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arXiv 2023年

作者： Chen, Aodong Xu, Fei Han, Li Dong, Yuan Chen, Li Zhou, Zhi Liu, Fangming The Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University 3663 N. Zhongshan Road Shanghai200062 China The School of Software Engineering East China Normal University 3663 N. Zhongshan Road Shanghai200062 China The School of Computing and Informatics University of Louisiana at Lafayette 301 East Lewis Street LafayetteLA70504 United States The Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Sun Yat-sen University 132 E. Waihuan Road Guangzhou510006 China Peng Cheng Laboratory Huazhong University of Science and Technology China

GPUs have become the defacto hardware devices for accelerating Deep Neural Network (DNN) inference workloads. However, the conventional sequential execution mode of DNN operators in mainstream deep learning frameworks cannot fully utilize GPU resources, even with the operator fusion enabled, due to the increasing complexity of model structures and a greater diversity of operators. Moreover, the inadequate operator launch order in parallelized execution scenarios can lead to GPU resource wastage and unexpected performance interference among operators. In this paper, we propose Opara, a resource- and interference-aware DNN Operator parallel scheduling framework to accelerate DNN inference on GPUs. Specifically, Opara first employs CUDA Streams and CUDA Graph to parallelize the execution of multiple operators automatically. To further expedite DNN inference, Opara leverages the resource demands of operators to judiciously adjust the operator launch order on GPUs, overlapping the execution of compute-intensive and memory-intensive operators. We implement and open source a prototype of Opara based on PyTorch in a non-intrusive manner. Extensive prototype experiments with representative DNN and Transformer-based models demonstrate that Opara outperforms the default sequential CUDA Graph in PyTorch and the state-of-the-art operator parallelism systems by up to 1.68× and 1.29×, respectively, yet with acceptable runtime overhead. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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ECO v1: Towards Event-Centric Opinion Mining

arXiv

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arXiv 2022年

作者： Xu, Ruoxi Lin, Hongyu Liao, Meng Han, Xianpei Xu, Jin Tan, Wei Sun, Yingfei Sun, Le Chinese Information Processing Laborator Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Data Quality Team WeChat Tencent Inc. China

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies. © 2022, CC BY.

关键词： data mining

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An identity-based anonymous signcryption for vehicular ad hoc networks 2nd

An identity-based anonymous signcryption for vehicular ad ho...

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2nd International Cognitive Cities Conference, IC3 2019

作者： Lu, Yong Yang, Huijie School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China

ISBN: (纸本)9789811561122

The vehicular ad hoc networks (VANETs) can share information of vehicles’ location, speed and route within a certain range by interconnecting vehicles, roadside units (RSU) and the cloud. As people’s daily traffic tools, vehicles reflect users’ daily routines and other privacy. Therefore, privacy protection is very critical in VANETs. However, in the existing schemes, a user’s temporary identity (TID) is generated by the user himself, which is inconvenient to be managed and can be easily forged. The reputation values of users also need to be considered. In this paper, an identity based signcryption scheme for VANETs is proposed. In the novel scheme, TID is generated by key generation center (KCG) and a personal reputation system is produced. The security analysis of the scheme shows that our scheme is practical. © Springer Nature Singapore Pte Ltd 2020.

关键词： Vehicular ad hoc networks

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MetaMask: revisiting dimensional confounder for self-supervised learning 22

MetaMask: revisiting dimensional confounder for self-supervi...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiangmeng Li Wenwen Qiang Yanan Zhang Wenyi Mo Changwen Zheng Bing Su Hui Xiong University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Gaoling School of Artificial Intelligence Renmin University of China Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guangzhou) Guangzhou HKUST Fok Ying Tung Research Institute

ISBN: (纸本)9781713871088

As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interference of task-irrelevant information and sample inefficiency, which are related to the recurring existence of trivial constant solutions. From the perspective of dimensional analysis, we find out that the dimensional redundancy and dimensional confounder are the intrinsic issues behind the phenomena, and provide experimental evidence to support our viewpoint. We further propose a simple yet effective approach MetaMask, short for the dimensional Mask learned by Meta-learning, to learn representations against dimensional redundancy and confounder. MetaMask adopts the redundancy-reduction technique to tackle the dimensional redundancy issue and innovatively introduces a dimensional mask to reduce the gradient effects of specific dimensions containing the confounder, which is trained by employing a meta-learning paradigm with the objective of improving the performance of masked representations on a typical self-supervised task. We provide solid theoretical analyses to prove MetaMask can obtain tighter risk bounds for downstream classification compared to typical contrastive methods. Empirically, our method achieves state-of-the-art performance on various benchmarks.1

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Towards Faster Fully Homomorphic Encryption Implementation with Integer and Floating-point Computing Power of GPUs

Towards Faster Fully Homomorphic Encryption Implementation w...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Guang Fan Fangyu Zheng Lipeng Wan Lili Gao Yuan Zhao Jiankuo Dong Yixuan Song Yuewu Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing China Ant Group Hangzhou China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Cyber Security University of Science and Technology of China Hefei China

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without knowledge of the plaintext message and currently has been the focus of both academia and industry. However, the performance issue hinders its large-scale application, highlighting the urgent requirements of high-performance FHE *** noticing the tremendous potential of GPUs in the field of cryptographic acceleration, this paper comprehensively investigates how to convert the available computing resources residing in GPUs into FHE workhorses, and implement a full set of low-level and middle-level FHE primitives based on two arithmetic units (i.e., INT32 and FP64 units) with three types of data precision (i.e., INT32, INT64 and FP64). This paper gives a comprehensive evaluation and comparison based on each road-map. Our implementations of fundamental functions outperform the implementations on the same platform by 1.7× to 16.7×. Taking CKKS FHE schemes as a case study, our implementation of homomorphic multiplication achieves 3.2× speedup over the state-of-the-art GPU-based implementation, even considering the difference of platforms. The detailed evaluation and comparison of this paper would offer a vital reference for the follow-up work to choose appropriate underlying arithmetic units and important primitive optimizations in GPU-based FHE implementations.

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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Heterophily-Aware Graph Attention Network

arXiv

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arXiv 2023年

作者： Wang, Junfu Guo, Yuanfang Yang, Liang Wang, Yunhong State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Shen Yuan Honors College Beihang University Beijing100191 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Hebei University of Technology Tianjin300401 China

Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess different labels or features. Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem. In this paper, we first propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i.e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, enabling nodes to acquire appropriate information from distinct neighbors. Then, we propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily, to handle the networks with different homophily ratios. To demonstrate the effectiveness of the proposed HA-GAT, we analyze the proposed heterophily-aware attention scheme and local distribution exploration, by seeking an interpretation from their mechanism. Extensive results demonstrate that our HA-GAT achieves state-of-the-art performances on eight datasets with different homophily ratios in both the supervised and semi-supervised node classification tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback

arXiv

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arXiv 2022年

作者： Li, Kai Li, Ying Cheng, Lei Shi, Qingjiang Luo, Zhi-Quan The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China The School of Software Engineering Tongji University Shanghai201804 China Pazhou Lab Guangzhou510310 China

The downlink channel covariance matrix (CCM) acquisition is the key step for the practical performance of massive multiple-input and multiple-output (MIMO) systems, including beamforming, channel tracking, and user scheduling. However, this task is challenging in the popular frequency division duplex massive MIMO systems with Type I codebook due to the limited channel information feedback. In this paper, we propose a novel formulation that leverages the structure of the codebook and feedback values for an accurate estimation of the downlink CCM. Then, we design a cutting plane algorithm to consecutively shrink the feasible set containing the downlink CCM, enabled by the careful design of pilot weighting matrices. Theoretical analysis shows that as the number of communication rounds increases, the proposed cutting plane algorithm can recover the ground-truth CCM. Numerical results are presented to demonstrate the superior performance of the proposed algorithm over the existing benchmark in CCM reconstruction. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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Distinguishing unitary gates on the IBM quantum processor

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science China(Information sciences) 2019年第7期62卷 210-216页

作者： Shusen LIU Yinan LI Runyao DUAN School of Data and Computer Science Sun Yat-sen University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney Centrum Wiskunde & Informatica and Research Center for Quantum Software Institute for Quantum Computing

An unknown unitary gate, which is secretly chosen from several known ones, can always be distinguished perfectly. In this paper, we implement such a task on IBM's quantum processor. More precisely,we experimentally demonstrate the discrimination of two qubit unitary gates, the identity gate and the 2/3π-phase shift gate, using two discrimination schemes — the parallel scheme and the sequential scheme. We program these two schemes on the ibmqx4, a 5-qubit superconducting quantum processor via IBM cloud,with the help of the QSI modules. We report that both discrimination schemes achieve success probabilities at least 85%.

关键词： quantum experiment IBM quantum computer software-aided quantum experiment sequential schemes for unitary discrimination parallel schemes for unitary discrimination

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NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation

arXiv

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arXiv 2024年

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Chen, Zhen Wang, Yi Li, Chenxin He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labor-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2024, CC BY.

关键词： Decoding

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