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Semi-Supervised learning for Cross-Device Visual Location Recognition 22

Semi-Supervised Learning for Cross-Device Visual Location Re...

引用

22nd international conference on Pattern Recognition (ICPR)

作者： Liu, Pengcheng Yang, Peipei Huang, Kaiqi Tan, Tieniu Hao, Hongwei Chinese Acad Sci Interact Digital Media Technol Res Ctr Beijing Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Ctr Res Intelligent Percept & Comp Beijing Peoples R China

ISBN: (纸本)9781479952083

The aim of this work is to localize a query mobile photograph by utilizing surveillance images, which naturally provide location information. We cast this cross-device visual localization problem as a classification task. By exploiting the surveillance network to collect reference images, the data acquisition process is significantly facilitated. However, the discrepancy between mobile images and surveillance images makes the training samples difficult to be used directly, and the scarcity of training samples caused by the immobility of surveillance cameras further degrades the performance. In contrast to most traditional domain adaptation problems and semi-supervised problems, the scarce labeled data and plentiful unlabeled data exist in different domains. Our location recognition method first exploits the unsupervised subspace alignment to weaken the discrepancy between the two domains, and then adopts the semi-supervised Laplacian SVM to reinforce the discriminant information utilizing the unlabeled mobile images. Experimental results show that our location recognition method significantly outperforms other related methods.

关键词： data acquisition

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Artificial neural networks in accelerometer-based human activity recognition

Artificial neural networks in accelerometer-based human acti...

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international conference on Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Paula Lubina Marcin Rudzki Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland

This paper presents a study aimed to assess applicability of artificial neural networks (ANNs) in human activity recognition from simple features derived from accelerometric signals. Secondary goal was to select the most descriptive signal features and sensor locations to be used as inputs to ANNs. Five triaxial accelerometers were attached to human body in the following places: one at back, two at waist laterally and two at both ankles. The set of activities to be recognized was established to include the most often performed actions in home environment. In total 25 subjects performed a set of predefined actions like walking, going up and down the stairs, sitting down and standing up from a chair. Acquired signals were divided into 0.5s time windows by a label defining the action performed. Several statistical signal features were calculated and used to train ANNs. learning and testing were performed on separate data sets. Analysis using Fisher Linear Discriminant showed that despite the fact that some of the calculated values play a significant role in the distinction between similar activities, none of the features or sensors could be omitted in the recognition of the activities considered in the study. Accuracy of 97% has been achieved for discriminating sitting and walking, 89% for standing, 72-75% for walking the stairs. Transient actions like standing up and sitting down have been detected with accuracy 56% and 38%, respectively. Even though there are studies declaring higher accuracy, none of them considered a set of activities analyzed in this research.

关键词： Legged locomotion Accelerometers Accuracy Hidden Markov models Neural networks Decision trees Sensitivity

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Representation learning for Contextual Object and Region Detection in Remote Sensing 22

Representation Learning for Contextual Object and Region Det...

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22nd international conference on Pattern Recognition (ICPR)

作者： Firat, Orhan Can, Gulcan Vural, Fatos T. Yarman Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey Idiap Res Inst Martigny Switzerland Middle E Tech Univ TR-06531 Ankara Turkey

ISBN: (纸本)9781479952083

The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features, amount of labelled data and the priors defined for contextual models. In this study, we examine the representation learning opportunities for remote sensing. First we attacked localization of contextual cues for complex object detection using disentangling factors learnt from a small amount of labelled data. The complex object, which consists of several sub-parts is further represented under the Conditional Markov Random Fields framework. As a second task, end-to-end target detection using convolutional sparse auto-encoders (CSA) using large amount of unlabelled data is analysed. Proposed methodologies are tested on complex airfield detection problem using Conditional Random Fields and recognition of dispersal areas, park areas, taxiroutes, airplanes using CSA. The method is also tested on the detection of the dry docks in harbours. Performance of the proposed method is compared with standard feature engineering methods and found competitive with currently used rule-based and supervised methods.

关键词： Remote sensing

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Humanoid Robot Imitation with Pose Similarity Metric learning 22

Humanoid Robot Imitation with Pose Similarity Metric Learnin...

引用

22nd international conference on Pattern Recognition (ICPR)

作者： Lei, Jie Song, Mingli Li, Ze-Nian Chen, Chun Xu, Xianghua Pu, Shiliang Zhejiang Univ Coll Comp Sci Hangzhou 310027 Zhejiang Peoples R China Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Hangzhou Dianzi Univ Sch Comp Sci Hangzhou 310018 Zhejiang Peoples R China Hangzhou Hikvis Digital Technol Co Ltd Hangzhou 310052 Zhejiang Peoples R China

ISBN: (纸本)9781479952083

Imitation is considered to be a kind of social learning that allows the transfer of information, actions, behaviours, etc. Whereas current robots are unable to perform as many tasks as human, it is a natural way for them to learn by imitations, just as human does. With the humanoid robots being more intelligent, the field of robot imitation has getting noticeable advance. In this paper, we focus on the pose imitation between a human and a humanoid robot and learning a similarity metric between human pose and robot pose. In contrast to recent approaches that capture human data using expensive motion captures or only imitate the upper body movements, our framework adopts a Kinect instead and can deal with complex, whole body motions by keeping both single pose balance and pose sequence balance. Meanwhile, different from previous work that employs subjective evaluation, we propose a pose similarity metric based on the shared structure of the motion spaces of human and robot. The qualitative and quantitative experimental results demonstrate a satisfactory imitation performance and indicate that the proposed pose similarity metric is discriminative.

关键词： humanoid robot imitation pose transfer similarity metric

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Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks 22

Pedestrian's Trajectory Forecast in Public Traffic with Arti...

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22nd international conference on Pattern Recognition (ICPR)

作者： Goldhammer, Michael Doll, Konrad Brunsmann, Ulrich Gensler, Andre Sick, Bernhard Univ Appl Sci Aschaffenburg Aschaffenburg Germany Univ Kassel Intelligent Embedded Syst Lab D-34125 Kassel Germany

ISBN: (纸本)9781479952083

This paper focuses on forecasting of pedestrian's short-time trajectories up to 2.5 s for traffic safety applications. We present a self-learning approach based on artificial neural network movement models and compare it to traditional constant velocity Kalman Filter prediction and extrapolation of polynomials fitted using a least-squares error. Trajectories of uninstructed pedestrians in public traffic at a real urban intersection are acquired by a wide angle stereo camera setup in combination with a 3D head tracking framework. Results using this real-world data show that the artificial neural network significantly improves forecast quality compared to other approaches especially for critical traffic scenes including velocity changes such as starting and stopping. For those velocity changes a reduction of position estimation errors of about 21% compared to the Kalman Filter and to extrapolation of polynomials is obtained. By means of a concrete pedestrian-vehicle scenario we demonstrate the benefit of the proposed approach for an advanced driver assistant system in terms of reaction time.

关键词： Artificial neural networks movement models pedestrian tracking traffic safety trajectory forecast

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Ubiquitous learning logs analytics 22

Ubiquitous learning logs analytics

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22nd international conference on Computers in Education, ICCE 2014

作者： Mouri, Kousuke Ogata, Hiroaki Uosaki, Noriko Liu, Songran Faculty of Arts and Science Graduate School of Information Science and Electrical Engineering University of Kyushu Japan Center for International Education and Exchange Osaka University Japan Advanced Technology and Science University of Tokushima Japan

ISBN: (纸本)9784990801434

This paper describes a system that can be used to visualize and analyze some ubiquitous learning logs to discover several learning patterns and trends. Visualization and analysis of the system are based on vast amount of learning data in ubiquitous learning environment. Ubiquitous learning Log (ULL) is defined as a digital record of what learners have learned in the daily life using ubiquitous technologies. It allows learners to log their learning experiences with photos, audios, videos, location, RFID tag and sensor data, and to share and to reuse ULL with others. This paper will reveal about the relationship between the ubiquitous learning logs and learners by using network graph.

关键词： data visualization

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learning analytics: An enabler for dropout prediction 22

Learning analytics: An enabler for dropout prediction

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22nd international conference on Computers in Education, ICCE 2014

作者： Tseng, Shu-Fen Chou, Chih-Yueh Chen, Zhi-Hong Chao, Po-Yao Department of Information Management Yuan Ze University Taiwan Department of Information Science and Engineering Yuan Ze University Taiwan Department of Information Communication Yuan Ze University Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taiwan

ISBN: (纸本)9784990801427

A key application of learning analytics is predicting students' learning performances and risks of dropping out. Heterogeneous data were collected from selected school to yield a model for predicting student's dropout. Results from this exploratory study conclude dropout prediction by learning analytics may provide more precise information on identifying at-risk students and factors causing them to be at risk.

关键词： Students

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learning by "search & log" 22

Learning by "search & log"

引用

22nd international conference on Computers in Education, ICCE 2014

作者： Yin, Chengjiu Flanagan, Brendan Hirokawa, Sachio Kyushu University Library Kyushu University Japan Graduate School of Information Science and Electrical Engineering Kyushu University Japan Research Institute for Information Technology Kyushu University Japan

ISBN: (纸本)9784990801410

Although previous research has demonstrated the benefits of the "learning by searching" strategy, there is a new problem which is how to measure and analyze the effectiveness of "learning by Searching" behaviors. In this paper, by using the record of the students' learning history, we have proposed a SNSearch system to analyze student web-searching behaviors of "learning by Searching".

关键词： data mining

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Visualizing ubiquitous learning logs using collocational networks 22

Visualizing ubiquitous learning logs using collocational net...

引用

22nd international conference on Computers in Education, ICCE 2014

ISBN: (纸本)9784990801427

This paper describes a system that can be used to visualize some ubiquitous learning logs using collocational networks to discover several learning patterns. Visualization of the system is based on vast amount of learning data in ubiquitous learning environment. Ubiquitous learning Log (ULL) is defined as a digital record of what learners have learned in the daily life using ubiquitous technologies. It allows learners to log their learning experiences with photos, audios, videos, location, RFID tag and sensor data, and to share and to reuse ULL with others. This paper will reveal about the relationship between the ubiquitous learning logs and learners by using network graph and collocational networks. Also, this paper will explicate the system through which learners can grasp their learning time, histories, knowledge and location.

关键词： Visualization

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Making sense of online learning behavior: A research on learning styles and collaborative learning data 22

Making sense of online learning behavior: A research on lear...

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22nd international conference on Computers in Education, ICCE 2014

作者： Sun, Meng Luo, Jiu-Tong Qian, Dong-Ming Gu, Xiao-Qing Department of Educational Information Technology East China Normal University China E-Educational System Engineering Research Center Shanghai Engineering Research Center of Digital Education Equipment East China Normal University China

ISBN: (纸本)9784990801427

This study focused on the relationship between learning styles, online behaviors and group collaborations. Sixty junior students from a university in China were taken as research object. Index of learning Styles was used as a measuring tool to test participants' learning styles. Relationships between variables were measured by using bi-variate correlations analysis and one-way analysis of variance respectively. The results revealed a meaningful relationship between learning styles and online collaborative behavior. In addition, groups' online collaborative performances could be significantly different. However, grouping by learning styles might not be the factor that make effects on group collaborations.

关键词： E-learning

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