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IISE Annual Conference and Expo 2024

作者： Wang, Dan Liu, Chang Tang, Lixin Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Ministry of Education Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China

ISBN: (纸本)9781713877851

Prediction of molten steel quality is a key issue in the converter steelmaking process. Due to high temperature and physical-chemical reaction, it is difficult to predict the temperature of molten steel in real time through mechanism analysis. This is a challenging problem with black box characteristics. Here we propose a Convolutional neural networks (CNN) and long short-term memory (LSTM) networks model based on multi-objective differential evolution algorithm (MODE) for the real-time prediction problem in the steelmaking process. Specifically, the CNN is used to extract feature between multiple variables that affect the temperature of molten steel, and the LSTM is suitable for the time series model based on the irregular trends information. The multi-step output CNN-LSTM method is used to construct the temperature prediction model of molten steel, and a knee solution strategy is applied to MODE, which can balance the model accuracy and generalization ability. The experiments were conducted using real data collected from the converter steelmaking process. The results verify the effectiveness of the proposed model on multiple steel grades and outperform other artificial intelligence methods in multi-task prediction. © IISE Annual Conference and Expo *** rights reserved.

关键词： Convolutional neural networks

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Hierarchical Scalable data Lake Design for Game Engine 8

Hierarchical Scalable Data Lake Design for Game Engine

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8th International Conference on Big data and Information Analytics, BigDIA 2022

作者： Yang, Mingbao Zhou, Peng Li, Shaobo Pu, Ruiqiang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

With the rapid development of information technology, people are more and more inseparable from smart devices, and people and devices produce a large amount of data all the time. How to store and analyze massive data has become a research hotspot of the times. However, researchers mainly study the high reliability, high availability, high fault tolerance, high throughput, and high concurrent processing of big data systems, but there are few studies on designing hierarchical and scalable data lakes. In this study, a layered and extensible design of the data lake is carried out, which reduces the complexity of implementing business indicators during data analysis, and at the same time improves code reuse, scalability, development efficiency and code security. In addition, in order to avoid the problem of data accumulation or even job execution failure due to the skew of ip dimension table data, this paper designs the ip dimension table rowkey. This paper designs a set of real-time and offline big data analysis data lake architecture based on game engine and applies it to the actual production environment. © 2022 IEEE.

关键词： Big data

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Semantic Correspondence with Peripheral Position Coding 13

Semantic Correspondence with Peripheral Position Coding

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Chen, Jinjian Li, Zuoyong Lai, Taotao Xie, Huosheng College of Computer and Data Science Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350319156

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM. © 2023 IEEE.

关键词： Image enhancement

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A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

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Computer Modeling in engineering & Sciences 2024年第1期138卷 939-956页

作者： Haifeng Lin Xiangfeng Liu Chen Chen Zhibo Liu Dexin Zhao Yiwen Zhang Weizhuang Li Mingsheng Cao College of Economics and Management Nanjing University of Aeronautics and AstronauticsNanjing211106China Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu610073China Academy of Military Sciences of PLA Beijing100091China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Ningbo Web King Technology Joint Stock Co. Ltd.Ningbo315000China The Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction *** the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management ***,an audio-based location distinction method is designed to detect the position change of the ***,the audio frequency response(AFR)of several frequency points is utilized as a device *** device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different *** on the features above,amovement detection algorithmfor device identification and location distinction is ***,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and *** experiments are conducted to evaluate the performance of the proposed *** experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

关键词： Acoustic hardware fingerprinting device management IoT location distinction

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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Identifying data Breaches in Dark Web through Prompt Active Learning 9

Identifying Data Breaches in Dark Web through Prompt Active ...

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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Xiang, Hui Wu, Yifan Wang, Xuebin Shi, Jinqiao Zhao, Can Zhao, Jiapeng Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China

ISBN: (纸本)9798350391367

Frequent data breaches have attracted people's attention. The dark web forums have become the crucial platform for data breach transactions. While some forums are well-organized with distinct sections, such as a dedicated area for leaks where most threads are related to data breaches, others lack clear segmentation, like Fear Forum. Hackers conduct a variety of discussions there so it is filled with tons of non-data breach information and building a data breach dataset for a classifier requires huge human resources. The content of these threads is diverse and complex, spanning various fields, and containing specific terminology in the dark web. Identifying data breach threads in these forums is extremely challenging for the model. With the influx of new threads every day the static model trained on old data may not perform well when processing new data, which also brings serious difficulty to identification. To address these challenges, we first design Prompt-based Active Learning (PAL) to guide the model to identify through prompts and select high-quality samples for annotation. Then we propose a system that combines PAL and DarkBERT. Our system can not only greatly reduce annotation costs but also continuously learn and update itself with the new informative data selected by PAL. Through rich experiments on a data breach dataset, we demonstrate that our system effectively identifies breach threads and PAL significantly outperforms other baselines. We also search for the optimal prompt templates to improve system performance. © 2024 IEEE.

关键词： Active learning

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Research on Tourist Destination Passenger Flow Prediction Based on Decomposition Clustering Technology for data Continuity Problem

Research on Tourist Destination Passenger Flow Prediction Ba...

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2024 International Conference on Culture-Oriented Science and Technology, CoST 2024

作者： Feng, Lianze Mu, Lin Yang, Yi Li, Caihong Lanzhou University School of Information Science and Engineering Lanzhou730000 China Ministry of Culture and Tourism Key Laboratory of Tourism Information Fusion Processing and Data Ownership Protection Lanzhou730000 China Tourism Quality Supervision and Management Institute of China Beijing100740 China

ISBN: (纸本)9798350380347

Tourist flow prediction faces challenges in achieving high accuracy due to the complex and dynamic nature of tourism data. This study addresses these challenges by proposing a multi-feature combined prediction model, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Incremental Entropy-K-means clustering-Time Convolutional Network-Coati Optimization Algorithm (CEEMDAN-IEK-TT-COA). The model integrates signal decomposition, clustering reconstruction, component prediction, and optimization algorithms. key features are selected through feature engineering, followed by data decomposition and reconstruction using CEEMDAN, Incremental Entropy, and K-means clustering. A model pool of Time Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory network (LSTM), and Transformer filters optimal sub-models for combined prediction. The Coati Optimization Algorithm (COA) refines the final prediction. Experimental results on Jiuzhaigou and Siguniangshan datasets demonstrate significant RMSE improvements of 21.81 % and 17.84 %, validating the model's effectiveness. © 2024 IEEE.

关键词： Long short-term memory

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Panicle-Cloud:An Open and Al-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

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Plant Phenomics 2023年第4期5卷 905-916页

作者： Zixuan Teng Jiawei Chen Jian Wang Shuixiu Wu Riqing Chen Yaohai Lin Liyan Shen Robert Jackson Ji Zhou Changcai Yang Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhou 350002China State Key Laboratory of Crop Genetics&Germplasm Enhancement academy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China Ningxia Academy of Agriculture and Forestry Sciences Yinchuan 750002China College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China Cambridge Crop Research National Institute of Agricultural Botany(NIAB)Cambridge CB3 OLEUK Key Laboratory of Smart Agriculture and Forestry(Fujian Agriculture and Forestry University) Fujian Province UniversityFuzhou 350002China Center for Agroforestry Mega Data Science School of Future TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China

Rice(Oryza sativa)is an essential stable food for many rice consumption nations in the world and,thus,the importance to improve its yield production under global climate *** evaluate different rice varieties'yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research ***,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological ***,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected *** facilitate the development of Al-powered detection models,we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists;then,we integrated several state-of-the-art deep learning models(including a preferred model called Panicle-AI)into the Panicle-Cloud platform,so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial *** trialed the Al models with images collected at different attitudes and growth stages,through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were ***,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice *** correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high ***,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice br

关键词： breeding cultivar hundreds

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Unidirectional Identity-Based Proxy Re-Signature with key Insulation in EHR Sharing System

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Computer Modeling in engineering & Sciences 2022年第6期131卷 1497-1513页

作者： Yanan Chen Ting Yao Haiping Ren Zehao Gan School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Basic Course Teaching Department Jiangxi University of Science and TechnologyNanchang330013China Institute of Electronic and Information Engineering University of Electronic Science and Technology of ChinaDongguan523808China Network and Data Security Key Laboratory of Sichuan Province Unversity of Electronic Science and Technology of ChinaChengdu610054China

The introduction of the electronic medical record(EHR)sharing system has made a great contribution to the management and sharing of healthcare *** referral treatment for patients,the original signature needs to be converted into a re-signature that can be verified by the new *** re-signature(PRS)can be applied to this scenario so that authenticity and nonrepudiation can still be insured for ***,the existing PRS schemes cannot realize forward and backward ***,this paper proposes the first PRS scheme that can provide key-insulated property,which can guarantee both the forward and backward security of the *** the leakage of the private key occurs at a certain moment,the forward and backward key will not be ***,the purpose of key insulation is ***’s more,it can update different corresponding private keys in infinite time periods without changing the identity information of the user as the public ***,the unforgeability of our scheme is proved based on the extended Computational Diffie-Hellman assumption in the random oracle ***,the experimental simulation demonstrates that our scheme is feasible and in possession of promising properties.

关键词： Proxy re-signature key insulation electronic medical record(EHR) random oracle model

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Structure-and Logic-Aware Heterogeneous Graph Learning for Recommendation 40

Structure-and Logic-Aware Heterogeneous Graph Learning for R...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Li, Anchen Yang, Bo Huo, Huan Hussain, Farookh Khadeer Xu, Guandong College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Aalto University Espoo Finland School of Computer Science University of Technology Sydney Sydney Australia Education University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798350317152

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure-and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets. © 2024 IEEE.

关键词： Semantics

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