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"abstract": "AR furniture aims at providing a platform to visualize a furniture model using augmented reality (AR) so that it will be easy to choose the furniture according to color combinations and combinations of furniture. This application provides a real representation of furniture as a 3D model interpreting in a real world. This Application let user choose the choice of furniture from a range of furniture and let them place 3D object of the same furniture on empty area so that they can look at the way it will feel at that area. You can view multiple objects at the same time. It will help in decorating empty room. By looking at multiple object user will be able to choose object that he/she desires. The project is developed on Unity Software and Visual Studio",
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"abstract": "Compared with the current question answering system, similarity matching methods are largely separated into two categories: deep learning methods and conventional ways. Conventional ways rely heavily on artificial features, have weak generalization ability, and insufficient accuracy. RNN and CNN also have text global feature extraction. Limitations. This paper proposes a BERT-based hybrid question answering matching model, which uses the BERT -base pre-training model to capture and represent the semantic information of the QAS sentence and the semantic relevance between the two. The feature vector generated by the BERT model is used as Bi-LSTM _ GCN for the input of the model, feature extraction is performed to further obtain the syntactic features of the sentence, and finally the attention mechanism is added to find the target answer, and the effectiveness of the proposed algorithm is verified on the two types of data sets.",
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"abstract": "There has been a growing interest in solving Visual Question Answering (VQA) tasks that require the model to reason beyond the content present in the image. In this work, we focus on questions that require commonsense reasoning. In contrast to previous methods which inject knowledge from static knowledge bases, we investigate the incorporation of contextualized knowledge using Commonsense Transformer (COMET), an existing knowledge model trained on human-curated knowledge bases. We propose a method to generate, select, and encode external commonsense knowledge alongside visual and textual cues in a new pre-trained Vision-Language-Commonsense transformer model, VLC-BERT. Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is capable of outperforming existing models that utilize static knowledge bases. Furthermore, through a detailed analysis, we explain which questions benefit, and which don’t, from contextualized commonsense knowledge from COMET. Code: https://github.com/aditya10/VLC-BERT",
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"content": "We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly. PolyMapper directly extracts the topological map of a city from overhead images as collections of building footprints and road networks. In order to unify the shape representation for different types of objects, we also propose a novel sequentialization method that reformulates a graph structure as closed polygons. Experiments are conducted on both existing and self-collected large-scale datasets of several cities. Our empirical results demonstrate that our end-to-end learnable model is capable of drawing polygons of building footprints and road networks that very closely approximate the structure of existing online map services, in a fully automated manner. Quantitative and qualitative comparison to the state-of-the-arts also show that our approach achieves good levels of performance. To the best of our knowledge, the automatic extraction of large-scale topological maps is a novel contribution in the remote sensing community that we believe will help develop models with more informed geometrical constraints.",
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"affiliation": "ETH Zurich",
"fullName": "Zuoyue Li",
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"affiliation": "ETH Zurich",
"fullName": "Jan Dirk Wegner",
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"affiliation": "ETH Zurich",
"fullName": "Aurelien Lucchi",
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"title": "2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
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"abstract": "Various redirected walking (RDW) techniques have been proposed, which unwittingly manipulate the mapping from the user’s physical locomotion to motions of the virtual camera. Thereby, RDW techniques guide users on physical paths with the goal to keep them inside a limited tracking area, whereas users perceive the illusion of being able to walk infinitely in the virtual environment. However, the inconsistency between the user’s virtual and physical location hinders passive haptic feedback when the user interacts with virtual objects, which are represented by physical props in the real environment.In this paper, we present a novel reinforcement learning approach towards RDW with passive haptics. With a novel dense reward function, our method learns to jointly consider physical boundary avoidance and consistency of user-object positioning between virtual and physical spaces. The weights of reward and penalty terms in the reward function are dynamically adjusted to adaptively balance term impacts during the walking process. Experimental results demonstrate the advantages of our technique in comparison to previous approaches. Finally, the code of our technique is provided as an open-source solution.",
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"content": "Various redirected walking (RDW) techniques have been proposed, which unwittingly manipulate the mapping from the user’s physical locomotion to motions of the virtual camera. Thereby, RDW techniques guide users on physical paths with the goal to keep them inside a limited tracking area, whereas users perceive the illusion of being able to walk infinitely in the virtual environment. However, the inconsistency between the user’s virtual and physical location hinders passive haptic feedback when the user interacts with virtual objects, which are represented by physical props in the real environment.In this paper, we present a novel reinforcement learning approach towards RDW with passive haptics. With a novel dense reward function, our method learns to jointly consider physical boundary avoidance and consistency of user-object positioning between virtual and physical spaces. The weights of reward and penalty terms in the reward function are dynamically adjusted to adaptively balance term impacts during the walking process. Experimental results demonstrate the advantages of our technique in comparison to previous approaches. Finally, the code of our technique is provided as an open-source solution.",
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"title": "2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)",
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"title": "Automated cross-sensor registration, orthorectification and geopositioning using LIDAR digital elevation models",
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"abstracts": [
{
"abstractType": "Regular",
"content": "Cross-sensor image registration, orthorectification, and geopositioning of imagery are well-known problems whose solutions are difficult, if not impossible, to automate. Registration of radar to optical imagery typically requires a manual solution, as does the registration of imagery over rugged terrain or urban areas, where foreshortening and layover present formidable obstacles to successful automation. We have developed an automated solution that is based on the registration of imagery to high-precision digital elevation models (DEMs) derived from Lidar data. The key idea is the generation of a simulated image using Lidar data, the image camera model and the illumination conditions. The simulated image is then registered to the actual image with normalized cross-correlation methods. The result is an effective and completely automated technique for registering imagery to DEMs. It has been shown to work with BuckEye Lidar, ALIRT Lidar, commercial satellite imagery and commercial synthetic aperture radar imagery over diverse terrain types, including mountains, cities, and forests. It provides an automated solution to many difficult geospatial problems, including cross-sensor registration of radar and optical imagery, image registration over rugged terrain, geopositioning of imagery and orthorectification. Its use of Lidar enables it to handle three-dimensional features that are foreshortened or laid over in different directions. Its use of simulated imagery enables it to bypass the problem of disparate features in cross-sensor registration. Statistical analyses of the registration accuracy are presented along with results on commercial satellite imagery and Lidar data over Iraq, Afghanistan, Haiti and the U.S.",
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"abstract": "Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential movement of dynamic objects. Most current approaches for multi-sensor multi-object tracking are either lack of reliability by tightly relying on a single input source (e.g., center camera), or not accurate enough by fusing the results from multiple sensors in post processing without fully exploiting the inherent information. In this study, we design a generic sensor-agnostic multi-modality MOT framework (mmMOT), where each modality (i.e., sensors) is capable of performing its role independently to preserve reliability, and could further improving its accuracy through a novel multi-modality fusion module. Our mmMOT can be trained in an end-to-end manner, enables joint optimization for the base feature extractor of each modality and an adjacency estimator for cross modality. Our mmMOT also makes the first attempt to encode deep representation of point cloud in data association process in MOT. We conduct extensive experiments to evaluate the effectiveness of the proposed framework on the challenging KITTI benchmark and report state-of-the-art performance. Code and models are available at https://github.com/ZwwWayne/mmMOT.",
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"content": "Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential movement of dynamic objects. Most current approaches for multi-sensor multi-object tracking are either lack of reliability by tightly relying on a single input source (e.g., center camera), or not accurate enough by fusing the results from multiple sensors in post processing without fully exploiting the inherent information. In this study, we design a generic sensor-agnostic multi-modality MOT framework (mmMOT), where each modality (i.e., sensors) is capable of performing its role independently to preserve reliability, and could further improving its accuracy through a novel multi-modality fusion module. Our mmMOT can be trained in an end-to-end manner, enables joint optimization for the base feature extractor of each modality and an adjacency estimator for cross modality. Our mmMOT also makes the first attempt to encode deep representation of point cloud in data association process in MOT. We conduct extensive experiments to evaluate the effectiveness of the proposed framework on the challenging KITTI benchmark and report state-of-the-art performance. Code and models are available at https://github.com/ZwwWayne/mmMOT.",
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"affiliation": "The University of Sydney",
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"affiliation": "Nanyang Technological University",
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"title": "2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)",
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"title": "FIRE: Unsupervised bi-directional inter- and intra-modality registration using deep networks",
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"abstract": "Magnetic resonance imaging (MRI) benefits from the acquisition of multiple sequences (thereafter, referred to as “modalities”) under a single imaging session. Each modality offers different complementary spatial and functional information in the clinical setting. Inter- and intra (across MR sequence slices)-modality image registration is an important pre-processing step across multiple applications in routine clinical workflows, such as when visual or quantitative imaging biomarkers need to be assessed across multi-sequence/multi-slice MRI data. This paper presents an unsupervised deep learning-based registration network that can learn affine and non-rigid transformations, simultaneously. Inverse-consistency is an important property that is commonly ignored in recent deep learning-based inter-modality registration algorithms. We address this issue through our proposed multi-task, cross-domain image synthesis architecture, in which we incorporated a new comprehensive transformation network. The proposed model learns a modality-independent latent representation to perform cycle-consistent cross-modality synthesis and uses an inverse-consistency loss to learn paired transformations, to align the synthesized with the target image. We name this proposed framework as “FIRE” due to the shape of its structure and we focus on interpreting model components to enhance model interpretability for clinical MR applications. Our method shows comparable and better performances against a well-established baseline method in experiments on multi-sequence brain MR data and intra-modality 4D cardiac Cine-MR data.",
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"title": "Analysis on Deep Reinforcement Learning in Industrial Robotic Arm",
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"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
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"volume": "0",
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"year": "2019",
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"article": {
"id": "1cJ0W5YaAtq",
"doi": "10.1109/VR.2019.8798351",
"title": "Parasitic Body: Exploring Perspective Dependency in a Shared Body with a Third Arm",
"normalizedTitle": "Parasitic Body: Exploring Perspective Dependency in a Shared Body with a Third Arm",
"abstract": "With advancements in robotics, systems featuring wearable robotic arms teleoperated by a third party are appearing. An important aspect of these systems is the visual feedback provided to the third party operator. This can be achieved by placing a wearable camera on the robotic arm's “host,” or Main Body Operator (MBO), but such a setup makes the visual feedback dependant on the movements of the main body. Here we introduce a VR system called Parasitic Body to explore a VR shared body concept representative of the wearable robotic arms “host” (the MBO) and of the teleoperator (here called the Parasite Body Operator (PBO)). 2 users jointly operate a shared virtual body with a third arm: The MBO controls the main body and the PBO controls a third arm sticking out from the left shoulder of the main body. We focused here on the perspective dependency of the PBO (indeed, the PBO view is dependant of the movement of the MBO) in a “finding and reaching” task.",
"abstracts": [
{
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"content": "With advancements in robotics, systems featuring wearable robotic arms teleoperated by a third party are appearing. An important aspect of these systems is the visual feedback provided to the third party operator. This can be achieved by placing a wearable camera on the robotic arm's “host,” or Main Body Operator (MBO), but such a setup makes the visual feedback dependant on the movements of the main body. Here we introduce a VR system called Parasitic Body to explore a VR shared body concept representative of the wearable robotic arms “host” (the MBO) and of the teleoperator (here called the Parasite Body Operator (PBO)). 2 users jointly operate a shared virtual body with a third arm: The MBO controls the main body and the PBO controls a third arm sticking out from the left shoulder of the main body. We focused here on the perspective dependency of the PBO (indeed, the PBO view is dependant of the movement of the MBO) in a “finding and reaching” task.",
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"authors": [
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"affiliation": "Keio University",
"fullName": "Ryo Takizawa",
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"surname": "Takizawa",
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{
"affiliation": "The University of Tokyo",
"fullName": "Adrien Verhulst",
"givenName": "Adrien",
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"affiliation": "RIKEN AIP",
"fullName": "Katie Seaborn",
"givenName": "Katie",
"surname": "Seaborn",
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{
"affiliation": "Keio University",
"fullName": "Masaaki Fukuoka",
"givenName": "Masaaki",
"surname": "Fukuoka",
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{
"affiliation": "RIKEN AIP, The University of Tokyo",
"fullName": "Atsushi Hiyama",
"givenName": "Atsushi",
"surname": "Hiyama",
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{
"affiliation": "Toyohashi University of Technology",
"fullName": "Michiteru Kitazaki",
"givenName": "Michiteru",
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{
"affiliation": "The University of Tokyo",
"fullName": "Masahiko Inami",
"givenName": "Masahiko",
"surname": "Inami",
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{
"affiliation": "Keio University",
"fullName": "Maki Sugimoto",
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"surname": "Sugimoto",
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"id": "trans/tg/5555/01/10048575",
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"abstractUrl": "/proceedings-article/aivr/2019/560400a025/1grOkUHmcMM",
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"proceeding": {
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"title": "2020 7th International Forum on Electrical Engineering and Automation (IFEEA)",
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"article": {
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"doi": "10.1109/IFEEA51475.2020.00169",
"title": "Simple and Reliable: Mechanics Analysis for Robotic Arm of Strawberry Picking Robot",
"normalizedTitle": "Simple and Reliable: Mechanics Analysis for Robotic Arm of Strawberry Picking Robot",
"abstract": "In most of the countries, the method of ridge cropping is adopted to plant strawberry, but most countries rely mainly on manual picking. Under the circumstances, developing a strawberry picking mechanism and its automation to replace manual work is an urgent problem waiting to be solved. In this study, based on 3D modelling, stress analyzing and motion simulation, an automatic picking and simple structure robot was innovatively designed. This paper mainly focuses on the part of two robotic arms. The single robotic arm consists of 5 rotational joints, 5 small motors at the joint, 4 Aluminium alloy skeletons and a pair of claws. The claw is made from light materials, and a pliant component is mounted to the surface. Through calculations of its motion simulation, it indicates that each part of the arm can run flexibly with bearing the requests of stress. Moreover, the claws can reduce damage to the fruit during picking by the soft structure. The results of dynamic simulation show that robotic arms can satisfy the requirements of automatic low-damage picking for strawberries in the greenhouse under the ridge.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In most of the countries, the method of ridge cropping is adopted to plant strawberry, but most countries rely mainly on manual picking. Under the circumstances, developing a strawberry picking mechanism and its automation to replace manual work is an urgent problem waiting to be solved. In this study, based on 3D modelling, stress analyzing and motion simulation, an automatic picking and simple structure robot was innovatively designed. This paper mainly focuses on the part of two robotic arms. The single robotic arm consists of 5 rotational joints, 5 small motors at the joint, 4 Aluminium alloy skeletons and a pair of claws. The claw is made from light materials, and a pliant component is mounted to the surface. Through calculations of its motion simulation, it indicates that each part of the arm can run flexibly with bearing the requests of stress. Moreover, the claws can reduce damage to the fruit during picking by the soft structure. The results of dynamic simulation show that robotic arms can satisfy the requirements of automatic low-damage picking for strawberries in the greenhouse under the ridge.",
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"normalizedAbstract": "In most of the countries, the method of ridge cropping is adopted to plant strawberry, but most countries rely mainly on manual picking. Under the circumstances, developing a strawberry picking mechanism and its automation to replace manual work is an urgent problem waiting to be solved. In this study, based on 3D modelling, stress analyzing and motion simulation, an automatic picking and simple structure robot was innovatively designed. This paper mainly focuses on the part of two robotic arms. The single robotic arm consists of 5 rotational joints, 5 small motors at the joint, 4 Aluminium alloy skeletons and a pair of claws. The claw is made from light materials, and a pliant component is mounted to the surface. Through calculations of its motion simulation, it indicates that each part of the arm can run flexibly with bearing the requests of stress. Moreover, the claws can reduce damage to the fruit during picking by the soft structure. The results of dynamic simulation show that robotic arms can satisfy the requirements of automatic low-damage picking for strawberries in the greenhouse under the ridge.",
"fno": "962700a806",
"keywords": [
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"Manipulator Kinematics",
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"authors": [
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"affiliation": "School of Engineering, University of Mississippi,Oxford,Mississippi",
"fullName": "Wenquan Cai",
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{
"affiliation": "School of Engineering, University of Mississippi,Oxford,Mississippi",
"fullName": "Yuhui Shi",
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"article": {
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"doi": "10.1109/ICHCI51889.2020.00094",
"title": "Analysis on Deep Reinforcement Learning in Industrial Robotic Arm",
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"abstract": "Deep reinforcement learning is a combination of reinforcement learning and deep learning. It allows the robot to learn new tasks on its own. In recent years, many studies have applied deep reinforcement learning algorithms to the manipulation of robotic arms, and have achieved excellent results. This article described the basic knowledge of deep reinforcement learning and analyzed the current problems faced by industrial robotic arms. By reviewing the main research that researchers have applied deep reinforcement learning algorithms to the field of manipulator operation in recent years and the development of related deep reinforcement learning algorithms. It concluded that how deep reinforcement learning can solve the problems faced by industrial robotic arms. Finally, this article referred to the challenges faced by the application of deep reinforcement learning and its application in the field of industrial robotic arms and then made a detailed analysis and explanation.",
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"abstractType": "Regular",
"content": "Deep reinforcement learning is a combination of reinforcement learning and deep learning. It allows the robot to learn new tasks on its own. In recent years, many studies have applied deep reinforcement learning algorithms to the manipulation of robotic arms, and have achieved excellent results. This article described the basic knowledge of deep reinforcement learning and analyzed the current problems faced by industrial robotic arms. By reviewing the main research that researchers have applied deep reinforcement learning algorithms to the field of manipulator operation in recent years and the development of related deep reinforcement learning algorithms. It concluded that how deep reinforcement learning can solve the problems faced by industrial robotic arms. Finally, this article referred to the challenges faced by the application of deep reinforcement learning and its application in the field of industrial robotic arms and then made a detailed analysis and explanation.",
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"normalizedAbstract": "Deep reinforcement learning is a combination of reinforcement learning and deep learning. It allows the robot to learn new tasks on its own. In recent years, many studies have applied deep reinforcement learning algorithms to the manipulation of robotic arms, and have achieved excellent results. This article described the basic knowledge of deep reinforcement learning and analyzed the current problems faced by industrial robotic arms. By reviewing the main research that researchers have applied deep reinforcement learning algorithms to the field of manipulator operation in recent years and the development of related deep reinforcement learning algorithms. It concluded that how deep reinforcement learning can solve the problems faced by industrial robotic arms. Finally, this article referred to the challenges faced by the application of deep reinforcement learning and its application in the field of industrial robotic arms and then made a detailed analysis and explanation.",
"fno": "231600a426",
"keywords": [
"Control Engineering Computing",
"Deep Learning Artificial Intelligence",
"Industrial Manipulators",
"Production Engineering Computing",
"Industrial Robotic Arm",
"Deep Reinforcement Learning Algorithms",
"Manipulator Operation",
"Human Computer Interaction",
"Service Robots",
"Shape",
"Virtual Environments",
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"authors": [
{
"affiliation": "Shandong University,Shandong,Chian",
"fullName": "Hengyue Guan",
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"abstract": "Hazardous environments gave a big challenge for the human to accomplish the desired tasks, especially in sampling and collecting materials either for securing the locality or research purposes. Hence, teleoperated robots with robotic arms have been proposed to execute any tasks that appointed. However, most robotic arms on the teleoperated robots only serve specific tasks and have no ability to retract arm with precise and smooth movement, which will compromise its functionality. In this paper, five designs of robotic arms were proposed with different mechanisms for its retractions. Evaluation and selection for the final design was decided based on the decision-making process for all designs. Considering important criterion, evaluation was performed using concept scoring. All concept designs were modelled and analysed in SolidWorks. Load was applied on the end effector of robotic arm to observe Von Mises criteria of designed arms. Both methods indicate that design 5 with linear motor mechanism has the best endurance and thus, chosen as the most suitable design to be mounted on the track vehicle.",
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"content": "Hazardous environments gave a big challenge for the human to accomplish the desired tasks, especially in sampling and collecting materials either for securing the locality or research purposes. Hence, teleoperated robots with robotic arms have been proposed to execute any tasks that appointed. However, most robotic arms on the teleoperated robots only serve specific tasks and have no ability to retract arm with precise and smooth movement, which will compromise its functionality. In this paper, five designs of robotic arms were proposed with different mechanisms for its retractions. Evaluation and selection for the final design was decided based on the decision-making process for all designs. Considering important criterion, evaluation was performed using concept scoring. All concept designs were modelled and analysed in SolidWorks. Load was applied on the end effector of robotic arm to observe Von Mises criteria of designed arms. Both methods indicate that design 5 with linear motor mechanism has the best endurance and thus, chosen as the most suitable design to be mounted on the track vehicle.",
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"affiliation": "Universiti Pertahanan Nasional Malaysia,Faculty of Engineering,Kuala Lumpur,Malaysia",
"fullName": "Norfarahana Adibah Raffie",
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{
"affiliation": "Universiti Pertahanan Nasional Malaysia,Faculty of Engineering,Kuala Lumpur,Malaysia",
"fullName": "Noor Hafizah Amer",
"givenName": "Noor Hafizah",
"surname": "Amer",
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{
"affiliation": "Universiti Pertahanan Nasional Malaysia,Faculty of Engineering,Kuala Lumpur,Malaysia",
"fullName": "Syed Mohd Fairuz Syed Mohd Dardin",
"givenName": "Syed Mohd Fairuz Syed",
"surname": "Mohd Dardin",
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{
"affiliation": "Universiti Pertahanan Nasional Malaysia,Faculty of Engineering,Kuala Lumpur,Malaysia",
"fullName": "Khisbullah Hudha",
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{
"affiliation": "Universiti Pertahanan Nasional Malaysia,Faculty of Engineering,Kuala Lumpur,Malaysia",
"fullName": "Saiddi Ali Firdaus Mohamed Ishak",
"givenName": "Saiddi Ali Firdaus Mohamed",
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"abstract": "Customer satisfaction is the key building block for developing and managing customer relationships. In the ever-increasing competitive society, customer satisfaction has become the most important factor that determines the survival and development of enterprises. Based on customer satisfaction theory, this paper introduces the evaluation model which indicates customer satisfaction depends on the following four factors: customer expectation, previous consumption experiences, perceived product quality and perceived service quality. As far as the characteristics of retail businesses are concerned, three-tier evaluation index system is constructed. Customer satisfaction can be accordingly evaluated by analytic hierarchy process (AHP) with the variables included in the index system. With the aim of providing beneficial reference for retail enterprises on how to improve customer satisfaction, this paper specially make an empirical study on Wal-Mart in order to analyze and verify the causalities between customer satisfaction and each influencing factors.",
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"doi": "10.1109/ICMB.2009.27",
"title": "Customer Satisfaction and Loyalty of Mobile Services",
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"abstract": "With the rapid development of mobile technology and users’ wide adoption of mobile phones, mobile services have been a huge success in China. Even though the literature on the adoption of mobile services is quite extensive, few studies focus on customer satisfaction and loyalty in China. This study investigated the impact of perceived customer value, perceived service quality, and trust on customer satisfaction, and the influence of satisfaction and perceived switching cost on customer loyalty. The research results can give some implications on mobile instant message development strategies in China.",
"abstracts": [
{
"abstractType": "Regular",
"content": "With the rapid development of mobile technology and users’ wide adoption of mobile phones, mobile services have been a huge success in China. Even though the literature on the adoption of mobile services is quite extensive, few studies focus on customer satisfaction and loyalty in China. This study investigated the impact of perceived customer value, perceived service quality, and trust on customer satisfaction, and the influence of satisfaction and perceived switching cost on customer loyalty. The research results can give some implications on mobile instant message development strategies in China.",
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"normalizedAbstract": "With the rapid development of mobile technology and users’ wide adoption of mobile phones, mobile services have been a huge success in China. Even though the literature on the adoption of mobile services is quite extensive, few studies focus on customer satisfaction and loyalty in China. This study investigated the impact of perceived customer value, perceived service quality, and trust on customer satisfaction, and the influence of satisfaction and perceived switching cost on customer loyalty. The research results can give some implications on mobile instant message development strategies in China.",
"fno": "3691a115",
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"affiliation": null,
"fullName": "Zhahua Deng",
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{
"affiliation": null,
"fullName": "Jinlong Zhang",
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{
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"fullName": "Ling Zhao",
"givenName": "Ling",
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{
"affiliation": null,
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"givenName": "Yaobin",
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{
"affiliation": null,
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"givenName": "K. K.",
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"abstract": "The products of higher education provided to the customers by colleges and universities are service. Service attribute is the basic characteristic of higher education. Whether customer is satisfied is very important to the future of college. The model of service customer satisfaction in higher education is established to explore the relationship of service quality, image of colleges and universities, customer perceived service value, customer trust, customer satisfaction and customer behavior in higher education. The empirical results show that customer satisfaction has remarkable influence to the customer behavior. Service quality and image of colleges has indirect influence to customer satisfaction and customer behavior in higher education through customer perceived service value and customer trust.",
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"abstract": "Image-based virtual try-on systems with the goal of transferring a desired clothing item onto the corresponding region of a person have made great strides recently, but challenges remain in generating realistic looking images that preserve both body and clothing details. Here we present a new virtual try-on network, called VTNFP, to synthesize photo-realistic images given the images of a clothed person and a target clothing item. In order to better preserve clothing and body features, VTNFP follows a three-stage design strategy. First, it transforms the target clothing into a warped form compatible with the pose of the given person. Next, it predicts a body segmentation map of the person wearing the target clothing, delineating body parts as well as clothing regions. Finally, the warped clothing, body segmentation map and given person image are fused together for fine-scale image synthesis. A key innovation of VTNFP is the body segmentation map prediction module, which provides critical information to guide image synthesis in regions where body parts and clothing intersects, and is very beneficial for preventing blurry pictures and preserving clothing and body part details. Experiments on a fashion dataset demonstrate that VTNFP generates substantially better results than state-of-the-art methods.",
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"content": "Image-based virtual try-on systems with the goal of transferring a desired clothing item onto the corresponding region of a person have made great strides recently, but challenges remain in generating realistic looking images that preserve both body and clothing details. Here we present a new virtual try-on network, called VTNFP, to synthesize photo-realistic images given the images of a clothed person and a target clothing item. In order to better preserve clothing and body features, VTNFP follows a three-stage design strategy. First, it transforms the target clothing into a warped form compatible with the pose of the given person. Next, it predicts a body segmentation map of the person wearing the target clothing, delineating body parts as well as clothing regions. Finally, the warped clothing, body segmentation map and given person image are fused together for fine-scale image synthesis. A key innovation of VTNFP is the body segmentation map prediction module, which provides critical information to guide image synthesis in regions where body parts and clothing intersects, and is very beneficial for preventing blurry pictures and preserving clothing and body part details. Experiments on a fashion dataset demonstrate that VTNFP generates substantially better results than state-of-the-art methods.",
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"affiliation": "Northeastern University",
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"affiliation": "Northeastern University",
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"affiliation": "University of California. Irvine",
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"abstract": "Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these use recognition of high level image features to predict reasonably looking depth maps, the result often has poor metric accuracy. Moreover, the standard feed forward architecture does not allow modification of the prediction based on cues other than the image.In this paper we relax the monocular depth estimation task by proposing a network that allows us to complement image features with a set of auxiliary variables. These allow disambiguation when image features are not enough to accurately pinpoint the exact depth map and can be thought of as a low dimensional parameterization of the surfaces that are reasonable monocular predictions. By searching the parameterization we can combine monocular estimation with traditional photoconsistency or geometry based methods to achieve both visually appealing and metrically accurate surface estimations. Since we relax the problem we are able to work with smaller networks than current architectures. In addition we design a self-supervised training scheme, eliminating the need for ground truth image depth-map pairs. Our experimental evaluation shows that our method generates more accurate depth maps and generalizes better than competing state-of-the-art approaches.",
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"abstract": "Virtual characters are an integral part of many game and learning environments and have practical applications as tutors, demonstrators or even representations of the user. However, creating virtual character behaviors can be a time-consuming and complex task requiring substantial technical expertise. To accelerate and better enable the use of virtual characters in social games, we present a virtual character behavior toolkit for the development of expressive virtual characters. It is a midlleware toolkit which sits on top of the game engine with a focus on providing high-level character behaviors to quickly create social games. The toolkit can be adapted to a wide range of scenarios related to social interactions with individuals and groups at multiple distances in the virtual environment and supports customization and control of facial expressions, body animations and group formations. We describe the design of the toolkit, providing an examplar of a small game that is being created with it and our intended future work on the system.",
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"abstract": "Two problems dealing with electromagnetic properties of anisotropic shells having arbitrary cylindrical shapes are examined. The first problem investigates the scattering response of the shell under external, normally incident plane wave excitation. The other problem involves the study of electromagnetic fields radiated through the shell due to a line source placed inside it. These two cases, in practice, simulate antenna radomes used to protect internally placed radiating elements from both environmental as well as external electromagnetic interference. The medium characteristics of the shell are assumed to be anisotropic, but homogeneous as well. Numerical results are presented for both the radar scattering cross section (RCS) of the radome, as well as radiation patterns of line sources located inside the shell.<>",
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"abstract": "According to the current situation that the existing electromagnetic signal visualization can only be applied in part of the spacial scale of middle or low latitude on earth, this paper built a earth shell space grid model applied to describing the environment of terrestrial space through employing the method of QTM sphere octree subdivision to subdivide the three-dimensional space of earth shell space on account of the earth shell space grid theory. And the paper utilized model to improve the ray casting arithmetic based on GPU as it seeks to reduce the memory of the whole gradient and raise the draw efficiency effectively. Based on the foundation above, the paper implemented the calculation of electromagnetic signal's form, field intensity and visualization by approaching the circular cone to signal beam and applying the certain propagation forecasting methods, which has the practical application value.",
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"content": "Lower-limb powered exoskeletons have the potential to help patients who suffer from mobility impairments to regain their independence. Unlike traditional exoskeletons with predefined gait patterns, some recently developed exoskeletons are capable of planning gait patterns autonomously based on environmental information and human perception. However, patients may have difficulty navigating through unfamiliar environments due to the slow or miscommunication between the exoskeletons and users. Verbal or manual adjustments cannot continuously or adaptively manage the mechanical motions either. In this study, we propose and demonstrate an autonomous walking pattern generator based on shared visual information between human and a powered lower-limb exoskeleton. This control scheme is to understand the user's intention during walking and help the patient who has trouble walking to overcome obstacles. Human gazing positions in a 3-D environment are measured and evaluated via an eye tracker system to detect objects in the walking path. A model predictive controller (MPC) is formulated, which generates and modifies the footsteps and the center of mass trajectory of the powered exoskeleton in real time considering the visual feedback. Preliminary experiments are performed by having a human subject walking toward a target sign indicating a physical obstacle. Our results show the feasibility of integrating the walking pattern generator with visual feedbacks to achieve autonomous control for a lower-limb powered exoskeleton.",
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"abstract": "With indoor intelligent surveillance gradually integrated into daily life, privacy-preserving of user is facing a major challenge. In order to solve the contradiction between user privacy-preserving and action recognition, we propose a partially coupled convolutional neural network (CNN) model that only uses anonymous videos of low resolution (LR). Firstly, in the pre-process, the optical flow characteristics of low resolution (16×12 pixels) and high resolution (HR) (64×48 pixels) videos are extracted respectively, and continuous multi-frame optical flow superposition is performed to represent the video action. Secondly, super-resolution technology is used to improve the characteristics of LR optical flow. Finally, a partially coupled CNN model is trained. HR data is added to the training, improve the recognition performance of LR data in test. Experiments on two public datasets show that the method we proposed is better than other state-of the-art methods, which has the dual advantages of privacy-preserving and high recognition accuracy.",
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"abstract": "Eye movement reflects the shift of overt visual attention. Eye movement trajectories from a group of observers can be expressed by a representative scanpath. The representative scan-path can work as a baseline for studies on scanpath prediction as well as provide useful knowledge about group behavior in psychological studies. In this paper, we propose a new framework to summarize a representative scanpath from individual scanpaths, taking into account the spatial distribution of scan-paths rather than simply treating them as strings of characters. It consists of three steps: extract areas of interest (AOI), remove outliers and summarize scanpaths. In the last step, we develop an algorithm termed Candidate-constrained DTW Barycenter Algorithm (CDBA) by imposing 3 constraints: (1) the components of the representative scanpath must be chosen from candidates (extracted AOIs); (2) the occurrence count of each AOI in the representative scanpath cannot exceed its maximum occurrence count in individual scanpaths; (3) any two contiguous AOIs in the representative scanpath must be contiguous in at least one individual scanpath. The experiments demonstrate that the proposed method outperforms other state-of-the-art scanpath mining methods.",
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"content": "Eye movement reflects the shift of overt visual attention. Eye movement trajectories from a group of observers can be expressed by a representative scanpath. The representative scan-path can work as a baseline for studies on scanpath prediction as well as provide useful knowledge about group behavior in psychological studies. In this paper, we propose a new framework to summarize a representative scanpath from individual scanpaths, taking into account the spatial distribution of scan-paths rather than simply treating them as strings of characters. It consists of three steps: extract areas of interest (AOI), remove outliers and summarize scanpaths. In the last step, we develop an algorithm termed Candidate-constrained DTW Barycenter Algorithm (CDBA) by imposing 3 constraints: (1) the components of the representative scanpath must be chosen from candidates (extracted AOIs); (2) the occurrence count of each AOI in the representative scanpath cannot exceed its maximum occurrence count in individual scanpaths; (3) any two contiguous AOIs in the representative scanpath must be contiguous in at least one individual scanpath. The experiments demonstrate that the proposed method outperforms other state-of-the-art scanpath mining methods.",
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"abstract": "Understanding the mechanisms underlying human visual attention is an important research problem in cognitive neuroscience and computer vision. While existing models predict salient regions (i.e., saliency maps) and temporal sequences of eye fixations (i.e., scanpaths) in images, their designs often partially follow theoretical frameworks. Here, we introduce ScanpathNet, a deep learning model inspired by the latest theoretical model in neuroscience. It is ’guided’ by a dynamic priority map influenced by semantic content and fixation history. The model leverages convolutional neural networks to extract rich semantic features, convolutional long short-term memory networks to model the inhibition of return mechanism and sequential dependencies of fixations, and mixture density networks to predict probability distributions of fixations for each pixel. Simulated human scanpaths can then be generated by sequentially sampling the output of the proposed model. Despite its simplicity, ScanpathNet showed promising qualitative and quantitative scanpath prediction performance in extensive experiments on numerous eye-tracking benchmark datasets.",
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