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检索条件"主题词=Source coding"

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Vulnerability Detection by Sequential Learning of Program Semantics via Graph Attention Networks

Vulnerability Detection by Sequential Learning of Program Se...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Li Li Qihong Han Zhanqi Cui School of Computer Science Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Vulnerability detection is a crucial aspect of protecting software systems from cyber attacks. However, some types of vulnerabilities are difficult to detect and require analyzing the source code from multi-views. To address this, we propose a general and easily extensible framework, SGVD(Sequential Graph Attention Networks for Vulnerability Detection). SGVD consists of a sequential module that uses the GAT to learn the semantic representations of the code and a novel Fused-Prediction module that extracts useful features from the multi-view source code. We evaluated this framework on a dataset that includes two large-scale open-source C projects. The experiments showed that SGVD had a superior performance compared to the existing advanced graph learning vulnerability detection tools Devign and ReGVd,with an average increase of 12.25% in Accuracy, 13.65% in Precision, 12.04% in F1 score, and 9.14% in Recall.

关键词： Codes Accuracy source coding Semantics Feature extraction Software systems Cybernetics Cyberattack

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Robust Decision-Making for Off-Ramp in Intention-Based Deep Reinforcement Learning

Robust Decision-Making for Off-Ramp in Intention-Based Deep ...

引用

Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Huazhen Fang Li Liu Qing Gu Xiaofeng Xiao Yu Meng School of Mechanical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

Deep reinforcement learning has demonstrated its effectiveness in autonomous driving decision-making. The real-world vehicle decision-making process is complex and involves many types of information. Limitations in state information can hinder agents from making optimal decisions, increasing the risk of serious consequences like collisions. We introduce a ro-bust deep reinforcement learning methodology that incorporates driving intention recognition for the decision-making of highway autonomous vehicles during off-ramp scenarios. By using driving intention recognition results, this approach enhances driving safety. A series of experiments utilizing the SUMO and NGSIM US101 datasets support the effectiveness and feasibility of the proposed model. Comparative analytical experiments show that the model outperforms other approaches in average return value, average speed, average steps, and success rate. Additionally, the model exhibits consistent success rates under different road traffic density scenarios, highlighting its robustness. The source code is accessible at https://***/ColinFanghz/***.

关键词： Road transportation source coding Decision making Transportation Deep reinforcement learning Robustness Road traffic Safety Manufacturing Autonomous vehicles

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Multi bearer channel resource allocation for optimised transmission of video objects

Multi bearer channel resource allocation for optimised trans...

引用

IEEE International Conference on Image Processing

作者： S. Nasir S. Worrall M. Mrak A.M. Kondoz Centre for Communication Systems Research University of Surrey Guildford Surrey UK

This paper presents a novel channel optimisation scheme that enhances the quality of object based video, transmitted over a fixed bandwidth channel. The optimisation methodology is based on an accurate modelling of video packet distortion at the encoder. Video packets are ranked according to their expected distortion and are then mapped to one of a number of different priority radio bearers. In the proposed scheme the video compression technique uses motion compensated prediction and video frames are split into a number of video packets. The algorithm performance is demonstrated for object based MPEG-4 video transmission over a UMTS/FDD system. The results demonstrate that the performance gain achieved with the proposed scheme can reach 2 dB, compared with the equal error protection scheme for video transmission over a fixed bandwidth channel.

关键词： Resource management Video compression Protection Channel coding Bandwidth Streaming media 3G mobile communication Error correction codes source coding Optimization methods

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OpenThermalPose: An Open-source Annotated Thermal Human Pose Dataset and Initial YOLOv8-Pose Baselines

OpenThermalPose: An Open-Source Annotated Thermal Human Pose...

引用

International Conference on Automatic Face and Gesture Recognition

作者： Askat Kuzdeuov Darya Taratynova Alim Tleuliyev Huseyin Atakan Varol Institute of Smart Systems and Artificial Intelligence Nazarbayev University Astana Kazakhstan

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Human pose estimation has a variety of applications in action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. There is a substantial stream of datasets and deep learning-based models to attain robust human pose estimation within the visible domain. Nonetheless, there are certain obstacles in this domain, including insufficient illumination and privacy concerns. These issues can be addressed using thermal cameras. However, only a limited number of annotated thermal human pose datasets are available to train data-hungry deep learning models. In this regard, we introduce a novel open-source thermal human pose dataset named OpenThermalPose. The dataset contains 6,090 thermal images and 14,315 annotated human instances. The annotations include bounding boxes and 17 anatomical keypoints, following the annotation format of the MS COCO dataset. The dataset covers various fitness exercises, multiple-person activities, and outdoor walking in different locations and weather conditions. As a baseline, we trained and evaluated YOLOv8-pose models on our dataset. We have made the dataset, source code, and pretrained models publicly available at https://***/IS2AI/OpenThermalPose to bolster research in this area.

关键词： Privacy Annotations source coding Pose estimation Lighting Medical services Motion capture

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An outer bound for the vector Gaussian CEO problem

An outer bound for the vector Gaussian CEO problem

引用

IEEE International Symposium on Information Theory

作者： Ersen Ekrem Sennur Ulukus Department of Electrical and Computer Engineering University of Maryland College Park MD USA

We study the vector Gaussian CEO problem, and provide an outer bound for its rate-distortion region. We obtain our outer bound by evaluating an outer bound for the multiterminal source coding problem by means of a technique relying on the de Bruijn identity and the properties of the Fisher information. Next, we address the tightness of our outer bound, and show that our outer bound does not provide the ratedistortion region in general. In particular, we provide a specific example where the rate-distortion region is strictly contained in our outer bound.

关键词： Rate-distortion Vectors source coding Sensors Additives Entropy Mutual information

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Efficient codebook allocation for an arbitrary set of vector quantizers

Efficient codebook allocation for an arbitrary set of vector...

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

作者： Y. Shoham A. Cersho Department of Electrical & Computer Engineering University of California Santa Barbara CA USA

Traditional solutions to the bit allocation problem assume nicely behaved quantizers whose distortion versus rate characteristic is a standard exponentially decreasing function. This model is often inadequate and leads to suboptimal bit allocations for actual quantizer characteristics. This inadequacy prevails particularly in coding systems based on vector quantization (VQ). The distortion-rate characteristic for VQ suffers from irregularities because of suboptimality of the codebook and of the codebook search method. We propose a new algorithm which efficiently allocates a given quota of bits, based on the actually measured quantizer performance, without any prior assumptions on its behavior. The algorithm can enhance the performance-of traditional scalar coders as well as any modern coder which employs dynamic bit allocation.

关键词： Bit rate Speech Rate distortion theory Vector quantization Random variables Frequency Search methods Distortion measurement Statistics source coding

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Identifying Patterns of Vulnerability Incidence in Foundational Machine Learning Repositories on GitHub: An Unsupervised Graph Embedding Approach

Identifying Patterns of Vulnerability Incidence in Foundatio...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Agrim Sachdeva Ben Lazarine Ruchik Dama Sagar Samtani Hongyi Zhu Kelley School of Business Indiana University Bloomington Indiana USA Alvarez College of Business University of Texas San Antonio Texas USA

The rapid pace of the development of artificial intelligence (AI) solutions is enabled by leveraging foundational tools and frameworks that allow AI developers to focus on application logic and rapid prototyping. However, the security vulnerabilities present in foundation repositories might cause irreparable damage due to the AI solutions built using these libraries being deployed in production environments. Our research leverages source code hosted on the prevailing social coding platform GitHub to identify vulnerabilities in foundational repositories commonly used for modern AI development (Linux, BERT, PyTorch, and Transformers), as well as the AI repositories that utilize foundation repositories as dependencies. Using an unsupervised graph embedding approach, we generate graph embeddings that capture vulnerability information and the relationships between repositories. Based on these embeddings, we performed clustering as our downstream task to group similarly vulnerable repositories. Our research identifies patterns and similarities between repositories and will help develop effective mitigation of vulnerabilities present in groups of repositories based on foundational AI repositories. We also discuss the implications of identifying such clusters of vulnerable repositories.

关键词： source coding Production Machine learning Transformers Rapid prototyping Software Security

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Clustering and quantization of binary vectors with stochastic complexity

Clustering and quantization of binary vectors with stochasti...

引用

IEEE International Symposium on Information Theory

作者： M. Gyllenberg T. Koski M. Verlaan Department of Appl. Math. University of Turku Turku Finland Department of Math. Luleå University of Technology Lulea Sweden Department of Techn. Math. and Comp. Science Delft University of Technnology Delft Netherlands

Stochastic complexity (SC) is used for determining both the codebook and its size in a vector quantizer for binary strings given a training sequence.

Stochastic complexity (SC) is used for determining both the codebook and its size in a vector quantizer for binary strings given a training sequence.< >

关键词： Quantization Stochastic processes Frequency Clustering algorithms source coding Taxonomy Nearest neighbor searches Distortion measurement Entropy Probability distribution

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Classifying source Code: How Far Can Compressor-based Classifiers Go?

Classifying Source Code: How Far Can Compressor-based Classi...

引用

International Conference on Software Engineering - Companion, ICSE, Companion

作者： Zhou Yang Singapore Management University Singapore

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Pre-trained language models of code, which are built upon large-scale datasets, millions of trainable parameters, and high computational resources cost, have achieved phenomenal success. Recently, researchers have proposed a compressor-based classifier (CBC); it trains no parameters but is found to outperform BERT. We conduct the first empirical study to explore whether this lightweight alternative can accurately classify source code. Our study is more than applying Cbc to code-related tasks. We first identify an issue that the original implementation overestimates Cbc. After correction, Cbc's performance on defect prediction drops from 80.7% to 63.0%, which is still comparable to CodeBERT (63.7%). We find that hyperparameter settings affect the performance. Besides, results show that Cbc can outperform CodeBERT when the training data is small, making it a good alternative in low-resource settings.

关键词： Costs Codes source coding Computational modeling Training data Software Task analysis

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Improving Plasticity in Online Continual Learning via Collaborative Learning

Improving Plasticity in Online Continual Learning via Collab...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Maorong Wang Nicolas Michel Ling Xiao Toshihiko Yamasaki The University of Tokyo Univ Gustave Eiffel CNRS LIGM

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Online Continual Learning (CL) solves the problem of learning the ever-emerging new classification tasks from a continuous data stream. Unlike its offline counterpart, in online CL, the training data can only be seen once. Most existing online CL research regards catastrophic forgetting (i.e., model stability) as almost the only challenge. In this paper, we argue that the model's capability to acquire new knowledge (i.e., model plasticity) is another challenge in online CL. While replay-based strategies have been shown to be effective in alleviating catastrophic forgetting, there is a notable gap in research attention toward improving model plasticity. To this end, we propose Collaborative Continual Learning (CCL), a collaborative learning based strategy to improve the model's capability in acquiring new concepts. Additionally, we introduce Distillation Chain (DC), a collaborative learning scheme to boost the training of the models. We adapt CCL-DC to existing representative online CL works. Extensive experiments demonstrate that even if the learners are well-trained with state-of-the-art online CL methods, our strategy can still improve model plasticity dramatically, and thereby improve the overall performance by a large margin. The source code of our work is available at https://***/maorong-wang/CCL-DC.

关键词： Continuing education Training Adaptation models Computer vision Federated learning source coding Training data

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