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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Huo, Qizheng Li, Shaonan Li, Chengyang Xie, Yongqiang Li, Zhongbo Institution of Systems Engineering Academy of Military Sciences Beijing100141 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (纸本)9781510663473

In the current cloud-native usage scenario, the default algorithm is single. When the cluster resources are insufficient, the service guarantee is generally achieved by the expansion of the cluster, and preemptive scheduling cannot be achieved during the scheduling process. Therefore, the original strategy cannot guarantee the operation of important services. Cloud video system provides video conference, online education, video office and other services. Cloud video consists of different pods like Nginx, MySQL, Tomcat, etc. This article sets pod priorities by dividing the categories of Cloud video Pods. Aiming at resource-constrained scenarios, the Preemptive resource scheduling strategy based on Cloud video Pod priority is proposed, and cluster experiments are carried out. Experiments show that the algorithm can effectively guarantee the operation of pods with high priority. © 2023 SPIE.

关键词： E-learning

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Enhancing Secrecy in UAV RSMA Networks: Deep Unfolding Meets Deep Reinforcement Learning

Enhancing Secrecy in UAV RSMA Networks: Deep Unfolding Meets...

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2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Adam, Abuzar B. M. Elhassan, Mohammed A. M. School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science Zhejiang Normal University Zhejiang Jinhua China

ISBN: (纸本)9798350302561

In this paper, we consider the maximization of the secrecy rate in multiple unmanned aerial vehicles (UAV) rate-splitting multiple access (RSMA) network. A joint beamforming, rate allocation, and UAV trajectory optimization problem is formulated which is nonconvex. Hence, the problem is transformed into a Markov decision problem and a novel multiagent deep reinforcement learning (DRL) framework is designed. The proposed framework (named DUN-DRL) combines deep unfolding to design beamforming and rate allocation, data-driven to design the UAV trajectory, and deep deterministic policy gradient (DDPG) for the learning procedure. The proposed DUN-DRL have shown great performance and outperformed other DRL-based methods in the literature. © 2023 IEEE.

关键词： Reinforcement learning

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Uniform gradient magnetic field and spatial localization method based on Maxwell coils for virtual surgery simulation

Uniform gradient magnetic field and spatial localization met...

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作者： Huang, Yi Deng, Xutian Zhao, Xujie Xie, Wenxuan Yuan, Zhiyong Zhao, Jianhui School of Computer Science Wuhan University Wuhan China School of Computation Information and Technology Technical University of Munich Munich Germany School of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

With the development of virtual reality technology, simulation surgery has become a low-risk surgical training method and high-precision positioning of surgical instruments is required in virtual simulation surgery. In this paper we design and validate a novel electromagnetic positioning method based on a uniform gradient magnetic field. We employ Maxwell coils to generate the uniform gradient magnetic field and propose two positioning algorithms based on magnetic field, namely the linear equation positioning algorithm and the magnetic field fingerprint positioning algorithm. After validating the feasibility of proposed positioning system through simulation, we construct a prototype system and conduct practical experiments. The experimental results demonstrate that the positioning system exhibits excellent accuracy and speed in both simulation and real-world applications. The positioning accuracy remains consistent and high, showing no significant variation with changes in the positions of surgical instruments. © 2024 John Wiley & Sons Ltd.

关键词： Virtual reality

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Achieving Provably Effective Quantum Algorithms for Extensive Machine Learning Models 1

Achieving Provably Effective Quantum Algorithms for Extensiv...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Satheesh, N. Dixit, Rashmi Venkataramana, S. Anthoniraj, S. Reddy Vemi Reddy, Bala Gurivi Singh, Jitendra Department of Artificial Intelligence Karnataka Bangalore India Walchand Institute OfTechnology Department of Computer Science and Engineering Maharashtra Solapur India S.R.K.R.Engineering College Department of Information Technology AndhraPradesh Bhimavaram India School of Computer Science and Engineering Ramanagara India New Middle East International School Department of Physics Riyadh Saudi Arabia Srm Institute of Science and Technology Delhi - Ncr Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350365405

Massive machine learning models are cutting edge artificial intelligence technologies, but they have significant computational costs, power requirements, and tuning time constraints. We demonstrate in this work that verifiable well-organized solutions for nonspecific (stochastic) ramp succession procedures may be possible with fault-tolerant quantum computing. We demonstrate that comparable procedures are effective for (stochastic) ramp succession, the main machine learning procedure, based on previous efficient quantum algorithms for dissipative differential equations. We yardstick pictures of large machine learning models with between 7 million and 103 million restrictions in real-world scenarios. It has been observed that in the scarce keeping fit setting, a substantial enrichment can occur during the initial learning phase following model pruning. This finding provides impetus for a sparse parameter download and re-upload strategy. It is evident from our work that most large-scale, state-of-the-art machine-learning problems may benefit from the use of fault-tolerant quantum algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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WSports-50: An Image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

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4th International Conference on Frontiers in Computing and systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Tencent pretrain: A scalable and flexible toolkit for pre-Training models of different modalities 61

Tencent pretrain: A scalable and flexible toolkit for pre-Tr...

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61st Annual Meeting of the Association for Computational Linguistics, ACL-DEMO 2023

作者： Zhao, Zhe Li, Yudong Hou, Cheng Zhao, Jing Tian, Rong Liu, Weijie Chen, Yiren Sun, Ningyuan Liu, Haoyan Mao, Weiquan Guo, Han Guo, Weigang Wu, Taiqiang Zhu, Tao Shi, Wenhang Chen, Chen Huang, Shan Chen, Sihong Liu, Liqun Li, Feifei Chen, Xiaoshuai Sun, Xingwu Kang, Zhanhui Du, Xiaoyong Shen, Linlin Yan, Kimmo Tencent AI Lab School of Computer Science and Software Engineering Shenzhen University China School of Information and DEKE MOE Renmin University of China China

ISBN: (纸本)9781959429708

Recently, the success of pre-Training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-Training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-Training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-Training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pretraining models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-Training model. The modular design enables users to efficiently reproduce existing pre-Training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations. © ACL-DEMO 2023. All rights reserved.

关键词： Embeddings

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MCS Adaptation and its role in improving MAC Scheduling for Variable Packet Sizes in NR-V2X 6

MCS Adaptation and its role in improving MAC Scheduling for ...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： McCarthy, Brian Burbano-Abril, Andres O'Driscoll, Aisling University College Cork School of Computer Science and Information Technology Cork Ireland National Autonomous University of Mexico School of Engineering Mexico City Mexico

ISBN: (纸本)9798350329391

This paper investigates the significance of assuming an adaptive Modulation and Coding Scheme (MCS) when scheduling packets of varying sizes in New Radio V2X (NR-V2X). It is envisaged that many existing and future event driven vehicular services will exhibit significant variance in packet sizes, both between applications and within the same application. To ensure a sufficient quality of service, NR-V2X and its predecessor Cellular V2X (C-V2X) must be able to effectively schedule these diverse packet sizes. As such, this paper analyses the impact of fixed vs adaptive MCS selection and shows that assuming an adaptive approach is preferable in all cases, especially when dealing with varying packet sizes. A fixed MCS is commonly assumed in literature and this paper shows that such an assumption can have a large negative performance impact on the MAC layer scheduler. We further evaluate that considering an MCS range exceeding that specified in the current standard is extremely beneficial and evaluate the performance of alternative MCS selection schemes that minimise disruption in the MAC layer grant mechanism. We find that the default standardised MCS selection mechanism still remains preferable. © 2023 IEEE.

关键词： Quality of service

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network 7

Mobility and Behavior based Trustable Routing in Mobile Wire...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Praveen Kumar, B. Umamageswaran, J. Kalpana, A.V. Dhanalakshmi, R. Department of Computer Science and Engineering KL University Andhra Pradesh Vijayawada India Department of Information Technology RMK Engineering College Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features;they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN. © 2023 IEEE.

关键词： Sensor nodes

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Performance Analysis of Space-Air-Ground Integrated Network (SAGIN): UAV Altitude and Position Angle

Performance Analysis of Space-Air-Ground Integrated Network ...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Tan, Jingjing Tang, Fengxiao Zhao, Ming Kato, Nei Central South University School of Computer Science and Engineering Changsha410075 China Tohoku University The Graduate School of Information Sciences Sendai98008459 Japan

ISBN: (纸本)9798350345384

With the explosive growth of mobile data traffic, the Space-Air-Ground integrated network (SAGIN) has attracted the attention of academia and industry. As the communication infrastructure in the air network of the SAGIN system, Unmanned Aerial Vehicles (UAVs) are considered the air relay nodes to access ground and space networks. Due to the mobility of satellites in the space network, the formed variable position angle between the UAV and the satellite and the fixed UAV altitude can reduce the air-to-space transmission capability. Moreover, a UAV's battery is usually very limited, which may cause SAGIN transmission energy interruptions when the battery runs out. To address these issues in the SAGIN environment, we propose an integrated UAV channel fading and UAV energy consumption model to analyze the transmission performance. Based on the network performance analysis results, we derive the optimal UAV altitude expression. The numerical results verify the accuracy of the derived expressions and evaluate the impact of some key parameters on the optimal UAV altitude in the SAGIN uplink. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Hypergraph representation learning for identifying circRNA-disease associations

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Pattern Recognition 2025年 168卷

作者： Li, Yang Hu, Xuegang Li, Peipei Wang, Lei You, Zhuhong School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China College of Information Science and Engineering Zaozhuang University Zaozhuang277160 China School of Computer Science Northwestern Polytechnical University Xi'an710129 China

CircRNA-disease associations (CDA) are crucial for identifying circRNA biomarkers, significantly aiding the prevention, diagnosis, and treatment of complex human diseases. Traditional wet-lab methods for CDA prediction, while useful, are time-consuming, labor-intensive, and not always successful. Recently, computational methods have emerged as promising alternatives, offering more efficient CDA detection. Nevertheless, existing computational methods often overlook the multifaceted nature of CDAs, where each circRNA can associate with multiple diseases simultaneously, and vice versa. These methods typically fail to capture the beyond pairwise relationships and higher-order complex associations between circRNA-disease pairs. To this end, we propose a novel and effective biomarker computational method named HyperGRL-CDA, which is based on biological attribute information and hypergraph representation learning strategies. Its cornerstone is a hypergraph representation learning module that employs circRNA and disease similarity attributes to construct biological hypergraphs. This module leverages a symmetric hypergraph convolutional network to learn and reveal hidden, high-quality embedding representations, capturing the complex associations within these hypergraphs. Enhancing computational efficiency, HyperGRL-CDA incorporates the Extra Trees algorithm to determine CDA matching scores. Tested through five-fold cross-validation on the circR2Disease dataset, HyperGRL-CDA achieved an impressive accuracy of 92.22% and an AUC score of 96.08%. Furthermore, it demonstrated superior predictive performance on various related CDA datasets. These extensive experiments confirm HyperGRL-CDA as an efficient, accurate, and robust method for CDA prediction based on hypergraph representation learning. © 2025 Elsevier Ltd

关键词： Computational efficiency

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