检索结果-内蒙古大学图书馆

IEEE International Conference on Communications (ICC)

作者： Trent Reichenbach Chenglong Fu Xiaojiang Du Jia Di Yuede Ji Dept. of Electrical and Computer Engr Stevens Institute of Technology Hoboken NJ USA Dept. of Software and Information Systems UNC Charlotte Charlotte NC USA Dept. of Computer Science and Engr University of Arkansas Fayetteville AR USA Dept. Computer Science and Engr University of North Texas Denton TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

As smart home IoT systems gain popularity, they inevitably become targets for security risks and concerns. Among various cyber-attacks targeting these systems, the fake event attack poses significant issues due to its ability to manipulate secure devices through automation rules. In response to this threat, we propose TrustEvent - a system designed to offer end-to-end event signature verification. By integrating TrustEvent with existing home automation platforms, event authenticity is verified against signatures generated from edge devices before these events trigger automation rule execution. Notably, we have developed a signature proxy module, enhancing our system's compatibility across various platform scenarios. We have implemented a TrustEvent prototype in conjunction with existing commercial smart home IoT platforms, evaluating its overhead in the process. Our experimentation demonstrates that our system only marginally increases the automation execution latency, by an average of 3.74 seconds, representing a acceptable compromise for enhanced security.

关键词： Handwriting recognition Automation System performance Instruments Prototypes Smart homes Static analysis

来源：评论

学校读者我要写书评

暂无评论

On EDA-Driven Learning for SAT Solving

On EDA-Driven Learning for SAT Solving

引用

Design Automation Conference

作者： Min Li Zhengyuan Shi Qiuxia Lai Sadaf Khan Shaowei Cai Qiang Xu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong S.A.R Communication University of China China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic ‘1’ as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A COMPREHENSIVE STUDY ON OCCLUSION INVARIANT FACE RECOGNITION UNDER FACE MASK OCCLUSIONS

arXiv

引用

arXiv 2022年

作者： Hemathilaka, Susith Aponso, Achala Department of Computer Science and Software Engineering University of Westminster London United Kingdom Department of Computer Science Informatics Institute of Technology Colombo Sri Lanka

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face, and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features. © 2022, CC BY.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction

Reinforcement Learning for Language Grounding: Mapping Words...

引用

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Srinivasa Rao Adapa Himanshu Gupta Susmoy Mukherjee Praneeth Reddy Mallupalli Priyanka Avhad Kamal Upreti Software Engineer OpenLogix LLC Michigan USA Software Engineer Meta New Jersey USA Electrical and Electronics Engg. VIT Institute Tamil Nadu India Software Engineer Arcesium Hyderabad India Computer Engineering Veermata Jijabai Technological Inst. Mumbai India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction.

关键词： Grounding Reviews Scalability Human-robot interaction Collaboration Reinforcement learning Oral communication Robustness Noise measurement Robots

来源：评论

学校读者我要写书评

暂无评论

HPCNet: A Hybrid Progressive Coupled Network for Image Deraining

HPCNet: A Hybrid Progressive Coupled Network for Image Derai...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Qiong Wang Kui Jiang Jinyi Lai Zheng Wang Jianhui Zhang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Huawei Technologies Cloud BU College of Computer Science and Technology Hangzhou Dianzi University

Convolutional neural networks (CNNs) and Transformers have significantly succeeded in low-level vision tasks. Although prominent complementary characteristics exist regarding the larger receptive field and better convergence, only some efforts have compacted them efficiently due to their individual and nonnegligible weakness. In this paper, we propose a hybrid progressive coupled network (HPCNet) for rain perturbation removal, which integrates the advantages of these two architectures while maintaining both effectiveness and efficiency. In particular, we achieve the progressive decomposition and association of rain-free and rain features, designed as an asymmetrical dual-path mutual representation network to alleviate the computational cost. Meanwhile, we equip the network with high-efficiency convolutions and resolution rescaling strategy to trade off the computational complexity. Extensive experiments show that our method outperforms MPRNet on average while saving 87.2% and 61.1% of the computational cost and parameters.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Correlation And Order-Aware Rule Learning Method For Knowledge Graph Reasoning

A Correlation And Order-Aware Rule Learning Method For Knowl...

引用

International Conference on Parallel and Distributed systems (ICPADS)

作者： Yuefeng He Yang He Xu Zheng Bei Hui Bin Tian Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Naval University of Engineering Wuhan

Mining high-quality logical rules is crucial as they can provide beneficial interpretability for predictions. Recent methods that incorporate logical rules into learning tasks have been proven to yield high-quality logical rules successfully. However, existing methods either rely on the rule instances observed to support rule mining, or simply embed the rule head and the rule body to learn from them. Additionally, they can not fully utilize the rich semantic information contained in logical rules and overlook the intrinsic correlations between all relations within the domain. In this paper, we propose a model called Correlation and order-Aware Rule Learning (CARL) that captures deeper semantic information in rules by allowing relations to be co-aware of each other and paying attention to logical sequence sensitivity. CARL utilizes semantic consistency between the rule body and rule head as its learning objective, continuously introducing more semantic information and logically simplifying the rule body while considering logical sequence sensitivity. We explored the internal correlations between domain relations and used the thought of knowledge distillation to simplify modules so that relations in CARL can share or perceive each other’s information or state efficiently. Experiments on link prediction tasks have demonstrated that CARL can learn higher-quality rules and yield state-of-the-art results on four popular public datasets. https://***/burning5112/CARL

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine learning for battery quality classification and lifetime prediction using formation data

引用

Energy and AI 2024年第4期18卷 518-530页

作者： Jiayu Zou Yingbo Gao Moritz H.Frieges Martin F.Börner Achim Kampker Weihan Li Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives(ISEA)RWTH Aachen UniversityCampus-Boulevard 8952074 AachenGermany Center for Ageing Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems(CARL)RWTH Aachen UniversityCampus-Boulevard 89AachenGermany Department of Computer Science RWTH Aachen University52056 AachenGermany Chair of Production Engineering of E-Mobility Components(PEM) RWTH Aachen UniversityBohr 1252072 AachenGermany Juelich Aachen Research Alliance JARA-EnergyGermany

Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety ***,we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation *** extract three classes of features from the raw formation data,considering the statistical aspects,differential analysis,and electro-chemical *** correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing *** learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery *** validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25℃ and 45℃,***,the lifetime prediction model is able to predict the battery end-of-life with mean per-centage errors of 6.50% and 5.45%for the batteries aged at 25℃ and 45℃,*** work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.

关键词： Battery Formation Quality classification Life prediction Machine learning

来源：评论

学校读者我要写书评

暂无评论

ConZone: A Zoned Flash Storage Emulator for Consumer Devices

ConZone: A Zoned Flash Storage Emulator for Consumer Devices

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Dingcui Yu Jialin Liu Yumiao Zhao Wentong Li Ziang Huang Zonghuan Yan Mengyang Ma Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China College of Computer Science and Technology East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Considering the potential benefits to lifespan and performance, zoned flash storage is expected to be incorporated into the next generation of consumer devices. However, due to the limited volatile cache and heterogeneous flash cells of consumer-grade flash storage, adopting a zone abstraction requires additional internal hardware design to maximize its benefits. To understand and efficiently improve the hardware design on consumer-grade zoned flash storage, we present ConZone—the first emulator tailored to the characteristics of consumer-grade zoned flash storage. Users can explore the internal architecture and management strategies of consumer-grade zoned flash storage and integrate the optimization with software. We validate the accuracy of ConZone by realizing a hardware architecture for consumer-grade zoned flash storage and comparing it with the state-of-the-art. We also make a case study for read performance research with ConZone to explore the design of mapping mechanisms and cache management strategies.

关键词： Performance evaluation Accuracy Europe computer architecture Hardware software Planning Optimization Next generation networking

来源：评论

学校读者我要写书评

暂无评论

StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection

StixelNExT: Toward Monocular Low-Weight Perception for Objec...

引用

IEEE Symposium on Intelligent Vehicle

作者： Marcel Vosshans Omar Ait-Aider Youcef Mezouar Markus Enzweiler Institute for Intelligent Systems Which is Part of the Faculty of Computer Science and Engineering University of Applied Sciences Esslingen Germany Institut Pascal ISPR (Image Systems of Perception Robotics) Universite Clermont Auvergne INP / CNRS France

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In this work, we present a novel approach for general object segmentation from a monocular image, eliminating the need for manually labeled training data and enabling rapid, straightforward training and adaptation with minimal data. Our model initially learns from LiDAR during the training process, which is subsequently removed from the system, allowing it to function solely on monocular imagery. This study leverages the concept of the Stixel-World to recognize a medium level representation of its surroundings. Our network directly predicts a 2D multi-layer Stixel-World and is capable of recognizing and locating multiple, superimposed objects within an image. Due to the scarcity of comparable works, we have divided the capabilities into modules and present a free space detection in our experiments section. Furthermore, we introduce an improved method for generating Stixels from LiDAR data, which we use as ground truth for our network.

关键词： Training Space vehicles Adaptation models Laser radar Image recognition Intelligent vehicles Training data

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

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：