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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jiapeng Hui Guoyun Lian Jiansheng Wu Shuting Ge Jinfeng Yang Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen China School of Computer Science and Software Engineering University of Science and Technology Anshan China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Lane detection under extreme conditions presents a highly challenging task that requires capturing each crucial pixel to predict the complex topology of lane lines and differentiate the various lane types. Existing methods predominantly rely on deep feature extraction networks with substantial parameters or the fusion of multiple prediction modules, resulting in large model sizes, embedding difficulties, and slow detection speeds. This paper proposes a Proportional Feature Pyramid Network(P-FPN) through fusing the weights into the FPN for lane detection. In our method, the high-level features are explored to predict lanes coarsely while local- detailed features are leveraged to improve localization accuracy. Extensive experiments on the widely used lane detection dataset, CULane dataset, demonstrate that the proposed method achieves the competitive results compared with several state-of-the-art approaches.

关键词： Location awareness Lane detection Network topology Computational modeling Computer architecture Predictive models Feature extraction

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Meta-Learning with Task-Adaptive Selection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wan, Quan Wang, Maofa Shan, Weifeng Wang, Bin Zhang, Lu Leng, Zhixiong Yan, Bingchen Xu, Yanlin Chen, Huiling Beijing Information Science and Technology University School of Applied Science Beijing100192 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Institute of Disaster Prevention School of Emergency Management Langfang065201 China China Geological Survey Command Center of Natural Resources Comprehensive Survey Beijing100055 China Southern University of Science and Technology Department of Earth and Space Sciences Shenzhen518055 China Wenzhou University Department of Computer Science and Artificial Intelligence Wenzhou325035 China

The gradient-based meta-learning algorithm gains meta-learning parameters from a pool of tasks. Starting from the obtained meta-learning parameters, it can achieve better results through fast fine-tuning with only a few gradient descent updates. The two-layer meta-learning approach that shares initialization parameters has achieved good results in solving few-shot learning domain. However, in the training of multiple similar tasks in the inner layer, the difficulty and benefits of the tasks have been consistently overlooked, resulting in conflicts between tasks and ultimately compromising the model to unexpected positions. Therefore, this paper proposes a task-adaptive selection metalearning algorithm called TSML. Specifically, we construct a task selection trainer to assess the difficulty of tasks and calculate their future benefits. Designing more optimal training strategies for each task based on difficulty and benefit, altering the current compromise in multi-task settings, and balancing the impact of tasks on meta-learning parameters. Additionally, the outer meta-parameter updating method for traditional meta-learning has been adjusted, enabling the meta-parameters to attain a better position. By doing so, we can rapidly improve the generalization and convergence of the meta-learning parameters on unknown tasks. Experimental results indicate a 2.1% improvement over the base model in the 4-conv setting, with a more pronounced effect as the neural network is progressively complexified, reaching a 4.1% improvement in resnet12. © 1991-2012 IEEE.

关键词： Zero-shot learning

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Design of a Lower Limb Exoskeleton Rehabilitation Robot System Based on Forward Dynamics Simulation

Design of a Lower Limb Exoskeleton Rehabilitation Robot Syst...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Yudong Fang Yuefei Wang Liucun Zhu Zhen Liu Lei Shi Muxin Du Jianchu Lin School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Sentinel Institute of Science and Technology Beibu Gulf University Qinzhou China Nagasaki Institute of Applied Science Nagasaki Japan Guangzhou Shiyuan Electronic Technology Company Limited Guangzhou China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lower limb exoskeleton rehabilitation robots can effectively improve the rehabilitation of stroke patients and can substantially reduce the workload of rehabilitation therapists and improve work efficiency. In this paper, we propose a method to investigate the influence of muscle groups on the hip joint during the gait cycle through kinetic modeling simulation. The method is validated using electromyographic signals (EMG). The patients are treated according to their gait characteristics. Simulation with OpenSim; optimisation by Scone controller; joint motion change curves were analysed by Matlab simulation to verify the correctness of the mathematical model, and the simulation showed excellent following effect. The feasibility of the theory is verified, and a new scheme is proposed for the treatment of stroke patients.

关键词： Limbs Exoskeletons Stroke (medical condition) Muscles Assistive robots Mathematical models Kinetic theory MATLAB Robots Optimization

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Detection and Replay of Distributed Denial of Service Attacks In Smart Cities Using a Hybrid Deep Learning Approach

Detection and Replay of Distributed Denial of Service Attack...

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Business Analytics for technology and Security (ICBATS), International Conference on

作者： Muhammad Waseem Iqbal Ghassan F. Issa Muhammad Yousif Muhammad Atif Department of Software Engineering Superior University Lahore Pakistan School of Information Technology Skyline University College Sharjah UAE Department of Computer Science NCBAE Lahore. Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science NCBAE Lahore Pakistan

Infrastructure for smart cities is presently being built, mostly because of the (IoT) platform, which connects a wide variety of things by using the website's underlying infrastructure. As a result, it is possible to employ a uniform platform to automate the services that are delivered. Smart city infrastructure, on the other hand, is susceptible to cyberattacks owing to security flaws in IoT networks. Distributing denial of service (DDoS) and replay assaults, for example, violate the requirements for certification in smart cities. The lack of citations in this part backs up many of the statements it makes. Who needs a citation? Both assaults have the potential to substantially damage smart city infrastructure, which might also cost money and even result in human fatalities. This paper covers the creation of a blended deep- learning algorithm for replay and DDoS intrusion prevention on a growth-oriented smart city platform. This is made possible by combining proven machine-learning approaches with more recent advances in artificial intelligence. We simulate scattered denial of service and replay attacks on three datasets acquired from real-world smart cities to assess the efficacy of the proposed hybrid strategy on environmental, smart river, and smart soil dataset. The suggested model has shown excellent rates of accuracy. The ecosystem collection has an accuracy level of 98.37%, the smart riverbed dataset must have an accuracy of 98.13%, and the smart mud dataset must have an accuracy of 99.51%. The findings demonstrated that the proposed model outperformed past instances of evolutionary computation and machine learning techniques employed in the corpus of academic literature.

关键词： Analytical models Machine learning algorithms Smart cities Computational modeling Soil Probability distribution Real-time systems

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SDCM: An Efficient Camellia Oleifera Fruits Detection Algorithm in Dense Occlusion Scenes

SDCM: An Efficient Camellia Oleifera Fruits Detection Algori...

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Computing and Big Data (ICCBD), International Conference on

作者： Youyang Zhang Qiuxia Chen Wenhua Cui Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence of the Guang-dong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (数字)9798350317008

ISBN: (纸本)9798350317015

To realize intelligent management in the Camellia oleifera fruit industry and foster the efficient growth of its supply chain, the accurate identification and detection of Camellia oleifera fruits are essential. However, in natural environments, these fruits exhibit challenges such as clustering, mutual occlusion, and obstruction by leaves. This paper proposes a Camellia oleifera fruits detection algorithm based on an improved YOLOv7 deep convolutional neural network, which is referred to as the SDCM network model, and add the function of counting the number of fruits. We introduce an enhancement to the backbone layer of the YOLOv7 model by incorporating a module called SDCM (Shortcut and Dilation Convolution based Module). This module enriches multiscale edge information, enabling the network to extract features more effectively, thus improving detection accuracy. The experimental results show that the detection accuracy of the SDCM network model is significantly improved compared with the original YOLOv7 model, and the detection performance of the model in this paper is superior compared with other classical detection models. By enhancing the detection accuracy of the model and adding the function of fruit counting, the model in this paper can provide more reliable and accurate detection performance and the function of predicting fruit yield for the development of the Camellia oleifera fruit industry, improve the production efficiency, and reduce the artificial resources.

关键词： Industries Accuracy Image edge detection Supply chains Production Predictive models Feature extraction

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A Look at Service Meshes 12

A Look at Service Meshes

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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Koschel, Arne Bertram, Marvin Bischof, Richard Schulze, Kevin Schaaf, Marc Astrova, Irina University of Applied Sciences and Arts Hannover Faculty Iv Department of Computer Science Hannover Germany University of Applied Sciences Northwestern Switzerland Institute of Information Systems Olten Switzerland Tallinn University of Technology School of It Department of Software Science Tallinn Estonia

ISBN: (纸本)9781665400329

Service meshes can be seen as an infrastructure layer for microservice-based applications that are specifically suited for distributed application architectures. It is the goal to introduce the concept of service meshes and its use for microservices with the example of an open source service mesh called Istio. This paper gives an introduction into the service mesh concept and its relation to microservices. It also gives an overview of selected features provided by Istio as relevant to the above concept and provides a small sample setup that demonstrates the core features. © 2021 IEEE.

关键词： Mesh generation

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Cloud Computing: Serverless 12

Cloud Computing: Serverless

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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Koschel, Arne Klassen, Samuel Jdiya, Kerim Schaaf, Marc Astrova, Irina University of Applied Sciences and Arts Hannover Faculty Iv Department of Computer Science Hannover Germany Institute of Information Systems University of Applied Sciences Northwestern Switzerland Olten Switzerland Tallinn University of Technology School of It Department of Software Science Tallinn Estonia

ISBN: (纸本)9781665400329

A serverless architecture is a new approach to offering services over the Internet. It combines BaaS (Backend-as-a-service) and FaaS (Function-as-a-service). With the serverless architecture no own or rented infrastructures are needed anymore. In addition, the company does not have to worry about scaling any longer, as this happens automatically and immediately. Furthermore, there is no need any longer for maintenance work on the servers, as this is completely taken over by the provider. Administrators are also no longer needed for the same reason. Finally, many ready-made functions are offered, with which the development effort can be reduced. As a result, the serverless architecture is very well suited to many application scenarios, and it can save considerable costs (server costs, maintenance costs, personnel costs, electricity costs, etc.). The company only must subdivide the source code of the application and upload it to the provider's server. The rest is done by the provider. © 2021 IEEE.

关键词： Cloud computing

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INFEASIBILITY OF CONSTRUCTING A SPECIAL ORTHOGONAL MATRIX FOR THE DETERMINISTIC REMOTE PREPARATION STATE OF AN ARBITRARY N-QUBIT STATE

arXiv

引用

arXiv 2023年

作者： Liu, Wenjie Li, Zixian Yuan, Gonglin Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Information Science Center for Applied Mathematics of Guangxi Guangxi University Nanning530004 China

In this paper, we present a polynomial-complexity algorithm to construct a special orthogonal matrix for the deterministic remote state preparation (DRSP) of an arbitrary n-qubit state, and prove that if n > 3, such matrices do not exist. Firstly, the construction problem is split into two sub-problems, i.e., finding a solution of a semi-orthogonal matrix and generating all semi-orthogonal matrices. Through giving the definitions and properties of the matching operators, it is proved that the orthogonality of a special matrix is equivalent to the cooperation of multiple matching operators, and then the construction problem is reduced to the problem of solving an XOR linear equation system, which reduces the construction complexity from exponential to polynomial level. Having proved that each semi-orthogonal matrix can be simplified into a unique form, we use the proposed algorithm to confirm that the unique form does not have any solution when n > 3, which means it is infeasible to construct such a special orthogonal matrix for the DRSP of an arbitrary n-qubit state. © 2023, CC BY.

关键词： Matrix algebra

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A Study of Synthetic Double Sampling np Chart Based on Median Run Length 3

A Study of Synthetic Double Sampling np Chart Based on Media...

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3rd International Conference on Artificial Intelligence and Data sciences, AiDAS 2022

作者： Tuh, Moi Hua Kon, Cynthia Mui Lian Chua, Hong Siang Lau, Man Fai Hui Chang, Robin Yee Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Kuching Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Sarawak Kota Samarahan Malaysia School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia Universiti Teknologi Mara Faculty of Applied Sciences Sarawak Kota Samarahan Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

The Markov chain method has been adopted in this work to evaluate the performance of the synthetic double sampling (SynDS) np chart, which combines the double sampling (DS) np sub-chart and the conforming run length (CRL) sub-chart. In the literature, the performance of the existing SynDS np chart is investigated using average run length (ARL). It is found that the skewness of the run length (RL) distribution varies depending on the size of shift in the process fraction nonconforming, from being significantly right-skewed when the process is in-control (IC) or small shift to being nearly symmetric when the process shift is large. As a result, the true performance of the chart may differ from what the ARL displays. Thus, median run length (MRL) is recommended to represent the central tendency associated with the varying shapes of the RL distribution in accordance to the process shift sizes. This work suggests an optimal design approach for SynDS np chart based on MRL to monitor the process fraction nonconforming, under zero-state and steady-state modes. © 2022 IEEE.

关键词： Control charts

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Jin Xiao Hui Cui Ping Xuan Xikang Feng Leyi Wei Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University China School of Software Northwestern Polytechnical University China AIDD Faculty of Applied Science Macao Polytechnic University Macao SAR China School of Information Xiamen University Xiamen China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGA mm , which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer.

关键词： Knowledge engineering Representation learning Pathology Genomics Prediction methods Data models Gene expression Bioinformatics Prognostics and health management Cancer

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