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检索条件"任意字段=19th International Conference on Logic Programming"

共 2639 条记录，以下是2621-2630 订阅

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Conditional Score Matching for Image to Image Regression

Conditional Score Matching for Image to Image Regression

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international conference on Machine Learning and Applications (ICMLA)

作者： Hao Xin Michael Yu Zhu Purdue University West Lafayette United States

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

Image to image regression is an essential machine learning problem that is commonly encountered in computer vision tasks. Previous research works have been concentrating on task-dependent end-to-end regression models. Although such a discriminative approach has shown its proficiency in producing precise predictions, its architecture is usually overly complicated for interpretation and unable to provide uncertainty quantification for prediction. We propose a general image to image regression framework named CSMNet which is based on conditional score matching and Langevin dynamics. the proposed framework is generative and emphasize on the probabilistic perspective of general image to image regression. Discriminative models are also involved as sufficient feature extractors to complement the generative modeling of the distribution of the target image conditioned an input image. the proposed model is trained by multi-level denoising score matching. Subsequently, the learned model is used for sampling from the conditional distribution via annealed Langevin dynamics. We introduce a grid selection procedure of Langevin sampling which can produce robust prediction. Comprehensive experiments are conducted on diverse image to image regression problems. the proposed model surpasses other purely generative models and achieves comparable performance to some end-to-end regression methods. Our method also demonstrates remarkable generalization ability which further demonstrates the advantage of the proposed conditional score matching framework.

关键词： Image resolution Uncertainty Machine learning Feature extraction Probabilistic logic Regression analysis Task analysis

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Enabling Security Analysis of IoT Device-to-Cloud Traffic

Enabling Security Analysis of IoT Device-to-Cloud Traffic

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Eda Zhou Joseph Turcotte Lorenzo De Carli Department of Computer Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

End-to-end encryption is now ubiquitous on the internet. By securing network communications with TLS, parties can insure that in-transit data remains inaccessible to collection and analysis. In the IoT domain however, end-to-end encryption can paradoxically decrease user privacy, as many IoT devices establish encrypted communications with the manufacturer's cloud backend. the content of these communications remains opaque to the user and in several occasions IoT devices have been discovered to exfiltrate private information (e.g., voice recordings) without user authorization. In this paper, we propose Inspection-Friendly TLS (IF-TLS), an IoT-oriented, TLS-based middleware protocol that preserves the encryption offered by TLS while allowing traffic analysis by middleboxes under the user's control. Differently from related efforts, IF-TLS is designed from the ground up for the IoT world, adding limited complexity on top of TLS and being fully controllable by the residential gateway. At the same time it provides flexibility, enabling the user to offload traffic analysis to either the gateway itself, or cloud-based middleboxes. We implemented a stable, Python-based prototype IF-TLS library; preliminary results show that performance overhead is limited and unlikely to affect quality-of-experience.

关键词： Privacy Cloud computing Protocols Prototypes logic gates Middleboxes Encryption

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Dynamic Railway Simulation Using DIgSILENT programming Language

Dynamic Railway Simulation Using DIgSILENT Programming Langu...

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international conference on Electrical Machines and Systems

作者： Trinnapop Boonseng Nuapett Sarasiri Department of Electrical Power Engineering Mahanakorn University of Technology Thailand

ISBN: (纸本)9781467388634

this paper presents the modeling and simulation of electrical systems for railway transportation utilizing DIgSILENT PowerFactory software embedded application called DIgSILENT programming Language (DPL). the DPL command can be directly used to control single line grid based on tractive effort curve (TE), braking effort curve (BE) and train-speed profile. the simulations of train movements in this research have been considered as a sequence of three operating modes, namely accelerating mode, constant mode and braking mode. the simulation results show the electric power and phase voltage and distance varying by time and speed. this characteristic design leads to specific outcomes in order to improve railway transportation system and the further quality of railway system research.

关键词： DIgSILENT programming Language railway system train dynamic simulation model

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CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images

CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation ...

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international conference on Machine Learning and Applications (ICMLA)

作者： Linchen Qian Jiasong Chen Timur Urakov Weiyong Gu Liang Liang University of Miami Coral Gables FL USA

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

Ambiguity is inevitable in medical images, which often results in different image interpretations (e.g. object boundaries or segmentation maps) from different human experts. thus, a model that learns the ambiguity and outputs a probability distribution of the target, would be valuable for medical applications to assess the uncertainty of diagnosis. In this paper, we propose a powerful generative model to learn a representation of ambiguity and to generate probabilistic outputs. Our model, named Coordinate Quantization Variational Autoencoder (CQ-VAE) employs a discrete latent space with an internal discrete probability distribution by quantizing the coordinates of a continuous latent space. As a result, the output distribution from CQ-VAE is discrete. During training, Gumbel-Softmax sampling is used to enable backpropagation through the discrete latent space. A matching algorithm is used to establish the correspondence between model-generated samples and "ground-truth" samples, which makes a trade-off between the ability to generate new samples and the ability to represent training samples. Besides these probabilistic components to generate possible outputs, our model has a deterministic path to output the best estimation. We demonstrated our method on a lumbar disk image dataset, and the results show that our CQ-VAE can learn lumbar disk shape variation and uncertainty.

关键词： Training Uncertainty Shape Linear regression Estimation Probabilistic logic Probability distribution

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Human Perception of Swarm Fragmentation

Human Perception of Swarm Fragmentation

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ACM/IEEE international conference on Human-Robot Interaction (HRI)

作者： Aymeric Hénard Étienne Peillard Jérémy Rivière Sébastien Kubicki Gilles Coppin CNRS Lab-STICC Univ Brest Brest France CNRS Lab-STICC IMT Atlantique Brest France CNRS Lab-STICC ENIB Brest France

ISBN: (数字)9798400703225

ISBN: (纸本)9798331516987

In the context of robot swarms, fragmentation refers to a break¬down in communication and coordination among the robots. this fragmentation can lead to issues in the swarm self-organisation, especially the loss of efficiency or an inability to perform their tasks. Human operators influencing the swarm could prevent fragmenta¬tion. To help them in this task, it is necessary to study the ability of humans to perceive and anticipate fragmentation. this article stud¬ies the perception of different types of fragmentation occurring in swarms depending on their behaviour selected amongst swarming, flocking, expansion and densification. thus, we characterise human perception thanks to two metrics based on the distance separating fragmented groups and the separation speed. the experimentation protocol consists of a binary discrimination task in which partic¬ipants have to assess the presence of fragmentation. the results show that detecting fragmentation for expansion behaviour and anticipating fragmentation, in general, are challenging. Moreover, they show that humans rely on separation distance and speed to infer the presence or absence of fragmentation. Our study paves the way for new research that will provide information to humans to better anticipate and efficiently prevent the occurrence of swarm *** CONCEPTS• Human-centered computing → User studies; Graphical user interfaces; • Computing methodologies → Multi-agent systems; Perception; Self-organization.

关键词： Measurement Human computer interaction Protocols Robot kinematics Human-robot interaction Estimation logic

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Digital-Twin Enabled Construction System For Supply Chain Risk Management

Digital-Twin Enabled Construction System For Supply Chain Ri...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Wennan Zhang Chenglin Yu Jijie Xiao Dennis Cheung Ho Kin Ray Y Zhong Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong Mattex Asia Development Limited Hong Kong China

ISBN: (数字)9798350320695

ISBN: (纸本)9798350320701

the supply chain in the construction industry faces numerous risks during the holistic construction process due to complex materials management and intricate relationships among stakeholders. Risk management is essential for stakeholders to effectively operate supply chains and avoid unpunctual deliveries. this paper presents a supply chain risk management strategy that uses a digital-twin enabled construction system with Kanban. With the advent of Industry 4.0, technologies such as the Internet of things (IoT), big data, and artificial intelligence have emerged in supply chain management. these technologies are applied in Digital twin to precisely connect the physical and virtual worlds and achieve deep synchronization. It monitors the supply chain networks and ensures the visibility of risks. Kanban is a lean approach that intuitively displays the operational parameters and decision-making support. the warning mechanism on Kanban utilizes Gaussian Mixture Model (GMM) with probabilistic density fitting (PDF) of historical data to predict delays. the results show that the possibility of false positives is less than 15% before five days of arrival. As the forecast days get shorter, the accuracy rate will increase. the reflection of the Digital twin enabled construction system contributes to the research and practical supply chain risk management.

关键词： Supply chain management Supply chains Fitting Decision making Probabilistic logic Digital twins Delays

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Investigation of Processes of Heating a Non-Ferromagnetic Rod under the Action of an Unsteady Electromagnetic Field

Investigation of Processes of Heating a Non-Ferromagnetic Ro...

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international conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Roman Musii Khrystyna Drohomyretska Halyna Bilushchak Nataliia Melnyk Ihor Demkiv Andrii Kunynets Department of Mathematics Lviv Polytechnic National University Lviv Ukraine Department of Computational Mathematics and Programming Lviv Polytechnic National University Lviv Ukraine Department of Software Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798350378627

ISBN: (纸本)9798350378634

A physical and mathematical model is formulated for determining the axial component of the magnetic field intensity vector and specific Joule heat density in a non-ferromagnetic conductive rod under the conditions of its induction heating through an unsteady electromagnetic field. the initial relations for the initial-boundary value problem of electrodynamics were written for the considered rod. We chose the axial component of the magnetic field intensity vector as a determining function. To obtain the solution, the cubic approximation along the radial coordinate of distribution of the determining function along the radius of the rod was used. In consequence of this, the original initial-boundary value problem is reduced to Cauchy problem in time for the integral characteristics of the determining function in the radial variable. Applying the integral Laplace transform, the expression for the integral characteristics and the axial component of the magnetic field intensity vector is written down. Expressions of Joule heat and the determining function in the considered rod under the action of an unsteady electromagnetic field were obtained. A numerical analysis of change in time and distribution along radius of the rod of the axial component of the magnetic field intensity vector and Joule heat was performed depending on frequency of the carrier electromagnetic oscillations and duration of the unstable electromagnetic field.

关键词： Electromagnetic heating Time-frequency analysis Numerical analysis Vectors Mathematical models Magnetic fields Electromagnetic fields

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the Carabela Project and Manuscript Collection: Large-Scale Probabilistic Indexing and Content-based Classification

The Carabela Project and Manuscript Collection: Large-Scale ...

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international Workshop on Frontiers in Handwriting Recognition

作者： E. Vidal Verónica Romero A. H. Toselli J.A. Sánchez V. Bosch L. Quirós José Miguel Benedí Jose Ramón Prieto Moisés Pastor F. Casacuberta C. Alonso C. García L. Márquez C. Orcero PRHLT Research Center UPV Valencia Spain CAS IAPH Cádiz Spain

ISBN: (数字)9781728199665

ISBN: (纸本)9781728199672

the main aim of the Carabela project was to develop and apply techniques that allow textual searching on massive Spanish collections of 15th-19th century manuscripts. the project focused on a relatively small subset of 125 000 images of collections of interest to underwater archaeology. For this type of manuscripts, state-of-the-art automatic transcription techniques, generally fail to achieve usable transcription accuracy. therefore, rather than insisting in actual transcription, methodologies for probabilistic indexing of handwritten text images have been adopted. this has allowed us to effectively cope with the intrinsically high degree of uncertainty of the text contained in most historical manuscripts, leading to highly effective systems for textual search and retrieval. Carabela has gone one step further by developing new techniques to classify probabilistically indexed, but otherwise untranscribed, text images according to their textual content. these techniques have been successfully used to automatically classify Carabela bundels (each containing hundreds or thousands of pages) according to their “level of risk” of public exposure, in order to control their access and avoid as much as possible the plundering of Spanish underwater heritage.

关键词： Probabilistic logic Optical imaging Indexing Computational modeling Adaptive optics Training Optical network units

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Fuzzy logic Based Speed Control of an Induction Motor with Considering Core Loss, Stray Load Loss, and Variations of Motor Parameters

Fuzzy Logic Based Speed Control of an Induction Motor with C...

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international conference on Electrical Machines and Systems

作者： Effat Jahan Md. Rifat Hazari Mohammad Abdul Mannan Junji Tamura Department of Electrical and Electronic Engineering Kitami Institute of Technology (KIT) Japan Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Bangladesh

ISBN: (纸本)9781467388634

Most of the controllers of Induction Motors (IM) for industrial applications have been designed based on PI controller without consideration of core loss (CL) and stray load loss (SLL). In order to obtain precise performances of torque as well as rotor speed and flux, the mentioned losses should be considered. the overshoot problems are the common phenomena occurred in conventional PI based controller. Also the performance is degraded under the variations of motor parameters such as rotor resistance, moment of inertia, etc. To overcome the above mentioned drawbacks of PI based speed controller, in this paper a fuzzy logic based speed controller is proposed. Fuzzy logic Controller (FLC) is a perfect choice for smooth operation of IM during transient condition and during the variations of motor parameters. the effectiveness of the proposed control scheme is verified by simulation analysis using MATLAB/SIMULINK. the simulation analysis shows the proposed FLC performs well and gives satisfactory results under the variations of rotor speed, load torque, rotor resistance, and moment of inertia compared to conventional PI based speed control strategy.

关键词： vector control core loss stray load loss fuzzy logic

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An Efficient Skip Link-based Traffic Prediction Algorithm with Multi-Scale Feature Extraction

An Efficient Skip Link-based Traffic Prediction Algorithm wi...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chaoqun Liu Xuanpeng Li Guangyu Li Chen Gong PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology Southeast University Nanjing China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Traffic prediction is an important component in the development of Intelligent Transportation Systems (ITS). To improve the accuracy of traffic prediction, many existing studies focus on combining recurrent neural networks and graph neural networks and achieve certain effects, but these works still suffer from some limitations on realizing rigorous long-term traffic foreseeing in highly dynamic spatio-temporal environments. To address this issue, we propose an efficient skip link-based traffic prediction algorithm with multiscale feature extraction (SKMSGCN). Specifically, we firstly design an available Spatio-temporal Traffic Feature Extraction module (STFE) based on the established information fusion scheme, to sufficiently obtain long-term traffic feature correlations. In addition, by means of memory networks, we propose a Critical traffic Feature Classification module (CFC), to obtain notable features from traffic sensor nodes with different attributions and further increase traffic prediction accuracy. Moreover, in order to efficiently process the input data of enhanced traffic features from CFC module and achieve the precise traffic foreseeing, we construct a long-term traffic prediction module based on graph convolutional networks and gate recurrent units. the extensive experimental results on two typical benchmark datasets firmly demonstrate the effectiveness of the proposed SKMSGCN in quantitative aspects.

关键词： Accuracy Recurrent neural networks Time series analysis Predictive models logic gates Prediction algorithms Feature extraction

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