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Film Analysis from the Perspective of Cinemeitrics Based on OpenCV and Deep learning 22

Film Analysis from the Perspective of Cinemeitrics Based on ...

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22nd IEEE/ACIS international conference on Computer and Information Science, ICIS 2022

作者： Sun, Jiangnan Li, Chunfang Tang, Ruihan Communication University of China School of Computer Science and Cybersecurity Beijing China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

As a new method of film research, Cinemeitrics uses a systematic and digital way to measure and analyze film style, It focuses more on the measurability of objects and the accuracy of results. As an artistic medium, film has become a popular recording tool for creating historical documents. It is more and more actively getting rid of the shackles of text archives and playing an increasingly important role in recording the times, writing history. So this paper takes the historical film 'The Founding of a Republic' as an example, uses image processing tools such as OpenCV to quantitatively analyze the film. The main work includes: shot segmentation, key frame extraction, subtitles extraction, object recognition and face tracking, providing data can be used to analyze film's style, rhythm and emotion. © 2022 IEEE.

关键词： Computer vision

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A Lightweight Homomorphic Encryption Federated learning Based on Blockchain in IoV 19

A Lightweight Homomorphic Encryption Federated Learning Base...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Du, Dongcai Zhao, Wei Wei, Linna Lu, Siyang Wu, Xuangou Anhui University of Technology School of Computer Science and Technology Ma'anshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan China Beijing Jiaotong University School of Software Beijing China

ISBN: (纸本)9798350346558

Artificial intelligence combined with the Internet of Vehicles (IoV) can improve the performance of automatic driving and service quality of vehicles. However, data privacy protection of IoV has become a challenging problem. In order to effectively protect user data security and reduce resource consumption, we propose a lightweight homomorphic encryption federated learning framework based on blockchain. Firstly, we combine federated learning with blockchain to train a global model collaboratively without sharing their raw data. Meanwhile, the aggregation of shared models is conducted in the on-chain nodes of the blockchain instead of a single server with traditional federated learning. Secondly, considering the further security of the sharing model on the chain, we design a lightweight homomorphic encryption approach because of the high computational cost with the existing homomorphic encryption federated learning. Finally, we conducted comparison experiments with existing homomorphic encryption federated learning schemes, and the experimental results show that our scheme can effectively protect data privacy and reduce computational costs and storage space. © 2022 IEEE.

关键词： Blockchain

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Implementation of Anonymization Algorithms for Log data Analysis on a Cloud-Based learning Management System 27

Implementation of Anonymization Algorithms for Log Data Anal...

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27th international conference on Knowledge Based and intelligent Information and engineering Sytems, KES 2023

作者： Takaki, Osamu Hamamoto, Nobukuni Takefusa, Atsuko Yokoyama, Shigetoshi Aida, Kento Gunma University 4-2 Aramaki-machi Maebashi371-8510 Japan National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo101-8430 Japan

In this study, we aimed to construct a system to perform statistical analyses for educational improvement while preserving privacy. Various probabilistic statistical algorithms have been developed for data agitation. However, one of the major challenges in using these algorithms is determining appropriate parameters. To address this issue, our previous research proposed the "α-criterion" as a criterion that must be satisfied by appropriate parameters. This paper outlines a system that calculates the parameters that satisfy the α-criterion while performing real-time data agitation on the given aggregate data. To verify that our implementation actually works, we show a use case with sample data on kibana-Elastic log analysis provided by Elastic N. V. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0)

关键词： anonymization differential privacy educational data analysis elasticsearch learning management system (LMS) local multilevel assignment of parameter values α-criterion

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intelligent Routing Approach based on Machine learning and SDN for Heterogeneous IoTs 22

Intelligent Routing Approach based on Machine Learning and S...

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IEEE 22nd international conference on High Performance Switching and Routing (IEEE HPSR)

作者： Ben Mabrouk, Mouna Rehoune, Dyhia Fotouhi, Azade Altran Telecom & Media Altran R&I 2 Rue Paul Dautier F-78457 Velizy Villacoublay France

ISBN: (纸本)9781665440059

Internet of things (IoT) is a network of billions of objects connected through heterogeneous communication technologies such as Ethernet, WiFi, Bluetooth, ZigBee, etc. The diversity of communication technologies is a significant barrier preventing the management, the control and more specifically the interoperability using a unified interface. In this paper, the SDN layer, which aims at ensuring a universal management of the IoT heterogeneous network, is to develop a Machine learning algorithm that allows the classification of IoTs according to the data, this algorithm allows the COBOX to find the object that will be interested in the data sent by other IoT, finally integrate this algorithm in the SDN network. The proposed solution enables the building of a middleware that supports interoperability between heterogeneous devices.

关键词： Machine learning algorithms System performance Switches Routing Heterogeneous networks Classification algorithms Topology

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Comparative Approaches by using Machine learning Algorithms in Breast Cancer Prediction 2

Comparative Approaches by using Machine Learning Algorithms ...

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2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Mohapatra, Srikanta Kumar Jain, Arpit Anshika Sahu, Premananda Chitkara University Institute of Engineering and Technology Chitkara University Punjab India SRM Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh Modinagar Ghaziabad India

ISBN: (纸本)9781665437899

Cancer is most dangerous disease in the world now. It affects the unusual segments of the human body. A sum of Breast cancer patients in the world is second-most among all other cancers. Nowadays it is challenging to detect breast cancer in its early stage. If the researcher gets success to detect cancer in its early stage then it is quite easier to reduce the death rate caused due to this disease. Artificial Neural Network(ANN) can be the solution for automated disease detection in the early stage. Machine learning (ML) approaches can be the best suit for prediction as these algorithms have intelligent learning ability for improved performance. This work is meant for the prediction of breast cancer in women by using different ML ability-based algorithms and choosing the best performance model. For the total number of 569 data points number of features are included radius, texture, perimeter, area, smoothness, compactness, concavity, and concave points. Here three classification algorithms as Support Vector Classifier (SVC), Random Forest (RF), and XGBoost (XGB) are taken for the performance measure. the classifiers are trained, tested, and validated by taking different parameter settings. In conclusion, XGBoost (XGB) is the best classification in predicting breast cancer using the Kaggle dataset with an accuracy of 98.7%. © 2022 IEEE.

关键词： Diseases

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Iterative Geographic Entity Alignment with Cross-Attention 22nd

Iterative Geographic Entity Alignment with Cross-Attention

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22nd international Semantic Web conference (ISWC)

作者： Dsouza, Alishiba Yu, Ran Windoffer, Moritz Demidova, Elena Univ Bonn Data Sci & Intelligent Syst DSIS Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Bonn Germany

ISBN: (纸本)9783031472398;9783031472404

Aligning schemas and entities of community-created geographic data sources with ontologies and knowledge graphs is a promising research direction for making this data widely accessible and reusable for semantic applications. However, such alignment is challenging due to the substantial differences in entity representations and sparse inter-linking across sources, as well as high heterogeneity of schema elements and sparse entity annotations in community-created geographic data. To address these challenges, we propose a novel cross-attention-based iterative alignment approach called IGEA in this paper. IGEA adopts cross-attention to align heterogeneous context representations across geographic data sources and knowledge graphs. Moreover, IGEA employs an iterative approach for schema and entity alignment to overcome annotation and interlinking sparsity. Experiments on real-world datasets from several countries demonstrate that our proposed approach increases entity alignment performance compared to baseline methods by up to 18% points in F1-score. IGEA increases the performance of the entity and tag-to-class alignment by 7 and 8% points in terms of F1-score, respectively, by employing the iterative method.

关键词： Geographic Knowledge Graph Iterative Neural Entity Alignment

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Deep learning and Text-Embedding to Integrate Energy Consumption into Industrial Machine Production Planning 29

Deep Learning and Text-Embedding to Integrate Energy Consump...

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29th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Bonci, Andrea Prist, Mariorosario Pompei, Geremia Longarini, Lorenzo Di Biase, Alessandro Verdini, Carlo Polytechnic University of Marche Dept. of Information Engineering Ancona Italy Inst. for chemical-physical processes National Research Council of Italy Pisa Italy Dept. of Electrical and Information Engineering Polytechnic of Bari Italy Polytechnic University of Marche Ancona Italy Dept. of Research&Development Syncode Scarl Fermo Italy

ISBN: (纸本)9798350361230

In modern industrial manufacturing, simulation and accurate forecasting of energy consumption are critical to optimise resources and reduce waste meeting the significant energy demands and greenhouse gas emissions of this sector. Industry 5.0 (15.0), focused on sustainability and human-centered manufacturing, emphasises the use of Artificial Intelligence (AI), the Industrial Internet of Things (1IoT) and the Cyber Physical Systems (CPS) to monitor and optimise resource use in real time. This paper proposes a novel energy consumption simulation method using Neural Network (NN), specifically the Deep Echo State Network (DeepESN), integrated with a text embedding model, which allows to combine energy consumption data into production scheduling. Unlike existing approaches, our method considers both machine-level energy consumption and production workflows, enabling comprehensive optimisation of energy effi-ciency. Preliminary tests in a real production scenario show the potential of the approach, highlighting its ability to predict energy consumption simply by using smart meters without additional hardware. This work represents a significant advancement in in-tegrating energy consumption modeling into production planning and contributes to more sustainable industrial practices. © 2024 IEEE.

关键词： Smart manufacturing

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OVERCOMING MATHEMATICAL ANXIETY TO PROMOTE PROGRESS IN MATHEMATICS DURING UndERGRADUATE engineering STUDIES AT UNIVERSITY 22

OVERCOMING MATHEMATICAL ANXIETY TO PROMOTE PROGRESS IN MATHE...

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22nd international Scientific conference engineering for Rural Development, ERD 2023

作者： Arevee, Eve Vintere, Anna Estonian University of Life Sciences Estonia Latvia University of Life Sciences and Technologies Latvia

Mathematics (math) is one of the logical and practical academic disciplines, which is one of the basic competencies in engineering. Despite that, some students have difficulty to learn math, which is determined by several cognitive and emotional factors. Math anxiety is one of the emotional factors that causes learning difficulties in math. Many students experience anxiety, fear, tension, or discomfort when faced with math, what affect students’ achievement. Math anxiety can be caused by several factors: lack of appropriate math knowledge, learning strategies, application of math in real life, limited exam time, lack of specific materials as well as personality type, lack of confidence, and stereotypes related to math. It is already proved, that achievement in math increases when anxiety is controlled. Therefore, to promote progress in math during engineering studies at university, the aim is to identify the most important factors that cause mathematics anxiety, as well as existing experiences in overcoming them. To develop the methodology of this study, a study of the scientific literature on math anxiety is performed, considering the above-mentioned math learning difficulties. In the case of math anxiety three factors are analysed: student’s prior mathematical experience as well as learning strategies at pre-university and university education level. The article looks at three approaches to learning math that are commonly used by students in their studies of math at university: (1) The use of ICT tools and math software to understand the concepts of math and solve independent work tasks;(2) The link between teaching of math and real/ everyday life;(3) Cooperative learning during additional math classes. The effectiveness of these approaches is characterised using the data from the quality monitoring of the math study process at the authors’ universities and students’ survey results as well as math teachers focused interviews. © 2023 Latvia University of Life Scien

关键词： Students

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A Smart Eye Detection System Using Digital Certification to Combat the Spread of COVID-19 (SEDDC) 22nd

A Smart Eye Detection System Using Digital Certification to ...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Al-Rajab, Murad Alqatawneh, Ibrahim Jasmy, Ahmad Jasim Noman, Syed Muhammad College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (纸本)9783031274084

The spread of the COVID-19 pandemic deeply affected the lifestyles of many billions of people. People had to change their ways of working, socializing, shopping and even studying. Governments all around the world made great efforts to combat the pandemic and promote a rapid return to normality. These governments issued policies, regulations, and other means to stop the spread of the disease. Many mobile applications were proposed and utilized to allow entrance to locations such as governmental premises, schools, universities, shopping malls, and a multitude of other locations. The applications most used being the monitoring of PCR (polymerase chain reaction) test results and vaccination status. The development of these applications is recent, and thus they have limitations which need to be overcome to provide an accurate and fast service for the public. This paper proposes a mobile application with an enhanced feature which can be used to speed the control process whereby public enter controlled locations. The proposed application can be used at entrances by security guards or designated personnel. The application relies on artificial intelligence techniques such as deep learning algorithms to read the iris and automatically recognize the COVID-19 status for the person in terms of their PCR test results and vaccination status. This proposed application is of promise because it would enhance safety while simultaneously facilitating a modern lifestyle by saving time compared to current applications used by the public. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Towards training swarms for game AI 22

Towards training swarms for game AI

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22nd international conference on intelligent Games and Simulation, GAME-ON 2021

作者： Palacios, Estefano Peláez, Enrique Facultad de Ingeniería Eléctrica y Computación Escuela Superior Politécnica del Litoral Campus Gustavo Galindo Guayaquil Ecuador

ISBN: (纸本)9789492859174

This paper presents a game design pattern that can inspire the creation of new games featuring swarms of hostile non-player characters. It is driven by reinforcement learning through insane-Q: an on-l[in]e, [sa]mpling-based, [n]on-[e]pisodic [Q]-learning variant. To unify the swarm’s behaviors, individuals query a shared insane-Q instance. To unchain the individuals from behaving identically—that is, to create sub-classes in the swarm—the data they feed to insane-Q is entangled with a programmable handle, which enables individuals to, indirectly, manipulate how insane-Q interprets its shared learning. As a test bed, a challenging racing mini-game was created through this pattern. It pits players against a reckless stampede of eighty four autonomous driving carts staring two sub-classes: greedy carts always take the path with the least amount of obstacles, while lazy carts always take the shortest path towards their next destination. © 2021 22nd international conference on intelligent Games and Simulation, GAME-ON 2021. All rights reserved.

关键词： Reinforcement learning

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