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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Anestis Dalgkitsis Ashima Chawla Anne-Marie Bosneag Christos Verikoukis Iquadrat Informatica S.L. Barcelona Spain Network Management Research Lab LM Ericsson Athlone Ireland Computer Engineering & Informatics Dept. University of Patras Greece

ISBN: (纸本)9781665435413

The success of Software-Defined Networking and Network Function Virtualization enabled providers to partially automate the decision-making of various network operating workflows. Network slicing is an innate feature of the Fifth Generation (5G) and Beyond-5G (B5G) networks, bringing many advantages but also increasing the complexity of managing such networks. In this context, automation tools based on Artificial Intelligence (AI) have become a preeminent instrument of automation used by the industry, as it would be impossible to manually provision and manage such a vast and dynamic infrastructure. Safe interaction of the AI agents with the network is one of the predominant challenges, especially when Reinforcement Learning (RL) is used in critical environments. This is particularly important when the RL agent actions have a high or irreversible impact on the network or service. Slice management is one of the major features that operators want to automate, to offer future services at large scales with manageable complexity to the operator. However, during the exploration phase, RL agents can cause significant performance degradation during operation and possibly introduce irreversible damage to the service being offered. To address this major challenge, we propose a multi-agent, modular, SafeRL architecture for distributed slice orchestration. We study the problem of zero-touch slice management and orchestration, in the context of Ultra-Reliable Low Latency Communication services for B5G networks. Our results demonstrate improved performance over competing solutions, while ensuring the safety of the performed actions during real-time slice orchestration.

关键词： Automation Scalability Reinforcement learning Ultra reliable low latency communication Real-time systems Space exploration Safety

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Combating Deepfakes: Multi-LSTM and Blockchain as Proof of Authenticity for Digital Media

Combating Deepfakes: Multi-LSTM and Blockchain as Proof of A...

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2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G 2020

作者： Ki Chan, Christopher Chun Kumar, Vimal Delaney, Steven Gochoo, Munkhjargal Chaoyang University of Technology Dept. of Information Management Taichung Taiwan Ryerson University and Capital Blockchain Inc. Dept. of Computer Science Toronto Canada Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781728170312

Malicious use of deep learning algorithms has allowed the proliferation of high realism fake digital content such as text, images, and videos, to exist on the internet as readily available and accessible consumable content. False information provided through algorithmically modified footage, images, audios, and videos (known as deepfakes), coupled with the virality of social networks, may cause major social unrest. The emergence of misinformation from fabricated digital content suggests the necessity for anti-disinformation methods such as deepfake detection algorithms or immutable metadata in order to verify the validity of digital content. Permissioned blockchain, notably Hyperledger Fabric 2.0, coupled with LSTMs for audio/video/descriptive captioning is a step towards providing a feasible tool for combating deepfake media. Original content would require the original artist attestation of untampered data. The smart contract combines a varied multiple LSTM networks into a process that allows for the tracing and tracking of a digital content's historical provenance. The result is a theoretical framework that enables proof of authenticity (PoA) for digital media using a decentralized blockchain using multiple LSTMs as a deep encoder for creating unique discriminative features;which is then compressed and hashed into a transaction. Our work assumes we trust the video at the point of reception. Our contribution is a decentralized blockchain framework of deep discriminative digital media to combat deepfakes. © 2020 IEEE.

关键词： Smart contract

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Best Optimized Route in MANET using Token Economy management System 4

Best Optimized Route in MANET using Token Economy Management...

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4th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2022

作者： Sugumaran, S. Sivasankaran, V. Chitra, M.G. Sivasakthiselvan, S. Jayarajan, P. Sreenivasa Institute of Technology and Management Studies Dept. of ECE AP Chittoor India VIT Bhopal University School of EEE Bhopal India Computer Science M.M.E.S Women's Arts and Science College Melvisharam India Saveetha Institute of Medical and Technical Sciences Dept of ECE Saveetha School of Engineering Chennai602105 India Sri Krishna College of Technology Dept of ECE Coimbatore India

ISBN: (纸本)9781665402392

The Mobile Ad-hoc Network [MANET] is an emerging trend in wireless *** provides network service at anytime and everywhere, because they are self-organized Infrastructure less networks. But, route creation from the source to destination and route maintenance up to the end of all packet transmissions are very challenging issues between the nodes due to lack of less centralization unit and dynamic topology. In this work, we have proposed the best optimized route from the various accessible of DSR using Token Economy management [TEM].This algorithm assigned a token to all nodes based on relative velocity, delay in time, and route cost and energy usage of *** the best path optimised by the token's cumulative value for that particular *** results are simulated by ns2 tools and compared to other well-known token *** TEM result indicates that the token system selection enhanced the velocity, less route cost, less time delay and less energy utilization in the network. © 2022 IEEE.

关键词： Mobile ad hoc networks

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Using AoI forecasts in communicating and robust distributed model-predictive control

arXiv

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arXiv 2021年

作者： Hahn, Jannik Schoeffauer, Richard Wunder, Gerhard Stursberg, Olaf Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Germany Heisenberg CIT Group Free University of Berlin Dept. of Mathematics and Computer Science Germany

In order to enhance the performance of cyber-physical systems, this paper proposes the integrated design of distributed controllers for distributed plants and the control of the communication network. Conventional design methods use static interfaces between both entities and therefore rely on worst-case estimations of communication delay, often leading to conservative behavior of the overall system. By contrast, the present approach establishes a robust distributed model-predictive control scheme, in which the local subsystem controllers operate under the assumption of a variable communication schedule that is predicted by a network controller. Using appropriate models for the communication network, the network controller applies a predictive network policy for scheduling the communication among the subsystem controllers across the network. Given the resulting time-varying predictions of the age of information, the paper shows under which conditions the subsystem controllers can robustly stabilize the distributed system. To illustrate the approach, the paper also reports on the application to a vehicle platooning scenario. © 2021, CC BY.

关键词： Networked control systems

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Repairing with Zero Skip Cost

arXiv

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arXiv 2024年

作者： Zhang, Wenqin Chee, Yeow Meng Dau, Son Hoang Etzion, Tuvi Kiah, Han Mao Luo, Yuan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Dept. of Industrial Systems Engineering and Management National University of Singapore Singapore School of Computing Technologies RMIT University MelbourneVIC Australia Computer Science Faculty Technion Israel Institute of Technology School of Physical and Mathematical Sciences Nanyang Technological University Singapore

To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractio...

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Exploring the Potential of DeepSeek-R1 Model in Transforming Healthcare Solutions: An Overview

Exploring the Potential of DeepSeek-R1 Model in Transforming...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Muhammad Raheel Raza Shahbaz Ahmed Fahad Ahmed Khokhar Asaf Varol Peter Faber Business School Australian Catholic University North Sydney Australia Department of Computer Science Universitat Koblenz Koblenz Germany Department of Informatics & Mathematics University of Florence Firenze FI Italy Department of Engineering Management & Technology College of Eng. & Comp. Science Uni. of Tennessee at Chattanooga Chattanooga TN US Dept. of Computer Engineering College of Eng. & Nat. Sciences Maltepe University Istanbul Turkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Over the past few decades, artificial intelligence (AI) has become more integrated into healthcare, with Large Language Models (LLMs) being a key component in improving healthcare decision-making. These LLMs' capacity to produce and interpret human-like texts has the potential to revolutionize healthcare procedures, where efficient data processing and communication are crucial. DeepSeek-Rl, created by DeepSeek AI, has drawn notice globally for its reasoning-based design that uses reinforcement learning (RL) to enhance problem-solving skills. Keeping this in view, the paper emphasizes the significance of the DeepSeek- Rl model and its application in the healthcare industry. Highlighting some hypothetical potential scenarios, we believe that DeepSeek models can playa crucial role in transforming existing medical approaches and revolutionizing the future of AI in health care.

关键词： Industries Biological system modeling Large language models Digital forensics Decision making Medical services Reinforcement learning Data processing Security Problem-solving

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Investigation of DQN Algorithm for Driving control for Smart Cities and Traffic Safety

Investigation of DQN Algorithm for Driving Control for Smart...

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2023 4th International Informatics and Software engineering Conference (IISEC)

作者： Yıldıray Yiğit Murat Karabatak Asaf Varol Ahad Nasab Vocational School of Ahlat Bitlis Eren University Bitlis Türkiye College of Technology Firat University Elazığ Türkiye Dept. of Engineering Management and Technology The University of Tennessee at Chattanooga Chattanooga TN US College of Engineering and Computer Science The University of Tennessee at Chattanooga Chattanooga TN US

With the rapid increase in the world population day by day, the migration of people from rural areas to cities is also increasing rapidly. This rapid population growth in cities brings with it many problems such as transportation, accommodation and education that need to be solved. Traffic and transportation problems in cities are at the forefront of these problems. Due to the increasing traffic day by day, the time people spend in traffic is increasing, and depending on the traffic density, there can be great variability in the acceleration and deceleration of the drivers. In addition, studies have shown that accidents due to excessive acceleration and deceleration are more common at times when traffic density is low. The fact that vehicle drivers can drive comfortably and safely in traffic can bring many advantages. Many parameters, from driving safety to fuel consumption, from environmental pollution to comfort, can be provided by driving control. The main goal of this work is to create an action strategy that maximizes the overall reward in the long run. In this study, an optimum speed has been tried to be suggested for vehicles in urban traffic by using deep reinforcement learning.

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Enabling Smart Agriculture in Nigeria: Application of Digital-Twin Technology 1

Enabling Smart Agriculture in Nigeria: Application of Digita...

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1st International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2021

作者： Elijah, Olakunle Rahim, Sharul Kamal A. Emmanuel, Abioye Abiodun Salihu, Yahaya Otuoze Usman, Zubair Gatugel Jimoh, Adinoyi Momoh Wireless Communication Center School of Electrical Engineering University of Technology Malaysia Johor Bahru Malaysia School of Electrical Engineering University of Technology Malaysia Control and Mechatronics Engr. Dept. Johor Bahru Malaysia Kaduna Polytechnic Computer Engineering Department Kaduna Nigeria Saudi Telecommunication Company Riyadh Saudi Arabia

ISBN: (纸本)9781665434935

The digital twin (DT) is an emerging technology and one of the industry 4.0 technologies. The research in the application of DT in agriculture is currently attracting research interest. In this paper, we provide an overview of DT and discuss possible areas in which it can enable smart agriculture in Nigeria. The benefits and challenges of deploying the DT are also presented. Finally, methods that can be adopted in the application of DT towards solving the problems facing Nigeria's agriculture sector are proposed. © 2021 IEEE.

关键词： Agriculture

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Deep Learning Applications for Detecting Crop Diseases from Image Data

Deep Learning Applications for Detecting Crop Diseases from ...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： RVS Praveen Mudit Mittal Prasanta Parida Rowsonara Begum Yogendra Kumar Ginni Nijhawan Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun School of Rural Management KIIT University Bhubaneswar Odisha India Department of Comp. Sc. & Engg. MLR Institute of Technology Hyderabad Telangana Department of Electrical Engineering GLA University Mathura India Dept. of Comp. Sc. & Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In order to optimize the productivity of crops and ensure food security the crop diseases should be identified as soon as possible. In this study, we study the image based crop disease identification using Deep Learning Methods. However, the traditional illnesses detection techniques are typically based on specialist knowledge and human inspection, and the latter also has the disadvantages of being very time consuming and error prone. Use of convolutional neural networks (CNNs), in particular, have made deep learning powerful for many imaging problems such as precise and automated identification of disease. The ultimate goal of this research is to build a CNN based model which, through the analysis of photos of a damaged leaf, can identify and categorize various crop diseases. With this large data set with excellent images of both healthy and sick crops, we take advantage of. The model is trained and tested based on F1 scores, accuracy, precision and recall. The findings indicate that the model proposed in this research outperforms generic machine learning techniques in diagnosing diseases such as rust, mildew, and blight at high accuracy. The real time implementation of the system is also scalable enough to monitor big areas in the smart farming set up. I show in this study that deep learning has the potential to significantly enhance agricultural disease management and to pave the way to new and more effective and sustainable ways to grow crops.

关键词： Deep learning Smart agriculture Training Visualization Accuracy Transfer learning Crops Data models Convolutional neural networks Diseases

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Improving super-resolution performance using meta-attention layers

arXiv

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arXiv 2021年

作者： Aquilina, Matthew Galea, Christian Abela, John Camilleri, Kenneth P. Farrugia, Reuben A. Dept. of Communications & Computer Engineering Faculty of ICT University of Malta UM Msida Malta Malta Deanery of Molecular Genetic & Population Health Sciences University of Edinburgh Scotland Edinburgh United Kingdom Dept. of Communications & Computer Engineering Faculty of ICT UM Dept. of Computer Information Systems Faculty of ICT UM Dept. of Systems & Control Engineering Faculty of Engineering UM

Convolutional Neural Networks (CNNs) have achieved impressive results across many super-resolution (SR) and image restoration tasks. While many such networks can upscale low-resolution (LR) images using just the raw pixel-level information, the ill-posed nature of SR can make it difficult to accurately super-resolve an image which has undergone multiple different degradations. Additional information (metadata) describing the degradation process (such as the blur kernel applied, compression level, etc.) can guide networks to super-resolve LR images with higher fidelity to the original source. Previous attempts at informing SR networks with degradation parameters have indeed been able to improve performance in a number of scenarios. However, due to the fully-convolutional nature of many SR networks, most of these metadata fusion methods either require a complete architectural change, or necessitate the addition of significant extra complexity. Thus, these approaches are difficult to introduce into arbitrary SR networks without considerable design alterations. In this paper, we introduce meta-attention, a simple mechanism which allows any SR CNN to exploit the information available in relevant degradation parameters. The mechanism functions by translating the metadata into a channel attention vector, which in turn selectively modulates the network’s feature maps. Incorporating meta-attention into SR networks is straightforward, as it requires no specific type of architecture to function correctly. Extensive testing has shown that meta-attention can consistently improve the pixel-level accuracy of state-of-the-art (SOTA) networks when provided with relevant degradation metadata. Despite average memory/runtime overheads of less than ≈2.6%/0.025 seconds for the datasets and models considered, meta-attention improves the performance for both PSNR and SSIM;for PSNR, the gain on blurred/downsampled (×4) images is of 0.2969 dB (on average) and 0.3320 dB for SOTA general and f

关键词： Image reconstruction

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