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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 718 条记录，以下是581-590 订阅

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Construction of Index System of international Economic Cooperation Innovation Model in Digital Era-based on TIF Theory and DIKWP Model

Construction of Index System of International Economic Coope...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Zhan Shanqi Fuliang Tang Yongmei Li Mei Qiurong Lu Zining Li Xing-sen Zhang Biyun School of Software and Microelectronics Peking University Beijing China DIKWP Research Group School of Computer and Technology Hainan University China College of Engineering Peking University Center of Chinese and Foreign Think Tanks International Economic and Cultural Exchange (Beijing) Beijing Financial Holdings Group Research Institute of Extenics and Innovation Methods Guangdong University of Technology Pricewaterhouse Coopers (PWC)

With the deepening of globalization, international economic cooperation has become a key factor in promoting economic growth and development. However, the rapid changes and technological advances brought about by the digital age are changing the rules and methods of international economic cooperation. In this digital age, data is no longer just a resource, it is more like a new economic currency, and the development of information technology has made it easier to move data across borders, providing unprecedented opportunities for all kinds of economic activities. However, international economic cooperation in the digital age also comes with a series of challenges. data privacy and security issues are more prominent, the digital divide persists, and regulatory and legal issues become more complex. The issues of fairness, fairness, efficiency and dialectical openness of digital platforms have become major issues that attract the attention of all countries. Therefore, in these turbulent times, we need innovative methods and indicators to assess and guide international economic cooperation to ensure that it leads to sustainable, beneficial, and balanced growth that benefits everyone around the world.

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AutoBloom: AI-Based Classification of cognitive Levels in Educational Content

AutoBloom: AI-Based Classification of Cognitive Levels in Ed...

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IEEE WIC ACM international conference on Web Intelligence (WI)

作者： Thanveer Shaik Xiaohui Tao Lin Li Yuan Sun Yan Li Ji Zhang Christopher Dann School of Mathematics Physics and Computing University of Southern Queensland Australia School of Computer Science and Artificial Intelligence Wuhan University of Technology China Information and Society Research Division NII Library Japan School of Education University of Southern Queensland Australia

Amidst the dynamic landscape of modern education, the effective use of classroom recordings remains a challenge. Particularly, the manual analysis of the cognitive depth and breadth of teaching materials can be dauntingly time-consuming and potentially biased. In response to this challenge, This research proposed AutoBloom, an AI-based framework that automates the classification of teaching materials according to Bloom's Taxonomy. By leveraging deep learning models to analyze multimodal content in online classroom recordings, AutoBloom accurately assigns attributes of Bloom's Taxonomy to instructional materials. This automation provides educators with valuable insights into the cognitive levels targeted by their teaching materials, enabling data-driven decisions in curriculum design and teaching method-ologies. AutoBloom not only addresses the need for efficient and objective analysis of educational content but also offers scalability and accuracy. The framework incorporates the concept of Shapley values to enhance explainability, ensuring transparency in the classification process. Additionally, AutoBloom facilitates continuous evaluation and improvement of teaching approaches, serves as a benchmarking tool for comparing strategies and content, showcases the potential of AI in analyzing multimodal data, and addresses ethical considerations related to data privacy and security. The findings highlight the transformative impact of AI in providing deeper insights for educators and institutions to enhance teaching effectiveness and improve learning outcomes in today's rapidly evolving educational landscape.

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Secure and Privacy-Preserving E-mobility Service Based on Blockchain and Hybrid Smart Contracts

Secure and Privacy-Preserving E-mobility Service Based on Bl...

引用

international Wireless Communications and Mobile computing conference, IWCMC

作者： Boutaina Jebari Mounir Ghogho Khalil Ibrahimi Faculty of Sciences LaRI Laboratory Ibn Tofail University Kenitra Morocco International University of Rabat TICLab Sala El Jadida Morocco University of Leeds Faculty of Engineering Leeds UK

The e-mobility infrastructure faces several challenges that hinder the general adoption of electric vehicles (EV). Indeed, the management system requires multiple actors to jointly act on different interdependent processes which makes it complex, time-consuming and inefficient. Moreover, the current state of the infrastructure raises several security and privacy concerns that render it non-compliant with security regulations and privacy laws. In this work, we propose a blockchain-based solution that allows a more efficient, secure and privacy-preserving management of the EV infrastructure. We used hybrid smart contracts and blockchain oracles to feed data to the blockchain in a secure and trusted manner.

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Exploiting Spatial and Temporal Features for Deep Learning Based Human Activity Recognition

Exploiting Spatial and Temporal Features for Deep Learning B...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Wenying Cao Gaotao Shi Tieguan Zhang International Engineering Institute Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

Human activity recognition using inertial sensors has gained significant popularity and widespread adoption in various fields, while deep learning has emerged as the dominant approach, playing a pivotal role in enhancing performance. Nevertheless, existing algorithms often neglect the heterogeneity of sensors and fail to effectively extract contextual features from long-term time series data, resulting in low accuracy in activity recognition, especially for complex activities. This paper proposes a novel method called FUsion Transformer hUman activity REcognition(FUTURE). FUTURE exploits the feature fusion mechanism and designs a multi-scaled DenseNet to extract the spatial features so that the relationship between heterogeneous sensors can be captured. Furthermore, a customized multi-head attention model with less computational complexity is employed in FUTURE to capture global dependencies within the sensor data. Experimental results on multiple datasets validate that FUTURE achieves an average recognition accuracy of 95% for complex activities and the evaluation against the state-of-the-art demonstrates the superior performance of the FUTURE model in accurately classifying complex activities.

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NCDFSA: Neural cognitive Diagnostic Focusing on Students' Attention to Knowledge Concepts

NCDFSA: Neural Cognitive Diagnostic Focusing on Students' At...

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IEEE international conference on Systems, Man and Cybernetics

作者： Guoxiong Wei Zhenyu He Quanlong Guan Liangda Fang Weiqi Luo Guanliang Chen College of Information Science and Technology Jinan University Guangzhou Guangdong China Guangdong Institution of Smart Education Jinan University Guangzhou Guangdong China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Faculty of Information Technology Monash University Australia

The primary aim of cognitive diagnosis is to predict students' performance and knowledge structures by analyzing their learning behavior and answering results, thereby enabling educators can provide personalized instruction. Scholars have proposed many cognitive diagnostic models. However, most of the models do not fully extract and utilize the relevant data and parameters of cognitive diagnosis. Moreover, some models only rely on artificially designed simple functions to analyze the cognitive process of students, which cannot fully capture the complex relationship between students and the exercises. To address these limitations, this paper proposes a neural cognitive diagnostic model named NCDFSA, which focuses on students' attention to knowledge concepts. The model utilizes neural networks to diagnose students' knowledge and considers students' implicit relationships with knowledge concepts. We introduce the concept of attention matrix(AM) and define the importance of knowledge concepts by the frequency of usage of knowledge concepts to improve the prediction effect. This paper compares NCDFSA with existing classical models on four real datasets and finds that the model has higher accuracy and rationality in predicting student performance.

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2nd international conference on Trends in Electronics and Health Informatics, TEHI 2022

2nd International Conference on Trends in Electronics and He...

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2nd international conference on Trends in Electronics and Health Informatics, TEHI 2022

ISBN: (纸本)9789819919154

The proceedings contain 35 papers. The special focus in this conference is on Trends in Electronics and Health Informatics. The topics include: cognitive Assessment and Trading Performance Correlations;molecular Docking Study of Oxido-Vanadium complexes with Proteins Involved in Breast Cancer;multi-Level Stress Detection using Ensemble Filter-based Feature Selection Method;a Hybrid Transfer Learning and Segmentation Approach for the Detection of Acute Lymphoblastic Leukemia;Logistic Regression Approach to a Joint Classification and Feature Selection in Lung Cancer Screening Using CPRD data;HI Applications for ADHD Children: A Case for Enhanced Visual Representations Using Novel and Adapted Guidelines;Trimmed-TDL-Based Time-to-Digital Converter for Time-of-Flight Applications Implemented on Cyclone V FPGA;CO2 Monitoring System to Warn of Possible Risk of Spread of COVID-19 in Classrooms;high-Power Analysis for Outage Probability and Average Symbol Error Probability over Non-identical κ - μ Double Shadowed Fading;Flexible Systolic Hardware Architecture for computing a Custom Lightweight CNN in CT Images Processing for Automated COVID-19 Diagnosis;hjorth Parameters in Event-Related Potentials to Detect Minimal Hepatic Encephalopathy;The V-Band Substrate Integrated Waveguide Antenna for MM Wave Application;the Influence of an Extended Optical Mode on the Performance of Microcavity Forced Oscillator;synthesis and Characterization of Fe3O4@SiO2 Core/shell Nanocomposite Films;optical and Structural Study of a Fibonacci Structure Manufactured by Porous Silicon and Porous SiO2;amyloid-β Can Form Fractal Antenna-Like Networks Responsive to Electromagnetic Beating and Wireless Signaling;how Does Microtubular Network Assists in Determining the Location of Daughter Nucleus: Electromagnetic Resonance as Key to 3D Geometric Engineering;computational Study of Absorption and Emission of Luteolin Molecule.

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Pooling Techniques in Hybrid Quantum-Classical Convolutional Neural Networks

Pooling Techniques in Hybrid Quantum-Classical Convolutional...

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Quantum computing and Engineering (QCE), IEEE international conference on

作者： Maureen Monnet Hanady Gebran Andrea Matic-Flierl Florian Kiwit Balthasar Schachtner Amine Bentellis Jeanette Miriam Lorenz Fraunhofer Institute for Cognitive Systems IKS Munich Germany LMU University Hopsital Munich Germany

Quantum machine learning has received significant interest in recent years, with theoretical studies showing that quantum variants of classical machine learning algorithms can provide good generalization from small training data sizes. However, there are notably no strong theoretical insights about what makes a quantum circuit design better than another, and comparative studies between quantum equivalents have not been done for every type of classical layers or techniques crucial for classical machine learning. Particularly, the pooling layer within convolutional neural networks is a fundamental operation left to explore. Pooling mechanisms significantly improve the performance of classical machine learning algorithms by playing a key role in reducing input dimensionality and extracting clean features from the input data. In this work, an in-depth study of pooling techniques in hybrid quantum-classical convolutional neural networks (QCCNNs) for classifying 2D medical images is performed. The performance of four different quantum and hybrid pooling techniques is studied: mid-circuit measurements, ancilla qubits with controlled gates, modular quantum pooling blocks and qubit selection with classical postprocessing. We find similar or better performance in comparison to an equivalent classical model and QCCNN without pooling and conclude that it is promising to study architectural choices in QCCNNs in more depth for future applications.

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The Construction of Intelligent Classroom for Blended Teaching of cognitive Load and College English Spoken Language Based on data Mining

The Construction of Intelligent Classroom for Blended Teachi...

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Intelligent Technologies (CONIT), international conference on

作者： Lu Liu Yingying Wei Nan Peng Department of Basic Teaching and Research East University of Heilongjiang China

Building an intelligent classroom that combines cognitive load and college English teaching is a key step in improving students' learning experience. Blended learning combines traditional face-to-face teaching with online learning, creating a more flexible and interactive learning environment. The use of technology in the classroom can help reduce cognitive load and enable students to focus on understanding complex concepts. Intelligent classrooms should be equipped with modern technologies such as interactive whiteboards, projectors, and audio-visual aids to facilitate the delivery of course content. In addition, the use of artificial intelligence can provide tailored feedback and suggestions based on the individual needs of each student, thereby helping them personalize their learning experience. Incorporating blended learning into college English courses can also provide opportunities for real language use through online communication tools, thereby improving language acquisition. This method can help students develop language skills in real life while reducing cognitive load. In short, building an intelligent classroom for blended learning is an important step in improving the quality of higher education. It provides an opportunity to integrate technology into traditional teaching methods, creating a more attractive and effective learning experience for students.

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Evaluating Role of Instagram’s Multimedia in Connective Action Leveraging Diffusion of Innovation and cognitive Mobilization Theories: Brazilian and Peruvian Social Unrest Case Studies

Evaluating Role of Instagram’s Multimedia in Connective Act...

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international conference on Behavior, Economic and Social computing (BESC)

作者： Mainuddin Shaik Niloofar Yousefi Nitin Agarwal Billy Spann COSMOS Research Center University of Arkansas Little Rock AR USA

In this research, we study the role of multimedia (images and videos) in propelling connective action from two perspectives. First, we examine multimedia adoption at different stages of a social movement categorized using the Diffusion of Innovations (DOI) theory. Second, we analyze how multimedia influences cognitive mobilization, measured using engagement rates. The two perspectives help us understand multimedia’s role in online mobilization. The data compiled for this investigation includes 78,478 images, 12,967 sidecars (collection of multiple permanent images/videos), and 8,810 videos extracted from Instagram, using posts from the latest wave of demonstrations in South America, specifically the protests in Brazil and Peru spanning from November 2022 to February 2023. Our study revealed unique multimedia usage patterns across the social movements’ DOI stages. For Brazil’s anti-government social movement, although videos were adopted more than sidecars on all DOI stages, sidecars generally had a higher engagement than videos. Similarly for Brazil’s pro-government and Peru’s anti-government social movements, sidecars consistently outperformed videos in engagement rates. Our in-depth study of multi-media usage on Instagram during social movements highlights the significant roles of different multimedia types at various stages of mobilization. Our results suggest that sidecars and videos, after images, are pivotal in promoting cognitive engagement and, subsequently, cognitive mobilization. The distinct diffusion patterns witnessed in the social movements studied emphasize the importance of understanding each multimedia’s unique characteristics and their contribution to driving connective action. This study demonstrates multimedia has become an indispensable facet of modern activism. Incorporating multimedia facilitates connective action and mobilization in a systematized manner, responding dynamically to the nature and needs of the ongoing social movements.

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A Reliable data Transmission Service for Tiansuan Constellation

A Reliable Data Transmission Service for Tiansuan Constellat...

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IEEE international conference on Web Services (ICWS)

作者： Xiaobin Xu Qi Wang Miaohe Niu Juan Fang Shangguang Wang Faculty of Information Technology Beijing University of Technology Beijing China Beijing-Dublin International College Beijing University of Technology Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Existing satellite constellations typically lack on-orbit processing capabilities, and on-orbit data backhaul face to reliability and effiency challenges. Tiansuan constellation is with the capability of providing satellite edge computing services, which can support complex data coding transmission methods. To effectively support effective data backhaul for existing satellite constellations, we propose a reliable data transmission service in Tiansuan constellation. The service adopts the adaptive coding transmission strategy to adjust the optimal size of the encoded data packet according to real-time satellite network conditions. An adaptive encoding transmission approach is proposed to reduce end-to-end delay. Experimental results based on the ground simulation platform demonstrate that the proposed method can effectively reduce end-to-end delay and improve effective data transmission rate.

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