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International Conference on data engineering

作者： Xu Xie Jin Niu Lifang Deng Dan Wang Jiandong Zhang Zhihua Wu Kaigui Bian Gang Cao Bin Cui School of CS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Alibaba Group China Lazada National Engineering Lab for Big Data Analysis and Applications Beijing Academy of Artificial Intelligence (BAAI) Institute of Computational Social Science Peking University Qingdao China

Click-through rate (CTR) prediction, whose purpose is to predict the probability of a user clicking on an item, plays a pivotal role in recommender systems. Capturing users’ accurate preferences from their historical interactions (e.g., clicks) is an essential step for handling this task and has aroused wide concern in both academia and industry. However, most of the previous methods focus on the users with abundant clicks and ill-serve the users who rarely click or purchase items. Though the ratio of these long-tailed users may be small on popular platforms, such as Amazon and Taobao, they are the majority on the newborn e-commerce company like Lazada. To extract the interests of long-tailed users, several works attempt to integrate the side information, such as demographic features. Nevertheless, these features are usually not available and may even lead to privacy concerns. Therefore, how to utilize the noisy and limited clicks becomes the key *** this paper, we propose a novel model called Hierarchical Interest Modeling (HIM). It hierarchically utilizes long-tailed users’ limited behaviors and captures their preferences from both personalized and group-wise perspectives. HIM consists of two main components, including User Behavior Pyramid (UBP) and User Behavior Clustering (UBC). The UBP module utilizes additional negative feedback to reduce the noises in positive feedback, thus obtaining reliable user personalized representations. Then, the UBC module automatically discovers latent user groups with self-supervised reconstruction loss and learns another interest representation for each user in a group-wise aspect. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines. Moreover, HIM has already been deployed on Lazada recommendation scenario and gains 3.38% on CTR prediction on average on the online A/B test. Our codes are available in https://***/xiaojin-nj/HIM.

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HOUGH-DCT Based Robust Watermarking for Medical Image 1

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12th International Symposium on Cyberspace Safety and Security, CSS 2020

作者： Mo, Ruiqi Liu, Jing Li, Jingbing Wang, Yulin Zhou, Jingjun School of Information and Communication Engineering Hainan University Haikou570228 China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan University Haikou570228 China Research Center for Healthcare Data Science Zhejiang Lab Zhejiang Hangzhou China

ISBN: (数字)9783030736712

ISBN: (纸本)9783030736705

The patients’ case files may be maliciously tampered with during network transmission. Once the patients’ medical record information is altered by criminals, serious medical accidents will occur. To solve the problem of information leakage, a robust medical image watermarking algorithm based on Hough transform and discrete cosine transform (HOUGH-DCT) is proposed. This method combines the feature vector of medical image, cryptography, hash function and zero watermark technology, and it makes up for the shortcoming that the traditional digital watermark method cannot protect the medical image. It has strong robustness and invisibility which can protect the patient's private information and medical image data security at the same time. Experimental results show that the algorithm can embed the watermark information at a faster speed without changing the medical image information and extract the watermark safely with better invisibility and robustness. © 2021, Springer Nature Switzerland AG.

关键词： Discrete cosine transforms

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Robot Manipulator Control via Solving Four-Layered Time-Variant Equations Including Linear, Nonlinear Equalities and Inequalities 11

Robot Manipulator Control via Solving Four-Layered Time-Vari...

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11th International Conference on Intelligent Control and Information Processing, ICICIP 2021

作者： Zhu, Xinhui Zhang, Li Shi, Yang Wang, Jing Li, Jian Xinyang Normal University School of Computer and Information Technology Xinyang464000 China Henan Key Lab. of Analysis and Application of Education Big Data Xinyang Normal University Xinyang464000 China Yangzhou University School of Information Engineering Yangzhou225127 China

ISBN: (纸本)9781665425155

Robot manipulator control is a complicated multi-tasking problem in reality. It includes not only basic tracking task, but also additional tasks, such as conquering joint angle limits, posture control. However, most existing works only consider the goal of tracking and formulate it as single-layered time-variant problems, which leads to impracticality. In this work, robot manipulator control problem is formulated as four-layered time-variant equations including linear, nonlinear equalities and inequalities. Each layer formulates one subtask: The first layer of nonlinear equality describes basic tracking task based on forward kinematics;The second layer and third layer are inequalities, which describe joint angle upper and lower limits;The last layer is a linear equality with respect to joint angle velocity, which could be designed by user to describe other task, such as posture control. To solve this complicated four-layered time-variant problem, it is converted as single-layered equation based on the zeroing neural dynamics method. Then, continuous-time solution is proposed. Furthermore, discrete-time algorithm is proposed based on a third-order time-discretization formula and continuous-time solution. Numerical experiments illustrate the effectiveness and superiority compared to existing work. © 2021 IEEE.

关键词： Industrial robots

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Taxi Station Location Model Based on Spatio-Temporal Demand Cube

Taxi Station Location Model Based on Spatio-Temporal Demand ...

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International Conference on Networking and Network Applications (NaNA)

作者： LuLu Gao XiaoNan Li ZhiCheng Wei Key Lab of Network and Information Security Shijiazhuang Hebei Province China College of Computer and Cyber Security Shijiazhuang Hebei Province China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

With the widespread use of mobile apps, empty taxis can easily receive new orders without having to roam around. Therefore, it is necessary to plan taxi stations where empty taxis can park. In the location selection problem, the spatial and temporal distribution of passenger demand needs to be considered to ensure that the selected location can meet the actual needs of passengers. In this paper, a Spatio-Temporal Cube Demand Coverage Model (STC-DCM) is proposed for taxi station location selection. Firstly, a spatiotemporal demand cube is constructed to obtain fine-grained user demands based on time and space, and genetic algorithm is used to maximize the demand coverage rate. Then, the final location selection results are obtained using the fusion module and adjustment optimization module, where the fusion module integrates multiple location selection solutions using ensemble learning method. Experimental results show that our proposed model can better cover dynamic passenger demand compared to traditional models.

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A Universal data-Driven Spatial Interpolation Framework for the Measurements of Environmental Sites

SSRN

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SSRN 2023年

作者： Cheng, Zhen Guo, Yuzhe Xue, Wenbo Du, Qinyi Shanghai Environmental Protection Key Lab of Environmental Big Data and Intelligent Decision-making School of Environmental Science and Engineering Shanghai Jiao Tong University Shanghai China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning Beijing100043 China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China

The accurate spatial distribution of environmental pollutant metrics is crucial for evaluating human exposure and managing the environment. Spatial interpolation, which relies on the measurements of limited environmental sites, is often necessary. However, commonly used geostatistical methods often introduce significant prediction biases, leading to misleading assessments and decision-making processes. Reported machine learning studies usually required intensive spatial datasets and sites’ measurements, as well as special complex algorithm for target pollutant. In this study, we propose and validate a simple and universal data-driven spatial interpolation framework for environmental sites’ measurements. We employ a conditional generative adversarial network (cGAN) as the data-driven algorithm to leverage spatial dataset. We demonstrate the implementation and validation of the framework using two representative ambient pollutants, namely ozone and PM2.5. The results reveal a relative mean absolute error of only 9% for ozone and 13% for PM2.5. Compared to the traditional inverse distance weight method and ordinary kriging method, the proposed framework reduces errors by 70% and 79% respectively. Furthermore, the R-squared values between independent measurements from additional geographic locations and the corresponding predicted results using the proposed method reach 0.96 for ozone and 0.97 for PM2.5, showcasing its excellent and consistent performance. The unique input dataset and universal algorithm architecture illustrate the distinct advantages compared to previous machine learning studies. With the rapid increase of remote sensing and numerical modeling datasets in the environmental field, we anticipate that data-driven methods will replace traditional mathematical approaches. This study provides a simple yet universal interpolation framework and is expected to accelerate the adoption of these advancements. © 2023, The Authors. All rights reserved.

关键词： Spatial distribution

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Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

arXiv

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arXiv 2023年

作者： Yuan, Haonan Sun, Qingyun Fu, Xingcheng Zhang, Ziwei Ji, Cheng Peng, Hao Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University China Department of Computer Science and Technology Tsinghua University China

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Chain-Based Outlier Detection for Complex data Scenarios

Chain-Based Outlier Detection for Complex Data Scenarios

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IEEE International Conference on Big data

作者： Huiwen Dong Qing-Guo Wang Wei Ding Institute of Artificial Intelligence and Future Networks Beijing Normal University Beijing China Guangdong Key Lab of AI and MM Data Processing Guangdong Provincial Key Laboratory IRADS IAS DST BNU-HKBU United International College Institute for Advanced Study Zhuhai China Faculty of Electrical Engineering and Automation Changshu Institute of Technology Changshu China

Outlier detection is a challenging problem due to the complexity of real-life data. Specifically, an effective outlier detection method should be able to handle (1) different types of outliers: local outliers, global outliers, and cluster outliers; (2) a lack of prior knowledge of outlier number or percentage; (3) neighboring clusters with different densities; and (4) real-time detection. Unfortunately, few algorithms can tackle all these challenges simultaneously. In this paper, we propose a chain-based theory to address these issues, where a minority of data points will be considered outliers if their distances from normal data points change abruptly. We present the parallel chaining method based on this theory. Experiments demonstrate that the proposed method exhibits comparable performance to other state-of-the-art methods in the synthetic datasets and outperform other methods in the real-life datasets.

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Suppress content shift: better diffusion features via off-the-shelf generation techniques 24

Suppress content shift: better diffusion features via off-th...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Zhiyong Yang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://***/Darkbblue/diffusion-content-shift.

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Violence Detection In Videos via Motion-Guided Global and Local Views

Violence Detection In Videos via Motion-Guided Global and Lo...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Ning Su Lijuan Sun Yutong Gao Jingchen Wu Xu Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT

Video violence detection aims to locate the time window in which violent behavior occurs. Most methods focus on utilizing RGB features directly or only fusing RGB and audio features, ignoring the effective exploitation of motion information carried in optical flow. This lack of emphasis on motion information may impact the overall accuracy of violence detection. Moreover, we observe that videos contain strong local correlations, so it is insufficient to analyze only from a holistic perspective without capturing finer details. Therefore, in this paper, we design a novel Global-and-Local Cross-Modal Network (GL-CMN) for violence detection, which effectively integrates motion information and multi-granularity features from target videos. Specifically, we first propose a Motion-Guided Attention Module (MGAM) to obtain enhanced visual features by calibrating RGB features through optical flow features. Secondly, The enhanced features are simultaneously fed into two parallel branches of the network. The global branch fuses the visual and audio features into holistic representations. The local branch extracts multi-scale temporal dependencies through dilated convolutions. Experiments demonstrate that our method exhibits significant improvement compared to previous state-of-the-art methods on the XD-Violence dataset.

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SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares

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Computers in Biology and Medicine 2024年 171卷 108225-108225页

作者： Gong, Lejun Cui, Xiong Liu, Yang Lin, Cai Gao, Zhihong Jiangsu Key Lab of Big Data Security & Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Department of Burn Wound Repair and Regenerative Medicine Center The First Affiliated Hospital of Wenzhou Medical University Zhejiang Wenzhou325000 China Zhejiang Engineering Research Center of Intelligent Medicine The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China

Background and objectives: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is equivalent to introducing noise into the data of cell gene expression entries. The data is contaminated, which affects the performance of downstream analyses, including clustering, cell annotation, differential gene expression analysis, and so on. Therefore, it is a crucial work to accurately determine which zeros are due to dropout events and perform imputation operations on them. Methods: Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations. Results: A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization. Conclusions: SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in

关键词： RNA

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