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IEEE Conference on Global Communications (GLOBECOM)

作者： Chun-An Yang Shih-Chieh Chen Yi-Hsuan Peng Yu-Wen Chen Jian-Jhih Kuo Ming-Jer Tsai Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Computer Systems Technology New York City College of Technology Brooklyn New York USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines.

关键词： Procurement Costs Simulation Streaming media Approximation algorithms Real-time systems Telecommunications Servers User preference Optimization

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A theory of higher-order subtyping with type intervals

A theory of higher-order subtyping with type intervals

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作者： Stucki, Sandro Giarrusso, Paolo G. Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden BedRock Systems Inc. Berlin Germany

The calculus of Dependent Object Types (DOT) has enabled a more principled and robust implementation of Scala, but its support for type-level computation has proven insufficient. As a remedy, we propose F··... 详细信息

关键词： Polymorphism

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An asynchronous P system with branch and bound for solving the subset sum problem 9

An asynchronous P system with branch and bound for solving t...

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9th International Symposium on Computing and Networking Workshops, CANDARW 2021

作者： Nagao, Kosei Fujiwara, Akihiro Kyushu Institute of Technology Graduate School of Computer Science And Systems Engineering Iizuka Fukuoka820-8502 Japan

ISBN: (纸本)9781665428354

Membrane computing is a computational model inspired by living cell activity. Using the membrane computing, a number of computationally hard problems have been solved by using exponential number of natural materials. However, existing algorithms for computationally hard problems define a huge number of evolution rules to execute arithmetic *** the present paper, we propose an asynchronous P system with branch and bound for solving the subset sum problem by applying existing P systems for elementary operations. Experimental results for the proposed P system show that the proposed method contributes to the large reduction of the numbers of evolution rules and membranes. © 2021 IEEE.

关键词： Branch and bound method

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Network Model to Generate Visual Realism Images from Textual Data

Network Model to Generate Visual Realism Images from Textual...

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International Conference on Advances in Information Technology (ICAIT)

作者： Pellakuri Vidyullatha Bhuvan Unhelkar Prasun Chakrabarti G S Pradeep Ghantasala Amjan Shaik Meghana Pyla Department Of Information Systems And Decision Sciences Muma College Of Business University of South Florida South Florida USA Department of CSE Koneru Lakshmaiah Educationn Foundation Vaddeswaram Guntur AP India Department of Computer Science and Engineering Sir Padampat Singhania University Udaipur Rajasthan India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering St.Peters Engineering College Maisammaguda TS India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Generative Adversarial Networks (GANs) have the ability to produce realistic images from random noise vectors and has attracted a lot of attention in recent years. The textual input is discontinuous and requires efficient semantic information capture, extending GANs to create images from text descriptions has special obstacles. In this research, a unique GAN-based text-to-image creation method that makes use of conditional GAN architectures and text embeddings. The generator neural network in our suggested model converts text embeddings into appropriate image representations, whereas the discriminator network separates real images from those produced by the generator. This paper shows the strategies like multi-modal fusion mechanisms and attention mechanisms in the network architecture to help textual descriptions and visual information match. This method uses the benchmark datasets and show that it can produce realistic and varied visuals that correspond with textual descriptions. Both qualitative and quantitative assessments demonstrate our model's capacity to grasp minute details and subtle semantic nuances found in the input text. In addition, we perform user studies to evaluate the fidelity and perceived quality of the generated images relative to ground truth images. Based on our experimental findings, it appears that the suggested framework for text-to-pixel image production provides both textual relevance and image quality. This paper illuminates how our methodology may be utilized in different settings, including virtual universes, PC supported plan, and the production of imaginative substance. We likewise accentuate future bearings and potential for profound learning and normal language handling procedures to further develop text- to-picture creating models' abilities.

关键词： Visualization Roads Semantics Noise Neural networks Text to image Production Generative adversarial networks Generators Vectors

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Motor Imagery Classification Enhancement using Generative Adversarial Networks for EEG Spectrum Image Generation

Motor Imagery Classification Enhancement using Generative Ad...

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Annual IEEE Symposium on computer-Based Medical systems

作者： Ahmed G. Habashi Ahmed M. Azab Seif Eldawlatly Gamal M. Aly Computer and Systems Engineering Department Ain Shams University Cairo Egypt Biomedical Engineering Department Technical Research Center Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt

The development of practical Brain-computer Interface (BCI) systems has been hindered by significant issues related to data, specifically the lack of sufficient data needed for training. To address this challenge, generating synthetic data that mimics real recorded data has been proposed to augment the real data. One promising technique for data augmentation is through the use of Generative Adversarial Networks (GANs), which have been successfully applied in many other fields. This paper proposes a novel GAN-based approach for generating synthetic spectrum images of Motor Imagery (MI) Electroencephalogram (EEG). The proposed GAN is examined with two Convolutional Neural Network (CNN) architectures in the context of MI classification. Using the public dataset BCI competition IV, our findings reveal that the generated EEG spectrum images using GANs exhibit temporal, spectral, and spatial characteristics similar to the real ones. The average classification accuracy of right-hand versus left-hand MI using the proposed GAN/CNN models has improved to 76.71% with an enhancement of 2.5% in comparison to using the CNN applied to the real data only. These results suggest that using GANs could improve MI BCI systems with limited data.

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Near-Optimal Battery Swapping Algorithm in Dockless Electric Bike Sharing systems

Near-Optimal Battery Swapping Algorithm in Dockless Electric...

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IEEE International Conference on Communications (ICC)

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Dockless electric bike (E-bike) sharing has become a new urban modality of green transportation to offer convenient services. Typically, the service provider arranges a truck starting from the depot to visit multiple parking locations to replace low-energy batteries. However, visiting many parking locations may cause a considerable tour cost. One efficient way is to aggregate low-energy E-bikes together. Some incentive mechanisms are thus adopted to encourage E-bike users to move their bikes to suitable parking locations, but leading to an incentive cost. The service provider would like to balance the tour cost of the truck and the incentive cost of E-bike users. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. The simulation results with the real dataset show that our algorithm outperforms the other baselines.

关键词： Costs Shared transport Simulation Aggregates Approximation algorithms Batteries Green transportation

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A Cross-subject Transfer Learning Method for CSI-based Wireless Sensing

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IEEE Internet of Things Journal 2025年

作者： He, Zhengran Bouazizi, Mondher Gui, Guan Ohtsuki, Tomoaki Keio University School of Science for Open and Environmental Systems Kanagawa 223-0061 Japan Keio University Department of Information and Computer Science Yokohama Japan Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

WiFi-based passive non-contact sensing is widely regarded as a leading technology in wireless sensing, owing to its extensive application scope and favorable growth outlook. Nevertheless, although current WiFi-based sensing techniques attain remarkable accuracy in identifying activities within particular scenarios, they need stronger generalization capabilities across different targets and environments, hindering further commercial development. To address this issue, this paper uses CNN, BLSTM, and Attention layers to propose a cross-subject transfer learning method based on the CNN-ABLSTM algorithm model. This method combines widely used transfer learning methods with deep neural network algorithms in cross-domain sensing. Specifically, this method leverages the performance advantages of the CNN-ABLSTM algorithm model in processing time-series data like channel state information (CSI) and utilizes transfer learning to fine-tune the pre-trained model from the source domain for application in the target domain with different subjects. This enables faster and more accurate achievement of cross-subject tasks. The simulated results show that the proposed new approach achieves higher recognition accuracy and shorter training times than traditional transfer learning methods for cross-subject tasks. In testing with the dataset used, it achieves up to around 85% performance of activity recognition accuracy in cross-subject tasks. © 2014 IEEE.

关键词： Contrastive Learning

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Robotics in Healthcare: Artificial Intelligence-Driven Application for Medical Diagnosis and Treatment

Robotics in Healthcare: Artificial Intelligence-Driven Appli...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Dhanya D A. Devendran S. Balaji Vaishali M R. Sujatha K.V. Sreelekha Department of Artificial Intelligence and Data Science Mar Ephraem College of Engineering and Technology Kanyakumari Tamil Nadu India School of Business Woxsen University Hyderabad Telangana India Department of Information Technology Sri Manakula Vinayagar Engineering College Puducherry India Deportmet of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science SubbalakshmiLakshmipathy College of Science Madurai Tamil Nadu India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The rapid development of deep learning methods presents a potentially game-changing opportunity in the realm of education, particularly in the promotion of student involvement and the comprehension of the subject matter. To exemplify learning gestures and resolve educational challenges, this exploratory study investigates the operation of deep learning algorithms to determine the interests of students. Our deep learning model can provide direct predictions on the areas of interest for individual students by analyzing enormous volumes of educational data. These data include pupil relations, performance standards, and behavioral patterns of students. By aligning instructional tactics with the preferences of students, this strategy not only makes it easier for students to become accustomed to newly presented educational material but also encourages active learning. The research reveals that deep learninghelps capture the intricacies of student engagement. It also provides preceptors with valuable insights that may be used to cultivate a learning landscape that is more engaging and investigative. The results of our research highlight the possibility that deep learning will be used to change educational procedures to make them more adaptable and sensitive to the various needs of students. In this paper, the practice of using deep learning for interest identification in education is discussed, along with its methodology, perpetrators, and counteraccusations

关键词： Deep learning Education Surgery Predictive models Robot sensing systems Prediction algorithms Real-time systems Pupils Medical diagnostic imaging Standards

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Coding Theorems for Repetition and Superposition Codes over Binary-Input Output-Symmetric Channels

arXiv

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arXiv 2024年

作者： Wang, Yixin Ma, Xiao The School of Systems Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

This paper is concerned with a class of low density generator matrix codes (LDGM), called repetition and superposition (RaS) codes, which have been proved to be capacity-achieving over binary-input output-symmetric (BIOS) channels in terms of bit-error rate (BER). We prove with a recently proposed framework that the RaS codes are also capacity-achieving over BIOS channels in terms of frame-error rate (FER). With this new framework, the theorem for the RaS codes can be generalized to source coding and joint source and channel coding (JSCC). In particular, we prove with this framework that the corresponding low-density parity-check (LDPC) codes, as an enlarged ensemble of quasi-cyclic LDPC (QC-LDPC) codes, can also achieve the capacity. To further improve the iterative decoding performance, we consider the convolutional RaS (Conv-RaS) code ensemble and prove it to be capacity-achieving over BIOS channels in terms of the first error event probability. The construction of Conv-RaS codes is flexible with rate (defined as the ratio of the input length to the encoding output length) ranging from less than one (typically for channel codes) to greater than one (typically for source codes), which can be implemented as a universal JSCC scheme, as confirmed by simulations. © 2024, CC BY.

关键词： Iterative decoding

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