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26th International World Wide Web Conference, WWW 2017 Companion

作者： Zhao, Shenglin Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics Application Shenzhen Research Institute Chinese University of Hong Kong Nanshan District Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9781450349147

Point-of-interest (POI) recommendation is an important application for location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. Previous studies show that modeling the sequential pattern of user check-ins is necessary for POI recommendation. Markov chain model, recurrent neural network, and the word2vec framework are used to model check-in sequences in previous work. However, all previous sequential models ignore the fact that check-in sequences on different days naturally exhibit the various temporal characteristics, for instance, "work" on weekday and "entertainment" on weekend. In this paper, we take this challenge and propose a Geo-Temporal sequential embedding rank (Geo-Teaser) model for POI recommendation. Inspired by the success of the word2vec framework to model the sequential contexts, we propose a temporal POI embedding model to learn POI representations under some particular temporal state. The temporal POI embedding model captures the contextual check-in information in sequences and the various temporal characteristics on different days as well. Furthermore, We propose a new way to incorporate the geographical influence into the pairwise preference ranking method through discriminating the unvisited POIs according to geographical information. Then we develop a geographically hierarchical pairwise preference ranking model. Finally, we propose a unified framework to recommend POIs combining these two models. To verify the effectiveness of our proposed method, we conduct experiments on two real-life datasets. Experimental results show that the Geo-Teaser model outperforms state-of-the-art models. Compared with the best baseline competitor, the Geo-Teaser model improves at least 20% on both datasets for all metrics. © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.

关键词： Location based services

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Review of Mathematical Optimization in Federated Learning

arXiv

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arXiv 2024年

作者： Yang, Shusen Zhao, Fangyuan Zhou, Zihao Shi, Liang Ren, Xuebin Xu, Zongben School of Mathematics and Statistics Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University China

Federated Learning (FL) has been becoming a popular interdisciplinary research area in both applied mathematics and information sciences. Mathematically, FL aims to collaboratively optimize aggregate objective functions over distributed datasets while satisfying a variety of privacy and system constraints. Different from conventional distributed optimization methods, FL needs to address several specific issues (e.g., non-i.i.d. data distributions and differential private noises), which pose a set of new challenges in the problem formulation, algorithm design, and convergence analysis. In this paper, we will systematically review existing FL optimization research including their assumptions, formulations, methods, and theoretical results. Potential future directions are also discussed. © 2024, CC BY.

关键词： Federated learning

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CDC: Classification driven compression for bandwidth efficient edge-cloud collaborative deep learning

arXiv

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arXiv 2020年

作者： Dong, Yuanrui Zhao, Peng Yu, Hanqiao Zhao, Cong Yang, Shusen National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Department of Computing Imperial College London United Kingdom

The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation. Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based classifiers, we present CDC, a Classification Driven Compression framework that reduces bandwidth consumption while preserving classification accuracy of edge-cloud collaborative DL. Specifically, to reduce bandwidth consumption, for resource-limited edge servers, we develop a lightweight autoencoder with a classification guidance for compression with classification driven feature preservation, which allows edges to only upload the latent code of raw data for accurate global training on the Cloud. Additionally, we design an adjustable quantization scheme adaptively pursuing the tradeoff between bandwidth consumption and classification accuracy under different network conditions, where only fine-Tuning is required for rapid compression ratio adjustment. Results of extensive experiments demonstrate that, compared with DNN training with raw data, CDC consumes 14.9 less bandwidth with an accuracy loss no more than 1.06%, and compared with DNN training with data compressed by AE without guidance, CDC introduces at least 100% lower accuracy loss. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Protein Structure Prediction Using A New Optimization-Based Evolutionary and Explainable Artificial Intelligence Approach

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Hong, Jun Zhan, Zhi-Hui He, Langchong Xu, Zongben Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou P. R. China Health Science Center School of Pharmacy Xi’an Jiaotong University Xi’an P. R. China National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an P. R. China College of Artificial Intelligence Nankai University Tianjin P. R. China

Protein structure prediction (PSP) is an important scientific problem because it helps humans to understand how proteins perform their biological functions. This paper models the PSP problem as a multi-objective optimization problem with three fast and accurate knowledge-based energy functions. This way, using evolutionary computation (EC)-based artificial intelligence (AI) approach to solve this multi-objective PSP problem to find the optimal structure is explainable. Considering that the multiple populations for multiple objectives (MPMO) framework shows efficient performance in solving lots of multi-objective benchmarks and real-world problems, this paper proposes a new AI approach named improved MPMO-based differential evolution (IMPMO-DE) to solve the multi-objective PSP problem. To our best knowledge, this is the first time that MPMO is applied to PSP, with three novel strategies. First, an adaptive archive-based mutation strategy is proposed to better balance the exploration and exploitation abilities by adaptively using different archive-based mutation operators in different evolutionary stages. Second, a mixed individual transfer strategy is proposed to share search information among the multiple populations to accelerate the convergence speed. Third, an evolvable archive update strategy is proposed to generate more promising solutions through evolving the archived solutions. IMPMO-DE is tested on 28 representative proteins and all the available template-free modeling proteins up to 404 residues in the famous Critical Assessment of Protein Structure Prediction (CASP14) competition. Experimental results show that IMPMO-DE performs better than the compared state-of-the-art EC-based PSP methods and ranks above average compared with all the CASP14 competitors. More importantly, IMPMO-DE is a new efficient AI approach that opens a promising optimization-based evolutionary and explainable way for efficient PSP rather than deep learning approaches like AlphaFold2,

关键词： Evolutionary algorithms

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SurveilEdge: Real-time video query based on collaborative cloud-edge deep learning

arXiv

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arXiv 2020年

作者： Wang, Shibo Yang, Shusen Zhao, Cong National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University China Department of Computing Imperial College London United Kingdom

The real-time query of massive surveillance video data plays a fundamental role in various smart urban applications such as public safety and intelligent transportation. Traditional cloud-based approaches are not applicable because of high transmission latency and prohibitive bandwidth cost, while edge devices are often incapable of executing complex vision algorithms with low latency and high accuracy due to restricted resources. Given the infeasibility of both cloud-only and edge-only solutions, we present SurveilEdge, a collaborative cloud-edge system for real-time queries of large-scale surveillance video streams. Specifically, we design a convolutional neural network (CNN) training scheme to reduce the training time with high accuracy, and an intelligent task allocator to balance the load among different computing nodes and to achieve the latency-accuracy tradeoff for real-time queries. We implement SurveilEdge on a prototype1 with multiple edge devices and a public Cloud, and conduct extensive experiments using real-world surveillance video datasets. Evaluation results demonstrate that SurveilEdge manages to achieve up to 7× less bandwidth cost and 5.4× faster query response time than the cloud-only solution;and can improve query accuracy by up to 43.9% and achieve 15.8× speedup respectively, in comparison with edge-only approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Bandwidth

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A Network Security Situation Assessment Method Based on Multi-attention Mechanism and HHO-ResNeXt 8th

A Network Security Situation Assessment Method Based on Mult...

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8th International Symposium on Security and Privacy in Social Networks and big data, SocialSec 2022

作者： Zhao, Dongmei Ji, Guoqing Zeng, Shuiguang School of College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9789811972416

Because the traditional convolutional neural network (CNN) cannot obtain the importance of each channel and its receptive field is limited, it is difficult to deal with the increasingly complex network environment. Aiming at the shortcomings, this paper combines the Contextual Transformer (COT) block and the Efficient Channel Attention (ECA) module with ResNeXt and uses Harris Hawks Optimization (HHO) algorithm to choose the most suitable hyperparameters to improve the model performance. This model enables the rich contexts among neighbor keys to be fully exploited and can obtain the importance of each channel to improve the weight of the useful channel and suppress the less useful channel, effectively making up for the shortcomings of the traditional convolutional neural network. The experiments on a network security dataset show that the model is superior to other models in the network security situation assessment effect and its comprehensive performance in accuracy, precision and F-score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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big OLAP data Cube Compression Algorithms in Column-Oriented Cloud/Edge data Infrastructures

Big OLAP Data Cube Compression Algorithms in Column-Oriented...

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IEEE International Conference on Multimedia big data (bigMM)

作者： Alfredo Cuzzocrea The Big Data Engineering and Analytics Laboratory University of Calabria Rende Italy Dept. of Computer Science University of Paris City Paris France

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-gener...

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Exploiting game theoretic analysis for link recommendation in social networks 15

Exploiting game theoretic analysis for link recommendation i...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhao, Tong Zhao, H. Vicky King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Electrical and Computer Engineering Department University of Alberta EdmontonAB Canada

ISBN: (纸本)9781450337946

The popularity of Online Social Networks (OSNs) has attracted great research interests in different fields. In Economics, researchers use game theory to analyze the mechanism of network formation, which is called Network Formation Game. While in Computer Science, much effort has been done in building machine learning models to predict future or missing links. However, there are few works considering how to combine game theoretic analysis and machine learning models. Therefore, in this paper, we study the problem of Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks. Our goal is to improve link recommendation accuracy via leveraging the power of Network Formation Games into machine learning models. We present two different approaches to solve this problem. First, we propose a three-phase method that straightforwardly combines game theoretic analysis with machine learning models. Second, we develop a unified model, BPRLGT, that incorporates Network Formation Game into a Bayesian ranking framework for link recommendation. Specifically, BPRLGT takes advantage of network topology and we design a game theoretic sampling approach to improve its training process. The experiments are conducted on four real world datasets and the results on all datasets demonstrate that both our proposed three-phase method and the unified ranking model outperform the baseline methods. © 2015 ACM.

关键词： Game theory

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An automated workflow for integrating environmental sustainability assessment into parametric part design through standard reference models 27

An automated workflow for integrating environmental sustaina...

引用

27th CIRP Life Cycle engineering Conference, LCE 2020

作者： Bernstein, William Z. Tensa, Melissa Praniewicz, Maxwell Kwon, Soonjo Ramanujan, Devarajan Engineering Laboratory National Institute of Standards and Technology MD United States School of Mechanical Industrial and Manufacturing Engineering Oregon State University OR United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGR United States Engineering Laboratory National Institute of Standards and Technology MD United States Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Mechanical Engineering Section Center for Digitalization Big Data and Data Analytics Aarhus University Aarhus8000 Denmark

Integrating environmental sustainability assessment into parametric design presents an opportunity for enabling sustainable product design. However, this is a challenging task as quantitative methods for sustainability assessment are poorly integrated with parametric design and optimization tools. Furthermore, current streamlined approaches for computing environmental impact during the design stage often ignore manufacturing-related impacts resulting from the geometric complexity of parts. To address these gaps, we present a systematic workflow for computing environmental indicators from parametric design models. Our workflow utilizes the unit manufacturing process information model to evaluate manufacturing-phase resource consumption from process planning data, and consequently enables quantitative correlation of design parameters to the calculated environmental indicators. We demonstrate our workflow through a case study involving the design of a rigid flange coupling, wherein we evaluate the influence of geometric design parameters on the corresponding environmental impact of the design. © 2020 The Author(s). Published by Elsevier B.V.

关键词： Computer aided design

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SUBSPACE OPTIMIZATION FOR LARGE LANGUAGE MODELS WITH CONVERGENCE GUARANTEES

arXiv

引用

arXiv 2024年

作者： He, Yutong Li, Pengrui Hu, Yipeng Chen, Chuyan Yuan, Kun Peking University China Beihang University China AI for Science Institute Beijing China National Engineering Laboratory for Big Data Analytics and Applications China

Subspace optimization algorithms, with GaLore (Zhao et al., 2024) as a representative method, have gained popularity for pre-training or fine-tuning large language models (LLMs) due to their memory efficiency. However, their convergence guarantees remain unclear, particularly in stochastic settings. In this paper, we unexpectedly discover that GaLore does not always converge to the optimal solution and substantiate this finding with an explicit counterexample. We then investigate the conditions under which GaLore can achieve convergence, demonstrating that it does so either in deterministic scenarios or when using a sufficiently large mini-batch size. More significantly, we introduce GoLore (Gradient random Low-rank projection), a novel variant of GaLore that provably converges in stochastic settings, even with standard batch sizes. Our convergence analysis can be readily extended to other sparse subspace optimization algorithms. Finally, we conduct numerical experiments to validate our theoretical results and empirically explore the proposed mechanisms. Codes are available at https://***/pkumelon/Golore. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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