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检索条件"主题词=Source coding"

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Debugging Unreproducible Builds using eBPF

Debugging Unreproducible Builds using eBPF

引用

Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Vyom Yadav Himanshu Computer Science Engineering Chandigarh University Mohali India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In software development, a key concept is “reproducible builds”. These are builds where the exact same output (identical bits) is generated every time the same source code is compliled with the same tools. Unfortunately, achieving this consistency isn’t always straightforward. Software artifacts built through a complex build system can introduce a challenge to pinpoint or give an idea about the reason for the unreproducibility of a build. With the build process spanning multiple stages, observability in the build system is crucial to determine the point of failure. eBPF solves the problem by allowing to tap into syscalls and process executions. In more straightforward terms, it helps to give an estimate on what could probably be the reason for the unreproducibility. Writing eBPF programs from scratch requires a great deal of knowledge and is not something every organisation would do to debug their unreproducible builds. This paper aims to provide a highlevel solution to observe the build process.

关键词： source coding Debugging Writing Software Observability Artificial intelligence Software development management

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Speech coding using modulo-PCM with side information

Speech coding using modulo-PCM with side information

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： V. Ramamoorthy Department of Electrical Engineering Linköping Institute of Technology Linkoping Sweden

The application of a new source coding scheme called Modulo-PCM with side information to speech coding is studied. The performance characteristics of an adaptive and a non-adaptive scheme are evaluated using two speec... 详细信息

关键词： Speech coding Decoding source coding Mean square error methods Distortion measurement Speech analysis Sequences Entropy coding Nonlinear distortion Nonlinear equations

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Communication-Efficient Federated Learning with Accelerated Client Gradient

Communication-Efficient Federated Learning with Accelerated ...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Geeho Kim Jinkyu Kim Bohyung Han ECE IPAI Seoul National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Federated learning often suffers from slow and unstable convergence due to the heterogeneous characteristics of participating client datasets. Such a tendency is aggravated when the client participation ratio is low since the information collected from the clients has large variations. To address this challenge, we propose a simple but effective federated learning framework, which improves the consistency across clients and facilitates the convergence of the server model. This is achieved by making the server broadcast a global model with a lookahead gradient. This strategy enables the proposed approach to convey the projected global update information to participants effectively without additional client memory and extra communication costs. We also regularize local updates by aligning each client with the overshot global model to reduce bias and improve the stability of our algorithm. We provide the theoretical convergence rate of our algorithm and demonstrate remarkable performance gains in terms of accuracy and communication efficiency compared to the state-of-the-art methods, especially with low client participation rates. The source code is available at our project page 1. 1. https://***/geehokim/FedACG:

关键词： Costs Federated learning source coding Performance gain Stability analysis Robustness Data models

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Neural Trajectory Model: Implicit Neural Trajectory Representation for Trajectories Generation

Neural Trajectory Model: Implicit Neural Trajectory Represen...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zihan Yu Yuqing Tang Systems Hub The Hong Kong University of Science and Technology(Guangzhou) International Digital Economy Academy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The multi-agent trajectory planning problem is a difficult problem in robotics due to its computational complexity and real-world environment complexity with uncertainty, non-linearity, and real-time requirements. Many existing solutions are either search-based or optimization-based approaches with simplified assumptions of environment, limited planning speed, and limited scalability in the number of agents. In this work, we first attempt to reformulate single-agent and multi-agent trajectory planning problems as query problems over an implicit neural representation of trajectories. We formulate such implicit representations as Neural Trajectory Models (NTM) which can be queried to generate nearly optimal trajectory in complex environments. We conduct experiments in simulation environments and demonstrate that NTM achieve (1) sub-millisecond planning time using GPUs, (2) almost avoiding all collisions, and (3) generating almost shortest paths. We also demonstrate that the same NTM framework can also be used for refining low-quality and conflicting multi-agent trajectories into nearly optimal solutions efficiently. (Open source code is available at https://***/laser2099/neural-trajectory-model)

关键词： Uncertainty Trajectory planning source coding Scalability Refining Real-time systems Trajectory Planning Optimization Intelligent robots

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Cross-Layer Optimization for H.264/AVC Video Transmission Over Space-Frequency (SF) Coded MIMO-OFDM Systems

Cross-Layer Optimization for H.264/AVC Video Transmission Ov...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Saurav K. Bandyopadhyay D.G. Srinivas Prasad Lisimachos P. Kondi W&W Communications Inc. Sunnyvale CA USA Qualcomm Inc.5775 Morehouse DriveSan Diego CA 92121 USA Dept. of Computer Science University of Ioannina Ioannina Greece

Multicarrier communication becomes a natural choice to transmit multimedia content at high data rates. Among multicarrier communication techniques, the combination of orthogonal frequency division multiplexing (OFDM) technology with multiple input multiple output (MIMO) systems is very effective for high data rate wireless communication. In this paper, we propose a cross layer optimization technique for transmitting H.264/AVC video data over a MIMO-OFDM wireless channel. A set of fast time-varying and frequency selective fading channels is considered. The channel is assumed to be unchanged for the duration of one OFDM block and change independently from one OFDM block to the other. The number of pilot symbols for each OFDM block is held constant and they are dispersed throughout the block for efficient channel estimation. The video transmission system uses a jointly optimal space frequency (SF) code design and pilot placement scheme. Rate compatible punctured convolutional (RCPC) codes are used for channel protection. The problem formulation selects the set of source and channel coding rates along with the optimal pilot placement for a total bit rate constraint. The performance of the optimal power allocation case is compared with the equal power distribution case. At the transmitter, we have developed a method for the estimation of the video distortion at the receiver for given channel conditions. The accuracy of this method is validated using experimental results. Computer simulation results are shown for a 2 x 2 MIMO-OFDM system but the results can be extended to MIMO-OFDM systems with any number of transmit and receive antennas.

关键词： OFDM Resource management Channel coding Automatic voltage control Channel estimation Wireless communication source coding

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Distributed compression for condition monitoring of wind farms

Distributed compression for condition monitoring of wind far...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shuang Wang Samuel Cheng Vladimir Stanković Lina Stanković School of Electrical and Computer Engineering University of Oklahama Tulsa OK USA Department of Electronic and Electrical Engineering University of Strathclyde Glasgow UK

In order to estimate the amount of energy that will be generated by a wind farm and provide efficient power distribution planning, it is necessary to deliver information of wind speed at all wind turbines. This paper proposes a scheme for compressing wind speed measurements exploiting both temporal and spatial correlation between the turbine readings via distributed source coding. The proposed scheme relies on a correlation model based on true measurements. A compression scheme proposed is of low encoding complexity and uses a particle-filtering based belief propagation decoder that adaptively estimates the nonstationary noise of the correlation model. Simulation results using realistic models show significant performance improvements compared to the scheme that does not dynamically refine correlation.

关键词： Correlation Decoding Wind turbines source coding Wind speed Noise

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Feedback communication over individual channels

Feedback communication over individual channels

引用

IEEE International Symposium on Information Theory

作者： Yuval Lomnitz Meir Feder Department of EE-Systems Tel-Aviv University Israel

ISBN: (纸本)9781424443123

We consider the problem of communicating over a channel for which no mathematical model is specified. We present achievable rates as a function of the channel input and output sequences known a-posteriori for discrete and continuous channels. Furthermore we present a rate-adaptive scheme employing feedback which achieves these rates asymptotically without prior knowledge of the channel behavior.

关键词： Feedback communications Additive noise Error probability Mutual information Decoding Gaussian noise Mathematical model Capacity planning Output feedback source coding

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Leveraging RAG-LLM to Translate C++ to Rust

Leveraging RAG-LLM to Translate C++ to Rust

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Assured Autonomy (ICAA), International Conference on

作者： Ahmet Okutan Samuel Merten Christoph C. Michael Ben Ryjikov Leidos Reston VA USA

ISBN: (数字)9798331521011

ISBN: (纸本)9798331521028

Despite their similarities, translating C++ code into the Rust language is a challenging task on account of differences in syntax, ecosystem, philosophy, and idioms. Large Language Models (LLMs) using Retrieval Augmented Generation (RAG) are effective at solving challenging programming language tasks, including source code generation and program translation. We leveraged RAG LLMs for translating C++ code into Rust and created Cpp2Rust, an LLM-based C++-to-Rust transpiler grounding the OpenAI ChatGPT4 model with similar C++ and Rust code snippet pairs from LeetCode. Preliminary analysis results show that 84% of the solutions produced by Cpp2Rust that had no compile errors were accepted as correct solutions on LeetCode.

关键词： Computer languages Codes Program processors Philosophical considerations Grounding source coding Large language models Ecosystems C++ languages Syntactics

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Quantizing Large-Language Models for Predicting Flaky Tests

Quantizing Large-Language Models for Predicting Flaky Tests

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International Conference on Software Testing, Verification, and Validation, ICST

作者： Shanto Rahman Abdelrahman Baz Sasa Misailovic August Shi University of Texas at Austin Austin TX USA University of Illinois Urbana-Champaign Urbana IL USA

ISBN: (数字)9798350308181

ISBN: (纸本)9798350308198

A major challenge in regression testing practice is the presence of flaky tests, which non-deterministically pass or fail when run on the same code. Previous research identified multiple categories of flaky tests. Prior research has also de-veloped techniques for automatically detecting which tests are flaky or categorizing flaky tests, but these techniques generally involve repeatedly rerunning tests in various ways, making them costly to use. Although several recent approaches have utilized large-language models (LLMs) to predict which tests are flaky or predict flaky-test categories without needing to rerun tests, they are costly to use due to relying on a large neural network to perform feature extraction and prediction. We propose FlakyQ to improve the effectiveness of LLM-based flaky-test prediction by quantizing LLM's weights. The quantized LLM can extract features from test code more efficiently. To make up for loss in prediction performance due to quantization, we further train a traditional ML classifier (e.g., a random forest) to learn from the quantized LLM-extracted features and do the same prediction. The final model has similar prediction performance while running faster than the non-quantized LLM. Our evaluation finds that FlakyQ classifiers consistently improves prediction time over the non-quantized LLM classifier, saving 25.4% in prediction time over all tests, along with a 48.4 % reduction in memory usage. Furthermore, prediction performance is equal or better than the non-quantized LLM classifier.

关键词： Training Software testing Quantization (signal) Codes Computational modeling source coding Artificial neural networks

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Illegal Parking Detection Based on Multi-Task Driving Perception

Illegal Parking Detection Based on Multi-Task Driving Percep...

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IEEE Symposium on Intelligent Vehicle

作者： Li-Chia Kuo Huei-Yung Lin Department of Electrical Engineering National Chung Cheng University Chiayi Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

With the development in sensors, computing resources and deep neural networks, the safety mechanisms of vehicles are continuously developed towards the fully automated driving system (FADS). One of the most crucial aspects of this technology is the environmental perception. Most of the existing works focus on recognizing specific targets in the scene, and often overlook the holistic information for sufficient use by FADS. In this paper, we adopt a multi-task learning approach to achieve more comprehensive recognition of environmental information. On the other hand, due to the increasing prominence of traffic issues in urban areas, the problem of illegal parking has gained more attention. Thus, a vision system to recognize illegal parking in road scenes based on environmental perception is proposed. We also collect an illegal parking dataset and make it available publicly for related research. source code and dataset are available publicly.

关键词： Target recognition Roads Machine vision source coding Urban areas Multitasking Sensor systems

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