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arXiv 2022年

作者： Zhong, Zhipeng Zhou, Fei Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng National Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China School of Computer Science The University of Nottingham United Kingdom

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS’s can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://***/PengZai/ARIC. © 2022, CC BY-NC-ND.

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Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network 1

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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Lin, Jing Mi, Chunqiao School of Computer Science and Engineering Huaihua University HuaihuaHunan418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities HuaihuaHunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province HuaihuaHunan418000 China

ISBN: (数字)9789811579844

ISBN: (纸本)9789811579837

The smart water meter in water supply network can directly affect water production and usage when faults occur. The traditional method of fault detection is inefficient with time lagging, which is not helpful for modernization of water supply system. The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply. In this paper, an automatic fault diagnosis method for the smart device is proposed based on BP neural network. And it was applied on Google Tensorflow platform. Fault symptom vectors were constructed using water meter status data and were used to train the neural network model. In order to improve the learning convergence speed and fault classification effect of the network, a method of weighted symptom was also employed. Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Fault detection

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Domain generalization for document authentication against practical recapturing attacks

arXiv

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arXiv 2021年

作者： Chen, Changsheng Zhang, Shuzheng Lan, Fengbo Huang, Jiwu The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against a reference sample, we proposed a document recapture detection scheme by employing Siamese network to compare and extract distinct features in a recapture document image. The proposed algorithm takes advantages of both metric learning and image forensic techniques. Instead of adopting Euclidean distance-based loss function, we integrate the forensic similarity function with a triplet loss and a normalized softmax loss. After training with the proposed triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushes the recaptured samples apart. In the experiment, we consider practical domain generalization problems, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed schemes with different network backbones have consistently outperformed the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents that produced by different devices, we have achieved less than 5.00% APCER (Attack Presentation Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5% BPCER decision *** Codes 94A08 Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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Predicting drug transcriptional response similarity using Signed Graph Convolutional Network

Predicting drug transcriptional response similarity using Si...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziyan Wang Chengzhi Hong Xuan Liu Zhankun Xiong Feng Liu Wen Zhang School of Computer Science Wuhan University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Agricultural Bioinformatics Key Laboratory of Hubei Province Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Huazhong Agricultural University Wuhan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Exploring the transcriptional response after employing chemical compounds assists in treating gene-related diseases and understanding biological activity of compounds. Calculating the similarity of drug transcriptional response can help to discover novel compounds that have the similar biological activity to known drugs for treating the same disease. Considering the transcriptional profiles of compounds are limited and harder to get than the structure of compounds, it is worth modeling the structure-transcriptional response similarity relationship. In this paper, we propose a signed graph convolutional network (SGCN)-based method, namely SGCN-DTRS, to predict drug transcriptional response similarity, which is quantitatively measured by Connectivity Map (CMap) scores, from their structures. SGCNDTRS constructs a CMap signed network from compounds and their CMap scores in the training data, which takes compounds as nodes, molecular structural representations of compounds as the attributes of nodes, and similarity relations between compounds as edges. Then SGCN-DTRS learns the CMap compound embeddings to predict CMap scores of pairwise compounds. Extensive experiments verify the superiority of the proposed method against the compared state-of-the-art methods and reveal that the relational information of the CMap data, which is learned from CMap signed network, is important for the CMap score prediction. SGCN-DTRS can not only work for the compounds in the training set but also is applicable to unseen compounds.

关键词： Drugs Training databases Training data Predictive models Biology Graph neural networks

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Optimal Location Privacy Preserving and Service Quality Guaranteed Task Allocation in Vehicle-Based Crowdsensing Networks

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2021年第7期22卷 4367-4375页

作者： Qian, Yongfeng Ma, Yujun Chen, Jing Wu, Di Tian, Daxin Hwang, Kai China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Data Sci Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518129 Peoples R China

With increasing popularity of related applications of mobile crowdsensing, especially in the field of Internet of Vehicles (IoV), task allocation has attracted wide attention. How to select appropriate participants is a key problem in vehicle-based crowdsensing networks. Some traditional methods choose participants based on minimizing distance, which requires participants to submit their current locations. In this case, participants' location privacy is violated, which influences disclosure of participants' sensitive information. Many privacy preserving task allocation mechanisms have been proposed to encourage users to participate in mobile crowdsensing. However, most of them assume that different participants' task completion quality is the same, which is not reasonable in reality. In this paper, we propose an optimal location privacy preserving and service quality guaranteed task allocation in vehicle-based crowdsensing networks. Specifically, we utilize differential privacy to preserve participants' location privacy, where every participant can submit the obfuscated location to the platform instead of the real one. Based on the obfuscated locations, we design an optimal problem to minimize the moving distance and maximize the task completion quality simultaneously. In order to solve this problem, we decompose it into two linear optimization problems. We conduct extensive experiments to demonstrate the effectiveness of our proposed mechanism.

关键词： Location privacy preserving mobile crowdsensing service quality task allocation vehicular network

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Disentangled Representation Learning with Transmitted Information Bottleneck

arXiv

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arXiv 2023年

作者： Dang, Zhuohang Luo, Minnan Jia, Chengyou Dai, Guang Wang, Jihong Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi’an710049 China The SGIT AI Laboratory Xi’an710048 China The State Grid Shaanxi Electric Power Company Ltd. State Grid Corporation of China Xi’an710048 China The School of Information Science and Technology University of Science and Technology of China Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc Beijing100085 China

Encoding only the task-related information from the raw data, i.e., disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Although significant advances have been made by regularizing the information in representations with information theory, two major challenges remain: 1) the representation compression inevitably leads to performance drop;2) the disentanglement constraints on representations are in complicated optimization. To these issues, we introduce Bayesian networks with transmitted information to formulate the interaction among input and representations during disentanglement. Building upon this framework, we propose DisTIB (Transmitted Information Bottleneck for Disentangled representation learning), a novel objective that navigates the balance between information compression and preservation. We employ variational inference to derive a tractable estimation for DisTIB. This estimation can be simply optimized via standard gradient descent with a reparameterization trick. Moreover, we theoretically prove that DisTIB can achieve optimal disentanglement, underscoring its superior efficacy. To solidify our claims, we conduct extensive experiments on various downstream tasks to demonstrate the appealing efficacy of DisTIB and validate our theoretical analyses. Copyright © 2023, The Authors. All rights reserved.

关键词： Information theory

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Multiagent Q-Learning for Multicrew Dynamic Scheduling and Routing in Road Network Restoration

Multiagent Q-Learning for Multicrew Dynamic Scheduling and R...

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Chinese Control and Decision Conference, CCDC

作者： Yufeng Shen Lianqi Duan Qianshun Zhu Zhaopin Su Guofu Zhang School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 School of Computer Science and Information Engineering Hefei University of Technology Hefei Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) Hefei Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology) Heifei

ISBN: (纸本)9781665478977

Road network restoration is an important issue in the post-disaster disposal and rescue, especially when extraordinarily serious natural disasters (e.g., floods and earthquakes) occur. Central to this endeavour is the problem of determining how to reasonably schedule and route the repair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand nodes. However, most existing work focuses on the activities of the single repair crew in the static road network, which is unable to adapt to the dynamic changes of the road network caused by secondary disasters, such as rock slides and debris flows. Consequently, this work is concentrated on multicrew dynamic scheduling and routing in road network restoration. Specifically, a model of multicrew dynamic scheduling and routing is first presented. Next, the Markov decision process is adopted to construct a decision model for repair crews, based on which a multiagent Q-learning algorithm is developed to find the best scheduling and routing strategy. Finally, experimental results demonstrate that the proposed method can make repair crews adjust their scheduling and routing strategies according to the dynamic changes of the damaged road network and provides a useful attempt to restore the damaged road network in complex, dynamic emergency scenarios of post-disaster.

关键词： Schedules Q-learning Roads Heuristic algorithms Maintenance engineering Markov processes Dynamic scheduling

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St-Kan-Former: A Novel Spatiotemporal Transformer Neural Network for Air Quality Prediction

SSRN

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SSRN 2025年

作者： Zhang, Wenyong Xia, Dawen Chang, Guoyan Zheng, Zhiquan Hu, Yang Huo, Yujia Wang, Ziqiang Feng, Fujian Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Engineering Research Center of Micro-nano and Intelligent Manufacturing Ministry of Education Kaili University Kaili556011 China Affiliated Hospital of Guizhou Medical University Guiyang550001 China State Key Laboratory of Public Big Data Guizhou University Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

Accurate and timely air quality prediction is important for supporting daily activities and safeguarding respiratory health. However, the complex aerodynamic properties of air pollutants pose significant challenges to accurate air quality prediction. Most existing spatiotemporal prediction models extract spatial features in a coarse-grained manner, which cannot fully utilize the spatial feature data, resulting in poor prediction accuracy. To this end, we propose a novel air quality prediction method based on a Spatiotemporal Transformer with a Kolmogorov-Arnold Network (ST-KAN-Former). Specifically, in the temporal dimension, we use the wavelet transform, KAN, and ProbAttention network to construct the temporal feature extraction model. Based on this, the temporal dimension data are decomposed into high-frequency and low-frequency components, and then feature extraction is performed for fine-grained acquisition of temporal dimension features. Then, in the spatial dimension, we divide the spatial region into four subregions according to the distance parameter between the master site and the slave site and build a spatial feature extraction model for each subregion using KAN and ProbAttention networks. Next, we simulate the attenuation of pollutants during their dispersion to introduce the concept of diffusion coefficients. This approach allows us to apply diffusion processing to data from each subregion, enabling a finer granularity in capturing spatial characteristics. Finally, we employ the KAN and ProbAttention networks to construct a spatiotemporal feature fusion model that integrates temporal and spatial characteristics. Extensive experiments on the Beijing dataset demonstrate that ST-KAN-Former achieves better results compared with the other 11 benchmarks. © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Attention Field Size Alters Patterns of Population Receptive Fields in the Early Visual Cortex

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Neuroscience Bulletin 2022年第2期38卷 205-208页

作者： Bo Liu Xiaochun Wang Le Wang Qiaojun Qu Wei Zhang Bin Wang Jie Xiang Ting Yan Tianyi Yan Hui Zhang College of Medical Imaging Shanxi Medical UniversityTaiyuan 030001China Department of Radiology First Clinical Medical CollegeShanxi Medical UniversityTaiyuan 030001China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030001China Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province Shanxi Medical UniversityTaiyuan 030001China Translational Medicine Research Center Shanxi Medical UniversityTaiyuan 030001China School of Life Science Beijing Institute of TechnologyBeijing 100081China Ministry of Industry and Information Technology Key Laboratory of Convergence Medical Engineering System and Healthcare TechnologyBeijing Institute of TechnologyBeijing 100081China

Dear Editor,Visual spatial attention is a cognitive process by which prior information about the relevance of spatial locations is used to improve perceptual performance[1].Visual attention is spread across the attention field(AF),a range within visual *** AF reshapes the distribution of activity throughout the visual pathway,including the early visual cortex(EVC)and all areas along the dorsal and ventral visual cortical pathways[2,3].Importantly,the effect of attention depends not only on the spatial position of the AF but also on its size[4].

关键词： Visual Cortex visual

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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