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arXiv 2023年

作者： Tian, Zhen Bai, Ting Zhao, Wayne Xin Wen, Ji-Rong Cao, Zhao Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia China Poisson Lab Huawei Beijing China

Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they reduce the high computational costs caused by the exponential growth of high-order feature combinations, they still suffer from the degradation of model capability due to the suboptimal learning of the restricted feature orders. The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler’s formula. EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way. Furthermore, EulerNet incorporates the implicit and explicit feature interactions into a unified architecture, which achieves the mutual enhancement and largely boosts the model capabilities. Such a network can be fully learned from data, with no need of pre-designed form or order for feature interactions. Extensive experiments conducted on three public datasets have demonstrated the effectiveness and efficiency of our approach. Our code is available at: https://***/RUCAIBox/EulerNet. © 2023, CC BY.

关键词： Forecasting

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Query-oriented data Augmentation for Session Search

arXiv

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arXiv 2024年

作者： Chen, Haonan Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China

Modeling contextual information in a search session has drawn more and more attention when understanding complex user intents. Recent methods are all data-driven, i.e., they train different models on large-scale search log data to identify the relevance between search contexts and candidate documents. The common training paradigm is to pair the search context with different candidate documents and train the model to rank the clicked documents higher than the unclicked ones. However, this paradigm neglects the symmetric nature of the relevance between the session context and document, i.e., the clicked documents can also be paired with different search contexts when training. In this work, we propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context, i.e., the current query, and train our model to rank the generated sequence along with the original sequence. This approach enables models to learn that the relevance of a document may vary as the session context changes, leading to a better understanding of users’ search patterns. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty. Through experimentation on two extensive public search logs, we have successfully demonstrated the effectiveness of our model. © 2024, CC BY.

关键词： Search engines

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A Stepwise Path Selection Scheme Based on Multiple QoS Parameters Evaluation in SDN 17th

A Stepwise Path Selection Scheme Based on Multiple QoS Param...

引用

17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Liu, Lin Zhou, Jian-Tao Xing, Hai-Feng Guo, Xiao-Yong College of Computer Science Ecological Big Data Engineering Research Center of the Ministry of Education Cloud Computing and Service Software Engineering Laboratory of Inner Mongolia Autonomous Region National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Social Computing and Data Processing Key Laboratory of Inner Mongolia Autonomous Region Big Data Analysis Technology Engineering Research Center of Inner Mongolia Autonomous Region Inner Mongolia University Inner Mongolia Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Inner Mongolia Hohhot China College of Computer Information and Management Inner Mongolia University of Finance and Economics Inner Mongolia Hohhot China

ISBN: (纸本)9783030926342

Nowadays, the best-effort service can not guarantee the quality of service (QoS) for all kinds of services. QoS routing is an important method to guarantee QoS requirements. It involves path selection for flows based on the current network status and the performance criteria of the service requirements. However, it is difficult for proposed solutions to obtain all the available paths owing to not fully considering all the QoS parameters of paths. In this paper, we propose SWQoS, a novel, universal, and stepwise QoS guarantee scheme based on multiple QoS parameter evaluation for selecting the available paths including preferred paths, satisfied paths and reluctant paths in SDN. The experiments show that SWQoS can select all the available paths that meet the performance criteria of the service requirements and have better QoS parameter performance compared with other path selection methods. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Quality of service

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Rethink Video Retrieval Representation for Video Captioning

SSRN

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SSRN 2024年

作者： Tian, Mingkai Li, Guorong Qi, Yuankai Wang, Shuhui Sheng, Quan Z. Huang, Qingming School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computing Macquarie University Australia Key Laboratory of Intelligent Information Processing Institute of Computer Technology Chinese Academy of Sciences China

Video captioning, a challenging task targeting the automatic generation of accurate and comprehensive descriptions based on video content, has witnessed substantial success recently driven by bridging video representations and textual semantics. Inspired by the nature of the video retrieval task, which learns visual features strongly related to text queries, we propose to take advantage of visual representation learning from the video retrieval framework to tackle video captioning tasks. However, a simple direct application of video retrieval models performs poorly due to the weak ability to capture sufficient video details and temporal information required for video captioning. To increase the attention on details, we propose a learnable token shift module, which flexibly captures subtle movements in local regions across the temporal sequence. Furthermore, we devise a Refineformer, which learns to integrate local video tokens strongly related to desired captions via a cross-attention mechanism. Extensive experiments on MSVD, MSR-VTT and VATEX demonstrate the favorable performance of our method. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Optimal Sizing of Ev Charging Stations Considering Charging Scheduling and Renewable Energy

SSRN

引用

SSRN 2022年

作者： Zhou, Kaile Hu, Dingding Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei University of Technology Hefei230009 China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China

The construction of electric vehicle (EV) charging stations is critical for the widespread use of EVs. Appropriate design of EV charging stations should fulfill charging requests while considering various economic issues. In this regard, we present an optimization model to address the sizing problem of EV charging stations with integration of renewable energy resources, energy storage system and multi-types of chargers. The proposed model aims at reducing the investment cost and improving the profitability of EV charging stations. In order to accurately estimate EV charging demand, the characteristics of EV charging behaviors are derived based on real-world charging data. Furthermore, a charging scheduling strategy that responds to time-of-use (TOU) price and charging demand levels is proposed to improve the utilization efficiency of chargers. Results show that the design of EV charging stations with integrated consideration of these components and charging scheduling strategy can achieve optimal economic efficiency. © 2022, The Authors. All rights reserved.

关键词： Renewable energy resources

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Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation

arXiv

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arXiv 2022年

作者： Tian, Zhen Bai, Ting Zhang, Zibin Xu, Zhiyuan Lin, Kangyi Wen, Ji-Rong Zhao, Wayne Xin Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing University of Posts and Telecommunications Beijing China Weixin Open Platform Tencent Guangzhou China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia China Beijing Key Laboratory of Big Data Management and Analysis Methods China

With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real industrial applications. Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference. However, they suffer from the degradation of model accuracy in knowledge distillation process. It is challenging to balance the efficiency and effectiveness of the shallow student models. To address this problem, we propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. The proposed lightweight student model DAGFM can learn arbitrary explicit feature interactions from teacher networks, which achieves approximately lossless performance and is proved by a dynamic programming algorithm. Besides, an improved general model KD-DAGFM+ is shown to be effective in distilling both explicit and implicit feature interactions from any complex teacher model. Extensive experiments are conducted on four real-world datasets, including a large-scale industrial dataset from WeChat platform with billions of feature dimensions. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-ofthe-art method on both online and offline experiments, showing the superiority of DAGFM to deal with the industrial scale data in CTR prediction task1 © 2022, CC BY.

关键词： Recommender systems

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Research On Tourist Destination Passenger Flow Prediction Based On Decomposition Clustering Technology For data Continuity Problem

Research On Tourist Destination Passenger Flow Prediction Ba...

引用

Culture-Oriented Science and Technology (CoST), International Conference on

作者： Lianze Feng Lin Mu Yi Yang Caihong Li School of information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Tourism Information Fusion Processing and Data Ownership Protection Ministry of Culture and Tourism Lanzhou China Tourism quality supervision and management institute of China Beijing China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

Tourist flow prediction faces challenges in achieving high accuracy due to the complex and dynamic nature of tourism data. This study addresses these challenges by proposing a multi-feature combined prediction model, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Incremental Entropy-K-means clustering-Time Convolutional Network-Coati Optimization Algorithm (CEEMDAN-IEK-TT-COA). The model integrates signal decomposition, clustering reconstruction, component prediction, and optimization algorithms. key features are selected through feature engineering, followed by data decomposition and reconstruction using CEEMDAN, Incremental Entropy, and K-means clustering. A model pool of Time Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory network (LSTM), and Transformer filters optimal sub-models for combined prediction. The Coati Optimization Algorithm (COA) refines the final prediction. Experimental results on Jiuzhaigou and Siguniangshan datasets demonstrate significant RMSE improvements of $21.81 \%$ and $17.84 \%$, validating the model’s effectiveness.

关键词： Adaptation models Clustering algorithms Predictive models Prediction algorithms Feature extraction Transformers data models

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A joint call for actions to advance taxonomy in China

引用

Zoological Systematics 2022年第3期47卷 188-197页

作者： Chaodong Zhu Arong Luo Ming Bai Michael COrr Zhonge Hou Siqin Ge Jun Chen Yibo Hu Xuming Zhou Gexia Qiao Hongzhi Kong Limin Lu Xiaohua Jin Lei Cai Xinli Wei Ruilin Zhao Wei Miao Qingfeng Wang Zhongli Sha Qiang Lin Meng Qu Jianping Jiang Jiatang Li Jing Che Xuelong Jiang Xiaoyong Chen Lianming Gao Zongxin Ren Chunlei Xiang Shixiao Luo Donghui Wu Dong Liu Yanqiong Peng Tao Su Chenyang Cai Tianqi Zhu Wanzhi Cai Xingyue Liu Hu Li Huaijun Xue Zhen Ye Xuexin Chen Pu Tang Shujun Wei Hong Pang Qiang Xie Feng Zhang Feng Zhang Xianjin Peng Aibing Zhang Taiping Gao Changfa Zhou Chen Shao Libin Ma Zhaoming Wei Yunxia Luan Ziwei Yin Wu Dai Cong Wei Xiaolei Huang Jingxian Liu Xiangsheng Chen Tianci Yi Zhisheng Zhang Zhulidezi Aishan Qin Li Hongying Hu CAS Key Laboratory of Zoological Systematics and Evolution Institute of ZoologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Animal Ecology and Conservation Biology Institute of ZoologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Systematic and Evolutionary Botany Institute of BotanyChinese Academy of SciencesBeijing 100093China State Key Laboratory of Mycology(SKLM) Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Aquatic Biodiversity and Conservation Institute of HydrobiologyChinese Academy of SciencesWuhan 430072China Wuhan Botanical Garden Chinese Academy of SciencesWuhan 430074China Department of Marine Organism Taxonomy&Phylogeny Institute of OceanologyChinese Academy of SciencesQingdao 266071China CAS Key Laboratory of Tropical Marine Bio-resources and Ecology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization&Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province Chengdu Institute of BiologyChinese Academy of SciencesChengdu 610041China State Key Laboratory of Genetic Resources and Evolution&Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain Kunming Institute of ZoologyChinese Academy of SciencesKunming 650221China CAS Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China CAS Key Laboratory of Plant Resource Conservation and Sustainable Utilization South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China CAS Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China CAS Key Laboratory of Tropical Forest Ecology Xishuangbanna Tropical Botanical GardenChinese Academy of SciencesMenglunMenglaYunnan 66630

Taxonomy plays an important role in understanding the origin, evolution, and ecological functionality of biodiversity. There are large number of unknown species yet to be described by taxonomists, which together with their ecosystem services cannot be effectively protected prior to description. Despite this, taxonomy has been increasingly underrated insufficient funds and permanent positions to retain young talents. Further, the impact factordriven evaluation systems in China exacerbate this downward trend, so alternative evaluation metrics are urgently necessary. When the current generation of outstanding taxonomists retires,there will be too few remaining taxonomists left to train the next generation. In light of these challenges, all co-authors worked together on this paper to analyze the current situation of taxonomy and put out a joint call for immediate actions to advance taxonomy in China.

关键词： Endangered taxonomists morphology-based taxonomy integrative taxonomy capacity building systematics

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Privacy-Preserved Neural Graph Similarity Learning

arXiv

引用

arXiv 2022年

作者： Hou, Yupeng Zhao, Wayne Xin Li, Yaliang Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China Alibaba Group China Beijing Key Laboratory of Big Data Management and Analysis Methods China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection issues, we first introduce the concept of attackable representation to systematically characterize the privacy attacks that each model can face. Inspired by the qualitative results, we propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. Instead, we learn multi-perspective graph representations based on learnable context vectors. To alleviate the attacks to graph properties, the obfuscated features that contain information from both graphs are communicated. In this way, the private properties of each graph can be difficult to infer. Based on the node-graph matching techniques while calculating the obfuscated features, PPGM can also be effective in similarity measuring. To quantitatively evaluate the privacy-preserving ability of neural GSL models, we further propose an evaluation protocol via training supervised black-box attack models. Extensive experiments on widely-used benchmarks show the effectiveness and strong privacy-protection ability of the proposed model PPGM. The code is available at: https://***/RUCAIBox/PPGM. © 2022, CC BY.

关键词： Graph neural networks

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Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

arXiv

引用

arXiv 2021年

作者： Wang, Yikai Sun, Fuchun Huang, Wenbing He, Fengxiang Tao, Dacheng State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China JD Explore Academy *** Inc China

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge—it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for multimodal and multitask dense image prediction. At its core, CEN adaptively exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of CEN compared to state-of-the-art methods. Detailed ablation studies have also been carried out, which demonstrate the advantage of each component we propose. Our code is available at https://***/yikaiw/CEN. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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