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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

science China(Information sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning. 2

Earthquake Damage Prediction and Rapid Assessment of Buildin...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Ranganathan, Preethi Akshaya Natarajan, Karthika KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9781665475174

A quick forecast of the region's structural damage following an earthquake is crucial for community relief and rescue efforts. Following an earthquake, getting detailed information about buildings in seismic disaster zones is frequently impossible. The information available is essential to estimate seismic damage to urban structures accurately. To address the aforementioned issues, we propose a novel method for quickly assessing building damage through extensive use of field research and data collected from earth observation. A model is created and trained to predict building damage from an earthquake. The algorithm used to indicate the building damage from the quake is Elastic net regression. The elastic net is a regularized regression method used to predict building damage from an earthquake. The proposed Elastic net regression is validated with standard accuracy and effectiveness compared with the Random forest and decision tree methods. The proposed model's outcomes show that elastic net regression is the most effective predictive model for predicting building damage. © 2023 IEEE.

关键词： Earthquakes

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Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing data

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 426-442页

作者： Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Malhan, Anu Department of Computer Engineering New Horizon Institute of Technology and Management Thane India Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Thane India

Accurate crop yield predictions are essential for agricultural planning and food security. Traditional methods relying on historical data are enhanced by modern techniques using satellite imagery, weather data, and soil conditions. This study presents an advanced crop yield forecasting model integrating deep reinforcement learning and multi-source remote sensing data. The Hybrid Reinforcement-Supervised Learning (HRSL) model combines reinforcement learning and supervised learning for enhanced classification and decision-making. Machine learning and artificial intelligence techniques analyze historical yield data and current season variables, improving prediction accuracy. Preprocessing steps like imputation, normalization, and atmospheric correction ensure robust datasets. Feature extraction with cascaded artificial neural networks (CANN) automates the learning of complex data relationships. Implemented using Python, this methodology achieves an accuracy of 97.8%. The proposed model demonstrates superior performance in yield prediction, evaluated through rigorous accuracy, precision, and training efficiency metrics. Integrating multi-source data with advanced machine learning models offers significant benefits for precision agriculture and food security. Future work will expand these techniques to broader applications and continuously improve predictive models with evolving data and computational capabilities. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Crop yield forecasting Deep reinforcement learning Food security Machine learning Multi-source data integration Precision agriculture Remote sensing

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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Responsible AI on National Cybersecurity Strategy with Enhancement of Cybersecurity in Education

Responsible AI on National Cybersecurity Strategy with Enhan...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Tingare, Bhagyashree Ashok Prasanna Lakshmi, G. Khedkar, Vijayshri Suresh Babu, Rakesh Thoppaen Dhar Diwan, Tarun Khatkale, Pravin B D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Department of Computer Science & Engineering Pune India HEXAWARE TECHNOLOGIES INC NJ United States Atal Bihari Vajpayee University Bilaspur India Sanjivani University Maharashtra Kopargaon India

ISBN: (纸本)9798350390049

For national cybersecurity to keep up with the digital transition, AI is more important than ever. Responsible AI improves cybersecurity and education by emphasizing openness and ethics. Ethical design is stressed from the outset in responsible AI. This article analyzes how artificial intelligence (AI) influences the national cybersecurity strategy, focusing on cybersecurity education development and effectiveness. The National Cybersecurity Strategy will frame this article. The major results show that responsible AI improves threat detection and response, cybersecurity infrastructure, talent shortage filling, collaborative research, and public trust. Educational institutions may now offer innovative training programs thanks to artificial intelligence. Completing this aim requires closing the gap between conventional methodologies and modern challenges. Closing gaps is vital. Functional artificial intelligence improves national cybersecurity and educational quality. This analysis shows that this conclusion is possible. This allows experts to properly react to shifting dangers. This is why it matters. © 2024 IEEE.

关键词： AI Integration AI-based Training Programs Cybersecurity Education Cybersecurity Infrastructure Ethical AI Governance National Cybersecurity Strategy Public Trust Responsible AI Skills Gap Threat Detection

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Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits

Transferability of a discrepancy model for the dynamics of e...

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93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2023

作者： Wohlleben, Meike Muth, Lars Peitz, Sebastian Sextro, Walter Faculty of Mechanical Engineering Dynamics and Mechatronics Paderborn University Paderborn Germany Department of Computer Science Data Science for Engineering Paderborn University Paderborn Germany

Modelling of dynamic systems plays an important role in many engineering disciplines. Two different approaches are physical modelling and data-driven modelling, both of which have their respective advantages and disadvantages. By combining these two approaches, hybrid models can be created in which the respective disadvantages are mitigated, with discrepancy models being a particular subclass. Here, the basic system behaviour is described physically, that is, in the form of differential equations. Inaccuracies resulting from insufficient modelling or numerics lead to a discrepancy between the measurements and the model, which can be compensated by a data-driven error correction term. Since discrepancy methods still require a large amount of measurement data, this paper investigates the extent to which a single discrepancy model can be trained for a physical model with additional parameter dependencies without the need for retraining. As an example, a damped electromagnetic oscillating circuit is used. The physical model is realised by a differential equation describing the electric current, considering only inductance and capacitance;dissipation due to resistance is neglected. This creates a discrepancy between measurement and model, which is corrected by a data-driven model. In the experiments, the inductance and the capacity are varied. It is found that the same data-driven model can only be used if additional parametric dependencies in the data-driven term are considered as well. © 2023 The Authors. Proceedings in Applied Mathematics and Mechanics published by Wiley-VCH GmbH.

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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An IoT Based Electromyography Signal Transmission from sEMG Electrodes to Client's Server with ICT Infrastructure 10

An IoT Based Electromyography Signal Transmission from sEMG ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pavithra, P.M. Balakrishnan, M. Anandaraj, A. Ramya, M. Sudharsan, R. Raja Sri Shanmugha College of Engineering and Technology Department of Artificial intelligence and Data Science Salem India Dr. Mahalingam College of Engineering and Technology Department of Artificial intelligence and Data Science Pollachi India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sri Shanmugha College of Engineering and Technology Department of Information Technology Salem India Sri Shanmugha College of Engineering and Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350384369

Bio-electric signal like Electromyography (EMG) can measure signal data from nerves and muscles separately in the particular region of affected nerve or muscles from a Human Being. For incorporating these measurements, can use a non-invasive technique. The Surface electrode can be used for measuring these problems in non-invasive fashion. Here, the idea of Internet of Things (IoT) can bring included advantage to applications with bio-electric signals to communicate to the doctors and patients such as human-PC cooperation by incorporating different advances for example, sensors, remote correspondence and information science. In this research, we worked on a remote bio-electric signals to another remote observing handling framework. A model with the contextual investigation of nerve and muscle signal acknowledgment utilizing 4-channel sEMG signals is executed. A Gaussian filter is prepared from one individual's sEMG signals with 4-nerve regions of human body such as ulnar, radial, median and radial. The introduced IoT application framework is actualized based on an 11-terminal bio-electric signal measuring device, with Arduino based Controller, Gateway, ESP8266 (Wifi) module, Server being the major mediums of remote transmission of signal data. The signal transmits at 35KB/s. In the framework, the ongoing sEMG signal from surface electrode is filtered from noise and other distortions using is given to the controller for performing the operation. © 2024 IEEE.

关键词： Gateways (computer networks)

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DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

Deep Learning for Channel Prediction in Non-Stationary Wirel...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9798350379525

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems. © 2024 IEEE.

关键词： Prediction models

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