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2023 World Wide Web Conference, WWW 2023

作者： Huang, Xu Lian, Defu Chen, Jin Zheng, Liu Xie, Xing Chen, Enhong School of Computer Science University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China University of Electronic Science and Technology of China Chengdu China Microsoft Research Asia Beijing China

ISBN: (纸本)9781450394161

Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency;while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker. In this work, we present a novel framework for the joint training of retriever and ranker, named CoRR (Cooperative Retriever and Ranker). With CoRR, the retriever is improved by deriving high-quality training signals from the ranker, while the ranker is improved by learning to discriminate hard negatives sampled by the retriever. We introduce two critical techniques. Firstly, we develop an adaptive and scalable sampler based on the retriever, to generate hard negative samples for the ranker's training. Compared with the widely-used exact top-k sampling, our method effectively alleviates the issues of false negative and item distribution shift, and thus improves the ranker's discriminability. Secondly, we propose a novel asymptotic-unbiased estimation of KL divergence, which serves as the objective for knowledge distillation. The new objective can be efficiently optimized with commonly-used optimizers. More importantly, it leads to better alignment of ranking order between retriever and ranker, which helps to improve the retrieval quality. We conduct comprehensive ex

关键词： Distillation

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Uncertain graph generating approach based on differential privacy for preserving link relationship of social networks

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International Journal of Security and Networks 2022年第1期17卷 28-38页

作者： Yan, Jun Tian, YuPan Liu, Hai Wu, ZhenQiang School of Computer Science Shaanxi Normal University Xi’an710119 China School of Mathematics and Computer Applications Shangluo College Shangluo726000 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China

With the widespread use of social networks in our daily life, the personal privacy in social networks has become a growing concern. To prevent the link relationship of social networks from disclosing users’ sensitive information when the social networks data is released, an uncertain graph approach based on differential privacy is introduced, which can resist attacks based on background knowledge and possesses better data utility. In this approach, we propose modification of edges based on random response (MERR) algorithm and injection of uncertainty based on k-edges-differential privacy (IUDP) algorithm. The MERR algorithm can modify the edge of original graph according to random response mechanism, while the IUDP algorithm injects uncertainty to generate an uncertain graph. For evaluating our approach, the expectation of editing distance between two graphs is adapted to measure the level of privacy preserving. In addition, our approach is conducted in different datasets and compared with other approaches. The experimental results indicate that this approach achieves differential privacy and has better data utility. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Privacy-preserving techniques

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Customized scheduling for shared bus with deadlines

Customized scheduling for shared bus with deadlines

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作者： Jin, Yong Xu, Jia Xu, Lijie Liu, Linfeng Xiao, Fu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science & Engineering Changshu Institute of Technology Jiangsu Changshu China

Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S-MBSA for the shared bus with strong endurance and E-MBSA for the large-scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S-MBSA and E-MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue. © 2022 John Wiley & Sons Ltd.

关键词： Scheduling

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Attention based simplified deep residual network for citywide crowd flows prediction

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Frontiers of computer science 2021年第2期15卷 51-62页

作者： Genan DAI Xiaoyang HU Youming GE Zhiqing NING Yubao LIU School of Data and Computer Science Sun Yat-Sen UniversityGuangzhou510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006China

Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the *** practice,emergency applications often require less training ***,there is a little work on how to obtain good prediction performance with less training *** this paper,we propose a simplified deep residual network for our *** using the simplified deep residual network,we can obtain not only less training time but also competitive prediction performance compared with the existing similar ***,we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time *** on the real datasets,we construct a series of experiments compared with the existing *** experimental results confirm the efficiency of our proposed methods.

关键词： crowd flows prediction spatio-temporal data mining attention

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a c... 详细信息

关键词： Dispersion compensation

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Adaptive Group Personalization for Federated Mutual Transfer Learning 41

Adaptive Group Personalization for Federated Mutual Transfer...

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41st International Conference on Machine Learning, ICML 2024

作者： Xu, Haoqing Shen, Dian Wang, Meng Wang, Beilun School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China College of Design and Innovation Tongji University Shanghai China

Mutual transfer learning aims to improve prediction with knowledge from related domains. Recently, federated learning is applied in this field to address the communication and privacy concerns. However, previous clustered federated learning (CFL) solutions lack theoretical guarantee of learnability recovery and require time-consuming hyper-parameter tuning, while centralized mutual transfer learning methods lack adaptability to concept drifts. In this paper, we propose the Adaptive Group Personalization method (AdaGrP) to overcome these challenges. We adaptively decide the recovery threshold with a nonparametric method, adaptive threshold correction, for tuning-free solution with relaxed condition. Theoretical results guarantee the perfect learnability recovery with the corrected threshold. Empirical results show AdaGrP achieves 16.9% average improvement in learnability structure recovery compared with state-of-the-art CFL baselines. Copyright 2024 by the author(s)

关键词： Federated learning

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HGFormer: Topology-Aware Vision Transformer with HyperGraph Learning

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IEEE Transactions on Multimedia 2025年

作者： Wang, Hao Zhang, Shuo Leng, Biao Beihang University School of Computer Science and Engineering Beijing100191 China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing100044 China

The computer vision community has witnessed an extensive exploration of vision transformers in the past two years. Drawing inspiration from traditional schemes, numerous works focus on introducing vision-specific inductive biases. However, the implicit modeling of permutation invariance and fully-connected interaction with individual tokens disrupts the regional context and spatial topology, further hindering higher-order modeling. This deviates from the principle of perceptual organization that emphasizes the local groups and overall topology of visual elements. Thus, we introduce the concept of hypergraph for perceptual exploration. Specifically, we propose a topology-aware vision transformer called HyperGraph Transformer (HGFormer). Firstly, we present a Center Sampling K-Nearest Neighbors (CS-KNN) algorithm for semantic guidance during hypergraph construction. Secondly, we present a topology-aware HyperGraph Attention (HGA) mechanism that integrates hypergraph topology as perceptual indications to guide the aggregation of global and unbiased information during hypergraph messaging. Using HGFormer as visual backbone, we develop an effective and unitive representation, achieving distinct and detailed scene depictions. Empirical experiments show that the proposed HGFormer achieves competitive performance compared to the recent SoTA counterparts on various visual benchmarks. Extensive ablation and visualization studies provide comprehensive explanations of our ideas and contributions. © 2025 IEEE.

关键词： Hypergraph Learning Image Classification Vision Transformer

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Knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

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data Intelligence 2023年第3期5卷 685-706页

作者： Feng Gao Jin Hou Jinguang Gu Lihua Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065Hubei The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Insitute of Scientic and Technical Information of ChinaBeijing 100038China Wuhan University of Science and Technology Big Data Science and Engineering Research Institute Wuhan 430065Hubei Eastchina Jiaotong University Nanchang 330013Jiangxi

Machine reading comprehension has been a research focus in natural language processing and intelligence ***,there is a lack of models and datasets for the MRC tasks in the anti-terrorism ***,current research lacks the ability to embed accurate background knowledge and provide precise *** address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic ***,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the *** eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the *** results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关键词： Machine reading comprehension Anti-terrorism domain Knowledge embedding Knowledge attention Mutual attention

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DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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