Specially , an adaptive prediction approach is presented to demand hotspots and their hotness , and then combing the driver 's location and the hotness , top candidates are recommended and visually presented to drivers . Based on the dataset provided by CAR INC . , the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation , with 15.21 % improvement on average f-measure for prediction and 79.6 % hit ratio for recommendation . Finally , the document includes an appendix describing the User Management System and how the models in the repository can be organized in courses . These courses play an important role in the recommendation of models . Crowd-sourced reviews are used daily by potential customers to learn relevant information about a business . While textual reviews have become prominent in many recommendation-based systems , the inclusion of images can significantly increase the effectiveness of a review . However , it is difficult to verify the accuracy and usefulness of the information provided by a contributing user . While textual reviews have become prominent in many recommendation-based systems , automated frameworks to provide relevant visual cues against text reviews where pictures are not available is a new form of task confronted by data mining and machine learning researchers . Suggestions of pictures that are relevant to the content of a review could significantly benefit the users by increasing the effectiveness of a review . One effective way to overcome thisproblem is by using Hierarchical Fuzzy System ( HFSs ) . This paper aims to explore the use of HFSs forRecommendation system . Specifically , we are interested in exploring and comparing the HFS and FLS forthe Career path recommendation system ( CPRS ) based on four key criteria , namely topology , the numberof rules , the rules structures and interpretability . Process mining complements BPM with methods , techniques , and tools that provide insights based on observed executions of business processes recorded in event logs of information systems . State-of-the-art discovery and conformance techniques completely ignore or only implicitly consider the information about the likelihood of processes , which is readily available in event logs , even though such stochastic information is necessary for simulation , prediction and recommendation in models . Furthermore , stochastic information can provide business analysts with further actionable insights on frequent and rare conformance issues . The semantic content of LOD in conjunction with the advanced searching and querying mechanisms provided by SPARQL has opened up unprecedented opportunities not only for enhancing existing applications , but also for developing new and innovative semantic applications . However , SPARQL is inadequate to deal with functionalities such as comparing , prioritizing , and ranking search results which are fundamental to applications such as recommendation provision , matchmaking , social network analysis , visualization , and data clustering . This paper addresses this problem by developing a systematic measurement model of semantic similarity between resources in Linked Data . We propose a methodology that takes a human factors engineering approach to develop systems-engineering-style requirements and design recommendations for both HIT and aspects beyond the scope of the HIT itself ( e.g. , policies and procedures ) . Our approach includes : ( 1 ) data collection and analysis ; ( 2 ) preliminary recommendations generation ; ( 3 ) recommendation review by methodological and domain subject matter experts ; ( 4 ) revised recommendation review by end users ; ( 5 ) revised recommendation review by HIT vendors ; and ( 6 ) final recommendation generation . We illustrate the methodology by presenting a case study focusing on home health care nurses using HIT during the home care admission process . Results of the experiment on the real dataset of the Douban website demonstrate that movie recommendation with the user characteristics mined by the LDA model and GraphGAN combined can achieve the best effect , which is much higher than the collaborative filtering based on user ratings or using only LDA theme . This proves that network embedding can promote the accuracy of recommendationation , while ensuring the dependability of the model . The method proposed in this paper is effective . Improving a websites performance by improving the usability , e.g. by introducing an online booking engine , an email acquisition facility or optimising a website for search engines is important , but only half of the job - there remains a lack of real meaning and intuitive usage in behind . This talk will elaborate on the potential and the enhancements Semantic Web technology can come up with in this situation and will include reflections on smart information searching , integration , recommendion and various intelligent services . Along with age estimation niu2016ordinal , popular applications for ordinal regression include predicting the progression of various diseases , such as Alzheimer ’s disease doyle2014predicting , Crohn ’s disease , artery disease , and kidney disease . Also , ordinal regression models are common choices for text message advertising and various recommender systems . While the field of machine learning field developed many powerful algorithms for predictive modeling , most algorithms were designed for classification tasks . document : DeepFM : A Factorization - Machine based Neural Network for CTR Prediction Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems . Despite great progress , existing methods seem to have a strong bias towards low - or high - order interactions , or require expertise feature engineering . In addition , DNNs model feature interactions at the bit - wise level , which is different from the traditional FM framework which models feature interactions at the vector - wise level . Thus , in the field of recommender systems , whether DNNs are indeed the most effective model in representing high - order feature interactions remains an open question . In this paper , we propose a neural network - based model to learn feature interactions in an explicit , vector - wise fashion . In real - world applications , size and speed are constraints to an efficient mobile and embedded deployment of deep models . Several relevant real - world applications depend on text classification tasks such as sentiment analysis , recommendation and opinion mining . The appeal for these applications combined with the boost in mobile devices usage motivates the need for research in restrained text classification models . In the recent years , deep neural networks and , in particular , convolutional neural networks ( CNNs ) [ reference ] have been applied with great success to numerous computer vision - related applications . However , original CNN models can not be directly applied to the recommendation problem to extract meaningful patterns in users , items and ratings because these data are not Euclidean structured , i.e. they do not lie on regular lattices like images but irregular domains like graphs or manifolds . This strongly motivates the development of geometric deep learning [ reference ] techniques that can mathematically deal with graph - structured data , which arises in numerous applications , ranging from computer graphics and vision [ reference ][ reference ][ reference ][ reference ] to chemistry [ reference ] . Several important real - world problems can be cast as a matrix completion problem , including remote sensing , system identification and recommendation systems . Throughout the paper , we will consider the recommendation system problem as an illustration of the matrix completion problem . Such systems have indeed become very common in many applications such as movie or product recommendation ( e.g. Netflix , Amazon , Facebook , and Apple ) . We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation . Despite widespread use in language modeling and economics , the multinomial likelihood receives less attention in the recommender systems literature . We introduce a different regularization parameter for the learning objective , which proves to be crucial for achieving competitive performance . Among these technologies , the main cornerstone of this paper is working with the RF technology . This paper entails an ambience section concerning the history and technology of autonomous vehicles . At present , several efficient traffic light control systems exist for autonomous vehicles and it is unfortunate that despite being one of the most popular technologies of the world , there is no complete and efficacious RF system till now . A new methodology for the design of navigation systems for autonomous vehicles is introduced . Using simple kinematic relationships , the problem of estimating the velocity and position of an autonomous vehicle is solved by resorting to special bilinear time-varying filters . The embedded deep learning object detection model competition in IEEE MMSP2019 focuses on the object detection for sensing technology in autonomous driving vehicles , which aims at detecting small objects in worse conditions through embedded systems . We provide a dataset with 89,002 annotated images for training and 1,500 annotated images for validation . Research on autonomous road vehicles , aircraft and vessels is presently underway at many research centres . The work is being enhanced by two factors : growing interest of future users and new technologies that make constructing autonomous vehicles possible . The domain of autonomous vehicles is of great interest to researchers and engineers and much has been performed in this field . The paper proposes a fuzzy control algorithm for autonomous electric vehicles which are moving next to the obstacle ( object ) boundaries , avoiding the collisions with them ( a “ guard motion ” ) . Four motion cycles ( programs ) which depend of the proximity levels and which are used by the vehicle on its trajectory are described . The development of autonomous connected vehicles , moving as a platoon formation , is a hot topic in the intelligent transportation system ( ITS ) research field . It is on the road and deployment requires the design of distributed control strategies , leveraging secure vehicular ad-hoc networks ( VANETs ) . Autonomous connected vehicles are a main concept in the future of Intelligent Transportation Systems ( ITS ) since they provide an increase in safety and road efficiency . The management and coordination of the connected vehicles is based on periodic communications among the vehicles involved in the network , and with their surrounding environment . Presents a visual navigation algorithm for autonomous on-road vehicles . The purpose of the research is to realize an autonomous vehicle navigation system which works as an intelligent driving support system . In this paper , a major challenge in the field of trajectory planning for autonomous on-road vehicles is addressed : to calculate trajectories efficiently and reliably . The proposed method is based on a cooperative maneuver automaton , in which each maneuver is formulated as a discrete-time hybrid automaton that defines constraints on the behavior of a group of autonomous vehicles . The proposed wireless video streaming mote is aimed at moving around an area under surveillance , gathering images and transferring the same to control station . The proposed system has potential applications including assistance to vehicle drivers during reversing , parking , autonomous driving of the vehicles , driving vehicles using mobile application etc . The proposed system designed has a myriad of real-time applications for domestic requirements with social cause too . Various combinations of cameras and 3D sensors are widely used in the field , and it is desirable to have a single algorithm that generalizes to as many different problem settings as possible . Many real - world robotic systems are equipped with multiple 3D sensors : for example , autonomous cars often have multiple lidars and potentially also radars . Yet , current algorithms often assume a single RGB - D camera , which provides RGB - D images , or a single lidar sensor , which allows the creation of a local front view image of the lidar depth and intensity readings . A major drawback of the global fusion network is that the variance of the regression target is directly dependent on the particular scenario . For autonomous driving , the system may be expected to detect objects from 1 m to over 100 m . This variance places a burden on the network and results in sub - optimal performance . Pedestrian detection aims to predict bounding boxes of all the pedestrian instances in an image . It has attracted much attention within the computer vision community in recent years as an important component for many human - centric applications , such as self - driving vehicles , person re - identification , video surveillance and robotics . Recently , many research works have been devoted to pedestrian detection . subsubsection : KITTI The challenging KITTI dataset consists of 7 , 481 training and 7 , 518 test images , which are captured from an autonomous driving platform . Evaluation is done at three levels of difficulty : easy , moderate and hard , where the difficulty is measured by the minimal scale of the pedestrians to be considered and the occlusion and truncation of the pedestrians . We quantify the performance of SegNet on two scene segmentation benchmarks using our Caffe implementation . The first task is road scene segmentation which is of current practical interest for various autonomous driving related problems . The second task is indoor scene segmentation which is of immediate interest to several augmented reality ( AR ) applications . section : Related Work The dramatic performance improvements from using CNNs for semantic segmentation have brought about an increasing demand for such algorithms in the context of autonomous driving scenarios . As a large amount of annotated data is crucial in order to train such deep networks , multiple new datasets have been released to encourage further research in this area , including Synthia [ reference ] , Virtual KITTI [ reference ] , and Cityscapes [ reference ] . section : Introduction Since the DARPA Grand Challenges for autonomous vehicles , there has been an explosion in applications and research for self - driving cars . Among the different environments for self - driving cars , highway and urban roads are on opposite ends of the spectrum . In contrast , residential or urban driving environments feature a much higher degree of unpredictability with many generic objects , inconsistent lane - markings , and elaborate traffic flow patterns . The relative regularity and structure of highways has facilitated some of the first practical applications of autonomous driving technology . Many automakers have begun pursuing highway auto - pilot solutions designed to mitigate driver stress and fatigue and to provide additional safety features ; for example , certain advanced - driver assistance systems ( ADAS ) can both keep cars within their lane and perform front - view car detection . Namely , today ’s self - driving cars are equipped with expensive but critical sensors , such as LIDAR , radar and high - precision GPS coupled with highly detailed maps . In today ’s production - grade autonomous vehicles , critical sensors include radar , sonar , and cameras . Long - range vehicle detection typically requires radar , while nearby car detection can be solved with sonar . section : Introduction The Fine - Grained Visual Classification ( FGVC ) task focuses on differentiating between hard - to - distinguish object classes , such as species of birds , flowers , or animals ; and identifying the makes or models of vehicles . FGVC datasets depart from conventional image classification in that they typically require expert knowledge , rather than crowdsourcing , for gathering annotations . The purpose of this research is to create a mathematical model of the flow of speech packets in telecommunication networks with wireless access . The work is of practical importance , since the proposed mathematical model can be used in the process of reckoning and designing of new local wireless telecommunication networks . The model can be integrated into a more complex system that describes the processes inside the wireless equipment for the network access . System dynamics ( SD ) modelling has recently reemerged to the reckoning vis-A -vis supply chain management (SCM)-related issues . Current trends in research in SD modelling vis-A -vis SCM are primarily focused on inventory management , time compression , demand amplification , design of supply chains ( and their integration ) , global supply chains and their planning and deployment . In this paper , we present the ApolloScape dataset [ 1 ] and its applications for autonomous driving . Compared with existing public datasets from real scenes , e.g. , KITTI [ 2 ] or Cityscapes [ 3 ] , ApolloScape contains much large and richer labelling including holistic semantic dense point cloud for each site , stereo , per-pixel semantic labelling , lanemark labelling , instance segmentation , 3D car instance , high accurate location for every frame in various driving videos from multiple sites , cities and daytimes . For each task , it contains at lease 15x larger amount of images than SOTA datasets .
Specially , an adaptive prediction approach is presented to demand hotspots and their hotness , and then combing the driver 's location and the hotness , top candidates are recommended and visually presented to drivers . Based on the dataset provided by CAR INC . , the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation , with 15.21 % improvement on average f-measure for prediction and 79.6 % hit ratio for recommendation .
Finally , the document includes an appendix describing the User Management System and how the models in the repository can be organized in courses . These courses play an important role in the recommendation of models .
Crowd-sourced reviews are used daily by potential customers to learn relevant information about a business . While textual reviews have become prominent in many recommendation-based systems , the inclusion of images can significantly increase the effectiveness of a review . However , it is difficult to verify the accuracy and usefulness of the information provided by a contributing user .
While textual reviews have become prominent in many recommendation-based systems , automated frameworks to provide relevant visual cues against text reviews where pictures are not available is a new form of task confronted by data mining and machine learning researchers . Suggestions of pictures that are relevant to the content of a review could significantly benefit the users by increasing the effectiveness of a review .
One effective way to overcome thisproblem is by using Hierarchical Fuzzy System ( HFSs ) . This paper aims to explore the use of HFSs forRecommendation system . Specifically , we are interested in exploring and comparing the HFS and FLS forthe Career path recommendation system ( CPRS ) based on four key criteria , namely topology , the numberof rules , the rules structures and interpretability .
Process mining complements BPM with methods , techniques , and tools that provide insights based on observed executions of business processes recorded in event logs of information systems . State-of-the-art discovery and conformance techniques completely ignore or only implicitly consider the information about the likelihood of processes , which is readily available in event logs , even though such stochastic information is necessary for simulation , prediction and recommendation in models . Furthermore , stochastic information can provide business analysts with further actionable insights on frequent and rare conformance issues .
The semantic content of LOD in conjunction with the advanced searching and querying mechanisms provided by SPARQL has opened up unprecedented opportunities not only for enhancing existing applications , but also for developing new and innovative semantic applications . However , SPARQL is inadequate to deal with functionalities such as comparing , prioritizing , and ranking search results which are fundamental to applications such as recommendation provision , matchmaking , social network analysis , visualization , and data clustering . This paper addresses this problem by developing a systematic measurement model of semantic similarity between resources in Linked Data .
We propose a methodology that takes a human factors engineering approach to develop systems-engineering-style requirements and design recommendations for both HIT and aspects beyond the scope of the HIT itself ( e.g. , policies and procedures ) . Our approach includes : ( 1 ) data collection and analysis ; ( 2 ) preliminary recommendations generation ; ( 3 ) recommendation review by methodological and domain subject matter experts ; ( 4 ) revised recommendation review by end users ; ( 5 ) revised recommendation review by HIT vendors ; and ( 6 ) final recommendation generation . We illustrate the methodology by presenting a case study focusing on home health care nurses using HIT during the home care admission process .
Results of the experiment on the real dataset of the Douban website demonstrate that movie recommendation with the user characteristics mined by the LDA model and GraphGAN combined can achieve the best effect , which is much higher than the collaborative filtering based on user ratings or using only LDA theme . This proves that network embedding can promote the accuracy of recommendationation , while ensuring the dependability of the model . The method proposed in this paper is effective .
Improving a websites performance by improving the usability , e.g. by introducing an online booking engine , an email acquisition facility or optimising a website for search engines is important , but only half of the job - there remains a lack of real meaning and intuitive usage in behind . This talk will elaborate on the potential and the enhancements Semantic Web technology can come up with in this situation and will include reflections on smart information searching , integration , recommendion and various intelligent services .
Along with age estimation niu2016ordinal , popular applications for ordinal regression include predicting the progression of various diseases , such as Alzheimer ’s disease doyle2014predicting , Crohn ’s disease , artery disease , and kidney disease . Also , ordinal regression models are common choices for text message advertising and various recommender systems . While the field of machine learning field developed many powerful algorithms for predictive modeling , most algorithms were designed for classification tasks .
document : DeepFM : A Factorization - Machine based Neural Network for CTR Prediction Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems . Despite great progress , existing methods seem to have a strong bias towards low - or high - order interactions , or require expertise feature engineering .
In addition , DNNs model feature interactions at the bit - wise level , which is different from the traditional FM framework which models feature interactions at the vector - wise level . Thus , in the field of recommender systems , whether DNNs are indeed the most effective model in representing high - order feature interactions remains an open question . In this paper , we propose a neural network - based model to learn feature interactions in an explicit , vector - wise fashion .
In real - world applications , size and speed are constraints to an efficient mobile and embedded deployment of deep models . Several relevant real - world applications depend on text classification tasks such as sentiment analysis , recommendation and opinion mining . The appeal for these applications combined with the boost in mobile devices usage motivates the need for research in restrained text classification models .
In the recent years , deep neural networks and , in particular , convolutional neural networks ( CNNs ) [ reference ] have been applied with great success to numerous computer vision - related applications . However , original CNN models can not be directly applied to the recommendation problem to extract meaningful patterns in users , items and ratings because these data are not Euclidean structured , i.e. they do not lie on regular lattices like images but irregular domains like graphs or manifolds . This strongly motivates the development of geometric deep learning [ reference ] techniques that can mathematically deal with graph - structured data , which arises in numerous applications , ranging from computer graphics and vision [ reference ][ reference ][ reference ][ reference ] to chemistry [ reference ] .
Several important real - world problems can be cast as a matrix completion problem , including remote sensing , system identification and recommendation systems . Throughout the paper , we will consider the recommendation system problem as an illustration of the matrix completion problem . Such systems have indeed become very common in many applications such as movie or product recommendation ( e.g. Netflix , Amazon , Facebook , and Apple ) .
We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation . Despite widespread use in language modeling and economics , the multinomial likelihood receives less attention in the recommender systems literature . We introduce a different regularization parameter for the learning objective , which proves to be crucial for achieving competitive performance .
Among these technologies , the main cornerstone of this paper is working with the RF technology . This paper entails an ambience section concerning the history and technology of autonomous vehicles . At present , several efficient traffic light control systems exist for autonomous vehicles and it is unfortunate that despite being one of the most popular technologies of the world , there is no complete and efficacious RF system till now .
A new methodology for the design of navigation systems for autonomous vehicles is introduced . Using simple kinematic relationships , the problem of estimating the velocity and position of an autonomous vehicle is solved by resorting to special bilinear time-varying filters .
The embedded deep learning object detection model competition in IEEE MMSP2019 focuses on the object detection for sensing technology in autonomous driving vehicles , which aims at detecting small objects in worse conditions through embedded systems . We provide a dataset with 89,002 annotated images for training and 1,500 annotated images for validation .
Research on autonomous road vehicles , aircraft and vessels is presently underway at many research centres . The work is being enhanced by two factors : growing interest of future users and new technologies that make constructing autonomous vehicles possible .
The domain of autonomous vehicles is of great interest to researchers and engineers and much has been performed in this field . The paper proposes a fuzzy control algorithm for autonomous electric vehicles which are moving next to the obstacle ( object ) boundaries , avoiding the collisions with them ( a “ guard motion ” ) . Four motion cycles ( programs ) which depend of the proximity levels and which are used by the vehicle on its trajectory are described .
The development of autonomous connected vehicles , moving as a platoon formation , is a hot topic in the intelligent transportation system ( ITS ) research field . It is on the road and deployment requires the design of distributed control strategies , leveraging secure vehicular ad-hoc networks ( VANETs ) .
Autonomous connected vehicles are a main concept in the future of Intelligent Transportation Systems ( ITS ) since they provide an increase in safety and road efficiency . The management and coordination of the connected vehicles is based on periodic communications among the vehicles involved in the network , and with their surrounding environment .
Presents a visual navigation algorithm for autonomous on-road vehicles . The purpose of the research is to realize an autonomous vehicle navigation system which works as an intelligent driving support system .
In this paper , a major challenge in the field of trajectory planning for autonomous on-road vehicles is addressed : to calculate trajectories efficiently and reliably . The proposed method is based on a cooperative maneuver automaton , in which each maneuver is formulated as a discrete-time hybrid automaton that defines constraints on the behavior of a group of autonomous vehicles .
The proposed wireless video streaming mote is aimed at moving around an area under surveillance , gathering images and transferring the same to control station . The proposed system has potential applications including assistance to vehicle drivers during reversing , parking , autonomous driving of the vehicles , driving vehicles using mobile application etc . The proposed system designed has a myriad of real-time applications for domestic requirements with social cause too .
Various combinations of cameras and 3D sensors are widely used in the field , and it is desirable to have a single algorithm that generalizes to as many different problem settings as possible . Many real - world robotic systems are equipped with multiple 3D sensors : for example , autonomous cars often have multiple lidars and potentially also radars . Yet , current algorithms often assume a single RGB - D camera , which provides RGB - D images , or a single lidar sensor , which allows the creation of a local front view image of the lidar depth and intensity readings .
A major drawback of the global fusion network is that the variance of the regression target is directly dependent on the particular scenario . For autonomous driving , the system may be expected to detect objects from 1 m to over 100 m . This variance places a burden on the network and results in sub - optimal performance .
Pedestrian detection aims to predict bounding boxes of all the pedestrian instances in an image . It has attracted much attention within the computer vision community in recent years as an important component for many human - centric applications , such as self - driving vehicles , person re - identification , video surveillance and robotics . Recently , many research works have been devoted to pedestrian detection .
subsubsection : KITTI The challenging KITTI dataset consists of 7 , 481 training and 7 , 518 test images , which are captured from an autonomous driving platform . Evaluation is done at three levels of difficulty : easy , moderate and hard , where the difficulty is measured by the minimal scale of the pedestrians to be considered and the occlusion and truncation of the pedestrians .
We quantify the performance of SegNet on two scene segmentation benchmarks using our Caffe implementation . The first task is road scene segmentation which is of current practical interest for various autonomous driving related problems . The second task is indoor scene segmentation which is of immediate interest to several augmented reality ( AR ) applications .
section : Related Work The dramatic performance improvements from using CNNs for semantic segmentation have brought about an increasing demand for such algorithms in the context of autonomous driving scenarios . As a large amount of annotated data is crucial in order to train such deep networks , multiple new datasets have been released to encourage further research in this area , including Synthia [ reference ] , Virtual KITTI [ reference ] , and Cityscapes [ reference ] .
section : Introduction Since the DARPA Grand Challenges for autonomous vehicles , there has been an explosion in applications and research for self - driving cars . Among the different environments for self - driving cars , highway and urban roads are on opposite ends of the spectrum .
In contrast , residential or urban driving environments feature a much higher degree of unpredictability with many generic objects , inconsistent lane - markings , and elaborate traffic flow patterns . The relative regularity and structure of highways has facilitated some of the first practical applications of autonomous driving technology . Many automakers have begun pursuing highway auto - pilot solutions designed to mitigate driver stress and fatigue and to provide additional safety features ; for example , certain advanced - driver assistance systems ( ADAS ) can both keep cars within their lane and perform front - view car detection .
Namely , today ’s self - driving cars are equipped with expensive but critical sensors , such as LIDAR , radar and high - precision GPS coupled with highly detailed maps . In today ’s production - grade autonomous vehicles , critical sensors include radar , sonar , and cameras . Long - range vehicle detection typically requires radar , while nearby car detection can be solved with sonar .
section : Introduction The Fine - Grained Visual Classification ( FGVC ) task focuses on differentiating between hard - to - distinguish object classes , such as species of birds , flowers , or animals ; and identifying the makes or models of vehicles . FGVC datasets depart from conventional image classification in that they typically require expert knowledge , rather than crowdsourcing , for gathering annotations .
The purpose of this research is to create a mathematical model of the flow of speech packets in telecommunication networks with wireless access . The work is of practical importance , since the proposed mathematical model can be used in the process of reckoning and designing of new local wireless telecommunication networks . The model can be integrated into a more complex system that describes the processes inside the wireless equipment for the network access .
System dynamics ( SD ) modelling has recently reemerged to the reckoning vis-A -vis supply chain management (SCM)-related issues . Current trends in research in SD modelling vis-A -vis SCM are primarily focused on inventory management , time compression , demand amplification , design of supply chains ( and their integration ) , global supply chains and their planning and deployment .
In this paper , we present the ApolloScape dataset [ 1 ] and its applications for autonomous driving . Compared with existing public datasets from real scenes , e.g. , KITTI [ 2 ] or Cityscapes [ 3 ] , ApolloScape contains much large and richer labelling including holistic semantic dense point cloud for each site , stereo , per-pixel semantic labelling , lanemark labelling , instance segmentation , 3D car instance , high accurate location for every frame in various driving videos from multiple sites , cities and daytimes . For each task , it contains at lease 15x larger amount of images than SOTA datasets .
A Framework for Passengers Demand Prediction and Recommendation Model-based and memory-based collaborative filtering algorithms for complex knowledge models Enhancing Yelp Data with Deep Learning and Information Reuse Is a Picture Worth Ten Thousand Words in a Review Dataset? An Exploratory Study of Hierarchical Fuzzy Systems Approach in Recommendation System Stochastic-Aware Conformance Checking: An Entropy-Based Approach A semantic similarity measure for linked data: An information content-based approach A Staged Approach for Facilitating Joint End User, Vendor, and Multi-Disciplinary Subject Matter Expert Input to Health Information Technology Recommendations LDA Model and Network Embedding-Based Collaborative Filtering Recommendation Invited Talk: To know where to go - Semantics in eTourism Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks DeepFM: A Factorization-Machine based Neural Network for CTR Prediction xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Squeezed Very Deep Convolutional Neural Networks for Text Classification Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks Matrix Completion on Graphs Variational Autoencoders for Collaborative Filtering Traffic signal interactive autonomous vehicle: An approach for intelligent path planning and steering control Navigation system design using time-varying complementary filters Summary Embedded Deep Learning Object Detection Model Competition Autonomous Ship - Responsibility Issues A Control Algorithm for Autonomous Electric Vehicles by Fuzzy Logic A Secure Adaptive Control for Cooperative Driving of Autonomous Connected Vehicles in the Presence of Heterogeneous Communication Delays and Cyberattacks. Secure Blockchain-Based Communication Scheme for Connected Vehicles Visual navigation of autonomous on-road vehicle Optimization-Based Maneuver Automata for Cooperative Trajectory Planning of Autonomous Vehicles Design and Development of a Mobile Wireless Video Streaming Mote PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation Scale-Aware Fast R-CNN for Pedestrian Detection SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes An Empirical Evaluation of Deep Learning on Highway Driving Pairwise Confusion for Fine-Grained Visual Classification Development of Speech Packet Flow Model in IEEE 802.11 Standard Radio Channel Evolution of system dynamics in supply chain management The ApolloScape Open Dataset for Autonomous Driving and Its Application
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SCC IRI DSA ArXiv IJCAI KDD ICANN NIPS NIPS 2014 WWW MMSP TST EuCNC IROS ECC 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition IEEE Transactions on Multimedia IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ECCV
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