检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Cheng, Chih-Hong Stöckel, Paul Zhao, Xingyu Department of Computer Science and Engineering Chalmers University of Technology Sweden Department of Informatics University of Hildesheim Germany WMG University of Warwick CV4 7AL United Kingdom

Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection. We focus on its role in safety-critical applications, introducing four types of instance-level fidelity that go beyond mere visual input characteristics. The aim is to ensure that applying testing on synthetic data can reveal real-world safety issues, and the absence of safety-critical issues when testing under synthetic data can provide a strong safety guarantee in real-world behavior. We suggest an optimization method to refine the synthetic data generator, reducing fidelity gaps identified by deep learning components. Experiments show this tuning enhances the correlation between safety-critical errors in synthetic and real data. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

CULTURAL EVOLUTION VIA ITERATED LEARNING AND COMMUNICATION EXPLAINS EFFICIENT COLOR NAMING SYSTEMS

arXiv

引用

arXiv 2023年

作者： Carlsson, Emil Dubhashi, Devdatt Regier, Terry Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Linguistics UC Berkeley BerkeleyCA United States

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that some other proposals such as iterated learning alone, communication alone, or the greater learnability of convex categories, do not yield the same outcome as clearly. We conclude that the combination of iterated learning and communication provides a plausible means by which human semantic systems become efficient. © 2023, CC BY.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

Optimizing CDN Architectures: Multi-Metric Algorithmic Break...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Md Nurul Absur Sourya Saha Sifat Nawrin Nova Kazi Fahim Ahmad Nasif Md Rahat Ul Nasib Department of Computer Science City University of New York New York USA Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia USA Samsung Austin Research Center Austin Texas USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments.

关键词： Content distribution networks Heuristic algorithms Scalability computer architecture Dynamic scheduling Fabrics Performance analysis Servers Resource management Optimization

来源：评论

学校读者我要写书评

暂无评论

Automated Boundary Identification for Machine Learning Classifiers

Automated Boundary Identification for Machine Learning Class...

引用

Search-Based and Fuzz Testing (SBFT), IEEE/ACM International Workshop on

作者： Felix Dobslaw Robert Feldt Dept. of Quality Mngmt Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (数字)9798400705625

ISBN: (纸本)9798350376012

AI and Machine Learning (ML) models are increasingly used as (critical) components in software systems, even safety-critical ones. This puts new demands on the degree to which we need to test them and requires new and expanded testing methods. Recent boundary-value identification methods have been developed and shown to automatically find boundary candidates for traditional, non-ML software: pairs of nearby inputs that result in (highly) differing outputs. These can be shown to developers and testers, who can judge if the boundary is where it is supposed to ***, we explore how this method can identify decision boundaries of ML classification models. The resulting ML Boundary Spanning Algorithm (ML-BSA) is a search-based method extending previous work in two main ways. We empirically evaluate ML-BSA on seven ML datasets and show that it better spans and thus better identifies the entire classification boundary(ies). The diversity objective helps spread out the boundary pairs more broadly and evenly. This, we argue, can help testers and developers better judge where a classification boundary actually is, compare to expectations, and then focus further testing, validation, and even further training and model refinement on parts of the boundary where behaviour is not ideal.

关键词： Training Conferences Machine learning Fuzzing Software systems Classification algorithms Testing

来源：评论

学校读者我要写书评

暂无评论

On Using Node Indices and Their Correlations for Fake Account Detection

On Using Node Indices and Their Correlations for Fake Accoun...

引用

IEEE International Conference on Big Data

作者： Sara Asghari Mostafa Haghir Chehreghani Morteza Haghir Chehreghani Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9781665480468

With the growing rate of online social networks, the number of fake accounts is multiplying day by day. There exist many approaches in the literature that try to distinguish fake accounts from real ones, for example, those that use machine learning and classification techniques to learn whether a user should be labeled as fake (bot) or not. In this paper, we follow a different approach and try to use node measurements in the field of complex networks analysis to identify fake accounts. We first model users’ interactions with a large graph. For example, in Twitter, we can form graphs of follower-following, comments, retweets, mentions, and so on. We then investigate different measurements, such as centrality indices and their correlations, to separate real and fake accounts. We find that measurements such as average path length, eigenvector centrality, harmonic centrality, degree, local reaching centrality and their correlations provide good indicators to distinguish real and fake accounts.

关键词： Correlation Social networking (online) Blogs Machine learning Complex networks Big Data Length measurement

来源：评论

学校读者我要写书评

暂无评论

Building Efficient Cnns Using Depthwise Convolutional Eigen-Filters (Decef)

SSRN

引用

SSRN 2022年

作者： Yu, Yinan Scheidegger, Samuel McKelvey, Tomas Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Asymptotic AI Gothenburg Sweden

Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their impressive capabilities. These models are typically composed of a large number of 2D convolutional (Conv2D) layers with numerous trainable parameters. % This comes with a high training cost and potentially leads to poor generalization performance due to the high variance in the results when the training data is not adequate. To reduce the complexity of a network, compression techniques can be applied. These methods typically rely on the analysis of trained deep learning models. However, in some applications, due to reasons such as particular data or system specifications and licensing restrictions, a pre-trained network may not be available. This would require the user to train a CNN from scratch. In this paper, we aim to find an alternative parameterization to Conv2D filters without relying on a pre-trained convolutional network. During the analysis, we observe that the effective rank of the vectorized Conv2D filters decreases with respect to the increasing depth in the network, which then leads to the implementation of the Depthwise Convolutional Eigen-Filter (DeCEF) layer. Essentially, a DeCEF layer is a low rank version of the Conv2D layer with significantly fewer trainable parameters and floating point operations (FLOPs). The way we define the effective rank is different from the previous work and it is easy to implement in any deep learning frameworks. To evaluate the effectiveness of DeCEF, experiments are conducted on the benchmark datasets CIFAR-10 and ImageNet using various network architectures. The results have shown a similar or higher accuracy using about 2/3 of the original parameters and reducing the number of FLOPs to 2/3 of the base network, which is then compared to the state-of-the-art techniques. © 2022, The Authors. All rights reserved.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Graph Clustering Using Node Embeddings: An Empirical Study

Graph Clustering Using Node Embeddings: An Empirical Study

引用

IEEE International Conference on Big Data

作者： Mahdi Ghanbari Mostafa Haghir Chehreghani Morteza Haghir Chehreghani Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9781665480468

A technique that has recently become popular to analyze graph data is node embedding learning. Many graph problems, such as node classification, link prediction and node clustering, can be solved using these embeddings. However, in the literature the efficiency of different embedding generation algorithms paired with different clustering algorithms is not extensively investigated. In this paper, we study the efficiency of well-known embedding generation algorithms in combination with clustering algorithms, to detect communities. We consider four embedding generation algorithms that use mechanisms such as convolution, attention, inductivity and shallowness; and three popular data clustering algorithms. Our experimental results reveal that the combination of GraphSAGE (that uses inductivity) with KMeans++ yields the best results. This can be due to high quality of embedding vectors generated by GraphSAGE and the regular shapes that the embeddings find in the vector space.

关键词： Convolution Shape Clustering algorithms Big Data Prediction algorithms Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Improved Tactical Decision Making and Control Architecture for Autonomous Truck in SUMO Using Reinforcement Learning

Improved Tactical Decision Making and Control Architecture f...

引用

IEEE International Conference on Big Data

作者： Deepthi Pathare Leo Laine Morteza Haghir Chehreghani Department of Computer Science and Engineering Chalmers University of Technology Göteborg Sweden Volvo Group Trucks Technology (Safe and Efficient Driving) Göteborg Sweden Department of Mechanics and Maritime Sciences Chalmers University of Technology Göteborg Sweden

We employ Reinforcement Learning (RL) techniques with improved state and action spaces for tactical decision making in an autonomous truck. Specifically, we implement Adaptive Cruise Control (ACC) and lane change maneuvers for the autonomous truck in a highway scenario. We show the results obtained using three reinforcement learning algorithms: Deep Q-Network (DQN), Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO). Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the RL agent and the low-level controllers based on physical models. Furthermore, we design a realistic reward function based on the Total Cost of Operation (TCOP) of the truck to guide the RL agent towards optimal driving strategy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

X-ray simulations with gVXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development 15

X-ray simulations with gVXR as a useful tool for education, ...

引用

15th SPIE Conference on Developments in X-Ray Tomography

作者： Vidal, Franck P. Afshari, Shaghayegh Ahmed, Sharif Atkins, Carolyn Béchet, Éric Bellot, Alberto Corbí Bosse, Stefan Chahid, Younes Chou, Cheng-Ying Culver, Robert Dixon, Lewis Friemann, Johan Garbout, Amin Hatton, Clémentine Henry, Audrey Leblanc, Christophe Leonardi, Alberto Létang, Jean Michel Lipscomb, Harry Manchester, Tristan Meere, Bas Middleburgh, Simon Mitchell, Iwan Perera, Liam Puig, Martí Tugwell-Allsup, Jenna Scientific Computing Ada Lovelace Centre Science Technology Facilities Council United Kingdom School of Computer Science & Engineering Bangor University United Kingdom Department of Biomechatronics Engineering National Taiwan University Taiwan DIAD Beamline Diamond Light Source United Kingdom UK Astronomy Technology Centre Royal Observatory Edinburgh United Kingdom Département d’Aérospatiale et Mécanique Université de Liège Belgium Escuela Superior de Ingeniería y Tecnología Universidad Internacional de La Rioja Spain Department of Computer Science University of Koblenz Koblenz Germany Department of Mechanical Engineering University of Siegen Siegen Germany The Manufacturing Technology Centre United Kingdom Department of Industrial and Materials Science Chalmers University of Technology Sweden Henry Royce Institute Henry Moseley X-Ray Imaging Facility Department of Materials The University of Manchester United Kingdom Scalian DS Rennes France INSA-Lyon Université Claude Bernard Lyon 1 CNRS Inserm CREATIS UMR 5220 U1294 Lyon France Department of Mechanical Engineering Eindhoven University of Technology Netherlands Department of Engineering Science University of Oxford United Kingdom Ysbyty Gwynedd United Kingdom

ISBN: (纸本)9781510679641

gVirtualXray (gVXR) is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT. © 2024 SPIE.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach

arXiv

引用

arXiv 2024年

作者： Kakooei, Mohammad Solska, Klaudia Daoud, Adel Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Institute for Analytical Sociology Linköping University Sweden Center for Advanced Study in the Behavioral Sciences Stanford University United States

Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs). Achieving this requires high-frequency, granular data to capture neighborhood-level changes, particularly in data scarce regions such as low- and middle-income countries. To fill in the data gaps, recent computer vision methods combining machine learning (ML) with earth observation (EO) data to improve poverty estimation. However, while much progress have been made, they often omit intra-annual variations, which are crucial for estimating poverty in agriculturally dependent countries. We explored the impact of integrating intra-annual NDVI information with annual multi-spectral data on model accuracy. To evaluate our method, we created a simulated dataset using Landsat imagery and nighttime light data to evaluate EO-ML methods that use intra-annual EO data. Additionally, we evaluated our method against the Demographic and Health Survey (DHS) dataset across Africa. Our results indicate that integrating specific NDVI-derived features with multi-spectral data provides valuable insights for poverty analysis, emphasizing the importance of retaining intra-annual information. © 2024, CC BY.

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：