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Disruptive Technologies (ICDT), International Conference on

作者： Jagjit Singh Dhatterwal Kuldeep Singh Kaswan Nitin Thapliyal Kiran Malik Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Engineering Galgotias University Greater Noida UP India Department of Computer Science and Engineering Graphic Era Hills University Dehradun Uttarakhand India Department of Computer Science and Engineering Matu Ram Institute of Engineering and Management Rohtak Haryana India

Adaptive learning in multiagent systems has emerged as a promising approach to enhance agents' capabilities to adapt to dynamic environments and optimize their performance. In this research paper, we investigate the integration of brain-computer interfaces (BCIs) as a novel means to facilitate adaptive learning in multiagent systems. BCIs establish direct communication channels between the human brain and external devices, enabling real-time monitoring and interpretation of neural activity. By leveraging BCIs, agents can capture valuable cognitive signals from human operators and use this information to adapt their decision-making processes and behaviors. We present a theoretical framework that outlines the incorporation of BCIs into multiagent systems and the mechanisms for adaptive learning. Additionally, we propose a comprehensive methodology for evaluating the effectiveness of this approach. The results of our experiments demonstrate the potential of adaptive learningin multiagent systems using BCIs, paving the way for new applications in various domains, including human-machine col-laboration, assistive technologies, and interactive gaming systems.

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Deep Learning for Uneven data in Industrial IoT Using a Distributed Bias-Aware Adversarial Network 5

Deep Learning for Uneven Data in Industrial IoT Using a Dist...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Gupta, Raj Kumar Naveena, N. Rao, B. Srinivasa Rs, Rajasree Sarkar, Swagata Kumar, Kallakunta Ravi Sardar Vallabhbhai Patel College Veer Kunwar Singh University Physics Department Bihar Ara Bhabua India Kongu Engineering College Computer Technology-UG Perundurai India Gokaraju Rangaraju Institute of Engineering & Technology Department of Computer Science and Engineering Hyderabad500090 India New Horizon College of Engineering Artificial Intelligence and Machine Learning Bangalore India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Sai Leo Nagar Chennai44 India Koneru Lakshmaiah Education Foundation Department of Ece AP Vaddeswaram India

ISBN: (纸本)9798350321425

In minority class and noisy data situations, supervised learning performs more favorably for the majority class but cannot generalize testing data. Performance in the aforementioned use cases might be improved with the help of neural-based data augmentation approaches for generating new data and deep convolutional models for classification. GANs (generative adversarial networks) have recently demonstrated impressive advancements in picture generation. To address the restrictions imposed by the distribution bias problem between the produced data and the unique data, and to provide a more vigorous data augmentation, the presented distribution bias aware collaborative GAN (DGAN) model for unbalanced deep learning in industrial IoT. By including an auxiliary classifier in the foundational GAN model, a comprehensive data augmentation system may be built. In particular, a provisional source of energy with random labels is envisioned and trained combatively with the classification model to appropriately augment the amount of data specimens in minority classes, and a mass fraction system is newly envisioned between two distinct feature extraction, allowing the cooperative adversarial training among some of the power source, voltage divider, and classification algorithm. The next step is to develop an augmentation approach for smart anomaly detection in class imbalance, which, by adjusting for dispersion bias using the properly balanced data, might significantly improve classification precision. Experiment assessments using real-world unbalanced datasets show the superior recital of the suggested model in addressing the distribution biased issue for multi-class classification in class imbalance for commercial IoT applications, as compared to five baseline techniques. © 2023 IEEE.

关键词： Generative adversarial networks

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Enhancing Residential Security with AI-Powered Intrusion Detection systems

Enhancing Residential Security with AI-Powered Intrusion Det...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Jeneetha Jebanazer J B.Vishnu Prabha B. Yasotha Jaisudha J C. Senthilkumar V.Samuthira Pandi Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Jeppiaar institute of Technology Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Tamil Nadu India

Using Intrusion Detection systems (IDS) powered by artificial intelligence is presented in the proposed work as a novel method for enhancing residential security. The overarching goal of the study is to design, develop, and evaluate a system that employs artificial intelligence techniques for real-time detection and prevention of unauthorized access in response to the rising demand for such measures. Using anomaly detection, neural networks, and decision trees, which are all examples of machine learning algorithms that benefit from the incorporation of data from multiple sensors, the proposed system guarantees the accurate identification of suspicious activities. Proposed work examines large datasets and compares them to conventional security measures to demonstrate the system's superior performance and prospective impact on reducing home intrusions. Proposed work contributes to the field of residential security by proposing a dependable, adaptable, and intelligent method for protecting homes against the ever-changing types of infiltration threats that exist today.

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Long Short-Term Memory-Based Next Keyword Prediction

Long Short-Term Memory-Based Next Keyword Prediction

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Rajkumar. K Karthikeyan. S U. Hariharan Dept. of Data Science and Business Systems College of Engineering and Technology SRM Institute of Science & Technology Kattankulathur Tamilnadu India Department of CSE FET Jain (Deemed-to-be University) Bangalore Department of CSE Apex Institute of Technology Chandigarh University Punjab India

This paper focuses on a method that uses an LSTM model to predict the next word of a sentence. Based on Katz's Backoff model, it has been designed to improve and further succeed in our previous work. The main intention here is to show how effective the LSTM model is when compared to Katz's backoff model in terms of the number of n-grams required to predict the next word, and it also helps you predict more accurately. Here LSTM is used for processing, prediction and classification. Since the LSTM model is efficient and has been modified to the task at hand, the model offers very highly accurate predictions even though have used only a tiny portion of the entire corpus. When comparing the existing model, the proposed model predicts the word with accuracy of 90%.

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Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity

arXiv

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arXiv 2024年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

Professional networks — the social networks among people in a given line of work — can serve as a conduit for job prospects and other opportunities. Here we propose a model for the formation of such networks and the transfer of opportunities within them. In our theoretical model, individuals strategically connect with others to maximize the probability that they receive opportunities from them. We explore how professional networks balance connectivity, where connections facilitate opportunity transfers to those who did not get them from outside sources, and congestion, where some individuals receive too many opportunities from their connections and waste some of them. We show that strategic individuals are over-connected at equilibrium relative to a social optimum, leading to a price of anarchy for which we derive nearly tight asymptotic bounds. We also show that, at equilibrium, individuals form connections to those who provide similar benefit to them as they provide to others. Thus, our model provides a microfoundation in professional networking contexts for the fundamental sociological principle of homophily, that "similarity breeds connection" [29], which in our setting is realized as a form of status homophily based on alignment in individual benefit. We further explore how, even if individuals are a priori equally likely to receive opportunities from outside sources, equilibria can be unequal, and we provide nearly tight bounds on how unequal they can be. Finally, we explore the ability for online platforms to intervene to improve social welfare and show that natural heuristics may result in adverse effects at equilibrium. Our simple model allows for a surprisingly rich analysis of coordination problems in professional networks and suggests many directions for further exploration. © 2024, CC BY.

关键词： Professional aspects

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Sinogram denoising via attention residual dense convolutional neural network for low-dose computed tomography

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Nuclear science and Techniques 2021年第4期32卷 70-83页

作者： Yin-Jin Ma Yong Ren Peng Feng Peng He Xiao-Dong Guo Biao Wei Key Laboratory of Optoelectronic Technology and Systems Ministry of EducationChongqing UniversityChongqing 400044China School of Data Science Tongren UniversityTongren 554300China School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA

The widespread use of computed tomography(CT)in clinical practice has made the public focus on the cumulative radiation dose delivered to ***-dose CT(LDCT)reduces the X-ray radiation dose,yet compromises quality and decreases diagnostic *** have made great efforts to develop various algorithms for LDCT and introduced deep-learning techniques,which have achieved impressive ***,most of these methods are directly performed on reconstructed LDCT images,in which some subtle structures and details are readily lost during the reconstruction procedure,and convolutional neural network(CNN)-based methods for raw LDCT projection data are rarely *** address this problem,we adopted an attention residual dense CNN,referred to as AttRDN,for LDCT sinogram ***,it was aided by the attention mechanism,in which the advantages of both feature fusion and global residual learning were used to extract noise from the contaminated LDCT ***,the denoised sinogram was restored by subtracting the noise obtained from the input noisy ***,the CT image was reconstructed using filtered *** experimental results qualitatively and quantitatively demonstrate that the proposed AttRDN can achieve a better performance than state-of-the-art ***,it can prevent the loss of detailed information and has the potential for clinical application.

关键词： Low-dose CT Sinogram denoising Deep learning Attention mechanism

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Improving Zero-Shot Coordination with Diversely Rewarded Partner Agents

Improving Zero-Shot Coordination with Diversely Rewarded Par...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peilin Wu Zhenhua Yang Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot coordination studies the training of well-generalizing human-AI coordination agents in the scenario where human data is unavailable. To obtain a coordination agent generalize to unseen humans, prevailing methods generate a population of partner agents as proxy models of human partners and then train a coordination agent with these partner agents. Constructed partner agents are expected to be as diverse as possible to cover a wide range of human behaviors, preventing a distribution shift between training and testing stages. Recent works concentrate on studying effective methods of creating a group of high-reward while diverse partner agents to model unseen human partners. However, the resulting high-reward partner agents do not accurately reflect real-world situations, considering that human decisions are not always optimal and may sometimes even hinder the progression of coordination. Therefore, these studies still struggle to capture the potential characteristics of human partners. In this work, reinforcement learning (RL) and supervised learning (SL) are integrated to train a reward-conditioned policy. By conditioned on different desired rewards, a reward-conditioned policy simulates both low-reward and high-reward partners. Additionally, a reward-bucketed replay buffer and curriculum learning are applied to enhance reward diversity and boost the training of coordination agents. Experiments demonstrate that the proposed reward-conditioned policy is capable of generating agents with different rewards. Moreover, the zero-shot coordination performance of agents trained with these partners surpasses previous methods in the majority of scenarios within the Overcooked human-AI coordination benchmark.

关键词： Training Impedance matching Supervised learning Neural networks Reinforcement learning Benchmark testing Trajectory

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RSMA-assisted SHAPTINs: secrecy performance under imperfect hardware and channel estimation errors

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Neural Computing and Applications 2024年 1-18页

作者： Feng, Zhou Kefeng, Guo Jian, Cheng Sunder Ali, Khowaja Kapal, Dev Reddy, Gadekallu Thippa Hussam Al, Hamadi School of Information Technology Yancheng Institute of Technology Yancheng224051 China School of Space Information Space Engineering University Beijing101407 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Faculty of Computing Digital and Data Technological University Dublin DublinD07EWV4 Ireland ADAPT Centre and Department of Computer Science Munster Technological University Cork BishopstownT12 P928 Ireland Department of institute of intelligent systems University of Johannesburg Auckland Park Johannesburg2006 South Africa Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Division of Research and Development Lovely Professional University Phagwara144001 India College of Engineering and IT College of Engineering and IT University of Dubai PO Box 14143 Dubai United Arab Emirates

Satellite high aerial platform terrestrial integrated networks have become the hot topic these years, which have been regarded as the major part of the intelligence of things for future networks. During this work, we investigate the secrecy performance for rate-splitting multiple access-assisted satellite high aerial platform terrestrial integrated networks. Besides, imperfect hardware and channel estimation errors are considered in the secrecy networks. Moreover, to enhance the energy utilization efficiency, rate-splitting multiple access scheme is utilized into the considered network, which is prior to that of the non-orthogonal multiple access scheme. What's more, to enhance the satellite transmission, multiple high aerial platforms are used to forward the transmission along with multiple eavesdroppers. In addition, the direct transmission link is not considered in the secrecy networks due to the heavy fading and obstacles. Relied on the former considerations, the exact and asymptotic analysis for the secrecy performance is further obtained to confirm the rightness of the analysis. Finally, some representative Monte Carlo simulations are carried out to validate the obtained results. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Asymptotic analysis

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Fundamental limits of distributed covariance matrix estimation under communication constraints 24

Fundamental limits of distributed covariance matrix estimati...

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Proceedings of the 41st International Conference on Machine Learning

作者： Mohammad Reza Rahmani Mohammad Hossein Yassaee Mohammad Ali Maddah-Ali Mohammad Reza Aref Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran and Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings.

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