检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Mamaghan, Amir Mohammad Karimi Dittadi, Andrea Bauer, Stefan Johansson, Karl Henrik Quinzan, Francesco KTH Royal Institute of Technology Sweden Helmholtz AI Helmholtz Center Munich Germany Max Planck Institute for Intelligent Systems Tübingen Germany Technical University of Munich Germany Department of Computer Science University of Oxford United Kingdom

Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect estimation and the identification of efficient and safe interventions. However, learning causal representations remains a major challenge, due to the complexity of many real-world systems. Previous works on causal representation learning have mostly focused on Variational Auto-Encoders (VAE). These methods only provide representations from a point estimate, and they are unsuitable to handle high dimensions. To overcome these problems, we proposed a new Diffusion-based Causal Representation Learning (DCRL) algorithm. This algorithm uses diffusion-based representations for causal discovery. DCRL offers access to infinite dimensional latent codes, which encode different levels of information in the latent code. In a first proof of principle, we investigate the use of DCRL for causal representation learning. We further demonstrate experimentally that this approach performs comparably well in identifying the causal structure and causal variables. © 2023, CC BY.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

BinaryAI: Binary Software Composition Analysis via intelligent Binary Source Code Matching

BinaryAI: Binary Software Composition Analysis via Intellige...

引用

International Conference on Software Engineering (ICSE)

作者： Ling Jiang Junwen An Huihui Huang Qiyi Tang Sen Nie Shi Wu Yuqun Zhang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China Tencent Security Keen Lab Shanghai China Department of Computer Science and Engineering Southern University of Science and Technol-ogy Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

While third-party libraries (TPLs) are extensively reused to enhance productivity during software development, they can also introduce potential security risks such as vulnerability propagation. Software composition analysis (SCA), proposed to identify reused TPLs for reducing such risks, has become an essential procedure within modern DevSecOps. As one of the mainstream SCA techniques, binary-to-source SCA identifies the third-party source projects contained in binary files via binary source code matching, which is a major challenge in reverse engineering since binary and source code exhibit substantial disparities after compilation. The existing binary-to-source SCA techniques leverage basic syntactic features that suffer from redundancy and lack robustness in the large-scale TPL dataset, leading to inevitable false positives and compromised recall. To mitigate these limitations, we introduce BinaryAI, a novel binary-to-source SCA technique with two-phase binary source code matching to capture both syntactic and semantic code features. First, BinaryAI trains a transformer-based model to produce function-level embeddings and obtain similar source functions for each binary function accordingly. Then by applying the link-time locality to facilitate function matching, BinaryAI detects the reused TPLs based on the ratio of matched source functions. Our experimental results demonstrate the superior performance of BinaryAI in terms of binary source code matching and the downstream SCA task. Specifically, our embedding model outperforms the state-of-the-art model CodeCMR, i.e., achieving 22.54% recall@l and 0.34 MRR compared with 10.75% and 0.17 respectively. Additionally, BinaryAI outperforms all existing binary-to-source SCA tools in TPL detection, increasing the precision from 73.36% to 85.84% and recall from 59.81% to 64.98% compared with the well-recognized commercial SCA product Black Duck. E-https://***

关键词： Codes Source coding Semantics Redundancy Syntactics Transformers Software

来源：评论

学校读者我要写书评

暂无评论

A New Application of Machine Learning: Detecting Errors in Network Simulations

A New Application of Machine Learning: Detecting Errors in N...

引用

Simulation Winter Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University St. Oxford OH USA

ISBN: (纸本)9781665476621

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

关键词： Training Machine learning algorithms Computational modeling Aggregates Stochastic processes Machine learning Transforms

来源：评论

学校读者我要写书评

暂无评论

Adapting Object-Centric Probabilistic Movement Primitives with Residual Reinforcement Learning

Adapting Object-Centric Probabilistic Movement Primitives wi...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： João Carvalho Dorothea Koert Marek Daniv Jan Peters Computer Science Department TU Darmstadt (TUDa) Institute for Intelligent Autonomous Systems TUDa Centre for Cognitive Science Research Department: Systems AI for Robot Learning German Research Center for AI (DFKI) Hessian. AI

ISBN: (纸本)9798350309805

It is desirable for future robots to quickly learn new tasks and adapt learned skills to constantly changing environments. To this end, Probabilistic Movement Primitives (ProMPs) have shown to be a promising framework to learn generalizable trajectory generators from distributions over demonstrated trajectories. However, in practical applications that require high precision in the manipulation of objects, the accuracy of ProMPs is often insufficient, in particular when they are learned in cartesian space from external observations and executed with limited controller gains. Therefore, we propose to combine ProMPs with the Residual Reinforcement Learning (RRL) framework, to account for both, corrections in position and orientation during task execution. In particular, we learn a residual on top of a nominal ProMP trajectory with Soft Actor-Critic and incorporate the variability in the demonstrations as a decision variable to reduce the search space for RRL. As a proof of concept, we evaluate our proposed method on a 3D block insertion task with a 7-DoF Franka Emika Panda robot. Experimental results show that the robot successfully learns to complete the insertion, which was not possible before with using basic ProMPs.

关键词： Three-dimensional displays Humanoid robots Reinforcement learning Aerospace electronics Probabilistic logic Generators Trajectory

来源：评论

学校读者我要写书评

暂无评论

Embodied Runtime Monitoring of Learning-enabled Robot Perception Components

Embodied Runtime Monitoring of Learning-enabled Robot Percep...

引用

IEEE International Conference on Automation science and Engineering (CASE)

作者： Deebul Nair Sathwik Panchangam Miguel A. Olivares-Mendez Nico Hochgeschwender Institute for Artificial Intelligence and Autonomous Systems Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Space Robotics Research Group (SpaceR) Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Department of Mathematics and Computer Science University of Bremen Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Monitoring during runtime, the estimated uncertainty from learning-enabled robot perception components, is a vital tool for assuring and evaluating model correctness. By utilizing different uncertainty estimation methods, monitors exploit the perceived sensor data and predictions generated by the perception model to make informed decisions, such as in safety-critical situations. In robotics, informed decisions should be aligned with embodied information regarding space and time. We argue and show that runtime monitors of learning-enabled components can be made more robust by including the embodiment information of the robot. However, it remains challenging to build uncertainty-based runtime monitors with embodiment information using only real-world datasets, which are tedious to collect. To this end, we propose a solution that leverages simulation-based datasets to characterize expected uncertainty estimates with robot embodiment. By constructing a nominal runtime monitor model based on simulated datasets, we establish a reference for the expected uncertainty along with the embodiment information that can be subsequently used to monitor real-world deployed perception components. We demonstrate the zero-shot sim-to-real transfer of our approach on a Kinova Gen3 manipulator picking task, for which the runtime monitor reliability model was trained using simulation-based data containing embodiment information of distance from the object being selected.

关键词： Runtime Uncertainty Computational modeling Estimation Predictive models Robot sensing systems Manipulators Reliability engineering Data models Monitoring

来源：评论

学校读者我要写书评

暂无评论

Editing a Woman's Voice

arXiv

引用

arXiv 2022年

作者： Costello, Anna M. Fedorova, Ekaterina Jin, Zhijing Mihalcea, Rada University of Chicago Booth School of Business United States ETH Zurich Department of Computer Science Switzerland Max Planck Institute for Intelligent Systems Tubingen Germany University of Michigan Department of Computer Science and Engineering United States

Prior work shows that men and women speak with different levels of confidence, though it's often assumed that these differences are innate or are learned in early childhood. Using academic publishing as a setting, we find that language differences across male and female authors are initially negligible: in first drafts of academic manuscripts, men and women write with similar levels of uncertainty. However, when we trace those early drafts to their published versions, a substantial gender gap in linguistic uncertainty arises. That is, women increase their use of cautionary language through the publication process more than men. We show this increase in the linguistic gender gap varies substantially based on editor assignment. Specifically, our author-to-editor matched dataset allows us to estimate editor-specific fixed effects, capturing how specific editors impact the change in linguistic uncertainty for female authors relative to male authors (the editor's author-gender gap). Editors' author-gender gaps vary widely, and correlate with observable editor characteristics such as societal norms in their country-of-origin, their work history, and the year that they obtained their PhD. Overall, our study suggests that a woman's "voice" is partially shaped by external forces, and it highlights the critical role of editors in shaping how female academics communicate. © 2022, CC BY-NC-ND.

关键词： Linguistics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

引用

IEEE International Conference on Automation science and Engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Invariance learning in deep neural networks with differentiable laplace approximations 22

Invariance learning in deep neural networks with differentia...

引用

Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Alexander Immer Tycho F.A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk Department of Computer Science ETH Zurich Switzerland and Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computing Imperial College London UK Department of Computer Science ETH Zurich Switzerland Department of Computer Science ETH Zurich Switzerland and Department of Engineering University of Cambridge UK

ISBN: (纸本)9781713871088

Data augmentation is commonly applied to improve performance of deep learning by enforcing the knowledge that certain transformations on the input preserve the output. Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network. Our approach relies on phrasing data augmentation as an invariance in the prior distribution on the functions of a neural network, which allows us to learn it using Bayesian model selection. This has been shown to work in Gaussian processes, but not yet for deep neural networks. We propose a differentiable Kronecker-factored Laplace approximation to the marginal likelihood as our objective, which can be optimised without human supervision or validation data. We show that our method can successfully recover invariances present in the data, and that this improves generalisation and data efficiency on image datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

arXiv

引用

arXiv 2023年

作者： Shen, You Zhang, Yunzhou Wu, Yanmin Wang, Zhenyu Yang, Linghao Coleman, Sonya Kerr, Dermot College of Information Science and Engineering Northeastern University Shenyang110819 China School of Electronic and Computer Engineering Peking University Shenzhen China Faculty of Robotics and Engineering Northeastern University Shenyang China The Department of Electronic and Computer Engineering Technical University in Munich Germany School of Computing Engineering and Intelligent Systems Ulster University United Kingdom

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://***/mystorm16/BSH-Det3D. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

引用

robotics, Engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：