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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431-440 订阅

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Differentiated Security in the Age of cognitive Internet of Things (CIoT)

Differentiated Security in the Age of Cognitive Internet of ...

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international conference on Distributed computing Systems

作者： Jiani Fan Kwok-Yan Lam Dusit Niyato School of Computer Science and Engineering Nanyang Technological University Singapore

Our IoT systems are transforming from “perceptive” to “cognitive,” where connected IoT devices behave as agents and interact with each other in a context-aware manner with minimum human intervention, learning from both the physical environment and experience. New security challenges surface as IoT applications are empowered with more cognitive capability. The vast amount of ever-growing data needed to equip our devices with artificial intelligence puts enormous pressure on the protection of sensitive data flow and the efficiency of network resource utilization. In order to address the potential security challenges for cognitive IoT systems, we first drafted a survey to study the security requirements and challenges of recent IoT proposals for future smart cities and then proposed a differentiated security architecture that enhances security and efficiency for data communication. In the future, we aim to build an incentive mechanism to encourage secure peer-to-peer data sharing using our proposed differentiated security architecture and deep learning techniques.

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Offload Shaping for Wearable cognitive Assistance

Offload Shaping for Wearable Cognitive Assistance

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IEEE international conference on Edge computing (EDGE)

作者： Roger Iyengar Qifei Dong Chanh Nguyen Padmanabhan Pillai Mahadev Satyanarayanan Carnegie Mellon University Intel Labs

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. This hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance.

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AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning 3

AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator...

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3rd Modeling, Estimation and Control conference (MECC)

作者： Sivashangaran, Shathushan Khairnar, Apoorva Eskandarian, Azim Virginia Tech Dept Mech Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781713872344

Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazardous, information poor environments such as regions where natural disasters occur, and extraterrestrial planets. The substantial training time required to learn an optimal DRL policy, which can be days or weeks for complex tasks, is a major hurdle to real-world implementation in AGV applications. Training entails repeated collisions with the surrounding environment over an extended time period, dependent on the complexity of the task, to reinforce positive exploratory, application specific behavior that is expensive, and time consuming in the real-world. Effectively bridging the simulation to real-world gap is a requisite for successful implementation of DRL in complex AGV applications, enabling learning of cost-effective policies. We present AutoVRL, an open-source high fidelity simulator built upon the Bullet physics engine utilizing OpenAl Gym and Stable Baselines3 in PyTorch to train AGV DRL agents for sim-to-real policy transfer. AutoVRL is equipped with sensor implementations of GPS, IMU, LiDAR and camera, actuators for AGV control, and realistic environments, with extensibility for new environments and AGV models. The simulator provides access to state-of-the-art DRL algorithms, utilizing a python interface for simple algorithm and environment customization, and simulation execution.

关键词： Autonomous Ground Vehicle cognitive Navigation Deep Reinforcement Learning Modeling Simulation to Real-World

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Industry 4.0 Networks' Federated Blockchain Learning Framework For cognitive computing

Industry 4.0 Networks' Federated Blockchain Learning Framewo...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Tannmay Gupta Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

cognitive computing is a ground-breaking notion in AI which replicates the thought pattern of the human mind. As a result, Industry 4.0 robotics is slowly flourishing owing to cognitive computing. The development of a variety of Machine learning and artificial intelligence techniques has already made it possible to see the beginning of the march toward improved decision-making and data-driven intelligent manufacturing. However, there are a number of additional issues that need to be addressed, such as issues with performance, limitations of data resources, and attacks including poisoning. Recent research efforts have just scratched the surface of the problem, which often leads to erratic performance, inefficiency, and the disclosure of private information. We built a decentralized paradigm for massive data-driven cognitive computing (D2C) in this research by mixing blockchain technology with federated learning. D2C stands for data-driven cognitive computing. The problem of a "data island" may be solved by federated learning if privacy is protected and efficient processing is carried out, while blockchain technology gives incentives in the form of a potent anti-poisoning system in a truly decentralized method. Convergence may be sped up with the use of blockchain technology enabled federated learning by taking advantage of better verifications and member choices. The results of a comprehensive review and assessment demonstrate that D2C is more effective than the designs and models that are now the industry standard.

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Wearables for In-Situ Monitoring of cognitive States: Challenges and Opportunities

Wearables for In-Situ Monitoring of Cognitive States: Challe...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Meera Radhakrishnan Thivya Kandappu Manoj Gulati Archan Misra Institute of High Performance Computing Agency for Science Technology and Research Singapore Management University School of Computing and Information Systems Singapore Management University

We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve as proxies for cognitive/affective states and show that earable-mounted pressure, EMG, and ultrasonic sensing hold promise for canturing such markers.

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Prediction of Electricity Consumption Demand Based on Long-Short Term Memory Network 1

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international conference on Green Energy, computing, and Intelligent Technology, GEn-CITy 2023

作者： Khan, Amanullah Maharum, Siti Marwangi Mohamad Harun, Faezah Shah, Jawad Ali Universiti Kuala Lumpur British Malaysian Institute Selangor Gombak53100 Malaysia International Islamic University Islamabad44000 Pakistan

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

Long Short-Term Memory (LSTM) networks are widely recognized for their ability to capture and retain long-term dependencies within time series data, making them a valuable tool for dealing with complex relationships between elements over extended periods of time. This research proposes a KerasTuner-based LSTM network to predict future electricity consumption and maximum demand. dataset used in this study is historical electricity consumption data of a plastic manufacturing plant in Malaysia, collected at 30-min intervals from 1st January 2017 to 31st December 2019. Both random selection and KerasTuner-based hyperparameter tuning were used to determine the best hyperparameters. The results demonstrated that the KerasTuner-based LSTM approach is effective in predicting future electricity consumption and captures the complex dependencies within the electricity consumption data. The evaluation metrics, training time, and limits of the future maximum demand indicated the effectiveness of the proposed model. This is proven when the proposed model outperformed other models and could improve the prediction accuracy while saving time. This research shows that the proposed model could serve as a valuable tool for predicting maximum electricity demand and could be applied in other industries to provide crucial insights for energy planning and management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Efficient Usage of Spectrum by Using Joint Optimization Channel Allocation Method

Efficient Usage of Spectrum by Using Joint Optimization Chan...

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international conference on data Analytics, Intelligent computing, and Cyber Security, ICDIC 2020

作者： Prasad, Padyala Venkata Vara Kiran, K.V.D. Kommaraju, Rajasekhar Gayathri, N. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India IT Department LakiReddy BaliReddy College of Engineering Mylavaram India

ISBN: (纸本)9789811941610

Traditionally, cognitive radio can be accessed by secondary user only when foremost user is absent, but the subordinate client needs to evacuate the idle spectrum when existence of foremost user is detected. Hence, the bandwidth is reduced in traditional scheme. To overcome the problem, non-orthogonal multiple access is used to increase the efficiency of spectrum in 5G communications. Non-orthogonal multiple access is used in this method to allow the subordinate user to enter the gamut even when forecast client is attending or not attending the conduit. Foremost client decryption technique and subordinate client decryption technique are introduced to decrypt the non-orthogonal signs. Hence, through the decoding techniques, secondary user throughput can be achieved;to increase the primary user throughput, duct channel energy must be in a limit. However due to the disturbance caused by the foremost client, the subordinate efficiency may be decreased. Orienting toward foremost user first deciphering and subordinate user first decoding. Here, we come up with two enhancement problems to enhance the efficiency of both primary client and secondary client. This is done using jointly optimizing spectrum resource (Gayatri et al. in Mobile Netw Appl 6:435–441 [1]). This citation embracing how much amount of sub-channel transmission power is used, and also, it enhances the number of sub-channels present in it. This type of citation is to enhance optimization problems. Jointly optimization algorithm is introduced to eliminate the existing problem. This is achieved by accepting the signs and calculating the time needed to sense gamut and the forecast client attending or not attending the conduit to decrypt the data sent while forecast client is absent (Ghasemi and Sousa in IEEE Commun Mag 46:32–39 [2]). The miniature outcomes will be shown the non-orthogonal-based multiple access cognitive radio’s predominant transmission efficiency. © 2023, The Author(s), under exclusive license

关键词： cognitive radio

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Comprehensive study and analysis for StarGAN and StarGAN-v2 in the task of image generation 2

Comprehensive study and analysis for StarGAN and StarGAN-v2 ...

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2nd international conference on computing Innovation and Applied Physics, CONF-CIAP 2023

作者： Cai, Yuanan Li, Yepeng Zhang, Zhenyu Department of Computer Engineering Baskin School of Engineering University of California Santa CruzCA95064 United States Department of Cognitive Science University of California - San Diego La Jolla CA92092 United States Department of Information Technology Monash University Wellington Rd ClaytonVIC3800 Australia

With the continuous development of deep learning and artificial intelligence industry, more and more neural networks have been developed. Among them, one typical network widely used is StarGAN. It solves the problem of requiring multiple generators and discriminators for multiple domains and style transitions. In this article, we will analyze and explore StarGAN v1 and StarGAN v2. including StarGAN's development history, advantages and disadvantages in the application, etc. At the same time, we will compare StarGAN with other neural networks such as CycleGAN and MSGAN through some discrimination criteria, and clearly demonstrate the influence of different networks on the generation of image data sets through quantitative and qualitative analysis. In addition, we also found a representative code on some of the actual uses of StarGAN for hair replacement through online sources. Since this code may be used to confuse the use of StarGAN v1 and v2, we will also improve this code in the article and put forward suggestions for changes. In this paper, we carried out the specific analysis of StarGAN's loss function based on the existing data and put forward some relevant insights. By taking the data we searched for StarGAN, it can be found that StarGAN could use as few resources as possible to achieve clearer and more varied results during the image generation task © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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20th international Symposium on Distributed computing and Artificial Intelligence, DCAI 2023

20th International Symposium on Distributed Computing and Ar...

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20th international Symposium on Distributed computing and Artificial Intelligence, DCAI 2023

ISBN: (纸本)9783031383328

The proceedings contain 36 papers. The special focus in this conference is on Distributed computing and Artificial Intelligence. The topics include: Using data Analytic for Social Media Posts to Optimise Recyclable Solid Waste Management Exemplary at the City of Valencia;Detection of Human Falls via Computer Vision for Elderly Care – An I3D/RNN Approach;generic Architecture for Multisource Physiological Signal Acquisition, Processing and Classification Based on Microservices;a Novel System Architecture for Anomaly Detection for Loan Defaults;enabling Distributed Inference of Large Neural Networks on Resource Constrained Edge Devices using Ad Hoc Networks;Using Neural Network to Optimize Bin-Picking in the SME Manufacturing Digital Transformation;neural Architecture Search: Practical Key Considerations;leveraging Smart City Services for Citizen Benefits Through a Unified Management Platform;step-Wise Model Aggregation for Securing Federated Learning;estimation of Occlusion Region Using Image Completion by Network Model Consisting of Transformer and U-Net;Federated Genetic Programming: A Study About the Effects of Non-IID and Federation Size;cognitive Reinforcement for Enhanced Post Construction Aiming Fact-Check Spread;chatbot Architecture for a Footwear E-Commerce Scenario;a Geolocation Approach for Tweets Not Explicitly Georeferenced Based on Machine Learning;classification of Scenes Using Specially Designed Convolutional Neural Networks for Detecting Robotic Environments;AMIR: A Multi-agent Approach for Influence Detection in Social Networks;extracting Knowledge from Testaments - An Ontology Learning Approach;AI-Based Platform for Smart Cities Urban Infrastructure Monitorization;bitcoin Price Prediction Using Machine Learning and Technical Indicators;RKTUP Framework: Enhancing Recommender Systems with Compositional Relations in Knowledge Graphs;operation of a Genetic Algorithm Using an Adjustment Function;a Classification Method for "Kawaii" Images Using Four Feat

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20th international Symposium on Distributed computing and Artificial Intelligence, DCAI 2023

20th International Symposium on Distributed Computing and Ar...

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20th international Symposium on Distributed computing and Artificial Intelligence, DCAI 2023

ISBN: (纸本)9783031386152

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