-IAGCL integrates joint graph convolution to capture implicit user preferences and employs intent-aware graph contrastive learning to extract explicit user intents from user–item interactions. By fusing these features, our method generates evenly distributed, intent-propensity-aware user/item representations. Theoretical analysis shows that JGC-IAGCL mitigates popularity bias while enhancing the exposure of long-tail items. Extensive experiments on four highly sparse public datasets validate its effectiveness, demonstrating superior recommendation accuracy and improved interpretability. © 2025

关键词： Self-supervised learning

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