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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Jiang, Hailiang Tian, Xin Tao, Ye Wang, Lulu Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Beijing HAOLINJU CVS Co. Ltd Beijing100088 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai201112 China

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers;fresh produce stores outperform regular stores in fruit sales of CGB;Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels. © 2024 The Authors.

关键词： community group-buying dark-colored fruits fresh produce stores subjectivity

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Unified Image Harmonization with Region Augmented Attention Normalization

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Annals of data Science 2024年第5期11卷 1865-1886页

作者： Hou, Junjie Zhang, Yuqi Su, Duo Sino-Danish College University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China

The image harmonization task endeavors to adjust foreground information within an image synthesis process to achieve visual consistency by leveraging background information. In academic research, this task conventionally involves the utilization of simple synthesized images and matching masks as inputs. However, obtaining precise masks for image harmonization in practical applications poses a significant challenge, thereby creating a notable disparity between research findings and real-world applicability. To mitigate this disparity, we propose a redefinition of the image harmonization task as "Unified Image Harmonization," where the input comprises only a single image, thereby enhancing its applicability in real-world scenarios. To address this challenge, we have developed a novel framework. Within this framework, we initially employ inharmonious region localization to detect the mask, which is subsequently utilized for harmonization tasks. The pivotal aspect of the harmonization process lies in normalization, which is accountable for information transfer. Nonetheless, the current background-to-foreground information transfer and guidance mechanisms are limited by single-layer guidance, thereby constraining their effectiveness. To overcome this limitation, we introduce Region Augmented Attention Normalization (RA2N), which enhances the attention mechanism for foreground feature alignment, consequently leading to improved alignment and transfer capabilities. Through qualitative and quantitative comparisons on the iHarmony4 dataset, our model exhibits exceptional performance not only in unified image harmonization but also in conventional image harmonization tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Image reconstruction

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Preface: Philosophy, Methodology, and Practice of big data

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Annals of data Science 2014年第3-4期1卷 281-282页

作者： Liu, Ying Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Research Laboratory on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China

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Group sensitive Classifier Chains for multi-label classification

Group sensitive Classifier Chains for multi-label classifica...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jun Huang Guorong Li Shuhui Wang Weigang Zhang Qingming Huang Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences ICT CAS Key Lab of Intelligent Information Processing (CAS) Beijing China Harbin Institute of Technology at Weihai Weihai China

In multi-label classification, labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the global label correlations. However, the label correlations may be different over different data groups. In this paper, we propose a simple and efficient framework for multi-label classification, called Group sensitive Classifier Chains. We assume that similar examples not only share the same label correlations, but also tend to have similar labels. We augment the original feature space with label space and cluster them into groups, then learn the label dependency graph in each group respectively and build the classifier chains on each group specific label dependency graph. The group specific classifier chains which are built on the nearest group of the test example are used for prediction. Comparison results with the state-of-the-art approaches manifest competitive performances of our method.

关键词： Correlation Birds Accuracy Boats Training data Training Measurement

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A pyramid scheme model based on "consumer rebate" frauds

arXiv

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arXiv 2019年

作者： Shi, Yong Li, Bo Long, Wen School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

There are various types of pyramid schemes which have inflicted or are inflicting losses on many people in the world. We propose a pyramid scheme model which has the principal characters of many pyramid schemes appeared in recent years: promising high returns, rewarding the participants recruiting the next generation of participants, and the organizer will take all the money away when he finds the money from the new participants is not enough to pay the previous participants interest and rewards. We assume the pyramid scheme carries on in the tree network, ER random network, SW small-world network or BA scale-free network respectively, then give the analytical results of how many generations the pyramid scheme can last in these cases. We also use our model to analyse a pyramid scheme in the real world and we find the connections between participants in the pyramid scheme may constitute a SW small-world network. Copyright © 2019, The Authors. All rights reserved.

关键词： Small-world networks

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An ES-based model with contemporaneous and temporal aggregation for forecasting intermittent and lumpy retail demand 11

An ES-based model with contemporaneous and temporal aggregat...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Erjiang, E. Tian, Xin Tao, Ye Sun, Huiqiang School of Management Guangxi Minzu University Nanning530006 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy Sciences Beijing100190 China Beijing Haolinju CVS Co. Ltd. Beijing100018 China Technology Development Co. Ltd. Beijing101399 China

The demand for products in the retail industry is often characterized by intermittent and volatile patterns. Specifically, at the SKU level, the demand exhibits intermittent and lumpy behavior, which presents challenges for accurate forecasting due to demand fluctuations and interval uncertainty. This study proposes an exponential smoothing (ES)-based model, called ES-C-T, that incorporates contemporaneous and temporal aggregation to forecast intermittent and lumpy retail demand. The ES-C-T model consists of three stages. Firstly, the ES model predicts the sales of each product by aggregating contemporaneously across all stores, and the resulting predictions are allocated to the SKU level using a weight vector. Secondly, the ES model forecasts the sales of each SKU by aggregating temporally within each store. Finally, the ES-C-T model derives a weight coefficient based on the predictions from the first and second stages, adjusts the initial predictions, and utilizes this as the final forecast. To validate the effectiveness of the ES-C-T model, sales data from a large Chinese convenience store chain are employed. The results demonstrate that the proposed ES-C-T model outperforms the benchmark model in terms of MAE, RMSE, and WRMSSE, effectively predicting retail sales for intermittent and lumpy demand. © 2024 The Authors.

关键词： Prediction models

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Image Orientation Estimation Based On Deep Learning - A Survey 11

Image Orientation Estimation Based On Deep Learning - A Surv...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Xu, Ruijie Shi, Yong Qi, Zhiquan School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China College of Information Science and Technology University of Nebraska Omaha68182 United States

In the realm of visual tasks, the estimation of orientation plays a pivotal role yet often remains an understudied aspect in the gamut of image analysis research. This paper endeavors to address this deficiency by conducting a comprehensive review of methods for orientation estimation within 2D imaging contexts. We aim to provide researchers with an exhaustive overview and detailed analysis of the techniques utilized for orientation estimation, thereby enhancing their understanding of this complex field. We introduce an organized classification of orientation estimation methods, segmented into approaches based on classification algorithms and those based on regression models. This classification not only sheds light on the distinct methodologies but also scrutinizes their underlying assumptions, practical applications. Additionally, the study identifies and underscores emerging research opportunities that hold the potential to enrich the discourse and technical advancements in orientation estimation, thereby encouraging deeper academic investigation and technological development. © 2024 The Authors.

关键词： Classification-based Orientation Estimation Regression-based Orientation Estimation

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SOVC: Subject-Oriented Video Captioning

arXiv

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arXiv 2023年

作者： Teng, Chang Ma, Yunchuan Li, Guorong Qi, Yuankai Qing, Laiyun Huang, Qingming School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100049 China School of Computing Macquarie University Australia

Describing video content according to users’ needs is a long-held goal. Although existing video captioning methods have made significant progress, the generated captions may not focus on the entity that users are particularly interested in. To address this problem, we propose a new video captioning task, Subject-Oriented Video Captioning (SOVC), which aims to allow users to specify the describing target via a bounding box. To support this task, we construct two subject-oriented video captioning datasets based on two widely used video captioning datasets: MSVD and MSRVTT, by annotating subjects in each video for each caption. These datasets pave the way for describing users’ interested targets. To tackle this task, we introduce a method tailored to this task, named SOVCNet. It consists of two key components: a subject-oriented sampling module that samples frames related to the subject to minimize irrelevant information;and a subject-oriented encoding module that utilizes the subject areas as hard prompts and integrates learnable soft prompts, enhancing the model’s focus on the subject’s activities and facilitating adaptation to the downstream generation task. Extensive experimental results demonstrate the effectiveness of our method on this new task. Copyright © 2023, The Authors. All rights reserved.

关键词： Video analysis

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Latest Technologies on dataset Distillation: A Survey 11

Latest Technologies on Dataset Distillation: A Survey

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Li, Muyang Qu, Yi Shi, Yong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

dataset distillation refers to the process of constructing a smaller dataset based on a larger dataset, so that the training model with the smaller dataset can obtain similar results to the larger dataset. In this paper, we first briefly review the basic methods and some recent advances in this field from the perspectives of meta-learning framework and data matching framework, including the backpropagation through time(BPTT), kernel ridge regression(KRR), decoupled techniques, gradient matching(DC), trajectory matching(MTT) and distribution matching(DM). The results of the plug-and-play approach in recent years are then presented. Finally, we discuss possible future developments from the perspectives of data distillation algorithms on large datasets, the relationship between compress ratio and information contained, more tasks and data modalities, and the use of generative models to improve the distillation results of datasets. © 2024 The Authors.

关键词： data assimilation

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Structure Aware Loss for Deep Unsupervised Industrial Image Registration 24

Structure Aware Loss for Deep Unsupervised Industrial Image ...

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24th IEEE International Conference on data mining Workshops, ICDMW 2024

作者： Shi, Yong Lyu, Mengjin Qi, Zhiquan Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science Beijing100190 China Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing100190 China University of Nebraska at Omaha College of Information Science and Technology OmahaNE 68182 United States University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100190 China University of Chinese Academy of Sciences School of Economics and Management Beijing100190 China

ISBN: (纸本)9798331530631

Image registration is extensively utilized in industrial applications. However, specific challenges inherent to these environments - such as low texture, significant displacement, and poor lighting conditions - complicate the registration process. This paper introduces a structure-aware loss module tailored to the unique characteristics of industrial images. This module promotes the network's focus on salient regions while emphasizing structural features. Additionally, it is designed as a plug-in component, facilitating integration with any loss function and homography estimation method. We develop an unsupervised registration network and create an industrial dataset to evaluate the module's effectiveness. A series of experiments demonstrate that our structure-aware module significantly enhances registration performance. © 2024 IEEE.

关键词： Image registration

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