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data science:Trends, perspectives, and prospects

data Science and Informetrics 2022年第3期2卷 1-21页

作者： Chaolemen Borjigin Chen Zhang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijingChina Information Resource Management School Renmin University of ChinaBeijingChina

data science is a rapidly growing academic field with significant implications for all conventional scientific studies. However, most relevant studies have been limited to one or several facets of data science from a specific application domain perspective and less to discuss its theoretical framework. data science is unique in that its research goals, perspectives, and body of knowledge are distinct from other sciences. The core theories of data science are the DIKW pyramid, data-intensive scientific discovery, data science life cycle, data wrangling or munging,big data analytics, data management, and governance, data products Dev Ops, and big data visualization. Six main trends characterize the recent theoretical studies on data science are:(1)the growing significance of data Ops,(2) the rise of citizen data scientists,(3) enabling augmented data science,(4) integrating data warehouse with data lake,(5) diversity of domain-specific data science, and(6) implementing data stories as data products. Further development of data science should prioritize four ways to turn challenges into opportunities:(1) accelerating theoretical studies of data science,(2) the trade-off between explainability and performance,(3) achieving data ethics, privacy and trust, and(4) aligning academic curricula with industrial needs.

关键词： CCS concepts General and reference Surveys and overviews data science big data data products data-driven management The DIKW pyramid

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Geometric modeling of Holocene large-river delta growth patterns,as constrained by environmental settings

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Science China Earth Sciences 2021年第2期64卷 318-328页

作者： Gaocong LI Qiong XIA Yaping WANG Zhiqiang LI Shu GAO Department of Marine Technology Guangdong Ocean UniversityZhanjiang 524088China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519000China Marine Resources Big Data Center of South China Sea Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang)(Zhanjiang Bay Laboratory)Zhanjiang 524088China State Key Laboratory for Estuarine and Coastal Research East China Normal UniversityShanghai 200046China

Topographic/bathymetric conditions of the continental shelf can significantly influence the long-term growth of river *** particular,these conditions constrain the accommodation space for sedimentation in the deltaic *** this study,we use a conceptual geometric model to evaluate the role played by this factor,on the basis of the principle of mass *** Ganges-Brahmaputra,Mekong and Nile deltas are examined as three typical examples,in terms of their different original seabed *** control variate method is applied to eliminate the effect of the difference in model input *** results show that,assuming a constant sediment load,the delta growth rate will decrease with time;a higher value of the original seabed slope leads to a lower shoreline progradation rate for the subaerial delta and a higher growth rate for the subaqueous ***,the original seabed morphology represented by slope is a critical factor affecting the evolution of Holocene large-river *** results explain the interrelationships between sediment load,deltaic plain area,and the original seabed slopes for the 27 large-river deltas worldwide,located in the middle/low latitudes,with different tectonic *** the future,the conceptual geometric model may be combined with sediment dynamic modeling to identify more details of the evolution of these deltas.

关键词： Large-river deltas Original topography/bathymetry Geometric modeling Growth geometry Holocene

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Fast incremental structure from motion based on parallel bundle adjustment

Fast incremental structure from motion based on parallel bun...

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作者： Cao, Mingwei Zheng, Liping Jia, Wei Liu, Xiaoping Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Structure from motion has attracted a lot of research in recent years, with new state-of-the-art approaches coming almost every year. One of its advantages over 3D reconstruction is that it can be used for any cameras (UAVs, depth sensor, light field) and produces relatively accurate point clouds and camera parameters. One of its disadvantages compared to other approaches is that it is computationally expensive. In this paper, we design a novel structure-from-motion framework to reduce the computational cost and implement a parallel bundle adjustment on GPU device for large-scale optimization. In our framework, the local bundle adjustment is added into the architecture of the incremental structure from motion;namely, the point clouds and camera’s parameters are optimized when an additional number of images was added. Then, the purpose is not only to improve the quality of the produced point clouds but also to reduce computation time via parallel bundle adjustment. We conduct extensively experiments on several challenging datasets and make comparison with the state-of-the-art methods. Experimental results show that the proposed method has the best performance in terms of accuracy and efficiency. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cameras

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Macroprudential policy in retail lending: Concept and results – a case study of the Bank of Russia

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Procedia Computer Science 2024年 242卷 1331-1338页

作者： Ekaterina Eremina Yong Shi School of Economics and Management University of Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China

Macroprudential regulation of the banking system is a significant instrument for ensuring the financial stability of the state. Unsecured consumer lending is growing at a steadily high rate (in August-October 2022, the average monthly debt growth was 0.9%), and it remains a priority segment for banks because of its high margins. The share of unsecured consumer loans provided to borrowers with debt burden indicator (DBI) of more than 80% amounted to more than 32% in the third quarter of 2022 (28% in the second quarter of 2022). The share of consumer loans for a term of more than 5 years remains at a high level (15% in July-September 2022). The share of MFO loans issued to borrowers with a DBI of more than 80% was 41% in the second quarter of 2022 (38% in the first quarter of 2022). A further increase in the citizens’ debt burden in the context of the economy restructuring may create additional macroeconomic risks. Problems of citizens with servicing loans and borrowings can lead to a reduction in demand in the economy, and losses of banks as a result of writing off “bad” loans can lead to a decrease in their ability to lend to the economy. To limit the risks associated with an increase in the citizens’ debt burden, since the first quarter of 2023, the Bank of Russia has established direct quantitative restrictions on the issuance of certain types of credits (loans) and the application of increased buffers to risk coefficients if creditors exceed established limits. This article discusses the main approaches to implementation of the macroprudential policy of the Central Bank of the Russian Federation, its goals and instruments, and peculiarities of application in the current conditions.

关键词： macroprudential policy banking system risks macroprudential instruments Bank of Russia

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BKA optimization algorithm based on sine-cosine guidelines

BKA optimization algorithm based on sine-cosine guidelines

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Yanqiu Zhou Xiao Wu Yuxiang Liu Xinghe Jiang Jiangnan Institute of Mechanical and Electrical Design Guiyang China School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In order to address the limitations of traditional BKA optimization algorithms in terms of stability, data processing capability, and computational speed, this study proposes a new and improved BKA algorithm that combines the sine-cosine criterion, i.e., SCBKA. This new algorithm significantly enhances its solution accuracy and convergence speed by integrating the sine-cosine oriented mechanism. We conducted systematic comparison experiments of the SCBKA algorithm with six recognized classical algorithms on the highest challenge dimension of the CEC 2020 benchmark test function, the 20th dimension. The experimental results clearly show that SCBKA not only obtains significant improvement in convergence and accuracy, but also demonstrates significant advantages in the stability of the algorithm. In addition, by applying the Friedman rank sum test, we further confirm the efficiency and reliability of the SCBKA algorithm.

关键词： Performance evaluation Accuracy Systematics Statistical analysis Stability criteria Orbits Reliability Optimization Standards Convergence

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Bilinear-Experts Network with Self-Adaptive Sampler for Long-Tailed Visual Recognition

SSRN

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SSRN 2024年

作者： Wang, Qin Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Long-tail distributed data hinders the practical application of state-of-the-art deep models in computer vision. Consequently, exclusive methodologies for handling the long-tailed problem are proposed, focusing on different hierarchies. For embedding hierarchy, existing works manually augment the diversity of tail-class features for specific datasets. However, prior knowledge about datasets is not always available for practical use, which brings unsatisfactory generalization ability in human fine-turned augmentation under such circumstances. To figure out this problem, we introduce a novel model named Bilinear-Experts Network (BENet) with Self-Adaptive Sampler (SAS). This model leverages model-driven perturbations to tail-class embeddings while preserving generalization capability on head classes through a designed bilinear experts system. The designed perturbations adaptively augment tail-class space and shift the class boundary away from the tail-class centers. Moreover, we surprisingly find that SAS automatically assigns more significant perturbations to specific tail classes rather than fewer-sample classes, which is against the common assumption that fewer-sample classes deserve greater attention. Also, experiments conducted across various long-tailed benchmarks validate the comparable efficacy of proposed BENet. © 2024, The Authors. All rights reserved.

关键词： Embeddings

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ENSIOT: A Stacking Ensemble Learning Approach for IoT Device Identification

ENSIOT: A Stacking Ensemble Learning Approach for IoT Device...

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International Workshop on Quality of Service

作者： Kangli Niu Shenghao Liu Lingzhi Yi Xianjun Deng Suning Chen Laurence T. Yang Minmin Cheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Safety Engineering Zhongnan University of Economics and Law

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

In order to resist network attacks on IoT devices, identifying IoT devices is the first step for ensuring device security. The traditional passive method identifies IoT devices by mining the potential relationship between traffic characteristics and devices. However, the form of selected traffic features are too singular without considering device behavioral characteristics and the classifier used is too specific with simple structure in these methods. This paper proposes a stacking ensemble learning approach for IoT device identification, ENSIOT, which fully considering the behavioral characteristics of devices and integrating the advantages of various machine learning methods to achieve efficient identification of IoT devices. Firstly, in the process of traffic processing, our method selects features from activity cycles, port numbers, signalling patterns, and cipher suites. Then, in model integration, many machine learning methods are used as base models to learn features selected, and output preliminary recognition results. Finally, the meta model learns the relationship between label and the recognition results of each base model and outputs the final device identification result. This stacking structure stacks the base models and the meta model to make a classifier with strong identification and generalization ability. Incremental learning is used to improve identification accuracy when traffic pattern changing. Comparative experiments are conducted on two datasets of UNSW and TMA-2021. The experimental results verify the effectiveness of ENSIOT, which achieve the accuracy of over 98% on two dataset and bring a noticeable improvement in terms of both accuracy and macro F1 score.

关键词： Ciphers Accuracy Incremental learning Stacking Resists Traffic control Internet of Things

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Generative Replay Method Based on Integrating Random Gating and Attention Mechanism

Generative Replay Method Based on Integrating Random Gating ...

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International Conference on Computer and Communications (ICCC)

作者： Yao He Jing Yang Qinglang Li Hongchao An Fengyuan Zhang School of Mechanical Engineering Guizhou University State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Mechanical Engineering Guizhou University Guiyang China

Artificial neural networks aspire to mimic human intelligence by constantly learning from a series of tasks without forgetting past knowledge. The most typical way to achieve this kind of learning is to store previously learned task data and replay previously stored data when training a new task. However, the performance of these methods will be affected with the reduction of memory during training. To solve this problem, a new generative replay algorithm is proposed in this paper —— A generative replay method based on the fusion of random gating and attention mechanism. This method is used to generate visual significance features for the model to remember past sample information. First, we introduce an attention mechanism into the generated model to address the issue of the quality of the generated replay data. This mechanism enhances the data salience of feature information during large-scale model training and allows the generated replay network to produce visual salience features, which essentially mimic the way a human observes. Secondly, because of the overfitting phenomenon in the training process of the model, a random gating mechanism is introduced to improve the generalization of the model and improve the accuracy of the model. Finally, we use the CIFAR100 and Permuted MNIST datasets to validate the efficacy of our method.

关键词：

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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big-MOE: BYPASS ISOLATED GATING MOE FOR GENERALIZED MULTIMODAL FACE ANTI-SPOOFING

arXiv

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arXiv 2024年

作者： Ma, Yingjie Yu, Zitong Lin, Xun Xie, Weicheng Shen, Linlin Great Bay University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators;(2) Gated networks’ susceptibility to input noise affecting decision-making;(3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (big-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues;(2) An isolation gating mechanism to counteract input noise;(3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/bigMoE. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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