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检索条件"主题词=Source coding"

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A high performance fixed rate compression scheme for still image transmission

A high performance fixed rate compression scheme for still i...

引用

Data Compression Conference (DCC)

作者： M.J. Ruf German Aerospace Research Establishment (DLR) Institute for Communications Technology Wessling Germany

Variable length source coding schemes have a very painful drawback when it comes to transmission of compressed images. Thus the authors restrict themselves to fixed rate source coding schemes, that do not have the problems of re-synchronization or complete loss of information in the case of uncorrectable transmission errors. In the paper, a high performance source coding scheme based on the discrete wavelet transform (DWT) with an additional DCT for the low frequency sub-image and a product pyramid vector quantizer (PPVQ) utilizing trellis coded quantization (TCQ) is proposed which gives close to optimum results compared to variable length coding (VLC). This together with the ability to calculate the bit-sensitivity of the compressed image data enables one to perform a highly efficient unequal error protection for image transmission over noisy channels. Simulation results show a source coding degradation of only 0.5-0.7 dB in PSNR due to fixed rate coding compared to the best found VLC scheme. In addition, applying combined source anti-channel coding and looking at the performance after transmission over an additive white Gaussian noise (AWGN) channel, the proposed scheme outperforms all other known fixed rate schemes, even a sophisticated VLC scheme based also on the DWT and especially has gains of up to 5 dB in PSNR and up to 3.5 dB in E/sub SN/sub 0/ compared to JPEG.< >

关键词： Image coding source coding Discrete wavelet transforms PSNR AWGN Additive white noise Propagation losses Discrete cosine transforms Frequency Quantization

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A Quantum Computation Language-based Quantum Compiler

A Quantum Computation Language-based Quantum Compiler

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International Conference on Computing and Networking Technology (ICCNT)

作者： S. Jaya Amruth T.M. Nigelesh V. Sai Shruthik Valluru Sateesh Reddy Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Quantum computing is a revolution in computational theory that uses the principles of quantum mechanics to perform computations that are beyond the capacity of classical computers. It works with the special features of the quantum bits or qubits to encode and transform the information in manners conventional bits cannot imagine. A quantum compiler is a software tool designed to translate high-level quantum language code into executable quantum instructions. This paper aims to develop a quantum compiler for translating Quantum Computation Language source codes into quantum bytecodes which can later be executed on a Quantum Virtual Machine. This compiler will have a combinational structure that includes a frontend for interpreting the QCL code, an intermediate representation for optimizing quantum algorithms, and a backend for generating efficient quantum bytecode. Optimization techniques will be applied to improve the functioning of the quantum circuits. The compiler will also include an error handling module to handle the errors. The successful deployment of a quantum compiler for QCL has many implications and unlocks opportunities for advancements in quantum technologies.

关键词： Quantum computing Codes Quantum algorithm source coding Qubit Quantum mechanics Transforms Quantum cascade lasers Software tools Quantum circuit

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U-Fuzz: Stateful Fuzzing of IoT Protocols on COTS Devices

U-Fuzz: Stateful Fuzzing of IoT Protocols on COTS Devices

引用

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

作者： Zewen Shang Matheus E. Garbelini Sudipta Chattopadhyay ASSET Research Group SUTD Singapore

ISBN: (数字)9798350308181

ISBN: (纸本)9798350308198

Internet-of-Things (IoT) devices have become widely popular and are being increasingly utilized in both home and industrial environments. Such devices use a variety of different protocols for communication. Considering the complex and stateful nature of these protocols, their implementations may contain security vulnerabilities and are subject to remote exploitation. To address this, we present U-Fuzz, a framework to systematically discover and replicate security vulnerabilities on arbitrary wired and wireless IoT protocol implementations. Given only a network capture file which contains the packet traces of normal (i.e., benign) communication, U-Fuzz automatically constructs a protocol state machine. Subsequently, this state machine is leveraged via a stateful fuzzing engine to arbitrarily manipulate and replay communicated packets. U-Fuzz carefully disintegrates the design of state machine construction from the fuzzing actions and optimizations, allowing U-Fuzz to work with an arbitrary number of protocols without any change in the stateful fuzzing engine. U-Fuzz does not require any access to the source code of the protocol and it also does not involve any instrumentation. This makes U-Fuzz to applicable out-of-the-box for fuzzing arbitrary IoT devices employing a variety of protocols. We implemented U-Fuzz and applied it against ten subject implementations including implementations on five commercial-off-the-shelf (COTS) devices employing three popular IoT protocols: 5G NR, Zigbee, and CoAP. As of today, U-Fuzz discovered a total of 11 new vulnerabilities (out of 16) and CVEs have already been assigned to all of them.

关键词： Wireless communication Protocols 5G mobile communication source coding Zigbee Fuzzing Internet of Things

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Distortion control for queues with deadlines

Distortion control for queues with deadlines

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Data Compression Conference (DCC)

作者： A. Faridi A. Ephremides Electrical & Computer Engineering Department University of Maryland College Park MD USA

We investigate the optimum transmission strategy that minimizes the overall distortion for delay-sensitive but distortion-tolerant data. We consider a set of source symbols residing at the transmitter that are encoded into a set of packets using multiresolution source coding. Each packet has a given deadline after which its transmission will be useless. Since multiresolution source codes are being used, the packet lengths can be adjusted by dropping less significant bits in order to allow for the more significant bits of a larger number of packets to be transmitted before the deadline. We find the optimum number of bits that must be transmitted of every packet to minimize the overall distortion when transmissions are error-free. We show that for strictly convex distortion functions the solution is unique and independent of the form of the function, and extend this result to the case where transmitted bits can be affected by noise and find the optimum strategy that leads to the minimum expected distortion. Finally we look at the case where packets arrive according to a given deterministic arrival schedule and present an algorithm that finds the optimum transmission strategy.

关键词： Delay Signal resolution Transmitters Scheduling algorithm Collaborative work Quantization Propagation losses Data engineering Educational institutions source coding

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Leveraging In-and-Cross Project Pseudo-Summaries for Project-Specific Code Summarization

Leveraging In-and-Cross Project Pseudo-Summaries for Project...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yupeng Wu Tianxiang Hu Ninglin Liao Rui Xie Minghui Zhang Dongdong Du Shujun Lin Peking University Beijing China Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology Beijing China Handan Institute of Innovation Peking University Handan China China Academy of Industrial Internet Beijing China Institute for Global Health and Development Peking University Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Code summarization is pivotal in software development, aiding developers in grasping the semantics of source code. However, existing research predominantly focuses on the general code summarization capabilities of models, neglecting project-specific summary characteristics. However, given the scarcity of project-internal code summary corpora, enhancing the model’s performance for a specific project presents a significant challenge. To tackle this issue, we introduces the use of In-and-Cross project pseudo-summaries to improve Project-Specific Code Summarization. Specifically, we employ models trained on other projects to generate cross-project pseudo-summaries and learn the distinctions from target-project through contrastive learning. Simultaneously, we utilize in-project pseudo-summaries generated by the current model, harnessing these data through semi-supervised learning to enhance performance. The experiment results show that the proposed method can effectively improve the performance of the summarization task in practical scenarios, and can also enhance the coordination of the model.

关键词： Codes Reviews source coding Semantics Refining Contrastive learning Semisupervised learning

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Enhancing Neural Aspect Term Extraction Using Part-Of-Speech and Syntactic Dependency Features

Enhancing Neural Aspect Term Extraction Using Part-Of-Speech...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiaxiang Chen Yu Hong Qingting Xu Jianmin Yao Guodong Zhou Computer Science and Technology Soochow University Suzhou China

We tackle Aspect Term Extraction (ATE), an important natural language processing task that automatically identifies words of domain-specific aspects. Recently, a variety of sophisticated neural models and learning strategies have been explored for enhancing ATE, and significant improvements have been obtained. We intend to strengthen the neural models by involving features of Part-Of-Speech (POS) and syntactic dependency into the encoding process. It is motivated by the empirical findings that features of linguistic structure help to refine the understanding of semantics. Accordingly, we propose a stepwise encoding approach, where POS and syntactic dependency are successively leveraged step by step, including 1) joint encoding over both word and POS sequences using a pretrained language model; 2) BiGRU-based representational refinement conditioned on semantics-aware POS information and POS-aware semantic information; 3) representational augmentation by convolutional encoding of dependency graph. We conduct experiments on the four benchmark datasets of Semantic Evaluation (SemEval) for ATE. Experimental results show that our method obtains substantial improvements on all the considered datasets, and the performance ( $F1$ -score) reaches 87.69%, 89.76%, 77.94% and 83.96% for L-14, R-14, R-15 and R-16, respectively. All the models and source codes in the experiments will be made publicly available to support reproducible research.

关键词： Convolutional codes source coding Semantics Syntactics Linguistics Feature extraction Encoding

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Exploring Branchformer-Based End-to-End Speaker Diarization with Speaker-Wise VAD Loss

Exploring Branchformer-Based End-to-End Speaker Diarization ...

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Oriental COCOSDA International Conference on Speech Database and Assessments

作者： Pei-Ying Lee Hau-Yun Guo Tien-Hong Lo Berlin Chen Department of Computer Science and Information Engineering National Taiwan Normal University Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Taipei Taiwan

ISBN: (数字)9798331506032

ISBN: (纸本)9798331506049

Speaker diarization involves partitioning an audio stream into segments according to the identity of the speaker. The encoder-decoder based attractors for the end-to-end neural diarization (EEND-EDA) model can handle overlapping speech and has shown promising performance compared to traditional methods. However, EEND-EDA fails to identify the number of speakers accurately. To address this limitation, we first replace the Transformer encoder in EEND-EDA with the Branchformer encoder. Additionally, we introduce speaker-wise VAD Loss (SAD Loss) to the self-attention mechanism of the Branchformer encoder, thereby improving the model's ability to distinguish different speakers. Extensive experimental results on the Mini-Librispeech and simulated dataset Sim2spk benchmark dataset suggest that our approach outperforms existing strong baselines by a substantial margin, achieving a significant improvement of more than 15% Diarization Error Rate (DER). We will release the source code on GitHub 1 for future research.

关键词： Error analysis Databases source coding Benchmark testing Transformers Software development management

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Selective Cross-Correlation Consistency Loss for Out-of-Distribution Generalization

Selective Cross-Correlation Consistency Loss for Out-of-Dist...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zining Chen Weiqiu Wang Zhicheng Zhao Fei Su Aidong Men School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Deep learning methods usually succeed in independent and identically distributed (IID) data distribution, but suffer from sharp performance decay in real-world out-of-distribution (OOD) data. OOD generalization emerges to alleviate the large distribution shift between source and target domains. Recently, domain-invariant learning has boosted the research on OOD generalization, but most methods indulge complex architectures and training strategies. Hence, we propose a simple yet effective Selective Cross-Correlation Consistency (SC 3 ) loss to align the cross-correlation matrix of features from identical categories. Specifically, we design the Semantic-Oriented Selection (SOS) algorithm in SC 3 loss to eliminate negative effects on spurious channels. Extensive experiments demonstrate that the SC 3 loss achieves superior performance on multiple OOD scenarios, including domain generalization (DG) and single domain gener-alization (SDG) tasks. Also, our loss consumes negligible computational resource which conforms to real-world applications. source code is available at https://***/znchen666/SC3.

关键词： Training Representation learning Deep learning source coding Semantics Distributed databases Computer architecture

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sourcePlag - source Code Plagiarism Detection Based on Abstract Syntax Trees

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Nakul Sharma Siddharth Shinde Swarup Bhosale Suyog Patil Department of Artificial Intelligence and Data Science Faculty of Engineering Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Data Science Student of Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities.

关键词： Java Codes Plagiarism source coding Syntactics Programming Security Reliability Python Testing

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Research on automatic generation of fuzz-target for software library functions

Research on automatic generation of fuzz-target for software...

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Ivannikov ISPRAS Open Conference (ISPRAS)

作者： Chi Thien Tran Dmitry Ponomarev Andrey Kuznhesov Ivannikov Institute for System Programming of the RAS Moscow Russia LLC “Fobos-NT” Moscow Russia

To avoid re-development, software libraries are widely used. However, the automated testing of software libraries remains challenging. This article focuses on the use of fuzzing technology and automatic code generation for testing software libraries. Since late of 2020, the instrument Futag [1] – Fuzz-targets automatic generator for software libraries – has been developed in Ivannikov Institute for system programming of Russian academy of science. The instrument gathers data on the building process, generates the fuzz-targets for library functions, and performs static analysis to find entity dependencies in library source code. As result, Futag has found bugs in software libraries such as libpng, tinyxml2, libpq-standalone, etc.

关键词： Software libraries Codes Instruments source coding Static analysis Fuzzing Programming

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