Spreading of misinformation on the web nowadays represents a serious issue, as their influence on peoples opinions may be significant. Fake news represents a specific type of misinformation. While its detection was mo...
ISBN:
(数字)9781728156255
ISBN:
(纸本)9781728156262
Spreading of misinformation on the web nowadays represents a serious issue, as their influence on peoples opinions may be significant. Fake news represents a specific type of misinformation. While its detection was mostly being performed manually in the past, automated methods using machine learning and related fields became more critical. On the other hand, deep learning methods became very popular and frequently used methods in the field of data analysis in recent years. the study presented in this paper deals withthe detection of fake news from the textual data using deep learning techniques. Our main idea was to train different types of neural network models using both entire texts from the articles and to use just the title text. the models were trained and evaluated on the Fake News dataset obtained from the Kaggle competition.
Withthe development of digital measurement technology and flexible pose adjustment technology, measurement assisted assembly (MAA) is adopted in large component alignment process to ensure assembly quality. Aiming to...
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ISBN:
(数字)9781728146201
ISBN:
(纸本)9781728146218
Withthe development of digital measurement technology and flexible pose adjustment technology, measurement assisted assembly (MAA) is adopted in large component alignment process to ensure assembly quality. Aiming to optimize alignment process and improve assembly quality, this paper proposes a feature-based pose optimization method. Firstly, the assembly constraint model based on assembly feature is elaborated and illustrates the constraint relationship between assembly feature, assembly characteristic and relative pose between components. Secondly, an assembly pose optimization method is introduced, which calculates the adjustment of component pose based on the coordination of mating characteristics and performance characteristics. this method includes coarse alignment pose optimization and target pose optimization. Finally, experiments are conducted in simulation environment, proving the proposed method is effective.
Space Exploration stands as one of the most challenging endeavors of our time. Extraterrestrial caves in particular have been identified by the scientific community as of great interest. they could be suitable for all...
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Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics. the problem is challenging due to the data variability among individual us...
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ISBN:
(纸本)9781538680940
Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics. the problem is challenging due to the data variability among individual users, and sensor artefacts. In this work, we propose an end-to-end framework for real-time cognitive workload classification with mixture Hyper Long Short Term Memory Networks (m-HyperLSTM), a novel variant of HyperNetworks. Evaluating the proposed approach on an eye-gaze pattern dataset collected from simulated driving scenarios of different cognitive demands, we show that the proposed framework outperforms previous baseline methods and achieves 83.9% precision and 87.8% recall during test. We also demonstrate the merit of our proposed architecture by showing improved performance over other LSTM-based methods.
the paper presents the components of the system for knowledge storage, distribution and processing in a large international company operating globally. It shows knowledge management processes which take into account b...
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ISBN:
(纸本)9781614998983;9781614998976
the paper presents the components of the system for knowledge storage, distribution and processing in a large international company operating globally. It shows knowledge management processes which take into account boththe standard activities for a number of years covering the entire company, and activities carried out gradually, consisting of an evolutionary development (building and verification) of further components tools for an automation of engineering processes. the evolutionary development includes a process of gradual, iterative creation and verification of subsequent KBE applications supporting the process of building FEM models for a specific product - a car seat. All implemented processes of building and developing KBE applications are focused on the evolutionary pursuit of creating an advanced design platform designed to carry out FEM analysis of car seats. Different elements of this system are characterized: tools for storing knowledge in the form of multimedia information for direct use by people, tools for automating selected elements of engineering processes etc.
the proceedings contain 21 papers. the special focus in this conference is on Informatics in Control, automation and robotics. the topics include: Parameter identification and model-based control of redundantly actuat...
ISBN:
(纸本)9783319550107
the proceedings contain 21 papers. the special focus in this conference is on Informatics in Control, automation and robotics. the topics include: Parameter identification and model-based control of redundantly actuated, non-holonomic, omnidirectional vehicles;passivity-based control design and experiments for a rolling-balancing system;time-optimal paths for a robotic batting task;an adaptive terminal sliding mode guidance law for head pursuit interception with impact angle considered;kinematic and dynamic approaches in gait optimization for humanoid robot locomotion;identification and control of the waelz process using infrared image processing;modeling and calibrating triangulation lidars for indoor applications;a comparison of discretization methods for parameter estimation of nonlinear mechanical systems using extended kalman filter: Symplectic versus classical approaches;dynamics calibration and real-time state estimation of a redundant flexible joint robot based on encoders and gyroscopes;visual servoing path-planning with elliptical projections;Mathematical model for the output signal’s energy of an ideal DAC in the presence of clock jitter;stochastic integration filter with improved state estimate mean-square error computation;fractional models of lithium-ion batteries with application to state of charge and ageing estimation;co-operation of biology related algorithms for solving opinion mining problems by using different term weighting schemes;bifurcation analysis and active control of surge and rotating stall in axial flow compressors via passivity;task controller for performing remote centre of motion;toward an automatic fongbe speech recognition system: Hierarchical mixtures of algorithms for phoneme recognition;spatial fusion of different imaging technologies using a virtual multimodal camera.
Alignment of two point clouds is an essential problem in medical robotics and computer-assisted surgery. In this paper, we first formally formulate the generalized point cloud registration problem in a probabilistic m...
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ISBN:
(纸本)9781538680940
Alignment of two point clouds is an essential problem in medical robotics and computer-assisted surgery. In this paper, we first formally formulate the generalized point cloud registration problem in a probabilistic manner. Specifically, not only positional but also the orientational information are incorporated into registration. Notably, the positional error is assumed to obey a multivariate Gaussian distribution to accommodate anisotropic cases. Expectation conditional maximization framework is utilized to solve the problem. In E-step, the correspondence probabilities between points in two generalized point clouds are computed. In M-step, the constrained optimization problem with respect to the transformation matrix is re-formulated as an unconstrained one. Extensive experiments are conducted to compare the proposed algorithm withthe state-of-the-art registration methods. the experimental results demonstrate the algorithm's robustness to noise and outliers, fast convergence speed.
Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleop...
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ISBN:
(纸本)9781538680940
Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform the underlying intent. We examine human inference on robot motion and furthermore validate our approach with a human subjects study that evaluates autonomy intent inference performance under a variety of goal scenarios and tasks, by novice subjects. Results show that our approach outperforms existing solutions and demonstrates that the probabilistic fusion of multiple observations improves intent inference and performance for shared-control operation.
this paper addresses the problem of localizing facial landmarks with deformable face models using cascaded regression strategies. Recently, these methods have become quite popular, standing out as simple and efficient...
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Grasping skill is a major ability that a wide number of real-life applications require for robotisation. State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural netw...
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ISBN:
(纸本)9781538680940
Grasping skill is a major ability that a wide number of real-life applications require for robotisation. State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks. However, such networks require huge amount of labeled data for training making this approach often impracticable in robotics. In this paper, we propose a method to generate a large scale synthetic dataset with ground truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D images and annotations of successful grasping positions based on grasp attempts performed in a simulated environment. We carried out experiments using an off-the-shelf CNN, withthree different evaluation metrics, including real grasping robot trials. the results show that Jacquard enables much better generalization skills than a human labeled dataset thanks to its diversity of objects and grasping positions. For the purpose of reproducible research in robotics, we are releasing along withthe Jacquard dataset a web interface for researchers to evaluate the successfulness of their grasping position detections using our dataset.
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