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Building Device Models Using LLMs for Requirements Simulation in Smart Homes

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Building Device Models Using LLMs for Requirements Simulatio...

IEEE International Requirements Engineering Conference Workshops (REW)

作者： Shi Chen Xiaohong Chen Zhi Jin Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of High-Confidence Software Technologies Peking University Ministry of Education China Department of Computer Science and Technology Peking University Beijing China

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

The implementation of model-based requirements simulations in smart homes is an effective strategy that allows users to comprehend the execution impact of their personalized needs before deployment. This necessitates the use of device models in advance. In this paper, we propose an automatic modeling method for smart home devices that utilizes a Large Language Model (LLM) to assist end users in modeling their devices. The method involves extracting information directly from device manuals using the extraction capabilities of the LLM, deducing the automata models using the reasoning capabilities of the LLM, and verifying the model quality through the properties of valid device models. Our evaluation has demonstrated that our method can generate high-quality device models. The resultant model can be utilized for smart home scenario simulations, assisting users in validating their requirements. Thus, this provides a cost-effective and scalable solution for offering device models to simulate user requirements in smart homes.

关键词： Large language models Conferences Automata Smart homes Manuals Information retrieval Cognition

Topic-Guided Self-Introduction Generation for Social Media Users

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arXiv 2023年

作者： Xu, Chunpu Li, Jing Li, Piji Yang, Min Department of Computing The Hong Kong Polytechnic University Hong Kong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user’s personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user’s tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework;it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user’s history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling. 1 © 2023, CC BY.

关键词： User profile

Preparing a commercial quantum key distribution system for certification against implementation loopholes

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Physical Review Applied 2024年第4期22卷 044076页

作者： Vadim Makarov Alexey Abrikosov Poompong Chaiwongkhot Aleksey K. Fedorov Anqi Huang Evgeny Kiktenko Mikhail Petrov Anastasiya Ponosova Daria Ruzhitskaya Andrey Tayduganov Daniil Trefilov Konstantin Zaitsev Skolkovo Moscow 121205 Russia Vigo Quantum Communication Center NTI Center for Quantum Communications Department of Physics Faculty of Science Institute for Quantum Computing Department of Physics and Astronomy Quantum technology foundation (Thailand) Bangkok 10110 Thailand QRate Skolkovo Moscow 143026 Russia Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology Moscow 119991 Russia atlanTTic Research Center School of Telecommunication Engineering Department of Signal Theory and Communications Moscow 101000 Russia

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarization encoding, a qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and postprocessing. We analyze the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure.

关键词： Quantum communication Quantum cryptography Quantum protocols

Generating hyperspectral data based on 3D CNN and improved wasserstein generative adversarial network using homemade high-resolution datasets 2020

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Generating hyperspectral data based on 3D CNN and improved w...

2020 International Conference on Wireless Communication and Sensor Networks, icWCSN 2020

作者： Li, Yin Huang, Da State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781450377638

Hyperspectral images contain rich information on the fingerprints of materials and are being popularly used in the exploration of oil and gas, environmental monitoring, and remote sensing. Since hyperspectral images cover a wide range of wavelengths with high resolution, they can provide rich features for enhancing the subsequent target detection and classification procedure. The recently proposed deep learning algorithms have been frequently utilized to extract features from hyperspectral images. However, these algorithms are all data-hungry, and need large volume of data to learn an even usable model. To overcome this deficiency, this paper proposes an improved 3D Wasserstein generative adversarial network (3D WGAN-GP) model to generate hyperspectral images with the help of a small amount of training homemade unlabeled hyperspectral images. In particular, WGAN-GP generates hyperspectral images through three-dimensional convolutional neural networks (3D-CNN) and discriminates it against the real ones through a discriminator network. We train the proposed 3D WGAN-GP model by jointly learning both networks with the Adam algorithm. To the best of our knowledge, this is the first work that augments hyperspectral images by using the deep learning algorithm. We evaluate the quality of the generated hyperspectral images by comparing their spectrums to the corresponding real ones. The experimental results confirm the effectiveness of the proposed model. © 2020 ACM.

关键词： Remote sensing

CNN+LSTM Accelerated Turbulent Flow Simulation with Link-Wise Artificial Compressibility Method 21

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CNN+LSTM Accelerated Turbulent Flow Simulation with Link-Wis...

50th International Conference on Parallel Processing, ICPP 2021

作者： Fan, Sijiang Fei, Jiawei Guo, Xiao-Wei Yang, Canqun Revell, Alistair Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China School of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom

ISBN: (纸本)9781450390682

The simulation of turbulent flow, the most common form of fluid, is indispensable in computational fluid dynamics (CFD). The synthetic eddy method (SEM) generates the turbulent inflow and is adopted as the inlet boundary condition of simulation. However, SEM is time-consuming and can significantly slow down the simulation process which occupies 58% of the whole computational time. This is highly inefficient especially since SEM is only used as the inlet. In this paper we propose an efficient alternative. In particular, we leverage CNN+LSTM to replace SEM to obtain the turbulence statistics and combine it with link-wise artificial compressibility method (LW-ACM), which is a fast numerical method of CFD. We validate the predicted results by CNN+LSTM and prove that our model can provide the correct turbulence statistics even after a long time. Experiment results show that our CNN+LSTM module achieves over 15 × speedup compared with SEM, which greatly reduces the time consumption of turbulent inflow generation (from 58% to 7%). As a result, the whole time of turbulent flow simulation is more than halved. Compared with a newly released GPU-accelerated standard lattice Boltzmann method solver, our combination of CNN+LSTM and LW-ACM is about 8.6 × faster. Among all studies reported to date, our work is the fastest implementation for simulating turbulent channel flow, an important step for the field of fast CFD analysis. © 2021 ACM.

关键词： Computational fluid dynamics

A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends

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arXiv 2023年

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

SPFNO: Spectral operator learning for PDEs with Dirichlet and Neumann boundary conditions

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arXiv 2023年

作者： Liu, Ziyuan Wu, Yuhang Huang, Daniel Zhengyu Zhang, Hong Qian, Xu Song, Songhe Department of Mathematics National University of Defense Technology Changsha410073 China Beijing International Center for Mathematical Research Peking University Beijing100871 China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha410073 China

Neural operators have been validated as promising deep surrogate models for solving partial differential equations (PDEs). Despite the critical role of boundary conditions in PDEs, however, only a limited number of neural operators robustly enforce these conditions. In this paper we introduce semi-periodic Fourier neural operator (SPFNO), a novel spectral operator learning method for solving PDEs with non-periodic BCs. This method extends our previous work (arXiv:2206.12698), which showed significant improvements by employing enhanced neural operators that precisely satisfy the boundary conditions. However, the previous work is associated with Gaussian grids, restricting comprehensive comparisons across most public datasets. In response to this, we present numerical results of SPFNO for various PDEs such as the viscous Burgers' equation, Darcy flow, incompressible pipe flow, and coupled reaction-diffusion equations. These results demonstrate the computational efficiency, resolution invariant property, and BC-satisfaction behavior of proposed model. An accuracy improvement of approximately 1.7X-4.7X over the non-BC-satisfying baselines is also achieved. Furthermore, our studies on SOL underscore the significance of satisfying BCs as a criterion for deep surrogate models of PDEs. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear equations

Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing

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arXiv 2022年

作者： Yang, Junyan Jiang, Yang Wang, Shuoyao College of Electronic and Information Engineering Shenzhen University China Institute of High-Performance Computing Singapore Guangdong Province Engineering Laboratory for Digital Creative Technology Shenzhen Key Laboratory of Digital Creative Technology China

The rapid development of multimedia and communication technology has resulted in an urgent need for high-quality video streaming. However, robust video streaming under fluctuating network conditions and heterogeneous client computing capabilities remains a challenge. In this paper, we consider an enhancement-enabled video streaming network under a time-varying wireless network and limited computation capacity. "Enhancement" means that the client can improve the quality of the downloaded video segments via image processing modules. We aim to design a joint bitrate adaptation and client-side enhancement algorithm toward maximizing the quality of experience (QoE). We formulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL)based framework, named ENAVS. As video streaming quality is mainly affected by video compression, we demonstrate that the video enhancement algorithm outperforms the super-resolution algorithm in terms of signal-to-noise ratio and frames per second, suggesting a better solution for client processing in video streaming. Ultimately, we implement ENAVS and demonstrate extensive testbed results under real-world bandwidth traces and videos. The simulation shows that ENAVS is capable of delivering 5% − 14% more QoE under the same bandwidth and computing power conditions as conventional ABR streaming. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs 39

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Hierarchical Context Pruning: Optimizing Real-World Code Com...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Lei Li, Yunshui Li, Jiaming Xia, Xiaobo Yang, Jiaxi Luo, Run Wang, Minzheng Chen, Longze Liu, Junhao Qu, Qiang Yang, Min Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University China School of Computing National University of Singapore Singapore University of Science and Technology of China China University of California Irvine United States MAIS Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)157735897X

Some of the latest released Code Large Language Models (Code LLMs) have been trained on repository-level code data, enabling them to perceive repository structures and utilize cross-file code information. This capability allows us to directly concatenate the content of repository code files in prompts to achieve repository-level code completion. However, in real development scenarios, directly concatenating all code repository files in a prompt can easily exceed the context window of Code LLMs, leading to a significant decline in completion performance. Additionally, overly long prompts can increase completion latency, negatively impacting the user experience. In this study, we conducted extensive experiments, including completion error analysis, topology dependency analysis, and cross-file content analysis, to investigate the factors affecting repository-level code completion. Based on the conclusions drawn from these preliminary experiments, we proposed a strategy called Hierarchical Context Pruning (HCP) to construct high-quality completion prompts. We applied the HCP to six Code LLMs and evaluated them on the CrossCodeEval dataset. The experimental results showed that, compared to previous methods, the prompts constructed using our HCP strategy achieved higher completion accuracy on five out of six Code LLMs. Additionally, the HCP managed to keep the prompt length around 8k tokens (whereas the full repository code is approximately 50k tokens), significantly improving completion throughput. Our code and data will be publicly available. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： high level languages