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Digital Twins in engineering Education, Preparing students for Industrial Digital Transformation 12

Digital Twins in Engineering Education, Preparing Students f...

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12th IEEE international conference on intelligent data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Tabunshchyk, Galyna Arras, Peter Wolff, Carsten Fachhochschule Dortmund Dortmund44227 Germany Ku Leuven Sint Katelijne Waver Belgium

ISBN: (纸本)9798350358056

Digital twins, one of the new important technologies which started to be omnipresent in literature since Industry 4.0 got started, are becoming an essential part of the digital transformation processes in industry. Simply put, a digital twin is a virtual model designed to accurately reflect a physical object and is connected to the real asset with a bi-directional digital thread. Despite the different soft- and hardware architectures and applied fields digital twins are aimed to unify general architectural solutions, speed up design processes and provide reliable tools to provide safe, reliable and secure solutions for running physical systems. At the same time it is can be used as an educational tool, a framework to connect different technologies and it could be a bridge between Industry and Academia. © 2023 IEEE.

关键词： digital twin

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Machine learning Experiments with Artificially Generated Big data from Small Immunotherapy datasets 21

Machine Learning Experiments with Artificially Generated Big...

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21st IEEE international conference on Machine learning and Applications, ICMLA 2022

作者： Yunas Mahmoud, Ahsanullah Neagu, Daniel Scrimieri, Daniele Rashad Ahmed Abdullatif, Amr University of Bradford Faculty of Engineering and Informatics Bradford United Kingdom

ISBN: (纸本)9781665462839

Big data and machine learning result in agile and robust healthcare by expanding raw data into useful patterns for data-enhanced decision support. The available datasets are mostly small and unbalanced, resulting in non-optimal classification when the algorithms are implemented. In this study, five novel machine learning experiments are conducted to address the challenges of small datasets by expanding these into big data and then utilising Random Forests. The experiments are based on personalised adaptable strategies for both balanced and unbalanced datasets. Multiple datasets from cryotherapy and immunotherapy are considered, however, hereby only immunotherapy is used. In the first experiment, artificially generated data is presented by increasing the observations of the dataset, each new data is four-time larger than the previous one, resulting in better classification. In the second experiment, the effect of volume on classification is considered based on the number of attributes. The attributes of each new dataset are built based on conditional probabilities. It did not make any difference, in obtained classification, when the number of attributes is increased to more than 879. In the third simulation experiment, classes of data are classified manually by dividing the data into a two-dimensional plane. This experiment is first performed on small data and then on expanded big data: by increasing observations, an accuracy of 73.68% is attained. In the fourth experiment, the visualisation of the enlarged data did not provide better insights. In the fifth experiment, the impact of correlations among datasets' attributes on classification is observed, however, no improvements in performance are achieved. The experiments generally improved performance by comparing the classification results using the original and artificial data. © 2022 IEEE.

关键词： Health care

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Application Research of Vertical Federated learning Technology in Banking Risk Control Model strategy 21

Application Research of Vertical Federated Learning Technolo...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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Work-in-Progress - Unity between the Two Worlds: Microscopic data-Based Immersive Experience 8

Work-in-Progress - Unity between the Two Worlds: Microscopic...

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8th international conference of the Immersive learning Research Network, iLRN 2022

作者： Tanabashi, Sayuri Tange, Toshiyuki The University of Tokyo Graduate School of Agricultural and Life Sciences Tokyo Japan The University of Tokyo Graduate School of Engineering Tokyo Japan

ISBN: (纸本)9781734899528

Since the 21st century, advanced technologies have prompted the divergence of learning materials. Recently, learning materials for immersive learning, adopting contents based on raw microscopic data, are emerging. As such, technologic advancement of microscopy has been emerging, as well as that of learning materials. This work-in-progress study describes the approach to envision the structure and function of organelles in plant cells based on more precise raw microscopic data as learning material for students. © 2022 Immersive learning Research Network.

关键词： Plants (botany)

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The Journey of Language Models in Understanding Natural Language 21st

The Journey of Language Models in Understanding Natural Lang...

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21st international conference on Web Information Systems and Applications (WISA)

作者： Liu, Yuanrui Zhou, Jingping Sang, Guobiao Huang, Ruilong Zhao, Xinzhe Fang, Jintao Wang, Tiexin Li, Bohan Nanjing Univ Aeronaut & Astronaut Coll Artificial Intelligence & Comp Sci & Technol Nanjing 211106 Peoples R China Beijing Shenzhou Aerosp Software Technol Co Ltd Beijing 100094 Peoples R China Beijing Inst Telemetry Beijing 100083 Peoples R China

ISBN: (纸本)9789819777068;9789819777075

Since the Turing Test was proposed in the 1950s, humanity began exploring artificial intelligence, with an aim to bridge the interaction gap between machines and human language. This exploration enables machines to comprehend how humans acquire, produce, and understand language, as well as the relationship between linguistic expression and the world. The paper explores the basic principles of natural language representation, the formalization of natural language, and the modeling methods of language models. The paper analyzes, summarizes and compares the mainstream technologies and methods, including vector space-based, topic model-based, graph-based, and neural network-based approaches. And how to improve the development trend and direction of language model understanding ability is predicted and further discussed.

关键词： Artificial intelligence Natural language understanding Vector space model Topic model Neural network Deep learning

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K-means ++ and Reinforcement learning-Based Clustering Method to Achieve Energy Efficiency in Underwater Acoustic Sensor Networks 21

K-means ++ and Reinforcement Learning-Based Clustering Metho...

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作者： Xiong, Tao Hou, Rui Zhou, Lingyun Xing, Guanglin Zeng, Deze South-Central Minzu University College of Computer Science Wuhan China China University of Geosciences Computer School Wuhan China

ISBN: (纸本)9798350329223

In recent years, underwater acoustic sensor networks (UASNs) have had a wide range of applications in the fields of underwater environment sensing, marine resource exploitation and water quality analysis. Sensor nodes operate in an underwater environment and are powered by embedded batteries, making it very difficult to replenish power. It is thus important to efficiently improve sensor energy efficiency in UASNs and to extend the network lifetime. Clustering can effectively reduce UASN energy consumption by dividing sensor nodes into clusters;however, energy efficiency is always affected by the imbalance in energy consumption due to the unreasonable selection of cluster heads (CHs) and routes. In this paper, a K-means ++ and reinforcement learning-based clustering (KRC) method is proposed to improve energy efficiency. First, the UASN clusters are uniformly distributed using the K-means ++ algorithm. Then, a combination of clustering and reinforcement learning is applied to CH selection, which calculates the energy of intercluster data transmission when each node in the cluster is a CH based on the node's residual energy and location and selects the appropriate CHs to equalize the energy consumption of each network node. The optimal transmission route constructed by reinforcement learning is used to select suitable nodes for relaying data for sensor nodes. The simulation results show that the KRC method effectively balances UASN node energy consumption and improves energy efficiency, thereby extending network survival. © 2023 IEEE.

关键词： Energy efficiency

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Resource Allocation and Task Offloading for Slicing-Based Communication and Computing in Space-Air-Ground Integrated Networks 21

Resource Allocation and Task Offloading for Slicing-Based Co...

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21st Annual IEEE international conference on Sensing, Communication, and Networking, SECON 2024

作者： Song, Yueqian Yuan, Guiyuan Zeng, Qingtian Chen, Geng College of Electronic and Information Engineering Shandong University of Science and Technology China College of Computer Science and Engineering Shandong University of Science and Technology China

ISBN: (纸本)9798331519186

In the upcoming 6G era, the demand for network communication and computing is expected to surge and diversify. A Space-Air-Ground Integrated Network (SAGIN) is introduced as a solution to provide seamless global connectivity. Meanwhile, network slicing technology can further enhance network capabilities by supporting customized services. However, the joint optimization of multi-dimensional resource allocation and task offloading decisions presents a significant challenge in dynamic and complex environments with diverse task types. In this work, we establish an SAGIN paradigm that integrates network slicing. A multi-level optimization scheme for resource allocation and task offloading is proposed to improve Key Performance Indicators (KPIs) and Quality of Service (QoS). Specifically, we propose a state Encoding-based Multi-Agent Soft Actor-Critic algorithm (SE-MASAC) within a Centralized Training with Decentralized Execution (CTDE) architecture. This algorithm processes current sensing states and historical information, leveraging reinforcement learning to make inter-slice resource and offloading scheduling decisions. Intra-slice decisions for intelligent User Equipments (iUEs) are determined based on their sensing data-driven priority. Simulation results demonstrate that our approach outperforms baselines in terms of system utility, QoS satisfaction, and various KPIs. © 2024 IEEE.

关键词： Quality of service

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A Novel Multi-scale Spatial Tiling Based Adaptive streaming Method for 360° Video 21

A Novel Multi-scale Spatial Tiling Based Adaptive Streaming ...

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作者： Du, Haipeng Liu, Huichuan Lv, Geli Lv, Xin Wang, Zhiwen Zhang, Weizhan Xi'an Jiaotong University School of Computer Science and Technology Bdke Lab Xi'an710049 China Xi'an Jiaotong University School of Continuing Education Xi'an710049 China Xi'an Jiaotong University Network Information Center Xi'an710049 China

ISBN: (纸本)9798350329223

The surging popularity of 360° videos has presented new challenges in the domains of video processing and streaming. In this paper, we introduce a novel 360° video adaptive streaming methodology grounded in multi-scale spatial tiling. This approach seamlessly navigates intricate network conditions, while assuring an optimal user experience. Firstly, through the utilization of a transformer-based saliency detection model, we pinpoint the salient regions within 360° videos that are poised to engage the user's attention. Subsequently, we undertake Region of Interest (RoI) encoding and multi-scale spatial tiling, facilitating the efficient representation and delivery of 360° videos. Finally, we utilize reinforcement learning to achieve effective adaptive bitrate *** results demonstrate the advantages of our approach in terms of enhancing the overall Quality of Experience (QoE) across intricate network environments. © 2023 IEEE.

关键词： Reinforcement learning

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Elevating Cybersecurity in Defence: A Collaborative and Technologically Driven Approach 6

Elevating Cybersecurity in Defence: A Collaborative and Tech...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ansar, Syed Anas Aggarwal, Shruti Arya, Swati Sayeed, Mohd Asim Soni, Nupur Pathak, Prabhash Chandra School of Computer Application Babu Banarasi das University Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350304480

As the world enters the 21st century, the imperative of safeguarding cybersecurity has intensified. Considering the expanding dependence on information technology (IT) systems for a broad spectrum of military operations, the stakes have never been higher for thwarting successful cyberattacks. Conventional security protocols have become increasingly precarious in the face of evolving cyber threats. This necessitates the adoption of avant-garde and nascent technologies to bolster the military's cybersecurity stance. The paper delves into an array of incipient technologies that proffers the prospect of enhancing cybersecurity in the realm of defence. These include artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing. AI can be leveraged to automate numerous cybersecurity functions, such as threat detection and response. ML can augment the precision of threat detection and response systems. Blockchain, a distributed ledger technology, can safeguard data and transactions. Quantum computing, a nascent technology, harbours the potential to revolutionize computation. It could be utilized to breach present-day encryption standards, thereby rendering data vulnerable to attackers. Moreover, the paper appraises the exigencies and possibilities in defence cyber security and scrutinizes the policy and practice ramifications of these emergent technologies and best practices. © 2023 IEEE.

关键词： artificial intelligence blockchain cybersecurity machine learning national security quantum computing zero-trust security

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On the Unreasonable Efficiency of state Space Clustering in Personalization Tasks 21

On the Unreasonable Efficiency of State Space Clustering in ...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Dereventsov, Anton Vatsavai, Raju Webster, Clayton G. Lirio LLC Lirio AI Res Knoxville TN 37923 USA Lirio LLC Behav Reinforcement Learning Lab Knoxville TN USA

ISBN: (纸本)9781665424271

In this effort we consider a reinforcement learning (RL) technique for solving personalization tasks with complex reward signals. In particular, our approach is based on state space clustering with the use of a simplistic K-means algorithm as well as conventional choices of the network architectures and optimization algorithms. Numerical examples demonstrate the efficiency of different RL procedures and are used to illustrate that this technique accelerates the agent's ability to learn and does not restrict the agent's performance.

关键词： clustering contextual bandit personalization reinforcement learning synthetic environment

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