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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zeng, Weixin Zhao, Xiang Tan, Zhen Tang, Jiuyang Cheng, Xueqi National University of Defense Technology Laboratory for Big Data and Decision China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China Institute of Computing Technology CAS China

ISBN: (纸本)9798350317152

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches KGs in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development. © 2024 IEEE.

关键词： Embeddings

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Risk and Potential:An Asset Allocation Framework with Applications to Robo-Advising

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Journal of the Operations Research Society of China 2022年第3期10卷 529-558页

作者： Xiang-Yu Cui Duan Li Xiao Qiao Moris S.Strub School of Statistics and Management Shanghai University of Finance and EconomicsShanghai200437China School of Data Science City University of Hong KongHong KongChina School of Data Science and Hong Kong Institute for Data Science City University of Hong KongHong KongChina Department of Information Systems and Management Engineering Southern University of Science and TechnologyShenzhen518055GuangdongChina

We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance *** utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility ***,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth *** naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and *** proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal *** establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its ***,we discuss applications of this framework to robo-advisors.

关键词： Mean-risk optimization Mean-variance Expected utility maximization Portfolio choice Risk Potential Robo-advising FinTech

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Temporal Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1586-1598页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Deng, Fuhu Chen, Leduo Qin, Zhiguang Raymond Choo, Kim-Kwang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu Sichuan610054 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610054 China School of Advanced Technology Suzhou Jiangsu215123 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fuzing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the interframe variances and generalizing graph convolutional network (GCN) operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatial-temporal cascaded aggregation (STCA) module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120. © 2020 IEEE.

关键词： Convolution

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A Novel Breast Cancer Detection and Classification Using Enhanced ANN Classification 5

A Novel Breast Cancer Detection and Classification Using Enh...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Mathiyalagan, P. Kiruthiga, G. Rasmi, A. Vadivel, M. Mahalakshmi, K.V. Poornappriya, T.S. Sri Ramakrishna Engineering College Department of CSE Coimbatore India Karpagam College of Engineering Dept of Artificial Intelligence and Data Science Coimbatore India RR Institute of Technology Information Science and Engineering Bangalore India Excel Engineering College Department of Information Technology TamilNadu Namakkal India B v Raju Institute of Technology CSE Telangana 502313 India Aspire Systems Pvt Ltd Data and Analytics Karnataka Bengaluru560037 India

ISBN: (纸本)9798331518097

The second most prevalent and leading cause of cancer death is breast cancer, which is the most common malignancy among women. Because the causes of breast cancer are not fully known, there is currently no reliable technique to prevent it. In recent years, breast cancer has become a widespread condition among women. Over the past several years, the issue of breast tumour categorization has become a significant issue. This article offers logical reasoning for the order in which to identify malignant lesions, a significant development in the mammography diagnosis of breast cancer. As a result, we concentrate on studying and analysing cancer imaging, with a particular emphasis on breast cancer. Mammography produces and processes pictures that are used in breast cancer detection. Sensitivity, specificity, and classification accuracy are the metrics used to assess the experimental findings. Results of an improved ANN classifier for differentiating between normal and abnormal cases in pictures from applied advanced mammography. © 2024 IEEE.

关键词： Lung cancer

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Discovering Personalized Characteristic Communities in Attributed Graphs 40

Discovering Personalized Characteristic Communities in Attri...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Niu, Yudong Li, Yuchen Karras, Panagiotis Wang, Yanhao Li, Zhao School of Computing and Information Systems Singapore Management University Singapore University of Copenhagen Department of Computer Science Copenhagen Denmark School of Data Science and Engineering East China Normal University Shanghai China Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350317152

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of hierarchically structured communities. We first propose a novel compressed COD evaluation approach to accelerate the influence estimation by eliminating redundant computations for overlapping communities. Then, we further devise a local hierarchical reclustering method to alleviate the skewness of hierarchical communities generated by global clustering for a specific query attribute. Extensive experiments confirm the effectiveness and efficiency of our solutions to COD: they find characteristic communities better than existing community search methods by several quality measures and achieve up to 25 x speedups against well-crafted baselines. © 2024 IEEE.

关键词： attributed graph characteristic community community hierarchy influence

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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Computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A language for graph database evolution and its implementation in Neo4j 42

A language for graph database evolution and its implementati...

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42nd International Conference on Conceptual Modeling: ER Forum, 7th Symposium on Conceptual Modeling Education, SCME 2023, Project Exhibitions, Posters and Demos, and Doctoral Consortium, ER-Companion 2023

作者： Hausler, Dominique Klettke, Meike Störl, Uta University of Regensburg Data Engineering Group Faculty of Informatics and Data Science Bajuwarenstrasse 4 Regensburg93053 Germany University of Hagen Chair of Databases and Information Systems Faculty of Mathematics and Computer Science Universitätsstrasse 1 Hagen58084 Germany

In this article, we present an evolution language for graph databases and a method to realize evolution operations on graph databases and their schema. Graph database management systems like Neo4j can be used for different scenarios: they can store graphs where no semantic constraints are checked. It is also possible to use them to store graphs where the structure of nodes and relationships is regular, for example, because the nodes and relationships of a graph have been generated by an application. In the latter case, these structures may change, that means they can undergo evolution. Currently, there is no established evolutionary language for graphs. However, evolution operations (such as add, rename, delete, merge, copy, split and move) have already been developed for other database models. In this article, we will not only use the established evolution operations but additionally extend the operations appropriated. After analyzing evolution operators explicitly in the context of graph databases, we also created a new graph-specific operation: transform. Moreover, we suggest an evolution language to graph data and show how it can be executed on the graph database schema as well as on the instances (i.e. the graphs themselves). In doing so, some operations can be implemented native based on Cypher, others non-native using the APOC library for Neo4j. © 2023 CEUR-WS. All rights reserved.

关键词： evolution graph databases evolution graph evolution language graph schema description

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Deep Reinforcement Learning algorithms for Low Latency Edge Computing systems 3

Deep Reinforcement Learning algorithms for Low Latency Edge ...

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3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Kumaran, K. Sasikala, E. SRM Institute of Science and Technology Chennai India Department of Information Technology Easwari Engineering College Chennai India SRM Institute of Science and Technology Kattankulathur Department of Data Science & Business Systems Chennai India

ISBN: (纸本)9798350320749

Nowadays, due to the increase of technological development in smart devices, more computational capabilities are needed with better performance. The maximization of the cloud computing resources used in mobile networks and Internet of Things makes good results. The data processing along with delay in the communication network will be easily handled by edge computing technology. The edge server brought as much as close to the end devices to avoid the frequent cloud access. In edge servers, the processing of the computational task is time consuming, which causes latency. The inference of deep learning along with reinforcement models makes the better resource allocation and task scheduling for complex systems. In this work, deep reinforcement models like Q learning, Double Q learning, Deep Q network, Double Deep Q learning has been compared. The developed Deep Reinforcement learning model has been deployed in the edge-based system. As a result, it is inferred that Double Deep Q learning is having the ability to make better decisions in resource allocation by maximizing the gains and rewards. The simulation result shows that Double Deep Q learning performs better with decreased latency and increases the performance of the edge systems. © 2023 IEEE.

关键词： Reinforcement learning

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Zero-DCE++Inspired Object Detection in Less Illuminated Environment Using Improved YOLOv5

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Computers, Materials & Continua 2023年第12期77卷 2751-2769页

作者： Ananthakrishnan Balasundaram Anshuman Mohanty Ayesha Shaik Krishnadoss Pradeep Kedalu Poornachary Vijayakumar Muthu Subash Kavitha Centre for Cyber Physical Systems Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Information and Data Sciences Nagasaki UniversityNagasaki8528521Japan

Automated object detection has received the most attention over the *** cases ranging from autonomous driving applications to military surveillance systems,require robust detection of objects in different illumination ***-of-the-art object detectors tend to fare well in object detection during daytime ***,their performance is severely hampered in night light conditions due to poor *** address this challenge,the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime ***,the preprocessing strategies involve using the Zero-DCE++approach to enhance lowlight *** is followed by optimizing the existing YOLOv5 architecture by integrating the Convolutional Block Attention Module(CBAM)in the backbone network to boost model learning capability and Depthwise Convolutional module(DWConv)in the neck network for efficient compression of network *** Night Object Detection(NOD)and Exclusively Dark(ExDARK)dataset has been used for this *** proposed framework detects classes like humans,bicycles,and *** demonstrate that the proposed architecture achieved a higher Mean Average Precision(mAP)along with a reduction in model size and total parameters,*** proposed model is lighter by 11.24%in terms of model size and 12.38%in terms of parameters when compared to baseline YOLOv5.

关键词： Object detection deep learning nighttime road scenes YOLOv5 DWConv Zero-DCE++ CBAM

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