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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Shi, Jia Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

The real academic network belongs to a heterogeneous network, therefore, for the link prediction tasks, some information on the network may be lost if only using homogeneous network methods. In order to make good use of the rich semantic information in a heterogeneous network for better predicting the cooperative relationship between scholars, this paper proposes a cooperative relationship prediction (CoRP) model. The CoRP model includes the following three steps. Firstly, a structural feature extraction module is designed based on meta path by using the similarity metric function to measure the meta path similarity between scholars. Secondly, to overcome the defects that the existing methods usually ignore the attribute information on nodes, three attribute feature presentation functions are constructed to fully utilize the attribute information of scholars in the academic network. In the end, a machine learning classifier which combines the structural similarity and attribute similarity is utilized to learn each attribute feature and complete the task of cooperative relationship prediction. The experimental results reveal that compared with the benchmark method, the proposed method has better performance and interpretability with improvement from 3% to 10% of AUC value for the AMiner academic data set. © 2021 IEEE.

关键词： Measurement Smart cities Semantics Machine learning Benchmark testing Predictive models Feature extraction

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Integrating Local Real data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning

arXiv

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arXiv 2023年

作者： Yang, Wenkai Chen, Deli Zhou, Hao Meng, Fandong Zhou, Jie Sun, Xu Center for Data Science Peking University China Pattern Recognition Center WeChat AI Tencent Inc. China MOE Key Lab of Computational Linguistics School of Computer Science Peking University China

Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner. However, the data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model severely biased to the head classes with the majority of the training samples. To alleviate this issue, decoupled training has recently been introduced to FL, considering it has achieved promising results in centralized long-tailed learning by re-balancing the biased classifier after the instance-balanced training. However, the current study restricts the capacity of decoupled training in federated long-tailed learning with a sub-optimal classifier re-trained on a set of pseudo features, due to the unavailability of a global balanced dataset in FL. In this work, in order to re-balance the classifier more effectively, we integrate the local real data with the global gradient prototypes to form the local balanced datasets, and thus re-balance the classifier during the local training. Furthermore, we introduce an extra classifier in the training phase to help model the global data distribution, which addresses the problem of contradictory optimization goals caused by performing classifier re-balancing locally. Extensive experiments show that our method consistently outperforms the existing state-of-the-art methods in various settings. © 2023, CC BY.

关键词： Privacy-preserving techniques

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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Automated Fruit Classification Based on Deep Learning Utilizing Yolov8 10

Automated Fruit Classification Based on Deep Learning Utiliz...

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Dewi, Christine Kamlasi, Oksam M Chhabra, Gunjan Dai, Guowei Kaushik, Keshav Khan, Inam Ullah Satya Wacana Christian University Department of Information Technology Salatiga Indonesia Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Agricultural Information Institute of Caas National Agriculture Science Data Center Beijing China School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Seas Isra University Department of Electronic Engineering Campus Islamabad Pakistan

ISBN: (纸本)9798350382471

The act of fruit identification entails the discernment and classification of various fruit varieties, predicated upon their visual attributes. This task can be accomplished through various methods, including manual inspection, traditional computer vision techniques, and more advanced approaches using machine learning and deep learning. Our work recognized 15 types of fruit. Our experiment used the fruit imagery dataset, consisting of class Avocado, Banana, Cherry, Apple Braeburn, Apple golden 1, Apricot, Grape, Kiwi, Mango, Orange, Papaya, Peach, Pineapple, Pomegranate and Strawberry. Yolov8 is one of the most popular learning models in the field of object detection. Convolutional neural networks (CNNs), thus simultaneously predicting multiple bounding boxes and probability classes from these bounding boxes. Yolov8 trains the entire image and directly optimizes detection performance, modeled as a Convolutional neural network (CNN). The percentage of detection of test results using *** for each of the 15 categories of fruit images with the average accuracy above is 0.97%, so it can be said to be quite good. After collecting data, Train training and system design implementation of Val Test Convolutional neural networks (CNN) algorithms enabled machine learning and were successfully detected using yolov8n. During the experimental process, we employ 100 epochs with the use of Google Collaboratory. Google Collaboratory has provided GPUs up to 12GB, making it easier and speeding up the fruit detection process. © 2023 IEEE.

关键词： Classification Convolutional Neural Network(CNN) Google Collaboratory YOLOv8

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An Efficient Graphics data Retrieval Method Based on data Conversion Schemes

An Efficient Graphics Data Retrieval Method Based on Data Co...

引用

International Conference on Electronics, Information and Communications (ICEIC)

作者： Nakhoon Baek School of Computer Science and Engineering Kyungpook National University Daegu Korea Graduate School of Data Science Kyungpook National University Daegu Korea Data-Driven Intelligent Mobility ICT Research Center Kyungpook National University Daegu Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Modern computer graphics systems usually manage a set of internal data to store control parameters and all kind of graphics data. The behind problem on these graphics system data is that they are frequently updated and retrieved with variety of data types, even they are initially stored as specific data types. To support these updates and retrievals with various data types, modern graphics systems typically use data type conversion features, and even in the worst cases, they should provide all combinations of internally stored data types and newly provided or retrieved data types. In this paper, we show how to efficiently provide all the required combinations of data type conversions, with motivations, analysis, and overall designs. We also show typical type conversion scenarios, with specific API function examples. Our design shows that it can improve the internal architecture and the internal efficiency of modern computer graphics systems. In its implementation, we use C++std:: map container for fast lookup of the best-suitable conversion function. This overall scheme was integrated in our existing systems, and shows its feasibility and correctness.

关键词： data conversion Pipelines Switches computer graphics C++ languages computer architecture Containers data retrieval

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Using Lock-Free Design for Throughput-Optimized Cache Eviction

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Proceedings of the ACM on Measurement and Analysis of Computing Systems 2025年第2期9卷 1-28页

作者： Chao Dong Fang Wang Hong Jiang Dan Feng Key Laboratory of Information Storage System Wuhan National Laboratory for Optoelectronics) Engineering Research Center of data storage systems and Technology School of Computer Science and Technology) Ministry of Education of China Huazhong University of Science and Technology Wuhan Hubei China Department of Computer Science and Engineering University of Texas at Arlington Arlington Texas USA

In large-scale information systems, storage device performance continues to improve while workloads expand in size and access characteristics. This growth puts tremendous pressure on caches and storage hierarchy in terms of concurrent throughput. However, existing cache eviction policies often struggle to provide adequate concurrent throughput due to their reliance on coarse-grained locking mechanisms and complex data structures. This paper presents a practical approach to cache eviction algorithm design, called Mobius, that optimizes the concurrent throughput of caches and reduces cache operation latency by utilizing lock-free data structures, while maintaining comparable hit ratios. Mobius includes two key designs. First, Mobius employs two lock-free FIFO queues to manage cache items, ensuring that all cache operations are executed efficiently in parallel. Second, Mobius integrates a consecutive detection mechanism that merges multiple modifications during eviction into a single operation, thereby reducing data races. Extensive evaluations using both synthetic and real-world workloads from high-concurrency clusters demonstrate that Mobius achieves a concurrent-throughput improvement ranging from 1.2× to 8.5× over state-of-the-art methods, while also maintaining lower latency and comparable cache hit ratios. The implementation of Mobius in CacheLib and RocksDB highlights its effectiveness in enhancing cache performance in practical scenarios.

关键词： cache eviction policy concurrency fifo queue lock-free throughput

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Attaining Human's Desirable Outcomes in Human-ai Interaction via Structural Causal Games

arXiv

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arXiv 2024年

作者： Liu, Anjie Wang, Jianhong Li, Haoxuan Chen, Xu Wang, Jun Kaski, Samuel Yang, Mengyue Department of Computer Science University College London United Kingdom Center for AI Fundamentals University of Manchester United Kingdom Center for Data Science Peking University China Gaoling School of Artificial Intelligence Renmin University of China China Aalto University Finland

In human-ai interaction, a prominent goal is to attain human's desirable outcome with the assistance of ai agents, which can be ideally delineated as a problem of seeking the optimal Nash Equilibrium that matches the human's desirable outcome. However, reaching the outcome is usually challenging due to the existence of multiple Nash Equilibria that are related to the assisting task but do not correspond to the human's desirable outcome. To tackle this issue, we employ a theoretical framework called structural causal game (SCG) to formalize the human-ai interactive process. Furthermore, we introduce a strategy referred to as pre-policy intervention on the SCG to steer ai agents towards attaining the human's desirable outcome. In more detail, a pre-policy is learned as a generalized intervention to guide the agents' policy selection, under a transparent and interpretable procedure determined by the SCG. To make the framework practical, we propose a reinforcement learning-like algorithm to search out this pre-policy. The proposed algorithm is tested in both gridworld environments and realistic dialogue scenarios with large language models, demonstrating its adaptability in a broader class of problems and potential effectiveness in real-world situations. © 2024, CC BY.

关键词： Game theory

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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Federated Learning for Smart Agriculture: Challenges and Opportunities 3

Federated Learning for Smart Agriculture: Challenges and Opp...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kondaveeti, Hari Kishan Sai, Guduru Balamanikanta Athar, Shaik Asneem Vatsavayi, Valli Kumari Mitra, Alakananda Ananthachari, Preethi School Of Computer Science And Engineering VIT-AP University Andhra Pradesh India Andhra University Department Of Cs & Se Andhra Pradesh India University Of Nebraska-Lincoln Nebraska Water Center LincolnNE United States Endicott College Of International Studies Woosong University Ai & BigData Dept Daejeon Korea Republic of

ISBN: (纸本)9798350318609

FL is a machine learning approach that allows knowledge sharing with privacy maintenance and cost reduction. FL has the potential to revolutionize the smart agriculture sector by enabling farmers to train and deploy machine learning models on their own devices, without the need to share their data with a central server. In this article, we review the applications of FL in smart agriculture, such as disease diagnosis and severity assessment, the Internet of Agricultural Things (IoAT), and yield forecasting. We also highlight the challenges and opportunities of using FL for smart agriculture. © 2024 IEEE.

关键词： Cost reduction

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Machine Learning With data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

引用

IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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