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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211-220 订阅

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Design Novel FCM-based Forecasting Model for Interval-valued Time Series: From Distance-metric Perspective 18

Design Novel FCM-based Forecasting Model for Interval-valued...

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18th international conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Yang, Fei Ouyang, Chenxi Yu, Fusheng Tang, Yuqing Jiang, Yanan School of Mathematical Sciences Beijing Normal University Beijing China

ISBN: (纸本)9798350318401

Predicting interval-valued time series (ITS) has long been a subject that has attracted researchers from a diverse range of fields including economics and finance. Since the data and relations in real world are usually highly sophisticated and inaccurate, modeling real complex systems is a challenging task especially for large scale, inaccurate and non stationary datasets. Fuzzy cognitive Map (FCM) has become a powerful tool for modeling and analyzing complex systems, and been widely used in various fields. As the increasing use of FCM, there is still very little known about implications of selecting the fitness function during the weight training process. Therefore, the purpose of this study is to explore the effects of different distances on the accuracy of learning algorithm in FCM by conducting experiments on different datasets. Findings reveal that the choice of distance indeed impacts on the final performance of the model. Moreover, this paper employs the enhanced overlap distance as the fitness function to optimize the weights. Suitability of this distance has been demonstrated, showcasing a significant advantage in accurately calculating the intervals' distance. © 2023 IEEE.

关键词： Fuzzy cognitive Maps

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EEG Channel Selection Based on Neuron Proportion with SNN for Motor Imagery Classification

EEG Channel Selection Based on Neuron Proportion with SNN fo...

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2023 international conference on Neuromorphic computing, ICNC 2023

作者： Sun, Zhihui Fan, Chaoqiong Jia, Tianyuan Li, Qing Wu, Xia Beijing Normal University School of Artificial Intelligence Beijing100875 China Beijing Normal University State Key Laboratory of Cognitive Neuroscience and Learning Beijing100875 China

ISBN: (纸本)9798350316889

The brain computer interface (BCI) technology based on motor imagery has great potential for various control and communication tasks. However, the presence of a large number of EEG channels leads to redundant information, which affects processing speed and classification accuracy. Spiking neural networks (SNN) have the potential to process EEG data by transmitting pulsing activity between synapses and neurons situated in space. Neucube is an SNN architecture inspired by the human brain structure that allows for end-to-end learning, classification, and understanding of spatiotemporal data at low power consumption, saving computing power and reducing operational complexity. By utilizing this model, the temporal and spatial information of EEG signals can be considered to explore the importance and correlation of spatial neurons corresponding to EEG channels during the classification process. Thus, this study aimed to use the Neucube model based on SNN to select the most influential EEG signal channels in the classification process. This improvement mainly focuses on improving classification accuracy and reducing energy consumption to enhance the practical application performance of BCI systems. The proposed method was tested on the BCI Competition IV dataset 2A. After deleting several unimportant EEG channels, the classification accuracy was improved, and the energy consumption was reduced. © 2023 IEEE.

关键词： Neural networks

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The Construction of Intelligent Classroom for Blended Teaching of cognitive Load and College English Spoken Language Based on data Mining 3

The Construction of Intelligent Classroom for Blended Teachi...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Liu, Lu Wei, Yingying Peng, Nan East University of Heilongjiang Department of Basic Teaching and Research 150060 China

ISBN: (纸本)9798350338607

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. © 2023 IEEE.

关键词： Students

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GAMR: A GUIDED ATTENTION MODEL FOR (VISUAL) REASONING 11

GAMR: A GUIDED ATTENTION MODEL FOR (VISUAL) REASONING

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11th international conference on Learning Representations, ICLR 2023

作者： Vaishnav, Mohit Serre, Thomas Artificial and Natural Intelligence Toulouse Institute Université de Toulouse France Carney Institute for Brain Science Dpt. of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre de Recherche Cerveau et Cognition CNRS Université de Toulouse France

Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. Here, we present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR), which instantiates an active vision theory - positing that the brain solves complex visual reasoning problems dynamically - via sequences of attention shifts to select and route task-relevant visual information into memory. Experiments on an array of visual reasoning tasks and datasets demonstrate GAMR's ability to learn visual routines in a robust and sample-efficient manner. In addition, GAMR is shown to be capable of zero-shot generalization on completely novel reasoning tasks. Overall, our work provides computational support for cognitive theories that postulate the need for a critical interplay between attention and memory to dynamically maintain and manipulate task-relevant visual information to solve complex visual reasoning tasks. © 2023 11th international conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Zero-shot learning

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Automatic Text Simplification for People with cognitive Disabilities: Resource Creation within the ClearText Project 2

Automatic Text Simplification for People with Cognitive Disa...

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2nd Workshop on Text Simplification, Accessibility and Readability, TSAR 2023

作者： Espinosa-Zaragoza, Isabel Abreu-Salas, José Moreda, Paloma Palomar, Manuel Centre of Digital Intelligence University of Alicante Spain University Institute for Computing Research University of Alicante Spain Department of Computing and Information Systems University of Alicante Spain

ISBN: (纸本)9789544520861

This paper presents the ongoing work conducted within the ClearText project, specifically focusing on the resource creation for the simplification of Spanish for people with cognitive disabilities. These resources include the CLEARSIM corpus and the *** tool. On the one hand, a description of the corpus compilation process with the help of APSA is detailed along with information regarding whether these texts are bronze, silver or gold standard simplification versions from the original text. The goal to reach is 18,000 texts in total by the end of the project. On the other hand, we aim to explore Large Language Models (LLMs) in a sequence-to-sequence setup for text simplification at the document level. Therefore, the tool's objectives, technical aspects, and the preliminary results derived from early experimentation are also presented. The initial results are subject to improvement, given that experimentation is in a very preliminary stage. Despite showcasing flaws inherent to generative models (e.g. hallucinations, repetitive text), we examine the resolutions (or lack thereof) of complex linguistic phenomena that can be learned from the corpus. These issues will be addressed throughout the remainder of this project. The expected positive results from this project that will impact society are threefold in nature: scientific-technical, social, and economic. © 2023 TSAR 2023 - 2nd Workshop on Text Simplification, Accessibility and Readability, associated with the 14th international conference on Recent Advances in Natural Language Processing 2023, RANLP 2023 - Proceedings of the Workshop. All rights reserved.

关键词： Natural language processing systems

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Proceedings of 3rd Eclipse Security, AI, Architecture and Modelling conference on Cloud to Edge Continuum, eSAAM 2023

Proceedings of 3rd Eclipse Security, AI, Architecture and Mo...

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3rd Eclipse Security, AI, Architecture and Modelling conference on Cloud to Edge Continuum, eSAAM 2023, co-located with EclipseCon 2023

ISBN: (纸本)9798400708350

The proceedings contain 12 papers. The topics discussed include: NEMO: building the next generation meta operating system;scalable and portable federated learning simulation engine;cognitive architecture for process industries;on the containerization and orchestration of RISC-V architectures for edge-cloud computing;unikernels motivations, benefits and issues: a multivocal literature review;modular monoliths the way to standardization;tool support for architectural pattern selection and application in cloud-centric service-oriented ides: decision support for architectural pattern selection and application;enabling compute and data sovereignty with infrastructure-level data spaces;and feature estimation for punching tool wear at the edge.

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Understanding User Perception of Biometric Privacy in the Era of Generative AI 4

Understanding User Perception of Biometric Privacy in the Er...

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4th international conference on Communication, computing and Industry 6.0, C216 2023

作者： Srinivasan, Srihari Cambridge International AS & A level GEMS New Millennium School - Al Khail Dubai United Arab Emirates

ISBN: (纸本)9798350327328

In the dynamic world of technology, the fusion of biometrics and artificial intelligence has ushered in an unprecedented era marked by convenience, security, and cutting-edge innovation. Biometrics, the science of using unique physical or behavioral attributes for identification and authentication, has seamlessly merged into daily routines. From unlocking smartphones with a fingerprint to clearing airport security with facial recognition, biometrics has transformed the way people interact with technology and the world. Simultaneously, Generative AI, powered by machine learning models, has unlocked new frontiers in generating remarkably realistic synthetic data, including human faces, voices, and fingerprints. This convergence of biometrics and Generative AI lies at the heart of a complex and rapidly evolving landscape, giving rise to profound questions concerning individual privacy and security. This study examines the correlation between demographic factors like age, gender, educational background, technological competence, and the regularity of employing biometric authentication, and their awareness about biometric technologies. Additionally, this research explores concerns regarding the potential misuse of biometric data and the notion that organizations should seek explicit consent before collecting such data. Lastly, it assesses the awareness of potential privacy risks and the belief that individuals should receive education regarding the utilization of their biometric data in AI systems. Descriptive research design has been used in this study. The first section of the questionnaire using Microsoft Forms covers the demographic factors, technological proficiency and frequency of using biometric authentication (e.g., fingerprint, facial recognition). The next section focuses on awareness and usage of biometric technologies, biometric privacy, trust, education, awareness of biometrics, generative AI and deepfakes using the Likert Scale. 53 samples were obtained th

关键词： Biometrics

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In-Vehicle Sensing and data Analysis for Older Drivers with Mild cognitive Impairment 20

In-Vehicle Sensing and Data Analysis for Older Drivers with ...

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20th IEEE international conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Moshfeghi, Sonia Jan, Muhammad Tanveer Conniff, Joshua Ara Ghoreishi, Seyedeh Gol Jang, Jinwoo Furht, Borko Yang, Kwangsoo Rosselli, Monica Newman, David Tappen, Ruth Smith, Dana College of Engg and Computer Science Florida Atlantic University Boca Raton United States Charles E. Schmidt College of Science Florida Atlantic University Boca Raton United States Christine E. Lynn College of Nursing Florida Atlantic University Boca Raton United States

ISBN: (纸本)9798350331110

Driving is a complex daily activity indicating age-and disease-related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults' driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with (MCI) to those without reveals that those with (MCI) exhibit smoother and safer driving patterns. This suggests that drivers with (MCI) are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation. © 2023 IEEE.

关键词： Telematics

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Machine Learning Technique for data Fusion and cognitive Load Classification Using an Eye Tracker 1st

Machine Learning Technique for Data Fusion and Cognitive L...

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1st international conference on Advances in computing Research, ACR’23

作者： Collins, Aaron Pillai, Parthana Balasingam, Balakumar Jaekel, Arunita University of Windsor WindsorONN9B 3P4 Canada

ISBN: (纸本)9783031337420

Advanced Driver Assistance Systems (ADAS) and other human-machine automation systems should be able to accurately recognize and adapt to the cognitive load of the user. For effective human-machine automation, it is important to develop techniques to automatically predict the cognitive load, based on data from non-invasive and low-cost sensors, such as eye-trackers. In this paper, we investigate the use of machine learning (ML) to classify the cognitive load of a participant performing n-back tasks. The ML models are trained using a large number of raw eye-tracking metrics. Our results demonstrate that tree-based algorithms are able to quickly predict cognitive load with a high degree of accuracy compared to other methods, indicating their potential usefulness for real-time applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Robust Wireless Network Anomaly Detection with Collaborative Adversarial Autoencoders

Robust Wireless Network Anomaly Detection with Collaborative...

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IEEE international conference on Communications (IEEE ICC)

作者： Katzef, Marc Cullen, Andrew C. Alpcan, Tansu Leckie, Christopher Univ Melbourne Dept Elect & Elect Engn Melbourne Vic Australia Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia

ISBN: (纸本)9781538674628

Anomaly detection is often deployed in centralised systems, for which critical failure points exist. However, the rising availability of low-cost, wireless-connected devices introduces opportunities for new anomaly detection techniques that leverage more robust topologies. In this paper, we propose a novel collaborative training scheme for anomaly detection models that involves sharing machine learning models amongst devices for incremental training. Using the Adversarial Autoencoder architecture, pseudo-rehearsal, and gossip-based communication, our framework provides all participating devices with a structured representation of other devices' data, so that training can continue even in the event of a device failure, with a 43% smaller performance degradation than state of the art alternatives. Under both optimal conditions and those with device failure, our model consistently exhibits better anomaly detection performance.

关键词： Anomaly Detection Wireless Networks cognitive Radio

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