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Smart World Congress (SWC), IEEE

作者： Feiran You Hongyang Du Jiawen Kang Wei Ni Dusit Niyato Abbas Jamalipour School of Electrical and Computer Engineering The University of Sydney Sydney NSW Australia Data61 Commonwealth Scientific and Industrial Research Organization (CSIRO) Sydney NSW Australia Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a Data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users’ privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users’ privacy within the PPO centralized management framework.

关键词： Privacy Solid modeling Simulation Noise reduction Noise Virtual reality Reinforcement learning Diffusion models Protection Optimization

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Learning-Based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

Learning-Based Sustainable Multi-User Computation Offloading...

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IEEE International Conference on Communications (ICC)

作者： Minrui Xu Dusit Niyato Jiawen Kang Zehui Xiong Mingzhe Chen School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA

In this paper, a novel paradigm of mobile edgequantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.

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LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics

arXiv

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arXiv 2025年

作者： Tang, Jialiang Chen, Shuo Gong, Chen Zhang, Jing Tao, Dacheng School of Computer Science and Engineering Nanjing University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore School of Intellgence Science and Technology Nanjing University China Department of Automation Institute of Image ProceProceedingssing and Pattern Recognition Shanghai Jiao Tong University China School of Computer Science Wuhan University China

Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Recent studies have revealed that Large Language Models (LLMs), with their powerful in-contextual modeling capabilities, hold significant potential for TSF. However, existing LLM-based methods usually perform suboptimally because they neglect the inherent characteristics of time series data. Unlike the textual data used in LLM pre-training, the time series data is semantically sparse and comprises distinctive temporal patterns. To address this problem, we propose LLM-PS to empower the LLM for TSF by learning the fundamental Patterns and meaningful Semantics from time series data. Our LLM-PS incorporates a new multi-scale convolutional neural network adept at capturing both short-term fluctuations and long-term trends within the time series. Meanwhile, we introduce a time-to-text module for extracting valuable semantics across continuous time intervals rather than isolated time points. By integrating these patterns and semantics, LLM-PS effectively models temporal dependencies, enabling a deep comprehension of time series and delivering accurate forecasts. Intensive experimental results demonstrate that LLM-PS achieves state-of-the-art performance in both short- and long-term forecasting tasks, as well as in few- and zero-shot settings. © 2025, CC BY.

关键词： Spatio-temporal data

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Design and Development of an Experimental Photovoltaic System

Design and Development of an Experimental Photovoltaic Syste...

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Energy Efficiency and Agricultural engineering (EE&AE), International Conference on

作者： Ventsislav Keseev Nicolay Mihailov Boris Evstatiev Tsvetelina Mladenova Vladislav Hinkov Nikolay Valov Department of Telecommunications University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electrical Power Engineering University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computer Systems and Technologies University of Ruse “Angel Kanchev” Ruse Bulgaria Center for Computing and Information Services University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Automation and Mechatronics University of Ruse “Angel Kanchev” Ruse Bulgaria

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

The world faces many challenges today and the distributed photovoltaic (PV) systems for local high-efficiency generation of electricity from sunlight seem to be one of the good solutions, but most of the population live in crowded urban environments, which creates a number of limiting factors. This study focuses on the design and implementation of an experimental photovoltaic installation in Ruse, Bulgaria, which allows the investigation of the impact of these factors on the efficiency of urban PV plants for optimization and education purposes. Initially, the goals towards the research facility are defined and the design and implementation of the installation is presented, including the orientation and positioning of the PV park, its schematic, and the key characteristics of the equipment used. Next, the first full year of operation of the research facility is analyzed by examining its energy generation profile. The winter months have about 6 times lower energy yields than the best summer ones and about 4-5 times lower on average compared to those for the other 3 seasons. This is due to the nearby buildings and trees, which create different shadows over the different seasons. The daily energy generation period during the winter is more than twice as short in comparison to that for the other seasons, while the peak daily winter energy yield is about $30 \%$ lower. The yearly energy yield per 1 kW peak installed PV panels is $1131.75 \mathrm{kWh} / \mathrm{kW}$, while the Global Solar Atlas suggests that the specific photovoltaic power output for Ruse is $1360.2 \mathrm{kWh} / \mathrm{kWp}$. The obtained exploitation results indicate that the experimental installation fully complies with the stated goals and allows investigating a number of urban scenarios.

关键词： Photovoltaic systems Azimuth Electricity Urban areas Buildings Production Solar power generation

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Code Clone Detection Using Boosting Algorithms

Code Clone Detection Using Boosting Algorithms

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Advanced Research in computing (ICARC), International Conference on

作者： M.V. Thanoshan Kuhaneswaran Banujan B.T.G.S Kumara S. Prasanth Zhenni Li Incheon Paik Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technologies Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan

To increase programming productivity, developers often copy and paste the source code with or without changing it. However, they may also introduce significant downsides in the long run, including complicating the software and raising maintenance costs. The activity of duplicating the code is known as code cloning. They are classified into four types - Type-1, Type-2, Type-3, and Type-4. In this paper, the author presents a machine-learning approach for detecting code clones of all kinds except for Type-2. Abstract Syntax Trees are used to extract features from the methods. A distance combination approach combines two feature vectors of a pair of methods and their class labels. Once the dataset is finalised, a machine- learning approach is utilised to classify the clone type. Moreover, boosting classifiers like XGBoost, CatBoost, LightGBM, Gradient Boosting and AdaBoost are evaluated for the highest classification accuracy. From the results obtained, LightGBM outperformed all the other classifiers with the highest F1 score of 0.81. This study would motivate future researchers to focus on identifying the Type-2 clones and extracting novel features in determining the clone types.

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Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission

arXiv

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arXiv 2024年

作者： Zhang, Ruichen Du, Hongyang Liu, Yinqiu Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by the complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregates them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated. © 2024, CC BY.

关键词： Satellite communication systems

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GraCo: Granularity-Controllable Interactive Segmentation

arXiv

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arXiv 2024年

作者： Zhao, Yian Li, Kehan Cheng, Zesen Qiao, Pengchong Zheng, Xiawu Ji, Rongrong Liu, Chang Yuan, Li Chen, Jie School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China -Preferred Program Peking University Shenzhen Graduate School China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Automation and BNRist Tsinghua University Beijing China

Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity output and multi-granularity output. The latter aims to alleviate the spatial ambiguity present in the former. However, the multi-granularity output pipeline suffers from limited interaction flexibility and produces redundant results. In this work, we introduce Granularity-Controllable Interactive Segmentation (GraCo), a novel approach that allows precise control of prediction granularity by introducing additional parameters to input. This enhances the customization of the interactive system and eliminates redundancy while resolving ambiguity. Nevertheless, the exorbitant cost of annotating multi-granularity masks and the lack of available datasets with granularity annotations make it difficult for models to acquire the necessary guidance to control output granularity. To address this problem, we design an any-granularity mask generator that exploits the semantic property of the pre-trained IS model to automatically generate abundant mask-granularity pairs without requiring additional manual annotation. Based on these pairs, we propose a granularity-controllable learning strategy that efficiently imparts the granularity controllability to the IS model. Extensive experiments on intricate scenarios at object and part levels demonstrate that our GraCo has significant advantages over previous methods. This highlights the potential of GraCo to be a flexible annotation tool, capable of adapting to diverse segmentation scenarios. The project page: https://***/GraCo. Copyright © 2024, The Authors. All rights reserved.

关键词： Pipelines

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

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IEEE Network 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Through nonlinear mapping of semantic representations, Deep Learning (DL)-based semantic communications further enhance communication efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities allow attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we propose a novel Mixture-of-Experts (MoE)-based SemCom system, comprising a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 1986-2012 IEEE.

关键词： Vehicular ad hoc networks

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Using Machine Learning to Study the Effects of Genetic Predisposition on Brain Aging in the UK Biobank

Using Machine Learning to Study the Effects of Genetic Predi...

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IEEE International Symposium on Biomedical Imaging

作者： Karen Ardila Emily Munro Fernando Vega Aashka Mohite Charlotte Curtis Amanda V. Tyndall M. Ethan MacDonald Department of Electronic and Automation Biomedical Engineering Universidad Autónoma de Manizales Manizales Colombia Department of Biomedical Engineering University of Calgary Calgary Canada Department of Electrical & Software Engineering University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Mitacs Globalink Research Internship Program Canada Department of Chemical Engineering University of Calgary Calgary Canada Department of Mathematics and Computing Mount Royal University Calgary Canada Department of Medical Genetics University of Calgary Calgary Canada

The influence of genetic predisposition on changes in brain morphology during aging remains largely unknown. This study explores the effects of genetic predisposition on three key brain regions: total brain volume (TBV), lateral ventricular volume (LVV), and total hippocampal volume (THV). The brain age gap estimate (BrainAGE) biomarker is used as an input to a genome-wide association study to determine which single nucleotide polymorphisms (SNPs) and genes are associated with accelerated brain aging. Six independent significant SNPs were found to contribute to accelerated morphological changes: TBV had associations on chromosome 17 linked with brain aging, and the total THV had independent significant associations in the APOC1 and TOMM40 gene regions related to neurodegeneration. Lastly, LVV presented a possible novel discovery in the gene NUAK1, known to play a role in cellular senescence. This study provides a framework to uncover complex associations between brain aging physiology and genetics.

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Coplane-Constrained Sparse Depth Sampling and Local Depth Propagation for Depth Estimation

SSRN

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SSRN 2023年

作者： Zhang, Jiehua Yang, Zhiwen Li, Liang Chen, Chuqiao Wang, Hongkui Wang, Tingyu Yan, Chenggang Gong, Yihong School of Software Engineering Xi’an Jiaotong University Xi’an710000 China Hangzhou Dianzi University Hangzhou310018 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Lishui Institute of Hangzhou Dianzi University Hangzhou310018 China Department of Automation

Depth estimation with sparse reference has emerged recently, and predicts depth map from a monocular image and a set of depth reference samples. Previous works randomly select reference samples by sensors, leading to severe depth bias as this sampling is independent to image semantic and neglects the unbalance of depth distribution in regions. This paper proposes a Coplane-Constrained sparse Depth (CCD) sampling to explore representative reference samples, and design a Local Depth Propagation (LDP) network for complete the sparse point cloud map. This can capture diverse depth information and diffuse the valid points to neighbors with geometry prior. Specifically, we first construct the surface normal map and detect coplane pixels by superpixel segmenting for sampling references, whose depth can be represented by that of superpixel centroid. Then, we introduce local depth propagation to obtain coarse-level depth map with geometric information, which dynamically diffuses the depth from the reference to neighbors based on local planar assumption. Further, we generate the fine-level depth map by devising a pixel-wise focal loss, which imposes the semantic and geometry calibration on pixels with low confidence in coarse-level prediction. Extensive experiments on public datasets demonstrate that our model outperforms SOTA depth estimation and completion methods. © 2023, The Authors. All rights reserved.

关键词： Semantics

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