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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461-470 订阅

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The Cluster-based cognitive Radio Sensor Networks that are Wireless Aware of PUEA and SSDF Attacks

The Cluster-based Cognitive Radio Sensor Networks that are W...

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Applied Intelligence and Sustainable computing (ICAISC), international conference on

作者： Hina Thakkar Sachin Goswami ISME - School of Management & Entrepreneurship ATLAS Skill Tech University Mumbai India School of Media Film & Television Studies IIMT University Meerut India

This paper presents a secure cognitive RadioWireless Sensor Network (CR-WSN) architecture. CR-WSN is a cluster based network in which the sensors are hierarchically structured. Each cluster is chosen with single cluster head that operates on ISM band frequencies. Sensor data from cluster members are aggregated by cluster head. Cluster heads forwards the collected data to sink based on selection of a shortest path using Dijkstra algorithm. Due to the problem of spectrum scarcity, cognitive Radios are subjected to two main attacks for utilizing the unlicensed spectrum. We resolve Spectrum Sensing data Falsification attack and Primary User Emulation attack. Dynamic Spectrum Sensing Technique is involved for performing spectrum sensing. We propose a regression based spectrum sensing data falsification attack detection technique and mathematical SHA with Digital Signature is applied for primary user emulation attack detection. Extensive implementation is handled in Network Simulator 3 and comparative results are illustrated for betterment of throughput and end-to-end delay efficiencies.

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Evolutionary Multi-Objective Neural Architecture Search for Generalized cognitive Diagnosis Models

Evolutionary Multi-Objective Neural Architecture Search for ...

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data-driven Optimization of complex Systems (DOCS), international conference on

作者： Shangshang Yang Cheng Zhen Ye Tian Haiping Ma Yuanchao Liu Panpan Zhang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.

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Energy-Efficient Collaborative DNN Inference in UAV Swarm

Energy-Efficient Collaborative DNN Inference in UAV Swarm

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international conference on Big data computing and Communications (BIGCOM)

作者： Weiqing Ren Yuben Qu Zhen Qin Chao Dong Hao Sun Lei Zhang Qihui Wu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China Noncommissioned Officer Academy of PAP China

Unmanned Aerial Vehicles (UAVs) have attracted great attention due to its high mobility and flexible deployment, which makes many people develop UAVs for different application scenarios. The unique performance of UAVs encourages the emergence of more critical and complex tasks in uncertain and potential harsh environments, many of which were not even envisaged decades ago, including military border surveillance and oil/gas offshore exploration. The large amount of data generated by these applications needs to be processed and analyzed by deep neural networks (DNNs). However, due to the resource constraints of UAVs, there are many challenges in how to deal with deep networks and complex models on UAVs. In this paper, we propose a fine-grained DNN inference partition method, aiming at allocating inference requests to the resource-constrained UAV swarm, classifying the captured images, and finding the minimum inference latency. We formulate the problem as an optimization problem that minimizes the inference latency of UAV swarm from capturing the image to outputting the result. The optimization problem is an NP-hard problem. Therefore, we introduce an online heuristic solution, namely EECIA, to find the computing task allocation strategy that gives the best latency among the available UAVs. The simulation results show that compared with the benchmarks, our algorithm can get relatively good performances under different configures and the running time meets the real-time requirements.

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2nd international conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

2nd International Conference on Emerging Trends in Expert Ap...

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2nd international conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

ISBN: (纸本)9789819919086

The proceedings contain 48 papers. The special focus in this conference is on Emerging Trends in Expert Applications and Security. The topics include: Securing Account Hijacking Security Threats in Cloud Environment Using Artificial Neural Networks;ioT-Based Smart Pill Box and Voice Alert System;early Identification of Plant Diseases by Image Processing Using Integrated Development Environment;usability Attributes and Their Mapping in Various Phases of Software Development Life Cycle;controlling Devices from Anywhere Using IoT Including Voice Commands;ioT-Based Air Quality Monitoring System;modeling of Order Quantity Prediction using Soft computing Technique: A Fuzzy Logic Approach;development of Classification Framework Using Machine Learning and Pattern Recognition System;Human Part Semantic Segmentation Using Custom-CDGNet Network;current Web Development Technologies: A Comparative Review;effect on Compressive Strength of Portland Pozzolana Cement on Adding Admixtures Using Machine Learning Technique;tech Track for Visually Impaired People;intelligent Compression of data on Cloud Storage;diagnosis of Diabetic Retinopathy Using Deep Neural Network;multi-parameter Sensor-Based Automation Farming;comparing Ensemble Techniques for Bilingual Multiclass Classification of Online Reviews;detection of Disease in Liver Image Using Deep Learning Technique;how to Quantify Software Quality Factors for Mobile Applications?: Proposed Criteria;dysgraphia Detection Using Machine Learning-Based Techniques: A Survey;Designing AI for Investment Banking Risk Management a Review, Evaluation and Strategy;R-Peak-Based Arrhythmia Detection as an Impact of COVID-19;a Neutrosophic cognitive Maps Approach for Pestle Analysis in Food Industry;assistive Agricultural Technology—Soil Health and Suitable Crop Prediction;smart Chatbot for Guidance About Children’s Legal Rights.

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A Two Dimensional Feature Engineering Method for Relation Extraction 5

A Two Dimensional Feature Engineering Method for Relation Ex...

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5th international conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： Wang, Hao Chen, Yanping Yang, Weizhe Lin, Chuan Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry Huaxi District Guizhou China State Key Laboratory of Public Big Data Huaxi District Guizhou China Guizhou University Huaxi District Guizhou China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

Transforming a sentence into a 2D representation, similar to table structures, reveals a semantic plane where elements can represent potential relations between paired entities. The 2D representation effectively addresses the common issue of overlapping relations. However, previous works directly transformed the representation from raw input, neglecting the utilization of prior knowledge, which is essential to support the entity relation extraction task. In this paper, we propose a 2D feature engineering method. The method innovatively integrates prior knowledge within 2D sentence representations, marking an advancement over previous strategies, and employs an attention mechanism to build the hitherto overlooked association between entities and prior knowledge. The fusion of prior knowledge and targeted attention enhances the model’s relational discernment capabilities. Evaluations on three benchmark datasets—ACE05 Chinese, ACE05 English, and CLTC—demonstrate the efficacy of the proposed method. It surpasses existing techniques, achieving state-of-the-art performance with F1 score improvements of 0.88%, 3.27%, and 2.83% respectively. The results indicate that 2D feature engineering can take advantage of a 2D sentence representation and make full use of prior knowledge. © 2024 SPIE.

关键词： Semantics

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Energy-Efficient Scheduling and Resource Allocation for Power-limited cognitive IoT Devices

Energy-Efficient Scheduling and Resource Allocation for Powe...

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IEEE international conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Kang Wang Peiran Wu Minghua Xia School of Electronics and Information Technology Sun Yat-Sen University Guangzhou China

Energy-efficient scheduling and resource allocation strategies help reduce interference and extend the lifetime of power-limited Internet of Things (IoT) devices. This paper focuses on improving the transmission efficiency and working time of power-limited data acquisition equipment, e.g., low-power consumption IoT sensors. In particular, the cognitive device tunes its transmission time and power rationally to avoid interference and recharges itself by conducting energy harvesting. Inspired by the concept of the age of information, we coin the concept of the value of update (VoU) and use it to guide devices to upload data in a timely manner and optimize the key parameters through a deep deterministic policy gradient (DDPG) neural network to maximize the long-term VoU. Finally, extensive simulations are conducted to demonstrate the effectiveness and robustness of the proposed scheme.

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Predicting Human-Robot Team Performance Based on cognitive Fatigue

Predicting Human-Robot Team Performance Based on Cognitive F...

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international conference on Automation and computing (ICAC)

作者： Yuhui Wan Chengxu Zhou School of Mechanical Engineering University of Leeds UK

Human-robot systems are increasingly employed across various industries, such as transportation, military, emergency response, and manufacturing. During human-robot collaboration, cognitive fatigue accumulation significantly impacts the human operator's performance, particularly in teleoperation and shared autonomy. This fatigue accumulation can be dangerous and may lead to incidents in robot operations. Consequently, modelling human performance is crucial for understanding and evaluating human-robot systems for risk mitigation and efficiency enhancement. In this work, we propose a prediction model for human-robot teams based on Fitts' Law and the SAFTE model. The model takes into account the operator's cognitive fatigue level and mission requirements to predict whether the operator is suitable for executing the mission and the time required for the human-robot team to complete it. Furthermore, we present a case study on a hypothetical scenario, drawing upon human study data, to assess the model's applicability.

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Defense Against Spectrum Sensing data Falsification Attacks Using Cluster Network Channeling with Distance-Based Attacker Detection in cognitive Radio Ad-Hoc Networks

Defense Against Spectrum Sensing Data Falsification Attacks ...

引用

international conference on Mobile computing and Ubiquitous Networking (ICMU)

作者： Amar Taggu Bryan Samuel Kharsohnoh Department of Computer Science and Engineering NERIST Nirjuli India

The growing demand for wireless and smart technologies has led to an unprecedented increase in the utilization of the radio spectrum. However, as the number of users and devices continues to surge, the spectrum is becoming over-crowded, impeding access and efficient utilization. To address this challenge, cognitive Radio (CR) technology is increasingly becoming the forerunner. cognitive Radio Ad-Hoc Networks (CRAHNs) consists of cognitive intelligent nodes where the CR nodes operating in an infrastructure-less mode, can sense and hop across different frequencies dynamically. CR nodes adapt broadcast constraints to minimize interference among primary and secondary users. By continuously sensing the radio frequency spectrum, cognitive users identify available licensed channels for communication, selecting optimal channels. CRAHNs are subjected to various security attacks which are unique to such networks only. Spectrum Sensing data Falsification (SSDF) attack is one such attack where the malicious CR nodes falsify sensing data in an attempt to influence the final fused data. In this work, we simulate a CRAHN consisting of clusters and cluster heads (CH). Apart from implementing routing across the clusters via the CHs, the current work also proposes a distance-based SSDF attacker detection method which operates primarily in the CHs. Comparison with an existing work [1] demonstrates improved detection.

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5th international conference on 3D Imaging Technologies—Multidimensional Signal Processing and Deep Learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies—Mul...

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5th international conference on 3D Imaging Technologies—Multidimensional Signal Processing and Deep Learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751808

The proceedings contain 59 papers. The special focus in this conference is on 3D Imaging Technologies—Multidimensional Signal Processing and Deep Learning. The topics include: Digital Twin Technology Approach Based on the Hierarchical IDP Tensor Decomposition;modeling Technology for complex Dynamic Operating Environment of Power Grid Based on Digital Twins;design of a Digital Twin Platform Based on Distributed computing and Resource Optimization Algorithms;deep Learning Network Optimization Combining 3D Imaging and Multidimensional Signal Processing;time Series Prediction Application of Deep Learning in Multidimensional Signal Processing;improving Abstractive Summarization with Graph Sequence Model;advancing Semantic Segmentation and Interpretation of 3D Images Through Integrated Deep Learning and Natural Language Processing Techniques;children’s Toy Product Design Based on Augmented Reality Technology;based on the Neural Network Classification of Human Behavior Research;development of an Independent Adversarial Sample Detection Model, Based on Image Features;Visualization and Analysis of CNN Adversarial Training;video Violence Detection Method Based on Multi-Feature and Graph Convolutional Network;Enhancing Realized Volatility Prediction: An Exploration into LightGBM Baseline Models;terminal Anomaly Discovery Technology Based on Service Behavior Deviation;An Efficient CRNN Model with the Multi-scale Feature Fusion for Text Recognition from Chinese Medical Reports;advancing Precision Drug Screening: Integrating Imaging Technology and Artificial Intelligence for Novel Models;a Short Video Retrieval Method Based on Spatio-Temporal Feature Fusion;spatial data Transformation and Fusion Expression Based on Geometric Algebra;EM2-YOLO: Lightweight Remote Sensing Image Detection;Tower-Type Detection of UAV Aerial Image Based on YOLOV5 Network Model.

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Efficient Online Edge Learning for UAV Object Detection via Adaptive Batch Size Fitting

Efficient Online Edge Learning for UAV Object Detection via ...

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international conference on Big data computing and Communications (BIGCOM)

作者： Feiyu Wu Yuben Qu Weijun Wang Chao Dong Yuqian Jing Lei Zhang Qihui Wu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China Nanjing University China

Benefiting from flexibility and mobility, Unmanned Aerial Vehicle (UAV) plays an important role in object detection. However, it is challenging to maintain high accurate inference results in an inherent dynamic scene. Because data drift caused by changeable weather, motion blur, etc leads to inference deviation from the pre-training model. Online learning is an effective method for data drift by supplementing actual environmental data. However, recent researches about online learning are unsuitable for resource-constrained and computationally complex UAV object detection, because of excessive training time for online learning. To improve recognition accuracy and shorten training time, this paper designs an efficient online edge learning for UAV object detection via adaptive batch size fitting. It comprehensively considers online samples and the model’s real-time performance to fit the batch size adaptively. Our method efficiently improves the convergence speed of model training and can be easily implemented on the embedded devices of UAVs. Experimental results show that compared with the existing batch size fitting methods, our method reduces the total training time by 30% and has strong robustness in different scenes.

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