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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381-390 订阅

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

3rd International Conference on Artificial Intelligence, Rob...

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

ISBN: (纸本)9789819721993

The proceedings contain 33 papers. The special focus in this conference is on Artificial Intelligence, Robotics, and Communication. The topics include: Design and Research of UAV Fire Emergency and Intelligent Inspection System in the Valve Hall of Converter Station;Development Overview and Application Prospects of Advanced UUVs;bisporus Mushroom 3D Reconstruction for Digital Twin of Smart Factory;Research on Automatic Music Generation Based on LSTM;specific Emitter Identification Method Based on Aggregation Model with Metric Learning in complex Field;fake Comment Detection Based on Generative Adversarial Networks;Shipping Risk Assessment Ship Inspection Method Based on Improved YOLOv5;spatiotemporal Prediction of Harmful Algal Blooms Based on Spatiotemporal Attention and Transformer;large-Scale Information Dissemination Algorithm Based on Random Mapping;Smoke Detection in complex Scenery Using Improved Faster-RCNN;CoT-Enhanced CNN-ViT Fusion with Adaptive Token Learning for Precise Facial Expression Recognition;evaluation Method of cognitive Level Based on Machine Learning and Wearable Device;ship Image Segmentation Based on Multi-dimensional Information and Feature Fusion;real-Time 6-DoF Object Pose Estimation Network Based on 2D-3D Coordinate Correspondence;traffic Light Detection Based on Deep Learning;robot Navigation Based on Reinforcement Learning: An Overview;active Control Technology of Supply Chain Intelligent Work Robot Based on Speech Enhancement Technology;research on Security Technology of Power Dispatching data Network Based on Deep Learning;An Improved RRT Path Planning Method Incorporating Deep Reinforcement Learning for Space Multi-arm Robot;a Classification Denoising Algorithm Based on 3D Point Cloud Curvature;Comparative Analysis of the Effect of KPCA in PSO-GRU Combination Model.

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Centralized Multicasting AODV Routing Protocol Optimized for Intermittent cognitive Radio Ad Hoc Networks 13th

Centralized Multicasting AODV Routing Protocol Optimized for...

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13th international conference on Mobile computing, Applications, and Services, MobiCASE 2022

作者： Phaswana, Phetho Velempini, Mthulisi Department of Computer Science University of Limpopo Polokwane South Africa

ISBN: (纸本)9783031318900

The advancement of wireless technology is affected by Spectrum scarcity and the overcrowding of free spectrum. cognitive Radio Ad Hoc Networks (CRAHNs) have emerged as a possible solution to both the scarcity and overcrowding challenges of the spectrum. The CRAHNs ensure that the Secondary Users (SUs) do co-exist with Primary Users (PUs) in a non-interfering manner. The SUs access the licensed spectrum opportunistically when they are idle. CRAHNs have many use cases which include intermittent networks here referred to as intermittent CRAHNs (ICRAHNs). For example, the Military (MCRAHNs). MCRAHN is complex and characterized by a dynamic topology which is subject to frequent partitioning and route breakages due to attacks and destruction in combat. This study optimizes the routing protocols for intermittent networks such as the MCRAHNs. ICRAHN routing is a challenge due to the network’s intermittent attribute, which is subject to destruction in the case of MCRAHN which is characterized by frequent link breakages. The performance of the proposed routing scheme was evaluated through network simulations using the following metrics: throughput, and Routing Path delay, Node Relay delay, Spectrum Mobility delay. The simulation results show that the MAODV is the best-performing algorithm. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Multicasting

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Dynamic Graph Neural Networks with Neighborhood Constraints 21

Dynamic Graph Neural Networks with Neighborhood Constraints

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21st international conference on Networking, Sensing and Control, ICNSC 2024

作者： Ren, Jiankang Lv, Xian Zhang, Jun Li, Xu Ni, Zheng Zhang, Haijun School of Computer Science and Technology Dalian University of Technology Dalian China Dalian University of Technology Key Laboratory of Social Computing and Cognitive Intelligence Dalian China School of Computer Science and Technology Xinjiang Normal University Xinjiang China Graduate School of Education Dalian University of Technology Dalian China

ISBN: (纸本)9798350365221

Graph data are well suited to represent complex interactions and dependencies owing to its inherent relational structure. Graph neural networks (GNNs) can effectively explore the topological structure and node feature information of graph data to perform perceptual inference tasks. In GNNs, the state of each node is iteratively updated by aggregating information from its neighbors. Note that some noise may be introduced due to the information aggregation of irrelevant neighbor nodes, leading to poor performance with higher computational overhead. This paper proposes a dynamic graph generation method based on neighborhood constraints, which learns the degrees of the nodes and imposes the neighborhood constraints to limit the neighbor-hood space of the nodes, resulting in a sparse graph with dynamic neighborhoods. Based on the sparse graph, the dynamic graph neural network selectively aggregates the information of the most important neighbors to improve the performance of the model. Furthermore, to ensure neighborhood constraints satisfaction during the learning process, a loss function is designed to penalize the nodes whose degrees exceed a specific threshold. The evaluation results demonstrate that our proposed method achieves a higher inference accuracy with the same or higher sparsity compared to state-of-the-art methods. © 2024 IEEE.

关键词： Graph neural networks

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Reconfigurable Intelligent Surface-Based Cooperative Spectrum Sensing Over Noisy Reporting Channel

Reconfigurable Intelligent Surface-Based Cooperative Spectru...

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international conference on Advances in data Science and computing Technologies, ASDC 2022

作者： Sharma, Girraj Vyas, Sandeep Nayyar, Anand Sharma, Ritu Jaipur Engineering College and Research Center Jaipur India Graduate School Faculty of Information Technology Duy Tan University Da Nang Viet Nam Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9789819936557

The term "cognitive radio (CR)" refers to revolutionary technique in which a transceiver can detect the empty radio spectrums. The transceiver then switches to a vacant channel, avoiding the ones that are currently in use. These can help to make the radio frequency (RF) spectrum more efficient. The reconfigurable intelligent surface (RIS) can regulate electromagnetic wave propagation by varying the surface's electric and magnetic characteristics. These surfaces can be used to change the properties of radio channels in a certain situation. This gives wireless systems new ways to improve their performance. The goal of this research is to investigate the notion of combining the two ideas inside a network in order to maximize the anticipated advantages based on both RIS and CR. The study looks at a variety of performance indicators for a cognitive radio network (CRN) based on cooperative spectrum sensing (CSS) and a RIS. The results show the influence of essential parameters and a significant increase in the sensing performance of a CR network using RIS technique. There is an increment of 33.3% in detection probability when RIS is employed to a non-RIS CR network. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： cognitive radio

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Does longitudinal, anodal tDCS improve working memory? A behavioral investigation 23

Does longitudinal, anodal tDCS improve working memory? A beh...

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16th ACM international conference on Pervasive Technologies Related to Assistive Environments (PETRA)

作者： Shah, Darshil Rao, Akash Bhavsar, Arnav Chowdhury, Shubhajit Roy Chandra, Sushil Dutt, Varun Free Univ Berlin Berlin Germany Indian Inst Technol Mandi Appl Cognit Sci Lab Mandi Himachal Prades India Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India Inst Nucl Med & Allied Sci Dept Biomed Engn New Delhi India

ISBN: (纸本)9798400700699

Transcranial direct current stimulation (tDCS) has shown promise for cognitive enhancement, particularly in improving working memory through single-session stimulation of the left dorsolateral prefrontal cortex (lDLPFC). However, the effectiveness of multi-session tDCS protocols for working memory enhancement remains controversial. This study aimed to investigate the effects of a 2mA anodal tDCS protocol over the lDLPFC on the performance of the n-back task and Sternberg Working Memory Task compared to a sham tDCS control group. The study recruited 40 healthy college students who completed the n-back and Sternberg tasks for 5 sessions, with pre-and post- intervention data recorded. In each session, the tDCS group received 20 minutes of 2mA anodal tDCS over the left dorsolateral prefrontal cortex (lDLPFC), while the active control group received sham stimulation. The results showed that both groups improved their task performance over time, with the greatest effects observed in the more complex task conditions. However, there were no significant differences in task performance between the tDCS and sham tDCS groups. These findings suggest that the effects of multi-session tDCS on WM performance may be limited and that daily practice may have a greater impact. Future research should investigate individual differences in tDCS response and the optimal design of multi-session tDCS protocols for cognitive enhancement.

关键词： cognitive enhancement working memory transcranial direct current stimulation

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Hybrid Intermission—cognitive Wireless Communication Network

Hybrid Intermission—Cognitive Wireless Communication Networ...

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international conference on Advances in data-driven computing and Intelligent Systems, ADCIS 2022

作者： Bindhu, M. Parasuraman, S. Yogeeswran, S. ECE Sri Venkateshwara College of Engineering Tamil Nadu Sriperumbudur Chennai602117 India ECE Karpaga Vinakaga College of Engineering and Technology Tamil Nadu Chengalpattu India ECE P.T. Leechengalvarya Naicker College of Engineering and Technology Tamil Nadu Veliyur India

ISBN: (纸本)9789819909803

We investigate a wirelessly powered cognitive wireless communication network (CWPCN) for Internet of Things (IoT) applications that consists of a main and secondary communication system. HTT and backscatter communication (BackCom) are two innovative methods that we propose for the secondary communications system. cognitive users have two alternatives when a main channel is overcrowded and the information receiver is unable to receive information: backscattering or collecting energy for future information transfers. Both are possibilities. To transfer information when the main channel is inactive, CUs employ bistatic scatter (BS) and high-frequency transmission (HTT) modes. Time allocation between AB mode and energy harvesting, as well as BS mode and HTT, is studied in order to maximise secondary communication system throughput. To be more specific, we discover the optimum closed-form solution for a particular CU situation as well as the best combination of operating modes. According to numerical statistics, our proposed hybrid HTT and BackCom mode beats the benchmark mode. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy harvesting

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Proceedings of the 23rd international conference on Innovations for Community Services, I4CS 2023

Proceedings of the 23rd International Conference on Innovati...

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Proceedings of the 23rd international conference on Innovations for Community Services, I4CS 2023

ISBN: (纸本)9783031408519

The proceedings contain 20 papers. The special focus in this conference is on Innovations for Community Services. The topics include: Impact of data Augmentation on Hate Speech Detection;efficient Quantum Solution for the Constrained Tactical Capacity Problem for Distributed Electricity Generation;quantum Approaches for Medoid Clustering;quantum Cloud computing from a User Perspective;a Feasibility Study of a Lightweight Fog computing Architecture Integrating Blockchain Technology for Smart E-Health Applications;towards Designing a User-Centered Local Community Platform to Foster Social Cohesion in a Multi-Generational Smart Community;efficient Internet of Things Surveillance Systems in Edge computing Environments: Accessible via Web Based Video Transmissions from Low-Cost Hardware;improved Euclidean Distance in the K Nearest Neighbors Method;AutoBookFinder: A Case Study of Automated Book Rack Identification in Library Through UGV;AIoT Chances in Resource-Constrained Environments via Manual Pruning;Enabling the JSON Web Signature Format to Support complex and Identity-Oriented Non-web Processes;end-to-End Quality of Service Management in the Physical Internet: A Digital Internet Inspired Multi-domain Approach;Bloom Filter-Based Realtime Risk Monitoring of SSH Brute Force Attacks;A Complete One-Time Passwords (OTP) Solution Using Microservices: A Theoretical and Practical Approach;impact of Speed Limitation on Urban Road Traffic;Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-data;carbonEdge: Collaborative Blockchain-Based Monitoring, Reporting, and Verification of Greenhouse Gas Emissions on the Edge;support for Fictional Story Writing and Copy Editing;AutoNLP: A System for Automated Market Research Using Natural Language Processing and Flow-based Programming.

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24th international conference on Artificial Intelligence in Education , AIED 2023

24th International Conference on Artificial Intelligence in ...

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24th international conference on Artificial Intelligence in Education , AIED 2023

ISBN: (纸本)9783031363351

The proceedings contain 123 papers. The special focus in this conference is on Artificial Intelligence in Education. The topics include: Simulating Learning from Language and Examples;learner Perception of Pedagogical Agents;how Useful Are Educational Questions Generated by Large Language Models?;"A Fresh Squeeze on data": Exploring Gender Differences in Self-efficacy and Career Interest in computing Science and Artificial Intelligence Among Elementary Students;preserving Privacy of Face and Facial Expression in Computer Vision data Collected in Learning Environments;towards Extracting Adaptation Rules from Neural Networks;predicting Student Scores Using Browsing data and Content Information of Learning Materials;item Difficulty Constrained Uniform Adaptive Testing;a Support System to Help Teachers Design Course Plans Conforming to National Curriculum Guidelines;virtual Agent Approach for Teaching the Collaborative Problem Solving Skill of Negotiation;Generative AI for Learning: Investigating the Potential of Learning Videos with Synthetic Virtual Instructors;evaluating the Rater Bias in Response Scoring in Digital Learning Platform: Analysis of Student Writing Styles;equity, Diversity, and Inclusion in Educational Technology Research and Development;it’s Good to Explore: Investigating Silver Pathways and the Role of Frustration During Game-Based Learning;evaluating Language Learning Apps for Behaviour Change Using the Behaviour Change Scale;Ghost in the Machine: AVATAR, a Prototype for Supporting Student Authorial Voice;AI Education for K-12: A Survey;annotating Educational Dialog Act with data Augmentation in Online One-on-One Tutoring;utilizing Natural Language Processing for Automated Assessment of Classroom Discussion;using Large Language Models to Provide Formative Feedback in Intelligent Textbooks;improving Code Comprehension Through Scaffolded Self-explanations;tutorial: Educational Recommender Systems;AI cognitive - Based Systems Supporting Learning Process

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TagVibra: push the limit of RFID-based vibration sensing 9

TagVibra: push the limit of RFID-based vibration sensing

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9th international conference on Big data computing and Communications, BigCom 2023

作者： Li, Jian Li, Ping Zhu, Biaokai Dong, Chong Gong, Chao Lv, Zhao Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China Anhui University of Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China Shanxi Police College Taiyuan030401 China

ISBN: (纸本)9798350331240

RFID-based mechanical vibration detection is considered a promising method for many Internet of Things (IoT) applications. However, existing methods are affected by ambient interference and leakage from the reader signature, which makes it difficult to obtain high-precision detection results. To overcome this limitation, this paper introduces TagVibra, a high-precision non-contact vibration sensing system based on RFID technology. By leveraging the nonlinear effect inside the tag and the modulation mechanism of RFID, TagVibra can achieve sub-millimeter level vibration detection accuracy. We implement TagVibra on our Universal Software Radio Peripheral (USRP) platform and evaluate its performance via experiments. The experimental results show that TagVibra can detect vibration with amplitude as low as 0.3mm, and the average estimation error below 0.7Hz when the vibration frequency is 100Hz. © 2023 IEEE.

关键词： Frequency estimation

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Cost-Effective data Caching Optimization with Distance-aware in Edge computing 2

Cost-Effective Data Caching Optimization with Distance-aware...

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2022 2nd conference on High Performance computing and Communication Engineering, HPCCE 2022

作者： Wang, Futian Han, Yinchao Wu, Zhimin Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

ISBN: (纸本)9781510663374

In recent years, edge data caching has been concerned by more and more research scholars. Caching data, especially popular data, on edge servers can significantly reduce users’ latency, while also greatly reducing direct data transmission between users and remote clouds, and slowing down the congestion of the backbone network. Most of the existing research on edge data caching is mainly aim at minimizing users’ latency, saving devices’ energy, and improving the hit rate of data caching, etc. From the perspective of an app vendor, how to formulate an edge data caching solution that can bring him the most profit while complying with a given budget constraint and considering the experience requirements of different users is a very realistic and challenging problem. In this paper, we formulate the Profit-Maximizing Edge data Caching(PMEDC) as a constrained optimization problem. For PMEDC in small scale scenarios, we propose an optimization model named PMEDC-IP that can derive the optimal caching strategy. For PMEDC in large scale scenarios, we propose an approximation algorithm named MPF(Maximum Profit First) that can find an approximate optimal caching scheme in a reasonable time. We conduct extensive experiments on a real-world dataset, and based on the final experimental results, PMEDC-IP and MPF proposed by us significantly outperform the other three representative methods. © 2023 SPIE.

关键词： Profitability

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