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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Weiyu Guo Shu Wu Liang Wang Tieniu Tan Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences College of Engineering and Information Technology University of Chinese Academy of Sciences

Pairwise learning algorithms are a vital technique for personalized ranking with implicit feedback. They usually assume that each user is more interested in items which have been selected by the user than remaining ones. This pairwise assumption usually derives massive training pairs. To deal with such large-scale training data, the learning algorithms are usually based on stochastic gradient descent with uniformly drawn pairs. However, the uniformly sampling strategy often results in slow convergence. In this paper, we first uncover the reasons of slow convergence. Then, we associate contents of entities with characteristics of data sets to develop an adaptive item sampler for drawing informative training data. In this end, to devise a robust personalized ranking method, we accordingly embed our sampler into Bayesian Personalized Ranking (BPR) framework, and further propose a Content-aware and Adaptive Bayesian Personalized Ranking (CA-BPR) method, which can model both contents and implicit feedbacks in a unified learning process. The experimental results show that, our adaptive item sampler indeed can speed up BPR learning and CA-BPR definitively outperforms the state-of-the-art methods in personalized ranking.

关键词： Business process re-engineering Training Convergence Training data Recommender systems Smart phones Collaboration

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LATENT WATERMARK: INJECT AND DETECT WATERMARKS IN LATENT DIFFUSION SPACE

arXiv

引用

arXiv 2024年

作者： Meng, Zheling Peng, Bo Dong, Jing Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China

Watermarking is a tool for actively identifying and attributing the images generated by latent diffusion models. Existing methods face the dilemma of image quality and watermark robustness. Watermarks with superior image quality usually have inferior robustness against attacks such as blurring and JPEG compression, while watermarks with superior robustness usually significantly damage image quality. This dilemma stems from the traditional paradigm where watermarks are injected and detected in pixel space, relying on pixel perturbation for watermark detection and resilience against attacks. In this paper, we highlight that an effective solution to the problem is to both inject and detect watermarks in the latent diffusion space, and propose Latent Watermark with a progressive training strategy. It weakens the direct connection between quality and robustness and thus alleviates their contradiction. We conduct evaluations on two datasets and against 10 watermark attacks. Six metrics measure the image quality and watermark robustness. Results show that compared to the recently proposed methods such as StableSignature, StegaStamp, RoSteALS, LaWa, TreeRing, and DiffuseTrace, LW not only surpasses them in terms of robustness but also offers superior image quality. Our code will be available at https://***/RichardSunnyMeng/LatentWatermark. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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A hierarchical contextual attention-based GRU network for sequential recommendation

arXiv

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arXiv 2017年

作者： Cui, Qiang Wu, Shu Huang, Yan Wang, Liang Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences

Sequential recommendation is one of fundamental tasks for Web applications. Previous methods are mostly based on Markov chains with a strong Markov assumption. Recently, recurrent neural networks (RNNs) are getting more and more popular and has demonstrated its effectiveness in many tasks. The last hidden state is usually applied as the sequence's representation to make recommendation. Benefit from the natural characteristics of RNN, the hidden state is a combination of long-term dependency and short-term interest to some degrees. However, the monotonic temporal dependency of RNN impairs the user's short-term interest. Consequently, the hidden state is not sufficient to reflect the user's final interest. In this work, to deal with this problem, we propose a Hierarchical Contextual Attention-based GRU (HCA-GRU) network. The first level of HCA-GRU is conducted on the input. We construct a contextual input by using several recent inputs based on the attention mechanism. This can model the complicated correlations among recent items and strengthen the hidden state. The second level is executed on the hidden state. We fuse the current hidden state and a contextual hidden state built by the attention mechanism, which leads to a more suitable user's overall interest. Experiments on two realworld datasets show that HCA-GRU can effectively generate the personalized ranking list and achieve significant improvement. Copyright © 2017, The Authors. All rights reserved.

关键词： Recurrent neural networks

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MULTI-TASK DEEP NEURAL NETWORK FOR MULTI-LABEL LEARNING

MULTI-TASK DEEP NEURAL NETWORK FOR MULTI-LABEL LEARNING

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IEEE International Conference on Image Processing

作者： Yan Huang Wei Wang Liang Wang Tieniu Tan Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479923427

This paper proposes a multi-task deep neural network (MT-DNN) architecture to handle the multi-label learning problem, in which each label learning is defined as a binary classification task, i.e., a positive class for "an instance owns this label" and a negative class for "an instance does not own this label". Multi-label learning is accordingly transformed to multiple binary-class classification tasks. Considering that a deep neural nets (DNN) architecture can learn good intermediate representations shared across tasks, we generalize one classification task of traditional DNN into multiple binary classification tasks through defining the output layer with a negative class node and a positive class node for each label. After a similar pretraining process to deep belief nets, we redefine the label assignment error of MT-DNN and perform the back-propagation algorithm to fine-tune the network. To evaluate the proposed model, we carry out image annotation experiments on two public image datasets, with 2000 images and 30,000 images respectively. The experiments demonstrate that the proposed model achieves the state-of-the-art performance.

关键词： Multi-Task Learning Deep Neural Network Multi-Label Learning Image Annotation Neural network Religious Missions Learning PARALLEL PROCESSING (COMPUTERS) images Public Opinion

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Artificial power system based on ACP approach

Artificial power system based on ACP approach

引用

2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012

作者： Dong, Xisong Xiong, Gang Hou, Jiachen Fan, Dong State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan China

ISBN: (纸本)9781467324007

Modern power system is a typical multi-level complex giant system consisting of physical infrastructures, human operators, and social resources, etc. The conventional analytical methods and simulation systems can't provide sufficient guidance for its operation and management, because they are mainly based on physical models, natural phenomenon, or other existing control methods which are based on reductionism. ACP approach, mainly consisting of artificial systems (A), computational experiments (C) and parallel execution (P), which is based on holism and complex system theory, has its specific advantages in the research on power systems. In this article, ACP approach is applied to build up artificial power system (APS) by using multi-agent complex networks. With the help of APS, actual power system's control, scheduling, optimization and management can be improved further via providing theoretical guidance and technical support for the rolling optimizations in normal situations and emergency management in abnormal situations. As a case study, an APS constructed with actual data from North China power grid is constructed and its vulnerability is simulated and analyzed under random, dynamic and static attacks. © 2012 IEEE.

关键词： Complex networks

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A Comparative Study on Autism Spectrum Disorder Detection via 3D Convolutional Neural Networks 25th

A Comparative Study on Autism Spectrum Disorder Detection vi...

引用

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Zhang, Kaijie Wang, Wei Guo, Yijun Shan, Caifeng Wang, Liang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Beijing China Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Artificial Intelligence Research Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030687625

The prevalence of Autism Spectrum Disorder (ASD) in the United States has increased by 178% from 2000 to 2016. However, due to the lack of well-trained specialists and the time-consuming diagnostic process, many children are not able to be promptly diagnosed. Recently, several research have taken steps to explore automatic video-based ASD detection systems with the help of machine learning and deep learning models, such as support vector machine (SVM) and long short-term memory (LSTM) model. However, the models mentioned above could not extract effective features directly from raw videos. In this study, we aim to take advantages of 3D convolution-based deep learning models to aid video-based ASD detection. We explore three representative 3D convolutional neural networks (CNNs), including C3D, I3D and 3D ResNet. In addition, a new 3D convolutional model, called 3D ResNeSt, is also proposed based on ResNeSt. We evaluate these models on an ASD detection dataset. The experimental results show that, on average, all of the four 3D convolutional models can obtain competitive results when compared to the baseline using LSTM model. Our proposed 3D ResNeSt model achieves the best performance, which improves the average detection accuracy from 0.72 to 0.85. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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Principal affinity based cross-modal retrieval

Principal affinity based cross-modal retrieval

引用

Asian Conference on Pattern Recognition (ACPR)

作者： Jian Liang Dong Cao Ran He Zhenan Sun Tieniu Tan Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (纸本)9781479961016

Multimedia content is increasingly available in multiple modalities. Each modality provides a different representation of the same entity. This paper studies the problem of joint representation of the text and image components of multimedia documents. However, most existing algorithms focus more on inter-modal connection rather than intramodal feature extraction. In this paper, a simple yet effective principal affinity representation (PAR) approach is proposed to exploit the affinity representations of different modalities with local cluster samples. Afterwards, multi-class logistic regression model is adopted to learn the projections from principal affinity representation to semantic labels vectors. Inner product distance is further used to improve cross-modal retrieval performance. Extensive experiments on three benchmark datasets illustrate that our proposed method obtains significant improvements over the state-of-the-art subspace learning based cross-modal methods.

关键词： Logistics Semantics Training Extraterrestrial measurements Testing Internet

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Framework of future innovative urban transport 19

Framework of future innovative urban transport

引用

19th IEEE International Conference on intelligent Transportation Systems, ITSC 2016

作者： Dong, Xisong Zhou, Jiehan Hu, Bin Riekki, Jukka Xiong, Gang Wang, Feiyue Zhu, Fenghua State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Center for Ubiquitous Computing University of Oulu Oulu Finland State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan China Qingdao Academy of Intelligent Industries Qingdao China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509018895

Rapidly growing urbanization is putting more pressure on the well-being of citizens and the environment. Applying the latest ICT technology to address urban transport challenges is a key strategy to release the new innovation powerhouse of urbanization. This paper introduces a framework of future Innovative Urban Transport (IUT), to support next generation urban transport, based on IoT, 4G, Big Data, Cloud computing and other novel ICT technologies. We present the construction of the framework, its reference architecture, and contribution to the environmental and socially important impacts. IUT aims to advance the existing smart city transport, to foster urban innovations that maximize the well-being of citizens while minimizing the negative effects like traffic congestion, air pollution high resource congestion, and traffic accidents. © 2016 IEEE.

关键词： Traffic congestion

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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

引用

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001 School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of Electrical and Information Engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

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Out-of-distribution Rumor Detection via Test-Time Adaptation

arXiv

引用

arXiv 2024年

作者： Tao, Xiang Zhang, Mingqing Liu, Qiang Wu, Shu Wang, Liang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Existing methods for rumor detection have achieved good performance, as they have collected enough corpus from the same data distribution for model training. However, significant distribution shifts between the training data and real-world test data occur due to differences in news topics, social media platforms, languages and the variance in propagation scale caused by news popularity. This leads to a substantial decline in the performance of these existing methods in Out-Of-Distribution (OOD) situations. To address this problem, we propose a simple and efficient method named Test-time Adaptation for Rumor Detection under distribution shifts (TARD). This method models the propagation of news in the form of a propagation graph, and builds propagation graph test-time adaptation framework, enhancing the model's adaptability and robustness when facing OOD problems. Extensive experiments conducted on two group datasets collected from real-world social platforms demonstrate that our framework outperforms the state-of-the-art methods in performance. © 2024, CC BY-NC-SA.

关键词： Social networking (online)

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