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第五届国际科技创新学术交流大会暨管理科学信息化与经济创新发展学术会议

作者： Min Li Weize Liao Jianrong Huang Jiebo Jiang Guangxi Xijiang Development Investment Group Co. Ltd.Lock Operation Management Branch Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control

Stock market forecasting based on Long Short-Term Memory networks(LSTMs) is a prevalent technique within the realms of Natural Language Processing and time-series *** stock market trends is widely acknowledged to be a highly intricate task,attributed to the multifaceted influences ranging from economic factors,policy changes,to market *** LSTM's superior capability in handling time-series data has thus made it a popular choice for such forecasting *** paper selects two stocks,Vanadium Titanium Shares(SZ.000629) and Crystal Technology(SH.603005),and applies both single variable LSTM(SV-LSTM) and multivariate LSTM(MV-LSTM) models to forecast the opening price,the lowest price,and the previous closing *** results indicate that the MV-LSTM yields more accurate predictions and exhibits relatively smaller errors than its SV-LSTM counterpart.

关键词： Stock LSTM Prediction Multivariate

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A control Strategy for Passing and Receiving in Virtual Human Soccer

A Control Strategy for Passing and Receiving in Virtual Huma...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jingjie Liao Hai Xiao Gong Hongyan Lu Yuling Yang Jiliang Lin Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China School of international education Xiamen University of Technology Xiamen China

Virtual human soccer is the concentrated embodiment of virtual human technology and kinematics. In order to realize the nature and reality of controlling the passing and receiving of the ball, the passing and receiving control of the virtual human and the movement track of the soccer ball is studied, and a message mechanism combined with inverse dynamics control strategy is proposed. The interaction of virtual human passing and receiving the ball is divided into execution behavior and contact ball effect. The execution behavior is controlled by the message mechanism to inform and receive the message, which is transmitted to the virtual human. The contact ball effect is in the charge of inverse dynamics, which adjusts the contact part when passing and receiving the ball. Finally, the simulation of football passing and receiving is realized, and the screen is smooth and natural. The experimental results verify the feasibility of this method. guangxi key laboratory of machine vision and intelligent control.

关键词： Tracking machine vision Process control Kinematics Programming Ubiquitous computing Behavioral sciences

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Visual Adapt for RGBD Tracking

Visual Adapt for RGBD Tracking

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guangtong Zhang Qihua Liang Zhiyi Mo Ning Li Bineng Zhong Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

Recent RGBD trackers have employed cueing techniques by overlaying Depth modality images as cues onto RGB modality images, which are then fed into the RGB-based model for tracking. However, the direct overlaying interaction method between modalities not only introduces more noise into the feature space but also exhibits the inadaptability of the RGB-based model to mixed-modality inputs. To address these issues, we introduce Visual Adapt for RGBD Tracking (VADT). Specifically, we maintain the input of the RGB-based model as the RGB modality. Additionally, we have devised a fusion module to enable modality interaction between depth and RGB features. Subsequently, a Depth Adapt module has been formulated to facilitate image interaction with the fused features. This module involves cross-attending to the obtained depth-assisted features and the RGB search frame features produced by the RGB-based model’s output. Experimental results indicate that our proposed tracker achieves state-of-the-art results on various RGBD benchmark tests.

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ODTrack: Online Dense Temporal Token Learning for Visual Tracking

arXiv

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arXiv 2024年

作者： Zheng, Yaozong Zhong, Bineng Liang, Qihua Mo, Zhiyi Zhang, Shengping Li, Xianxian Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University China Harbin Institute of Technology China

Online contextual reasoning and association across consecutive video frames are critical to perceive instances in visual tracking. However, most current top-performing trackers persistently lean on sparse temporal relationships between reference and search frames via an offline mode. Consequently, they can only interact independently within each image-pair and establish limited temporal correlations. To alleviate the above problem, we propose a simple, flexible and effective video-level tracking pipeline, named ODTrack, which densely associates the contextual relationships of video frames in an online token propagation manner. ODTrack receives video frames of arbitrary length to capture the spatio-temporal trajectory relationships of an instance, and compresses the discrimination features (localization information) of a target into a token sequence to achieve frame-to-frame association. This new solution brings the following benefits: 1) the purified token sequences can serve as prompts for the inference in the next video frame, whereby past information is leveraged to guide future inference;2) the complex online update strategies are effectively avoided by the iterative propagation of token sequences, and thus we can achieve more efficient model representation and computation. ODTrack achieves a new SOTA performance on seven benchmarks, while running at real-time speed. Code and models are available at https://***/GXNU-ZhongLab/ODTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Research on virtual human swarm football collaboration technology based on reinforcement learning

Research on virtual human swarm football collaboration techn...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiaoshan Mo Bin Hu Caixu Xu Yuling Yang Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Faculty of Arts and Humanities Macao University of Science and Technology Macao China

Reinforcement learning is used in many applications in artificial intelligence fields such as computer vision and environmental contextualized decision-making scenarios; virtual human swarms provide research directions for multi-intelligent body collaboration and environmental field detection in multi-intelligent body swarms. Football is a group sport, which is characterized by its holistic nature, confrontation, versatility, and ease of implementation, as well as the characteristics of both individual intelligence and group intelligence, and is a typical application scenario for multi-intelligence collaboration. This paper takes football as the research object to study the team collaboration problem and team gaming problem of the virtual human swarm in a specific environment and solves the cooperation and competition relationship between intelligence with reinforcement learning of multi-intelligence training.

关键词： Training Computer vision Decision making Collaboration Reinforcement learning Ubiquitous computing Artificial intelligence

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An individual dribbling control strategy for virtual humans combining kinetic animation and lightweight physics

An individual dribbling control strategy for virtual humans ...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yuling Yang Caichun Cen Xiaoshan Mo Jingjie Liao Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Faculty of Humanities and Arts Macau University of Science and Technology Macau China

In this paper, a virtual human dribbling strategy is proposed to realize the programmed animation of virtual human dribbling. First, the dribbling interactive elements are extracted and their shapes, sizes and positions are planned. Then the balls are attributed and divided. Also, the dominant of the ball trajectory is configured to a specific virtual human to solve the control problem of the ball movement curve in the virtual environment. Finally, according to the division of dribbling behavior, the states of the feet and the ball are analyzed and calculated under different behaviors, and the action transition of the virtual human is carried out through two basic actions to solve the number of action limitation, and combined with ball curve to realize the virtual human dribbling behavior strategy. And the actions are verified by the ”Cuju” virtual environment, which confirms the feasibility of individual dribbling of the virtual human.

关键词： Shape Virtual environments Modulation Animation Ubiquitous computing Behavioral sciences Trajectory

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Robust Tracking via Mamba-based Context-aware Token Learning

arXiv

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arXiv 2024年

作者： Xie, Jinxia Zhong, Bineng Liang, Qihua Li, Ning Mo, Zhiyi Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China

How to make a good trade-off between performance and computational cost is crucial for a tracker. However, current famous methods typically focus on complicated and time-consuming learning that combining temporal and appearance information by input more and more images (or features). Consequently, these methods not only increase the model’s computational source and learning burden but also introduce much useless and potentially interfering information. To alleviate the above issues, we propose a simple yet robust tracker that separates temporal information learning from appearance modeling and extracts temporal relations from a set of representative tokens rather than several images (or features). Specifically, we introduce one track token for each frame to collect the target’s appearance information in the backbone. Then, we design a mamba-based Temporal Module for track tokens to be aware of context by interacting with other track tokens within a sliding window. This module consists of a mamba layer with autoregressive characteristic and a cross-attention layer with strong global perception ability, ensuring sufficient interaction for track tokens to perceive the appearance changes and movement trends of the target. Finally, track tokens serve as a guidance to adjust the appearance feature for the final prediction in the head. Experiments show our method is effective and achieves competitive performance on multiple benchmarks at a real-time speed. Code and trained models will be available at https://***/GXNU-ZhongLab/TemTrack . Copyright © 2024, The Authors. All rights reserved.

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Knowledge Base Question Answering via Structured Query Generation using Question domain

Knowledge Base Question Answering via Structured Query Gener...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jiecheng Li Zizhen Peng Xiaoying Zhu Keda Lu Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The core problem of Knowledge Base Question Answering(KBQA) is to find queries from user questions to knowledge bases. Specifically, natural language questions need to be transformed into structured queries before associating with knowledge bases, and the answers can be found from the knowledge graph using the structured queries. We found structured queries in similar question domains tend to have repetitive reasoning steps. Also, humans often use cases identical to the question and information from these cases to assist in answering new questions. Hence, we propose a new KBQA framework based on similar question domains. We separately design the inference information retriever module to extract cases with a similar structure to the question and the relation information retriever module to narrow the scope of reasoning relation extraction. Finally, we used the retrieved inference cases and relation candidate sets as auxiliary information and generated an executable Knowledge-oriented Programming Language(KoPL) through the program generation module. Experiments have shown that the model can handle complex question answering and has a strong reasoning ability. Our methodology has resulted in new state-of-the-art performance on WebQSP and CWQ datasets.

关键词： Splicing Computational modeling Knowledge based systems Natural languages Knowledge graphs Programming Ubiquitous computing

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A Lightweight Model of VGG-U-Net for Remote Sensing Image Classification

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Computers, Materials & Continua 2022年第12期73卷 6195-6205页

作者： Mu Ye Li Ji Luo Tianye Li Sihan Zhang Tong Feng Ruilong Hu Tianli Gong He Guo Ying Sun Yu Thobela Louis Tyasi Li Shijun College of Information Technology Jilin Agricultural UniversityChangchun130118China Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center Changchun130118China Jilin Province Intelligent Environmental Engineering Research Center Changchun130118China Jilin Province Information Technology and Intelligent Agriculture Engineering Research Center Changchun130118China College of Engineering and Technology Jilin Agricultural UniversityChangchun130118China Department of Agricultural Economics and Animal Production University of LimpopoSovenga0727PolokwaneSouth Africa College of Information Technology Wuzhou UniversityWuzhou543003China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543003China

Remote sensing image analysis is a basic and practical research hotspot in remote sensing *** sensing images contain abundant ground object information and it can be used in urban planning,agricultural monitoring,ecological services,geological exploration and other *** this paper,we propose a lightweight model combining vgg-16 and u-net *** combining two convolutional neural networks,we classify scenes of remote sensing *** ensuring the accuracy of the model,try to reduce the memory of *** to the experimental results of this paper,we have improved the accuracy of the model to 98%.The memory size of the model is 3.4 *** the same time,The classification and convergence speed of the model are greatly *** simultaneously take the remote sensing scene image of 64×64 as input into the designed *** the accuracy of the model is 97%,it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low ***,the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.

关键词： VGG-16 U-Net fewer feature points nonlinear correction layer zero padding

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Identifying driver genes in cancer based on Pareto optimality consensus

Identifying driver genes in cancer based on Pareto optimalit...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zheng Deng Jingli Wu Xiaorong Chen Gaoshi Li College of Computer Science Information Technology Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision Intelligent Control Wuzhou University Wuzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

An important issue in cancer genomics is the identification of driver genes. It is significant for the discovery of key biomarkers and the development of effective personalized therapies. In this paper, a computated method PGScore is proposed. It scores genes at multilayer and integrates the scores to identify cancer driver genes based on Pareto Optimality Consensus(POC) strategy. PGScore uses random walks to reevaluate gene mutations, and integrates differential expression of mRNA and miRNA in normal and cancer samples. It measures the centrality of the gene in the network according to the weight of its direct and indirect neighbors, and finally integrates the above layers to get the final priority of the genes. We compare PGScore with state-of-the-art cancer driver genes prioritization methods on two real cancer datasets. The results show that PGScore can obtain better performance in identification accuracy and the partial area under the ROC(pAUC) curve on multiple reference databases.

关键词： Weight measurement Databases Medical treatment Genomics Pareto optimization Biomarkers Nonhomogeneous media

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