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arXiv 2023年

作者： Chipwanya, Ryan Academy of Computer Science and Software Engineering University of Johannesburg Johannesburg South Africa

The stock market has been established since the 13th century, but in the current epoch of time, it is substantially more practicable to anticipate the stock market than it was at any other point in time due to the tools and data that are available for both traditional and algorithmic trading. There are many different machine learning models that can do time-series forecasting in the context of machine learning. These models can be used to anticipate the future prices of assets and/or the directional bias of assets. In this study, we examine and contrast the effectiveness of three different machine learning algorithms—namely, logistic regression, decision tree, and random forest—to forecast the movement of the assets traded on the Japanese stock market. In addition, the models are compared to a feed forward deep neural network, and it is found that all of the models consistently reach above 50% in directional bias forecasting for the stock market. The results of our study contribute to a better understanding of the complexity involved in stock market forecasting and give insight on the possible role that machine learning could play in this *** Codes ACM-class: F.2.2, I.2.7 © 2023, CC BY.

关键词： Forecasting

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DKGV: A Dynamic Knowledge Graph Visualization Method Based on Force-Directed Layout 19

DKGV: A Dynamic Knowledge Graph Visualization Method Based o...

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19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI 2024

作者： Zhou, Hong Huang, Sunjie Zheng, Yushan Liu, Jinyan Chen, Yang Chen, Xiangyuan Li, Jun College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Academy of Inspection and Quarantine Shenzhen China Shenzhen University Shenzhen China

ISBN: (纸本)9798400713484

The dynamic knowledge graph is a data structure that adds temporal information to the nodes and edges of a traditional knowledge graph. It describes the changing processes of entities and relationships over time, thereby enriching and updating the expression of knowledge. The dynamic knowledge graph possesses characteristics such as time and labels;it is not only a knowledge graph but also a type of dynamic graph containing heterogeneous information. To address the issue that traditional dynamic graph visualization methods do not account for heterogeneous information in the layout, leading to a relatively random distribution of node labels in the visualization results, this paper proposes DKGV, a dynamic knowledge graph visualization method based on a force-directed layout. DKGV utilizes an improved GCN to generate the initial layout of the dynamic knowledge graph data, performs static force-directed layout iterative processing, and conducts dynamic force-directed layout calculations on the dynamic knowledge graph. Finally, it employs a force-directed layout with boundaries to support three-dimensional temporal display after view transformation. Experimental results show that DKGV can maintain the stability of the node layout during the evolution of the dynamic knowledge graph while making the overall layout more regular to better display the relationships between entities. © 2024 ACM.

关键词： Knowledge graph

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Unsupervised feature selection via multiple graph fusion and feature weight learning

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science China(Information sciences) 2023年第5期66卷 56-72页

作者： Chang TANG Xiao ZHENG Wei ZHANG Xinwang LIU Xinzhong ZHU En ZHU School of Computer Science China University of Geosciences State Key Laboratory for Novel Software Technology Nanjing University School of Computer National University of Defense Technology National Supercomputing Center in Jinan Qilu University of Technology (Shandong Academy of Sciences) College of Mathematics Physics and Information Engineering Zhejiang Normal University

Unsupervised feature selection attempts to select a small number of discriminative features from original high-dimensional data and preserve the intrinsic data structure without using data labels. As an unsupervised learning task, most previous methods often use a coefficient matrix for feature reconstruction or feature projection, and a certain similarity graph is widely utilized to regularize the intrinsic structure preservation of original data in a new feature space. However, a similarity graph with poor quality could inevitably afect the final results. In addition, designing a rational and efective feature reconstruction/projection model is not easy. In this paper, we introduce a novel and efective unsupervised feature selection method via multiple graph fusion and feature weight learning（MGF2WL） to address these issues. Instead of learning the feature coefficient matrix, we directly learn the weights of diferent feature dimensions by introducing a feature weight matrix, and the weighted features are projected into the label space. Aiming to exploit sufficient relation of data samples, we develop a graph fusion term to fuse multiple predefined similarity graphs for learning a unified similarity graph, which is then deployed to regularize the local data structure of original data in a projected label space. Finally, we design a block coordinate descent algorithm with a convergence guarantee to solve the resulting optimization problem. Extensive experiments with sufficient analyses on various datasets are conducted to validate the efficacy of our proposed MGF2WL.

关键词： unsupervised feature selection graph fusion feature weight learning local structure preservation

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No-Reference Image Quality Assessment via Multibranch Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 148-160页

作者： Pan, Zhaoqing Yuan, Feng Wang, Xu Xu, Long Shao, Xiao Kwong, Sam the School of Electrical and Information Engineering Tianjin University Tianjin300072 China the College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China the Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China the School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China the Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at https://***/NUIST-Videocoding/MB-CNN. © 2020 IEEE.

关键词： Neural networks

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Jaccard Index in Ensemble Image Segmentation: An Approach 22

Jaccard Index in Ensemble Image Segmentation: An Approach

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Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems

作者： Daniel Ogwok Elizabeth Marie Ehlers Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781450397612

Many methods have been applied to image segmentation, including unsupervised, supervised, and even deep learning-based models. Semantic and instance segmentation are the two most widely researched forms of segmentation. It is of value to use multiple methods to segment an image. In this paper, we present an image segmentation ensemble methodology. Multiple image segmentation methods are applied to an image and merged to create one segmentation using the proposed method. The technique uses the Jaccard index algorithm, sometimes called the Jaccard similarity coefficient and commonly known as Intersection over Union (IoU). This resulted in better segmentation results than the respective individual segmentation methods. This experiment was applied to mathematical expression recognition (MER), with the expressions taken from blackboards with varying degrees of noise, and lighting conditions, from different classroom environments. A summary of empirical results from the segmentation of multiple images is presented in the paper.

关键词： Jaccard index Region-based convolutional neural network Minimum spanning tree Image segmentation ensembles. Connected components labelling Image segmentation

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A Gene Expression Programming Inspired Evolution Symbiont Agent for Real-Time Strategy Generation 22

A Gene Expression Programming Inspired Evolution Symbiont Ag...

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Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems

作者： Siphesihle P. Sithungu Elizabeth M. Ehlers Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781450397612

AdaptiveSGA is a method for achieving Adaptive Game Artificial Intelligence-Based Dynamic Difficulty Balancing through the Symbiotic Game Agent Model. Previous work has shown that AdaptiveSGA can achieve Dynamic Difficulty Balancing in simulated soccer by effectively changing a team's strategy based on the opponent's performance. AdaptiveSGA pre-existing strategies and switches between them during runtime to increase the game's replayability by adapting the challenge it poses to the human player. Although this method works, its limitation is that if the human player surpasses the most intelligent strategy of the computer opponent, there is no way for the model to generate a new strategy during runtime that can potentially overcome the human player. AdaptiveSGA can only maintain engagement with the human player if the human player has not overcome the best strategy for the pool of pre-existing strategies. Current work addresses this limitation by introducing an Evolution Symbiont Agent whose purpose is to generate new strategies in real-time (during gameplay) through evolutionary mechanisms using Gene Expression Programming. Experimental results show that the presence of the evolution symbiont agent can use Gene Expression Programming to generate strategies capable of outperforming an opposing strategy.

关键词： gene expression programming dynamic difficulty balancing symbiotic game agent evolution symbiont agent

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第07期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic- like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. Firstly, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic- like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. The source code will be made publicly available. ©1979-2012 IEEE.

关键词： Latent semantic analysis

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VCApather: A Network as a Service Solution for Video Conference Applications 34

VCApather: A Network as a Service Solution for Video Confere...

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34th Workshop on Network and Operating System Support for Digital Audio and Video, NOSSDAV 2024

作者： He, Dongbiao Lin, Canshu Yu, Xian Westphal, Cedric Ming, Zhongxing Cui, Laizhong Zhou, Xu Garcia-Luna-Aceves, J.J. Sangfor Technologies Inc China College of Computer Science and Software Engineering Shenzhen University China Computer Network Information Center Chinese Academy of Sciences China China Electrical and Computer Engineering Department University of Toronto Canada Futurewei

ISBN: (纸本)9798400706134

We propose a network service as a solution for video conference applications by constructing network layer routing strategies. Our approach takes into account the characteristics of conferencing flows, addresses various self-customized metrics, and proactively ensures a positive user experience by preventing contention. The performance of VCApather is evaluated using a fully-meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather was found to satisfy 69.8% more QoE requirement and to yield an average bitrate improvement of 1.74×. © 2024 ACM.

关键词： Network layers

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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Defect detection of gear parts in virtual manufacturing

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 76-87页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Gears play an important role in virtual manufacturing systems for digital twins;however,the image of gear tooth defects is difficult to acquire owing to its non-convex *** this study,a deep learning network is proposed to detect gear defects based on their point cloud *** approach mainly consists of three steps:(1)Various types of gear defects are classified into four cases(fracture,pitting,glue,and wear);A 3D gear dataset was constructed with 10000 instances following the aforementioned classification.(2)Gear-PCNet++introduces a novel Combinational Convolution Block,proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology;(3)Compared with other methods,experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.

关键词： Defect detection Gear surface Gear dataset Combinational Convolution Block

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