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9th International Conference on Information technology and Quantitative Management, ITQM 2022

作者： Wang, Yunong Qu, Yi Chen, Zhensong 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 Management and Engineering Capital University of Economics and Business Beijing100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks. © 2022 The Authors. Published by Elsevier B.V.

关键词： Financial markets

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Povm-Based Quantum Self-Attention Neural Network 21

Povm-Based Quantum Self-Attention Neural Network

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21st International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Wei, Jiachun He, Zhimin Chen, Chuangtao Deng, Maijie Situ, Haozhen School of Mathematics and Big data Foshan University Foshan528000 China School of Electronic and Information Engineering Foshan University Foshan528000 China Macau University of Science and Technology Faculty of Innovation Engineering Macau China School of Mechatronic Engineering and Automation Foshan University Foshan528000 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

ISBN: (纸本)9798350303810

Quantum Self-Attention Neural Network (QSANN) has demonstrated remarkable potential. However, it is limited to measuring only partial information from qubits, thereby restricting its information extraction capability. Moreover, the measurement process requires multiple measurements for each qubit, leading to inefficiency in utilizing the feature space. To address these challenges, this paper introduces a novel approach called Positive Operator-Valued Measure based Quantum Self-Attention Neural Network (POVM-QSANN). It leverages POVM operators as observables, enabling the extraction of comprehensive QKV (Query-Key-Value) feature vectors from each qubit. This innovative mechanism significantly enhances information extraction capability and efficiently utilizes the feature space. The experimental results underscore the considerable advancements of POVM-QSANN on MC and RP datasets. Particularly noteworthy is its remarkable accuracy increase of 9.68% compared to QSANN on the RP dataset. © 2023 IEEE.

关键词： Positive Operator-Valued Measure Quantum machine learning Self-attention mechanism Variational quantum algorithm

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Validating SMT Solvers via Skeleton Enumeration Empowered by Historical Bug-Triggering Inputs

Validating SMT Solvers via Skeleton Enumeration Empowered by...

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International Conference on Software Engineering (ICSE)

作者： Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Huazhong University of Science and Technology China

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.

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Saliency Detection Based Pyramid Optimization of Large Scale Satellite Image

Saliency Detection Based Pyramid Optimization of Large Scale...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Han, Mingzhi Liu, Lanyu Xu, Tao He, Jie Liu, Zhen Zang, Junyuan School of Information Science and Engineering University of Jinan Jinan250022 China CHEOS Shandong Center of Data and Application Jinan250022 China Shandong Institute of Geological Surveying and Mapping Jinan250011 China Institute of Satellite Technology Shandong Institute of Industrial Technology Jinan China

ISBN: (纸本)9789819708543

Large-scale satellite images play an important role in disaster monitoring, ecological protection and other fields. However, due to its large size, it leads to slow browser loading and need more storage spaces. To solve the problem of spatiotemporal processing of satellite images, this paper proposes saliency detection based spatiotemporal pyramid optimization algorithm for network transmission, browser loading and visualization of large-scale satellite images. Saliency detection was firstly used to generate graphical result to analyze the area that people are easy to pay attention to in a remote sensing image, namely the significant area. Then, slice segmentation is carried out on the remote sensing image to separate the significant area and non-significant area, and the significant area is processed separately. In the experiment, cultivated land protection monitoring system based on WebGIS was used to load satellite images. Through comparison in the cultivated land protection monitoring system project, the loading speed and efficiency of the satellite image processed by the method in this paper are improved by about 14.57% compared with the satellite image without any processing. Compared with satellite images that construct a complete image pyramid, there is little difference in loading speed, but the storage efficiency of the proposed method is improved by about 11.98%. Experiments show that the satellite images processed by the method in this paper meet people's needs for image loading, and can be applied in actual project development to save computing resources and storage resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Loading

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Probabilistic Modeling of Fish Growth in Smart Aquaculture Systems

Probabilistic Modeling of Fish Growth in Smart Aquaculture S...

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作者： Kim, Jongwon Park, Eunbi Cho, Sungyoon Kwon, Kiwon Ko, Young Myoung Department of Industrial and Management Engineering Pohang University of Science and Technology 77 Cheongam-ro Nam-gu Pohang Gyeongbuk37673 Korea Republic of Korea Electronics Technology Institute Seoul Korea Republic of Open innovation Big Data Center Pohang University of Science and Technology 77 Cheongam-ro Nam-gu Pohang Gyeongbuk37673 Korea Republic of

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model. Copyright © 2023 KSII.

关键词： Fish

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Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization

arXiv

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arXiv 2023年

作者： Zhu, Mingli Wei, Shaokui Shen, Li Fan, Yanbo Wu, Baoyuan School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China JD Explore Academy China Tencent AI Lab United States

Backdoor defense, which aims to detect or mitigate the effect of malicious triggers introduced by attackers, is becoming increasingly critical for machine learning security and integrity. Fine-tuning based on benign data is a natural defense to erase the backdoor effect in a backdoored model. However, recent studies show that, given limited benign data, vanilla fine-tuning has poor defense performance. In this work, we provide a deep study of fine-tuning the backdoored model from the neuron perspective and find that backdoor-related neurons fail to escape the local minimum in the fine-tuning process. Inspired by observing that the backdoor-related neurons often have larger norms, we propose FT-SAM, a novel backdoor defense paradigm that aims to shrink the norms of backdoor-related neurons by incorporating sharpness-aware minimization with fine-tuning. We demonstrate the effectiveness of our method on several benchmark datasets and network architectures, where it achieves state-of-the-art defense performance. Overall, our work provides a promising avenue for improving the robustness of machine learning models against backdoor attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurons

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Image segmentation

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A Unified Visual Prompt Tuning Framework with Mixture-of-Experts for Multimodal Information Extraction 28th

A Unified Visual Prompt Tuning Framework with Mixture-of-Ex...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Bo Huang, Shizhou Du, Ming Wang, Hongya Song, Hui Xiao, Yanghua Lin, Xin School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Fudan-Aishu Cognitive Intelligence Joint Research Center Shanghai China East China Normal University Shanghai China

ISBN: (纸本)9783031306747

Recently, multimodal information extraction has gained increasing attention in social media understanding, as it helps to accomplish the task of information extraction by adding images as auxiliary information to solve the ambiguity problem caused by insufficient semantic information in short texts. Despite their success, current methods do not take full advantage of the information provided by the diverse representations of images. To address this problem, we propose a novel unified visual prompt tuning framework with Mixture-of-Experts to fuse different types of image representations for multimodal information extraction. Extensive experiments conducted on two different multimodal information extraction tasks demonstrate the effectiveness of our method. The source code can be found at https://***/xubodhu/VisualPT-MoE. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Social networking (online)

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Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification 29

Composition-based Heterogeneous Graph Multi-channel Attentio...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Niu, Hao Xiong, Yun Gao, Jian Miao, Zhongchen Wang, Xiaosu Ren, Hongrun Zhang, Yao Zhu, Yangyong Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Shanghai Financial Futures Information Technology Co. Ltd China

Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications. However, towards the sentence carried with more than one aspect, most existing works generate an aspect-specific sentence representation for each aspect term to predict sentiment polarity, which neglects the sentiment relationship among aspect terms. Besides, most current ABSA methods focus on sentences containing only one aspect term or multiple aspect terms with the same sentiment polarity, which makes ABSA degenerate into sentence-level sentiment analysis. In this paper, to deal with this problem, we construct a heterogeneous graph to model inter-aspect relationships and aspect-context relationships simultaneously and propose a novel Composition-based Heterogeneous Graph Multi-channel Attention Network (CHGMAN) to encode the constructed heterogeneous graph. Meanwhile, we conduct extensive experiments on three datasets: MAMS-ATSA, Rest14, and Laptop14, experimental results show the effectiveness of our method. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Sentiment analysis

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Vehicle Identification Model of Expressway Service Area Based on ETC Transaction data 15th

Vehicle Identification Model of Expressway Service Area Base...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： You, Yi Zou, Fumin Cai, Qiqin Guo, Feng Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Technology Fuzhou350118 China School of Mechanical Engineering and Automation Huaqiao University Xiamen361021 China College of Computer and Data Science Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819700677

This paper mainly focuses on vehicle identification model of expressway service area based on ETC transaction data, First Steps, Define the time when the service area entrance RSU identifies the vehicle as the vehicle order entry time;the second step, The identification time of the RSU at the service area is the export time of the vehicle order;the third step, Determine whether the vehicle order exit time minus the vehicle order entry time is greater than or equal to the threshold time T;In Step 4, If order exit time minus order entry time is greater than or equal to threshold time T, The order vehicle is considered as the vehicle entering the service area;If order exit time minus order entry time is less than threshold time T, Think that the order vehicle did not enter the service area, For the arterial road vehicles, Based on the method and system of identifying whether the vehicle enters the service area on the expressway, ETC effectively solves the expressway service area scenario, The difficult problem of the vehicle information collection in the service area being misread by the ETC equipment in the service area. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicles

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