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作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

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INT-MC: Low-Overhead In-Band network-Wide Telemetry Based on Matrix Completion 25

INT-MC: Low-Overhead In-Band Network-Wide Telemetry Based on...

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Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of computer Systems

作者： Xin Zeng Kun Xie Yanbiao Li Ke Xu Gaogang Xie Jigang Wen Yinchuan Cong Wei Liang College of Computer Science and Electronic Engineering Hunan University Changsha China Computer Network Information Center Chinese Academy of Sciences Beijing China and School Computer Science and Technology University of Chinese Academy of Sciences Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China

ISBN: (纸本)9798400715938

In-band network Telemetry (INT) enhances real-time, high-resolution network monitoring capabilities by incorporating fine-grained internal state information into packets. Utilizing INT for network-wide visualization can significantly bolster network management and operation. Although existing studies have made significant contributions, they have not been able to simultaneously meet the following two objectives: 1) Comprehensive visibility of the network's internal state, which refers to obtaining information on all switch ports and their corresponding link states; 2) Low measurement overhead, which involves reducing measurement bandwidth overhead and minimizing the impact of the measurement process on the network. We designed INT-MC to meet both of these objectives. INT-MC is an efficient and cost-effective network-wide telemetry solution based on matrix completion. By modeling link metadata as a matrix and leveraging its low-rank property, INT-MC selectively measures certain links. It then employs matrix completion algorithms to infer information about unmeasured links, thereby achieving low-overhead network-wide telemetry. Designing paths to cover the target links involves an NP-complete problem, and the high computational complexity may delay the measurement tasks. To circumvent this, we propose an improved scheme based on Eulerian digraph decomposition, transforming the path calculation problem into a high-information path selection problem, significantly reducing computational *** have implemented an INT-MC prototype within the NSFNet topology, consisting of 14 Tofino switches and 10 end-hosts, and conducted extensive experiments and evaluations. The results indicate that INT-MC incurs only 16% of the measurement overhead compared to existing network-wide telemetry solutions, while achieving nearly identical accuracy. Even under high-frequency measurements of 20 times per second, the bandwidth overhead of INT-MC is approximately 0.075% of the total bandwi

关键词： in-band network telemetry

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Knowledge Graph Augmented network Towards Multiview Representation Learning for Aspect-Based Sentiment Analysis

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IEEE Transactions on Knowledge and Data engineering 2023年第10期35卷 10098-10111页

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Du, Bo Jin, Hua Tao, Dacheng Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Wuhan430072 China Jd Explore Academy at *** Beijing100101 China Wuhan University Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Hubei Wuhan430072 China Hospital of Kunming University of Science and Technology Kunming650032 China

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets. © 1989-2012 IEEE.

关键词： Semantics

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AI-Enabled Multimedia Semantic Communications

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IEEE Communications Magazine 2025年第6期63卷 66-73页

作者： Li, Chenxing Duan, Yiping Wang, Liejun Tao, Xiaoming Tsinghua University Department of Electronic Engineering China State Key Laboratory of Space Network China Beijing National Research Center for Information Science and Technology China School of Computer Science and Technology Xinjiang University China

With increasing demand for mobile communication and the development of artificial intelligence, the development of the sixth-generation mobile communication network (6G) will be oriented toward more intelligent and multimodal services. It is necessary to consider the deep integration of information, communication, and data technologies and build a full life cycle resource management framework for computing, data, artificial intelligence models, and communication. To address the requirements of 6G intelligent applications and multimodal services, this article refers to the visual cognitive mechanism of the human brain to improve multimedia representation and communication efficiency and studies the architecture of semantic communication systems, privacy protection mechanisms, and quality evaluation and guarantee mechanisms. The "semantic-network-semantic" architecture was established to integrate semantic computing, intelligent communication, and endogenous security to improve transmission efficiency, service quality, and security credibility. Through prior semantic knowledge, semantic representation, and encoding transmission methods, the quality of service of semantic communication is improved. To build an efficient 6G semantic communication paradigm and system, the communication mode should be changed, and new ideas for future mobile communication should be provided. © 1979-2012 IEEE.

关键词： Multimedia services

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Research and implementation of a planimetric measurement technology based on machine vision

Research and implementation of a planimetric measurement tec...

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2022 International Conference on Signal Processing, computer networks, and Communications, SPCNC 2022

作者： Zhou, Qingsen Zhao, Laiding Bai, Xiaodong College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China National Local Joint Engineering Research Center for Communication and Network Technology Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China School of Computer Science and Technology Hainan University Haikou570228 China

ISBN: (纸本)9781510664616

To solve the issues with industrial traditional area measurement instruments equipped with infrared sensors, which are bulky and compute slowly, a planimetric measuring method based on machine vision is developed. The area of the plane is calculated through three processes: 3-D reconstruction by monocular camera calibration, top view by perspective transformation and image segmentation to obtain the proportion of the object to be measured. This paper enhances the calculation of the world coordinate points in the 3-D reconstruction process and lowers the measurement error from more than 1% to 0.28%. It solves the drawbacks of the conventional measuring device, which is huge and slow, and can correctly measure the planar area with less expensive hardware, which has certain application value. © 2023 SPIE.

关键词： Image segmentation

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Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

Intelligent Model to Predict Early Liver Disease using Machi...

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2022 International Conference on Business Analytics for technology and Security, ICBATS 2022

作者： Ghazal, Taher M. Rehman, Aziz Ur Saleem, Muhammad Ahmad, Munir Ahmad, Shabir Mehmood, Faisal Faculty of Information Science and Technology Ukm Network and Communication Technology Lab Center for Cyber Security Malaysia School of Computer Science NCBAandE Lahore Pakistan Gachon University Department of Computer Engineering Korea Republic of Gachon University Department of It Convergence Engineering Korea Republic of

ISBN: (纸本)9781665409209

Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it's more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate. © 2022 IEEE.

关键词： Forecasting

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IRL-PM: An Inverse Reinforcement Learning-based Power Minimization in Multi-User MISO networks 8

IRL-PM: An Inverse Reinforcement Learning-based Power Minimi...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Tian, Xingcong Gao, Bo Liu, Ming Xiong, Ke Fan, Pingyi Letaief, Khaled Ben Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781665456128

This paper aims to efficiently save energy consumption of multiple-input-single-output (MISO) networks. To this end, an optimization problem is formulated to minimize the transmit power of the base station (BS) while ensuring the minimal required information rate of each user. Reinforcement learning (RL) is a significant solution to tackle this non-convex problems. Nevertheless, designing efficient reward functions that accurately describe the involved optimization problems for RL is a challenging problem, which may result in low learning efficiency and even inaccurate learning. To cope with such an issue, this paper proposes an inverse reinforcement learning (IRL)-based approach to minimize the transmit power of MISO networks, in which the generative adversarial imitation learning (GAIL) is employed as the IRL framework and the reward function is represented by an artificial neural network (ANN) model, whose parameters are learned automatically in terms of an expert policy based on the semidefinite relaxation (SDR)-based approach. Since SDR is able to achieve the near optimal solution, the learned reward function is more accurate than traditional artificially designed one. Simulations show that, the proposed IRL-based approach reaches similar energy consumption to the SDR-based approach, and reduces about 50% transmit power compared to traditional RL-based one. © 2023 IEEE.

关键词： Reinforcement learning

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Sum Computation Rate Maximization in Self-Sustainable RIS-Assisted MEC

Sum Computation Rate Maximization in Self-Sustainable RIS-As...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Li, Han Liu, Ming Gao, Bo Xiong, Ke Fan, Pingyi Letaief, Khaled B. School of Computer and Information Technology Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665494274

This paper studies a self-sustainable reconfigurable intelligent surface (SRIS)-assisted mobile edge computing (MEC) network, where a SRIS first harvests energy from a hybrid access point (HAP), and then enhances the users' offloading performance with the harvested energy. To improve computing efficiency, a sum computation rate maximization problem is formulated. Based on the alternating optimization (AO) method, an efficient algorithm is proposed to solve the formulated non-convex problem. Simulations show that when the SRIS is deployed closer to the HAP, a higher performance gain can be achieved. © 2023 IEEE.

关键词： Mobile edge computing

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Neural network-driven parallel accelerated selective image encryption with semantic understanding

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The European Physical Journal Special Topics 2025年 1-16页

作者： Song, Wei Fu, Chong Zheng, Yu Chen, Junxin Zhang, Yanfeng School of Computer Science and Engineering Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China University of California Irvine Irvine USA School of Software Dalian University of Technology Dalian China

Existing batch image encryption schemes often directly encrypt images without considering their source. However, in real-life scenarios, these images are typically part of a larger collection stored in a directory on a computer or in the cloud. To efficiently protect those sensitive images distributed within the collection, we rethink selective image encryption (SIE) from the perspective of batch image encryption and proposes a novel SIE scheme using deep neural networks (DNN) and parallel computing. First, a DNN method is employed to identify sensitive images by analyzing their semantics. Next, a parallel cross-bitplane permutation algorithm is introduced to shuffle the retrieved images in bit-level. Then, a bi-directional diffusion strategy is employed to spread the encryption influence of a pixel to all images. Experimental results demonstrate the proposed scheme effectively understands the semantic of images, identifies sensitive images, and encrypts them with high permutation and diffusion efficiency. Further security analyses show that the proposed algorithm successfully resists various attacks with desired performance, including achieving high information entropy and low correlation coefficients in statistical analysis, and ideal values of the number of pixels changing rate (NPCR) and the unified averaged changing intensity (UACI) to resist differential attack, as well as high key sensitivity.

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A hybrid modeling approach incorporating physical modeling and deep learning for multiphase flow simulation 84

A hybrid modeling approach incorporating physical modeling a...

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84th EAGE Annual Conference and Exhibition

作者： Wang, Y. Zhang, J. Du, S. Yan, B. Song, H. Wang, J. Xie, C. China Texas A&M University United States University of Science and Technology Beijing China National & Local Joint Engineering Laboratory for Big-Data Analysis and Computing Technology King Abdullah University of Science and Technology Saudi Arabia Computer Network Information Center Chinese Academy of Sciences China

ISBN: (纸本)9781713884156

Multiphase flows are of great importance for the sustainable utilization of geological resources and ecological protection. Numerical simulation, as a general and powerful approach for multiphase flow modeling, faces great computational challenge for large scale and heterogeneous problems. This study explores the possibility of a hybrid modeling architecture for nonlinear multiphase flow, which integrates physical modeling and deep learning approaches. By breaking down the coupled system into pressure and saturation equations, we construct novel solvers using a physics-guided U-net and Fourier Neural Operator instead of the traditional time-consuming implicit pressure solver. The coupling of our carefully designed solver for pressure and explicit solver for saturation provides a new perspective for modeling large scale complex multiphase flows. Our results demonstrate that this hybrid modeling architecture exhibits superior predictive performance for pressure and saturation, with relative error about 0.2% and 0.6%, respectively. More importantly, our hybrid solver can improve computational efficiency by several orders of magnitude compared to traditional numerical simulation workflows. Our work showcases that the proposed hybrid modeling approach incorporating physical modeling and deep learning is promising to provide a more flexible and powerful alternative for multiphase flow modeling in geological formations. © 2023 84th EAGE Annual Conference and Exhibition. All rights reserved.

关键词： Multiphase flow

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