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The MOBOT Rollator Human-Robot Interaction Model and User Evaluation Process

The MOBOT Rollator Human-Robot Interaction Model and User Ev...

引用

IEEE Symposium Series on Computational Intelligence

作者： Eleni Efthimiou Stavroula-Evita Fotinea Theodore Goulas Maria Koutsombogera Panagiotis Karioris Anna Vacalopoulou Isidoros Rodomagoulakis Petros Maragos Costas Tzafestas Vassilis Pitsikalis Yiannis Koumpouros Alexandra Karavasili Panagiotis Siavelis Foteini Koureta Despoina Alexopoulou Institute for Language and Speech Processing/“Athena” RC Athens Greece Institute of Communication and Computer Systems-NTUA Athens Greece Technological Educational Institute of Athens Department of Informatics Aigaleo Greece Diaplasis Rehabilitation Center Kalamata Greece

ISBN: (纸本)9781509042418

In this paper we discuss the integration of a communication model in the MOBOT assistive robotic platform and its evaluation by target users. The MOBOT platform envisions the development of cognitive robotic assistant prototypes that act proactively, adaptively and interactively with respect to elderly humans with slight walking and cognitive impairments. The respective multimodal action recognition system has been developed to monitor, analyze and predict user actions with a high level of accuracy and detail. The robotic platform incorporates a human-robot communication model that has been defined with semantics of human actions in interaction, their capture and their representation in terms of behavioral patterns, to achieve an effective, natural interaction, aiming to support elderly users of slight walking and cognitive inability. The platform has been evaluated in a series of validation experiments with end users, the procedure and results of which are also presented in this paper.

关键词： multisensory data multimodal HRI model multimodal semantics multimodal human-robot communication natural HRI evaluation assessment

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Automatically scoring freshman writing: A preliminary investigation 10

Automatically scoring freshman writing: A preliminary invest...

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10th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2015 at the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL-HLT 2015

作者： Napoles, Courtney Callison-Burch, Chris Center for Language and Speech Processing Johns Hopkins University BaltimoreMD United States Computer and Information Science Department University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9781941643358

In this work, we explore applications of automatic essay scoring (AES) to a corpus of essays written by college freshmen and discuss the challenges we faced. While most AES systems evaluate highly constrained writing, we developed a system that handles open-ended, long-form writing. We present a novel corpus for this task, containing more than 3,000 essays and drafts written for a freshman writing course. We describe statistical analysis of the corpus and identify problems with automatically scoring this type of data. Finally, we demonstrate how to overcome grader bias by using a multi-task setup, and predict scores as well as human graders on a different dataset. Finally, we discuss how AES can help teachers assign more uniform grades. © NAACL-HLT 2015. All rights reserved.

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FrameNet+: Fast paraphrastic tripling of FrameNet 53

FrameNet+: Fast paraphrastic tripling of FrameNet

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53rd Annual Meeting of the Association for Computational Linguistics and 7th International Joint Conference on Natural language processing of the Asian Federation of Natural language processing, ACL-IJCNLP 2015

作者： Pavlick, Ellie Wolfe, Travis Rastogi, Pushpendre Callison-Burch, Chris Dredze, Mark Van Durme, Benjamin Computer and Information Science Department University of Pennsylvania United States Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781510808461

We increase the lexical coverage of FrameNet through automatic paraphrasing. We use crowdsourcing to manually filter out bad paraphrases in order to ensure a high-precision resource. Our expanded FrameNet contains an additional 22K lexical units, a 3-fold increase over the current FrameNet, and achieves 40% better coverage when evaluated in a practical setting on New York Times data. © 2015 Association for Computational Linguistics.

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Uncertainty estimation of DNN classifiers

Uncertainty estimation of DNN classifiers

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IEEE Workshop on Automatic speech Recognition and Understanding, ASRU 2015

作者： Mallidi, Sri Harish Ogawa, Tetsuji Hermansky, Hynek Center for Language and Speech Processing Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States Department of Computer Science Waseda University Tokyo Japan

ISBN: (纸本)9781479972913

New efficient measures for estimating uncertainty of deep neural network (DNN) classifiers are proposed and successfully applied to multistream-based unsupervised adaptation of ASR systems to address uncertainty derived from noise. The proposed measure is the error from associative memory models trained on outputs of a DNN. In the present study, an attempt is made to use autoencoders for remembering the property of data. Another measure proposed is an extension of the M-measure, which computes the divergences of probability estimates spaced at specific time intervals. The extended measure results in an improved reliability by considering the latent information of phoneme duration. Experimental comparisons carried out in a multistream-based ASR paradigm demonstrates that the proposed measures yielded improvements over the multistyle trained system and system selected based on existing measures. Fusion of the proposed measures achieved almost the same performance as the oracle system selection. © 2015 IEEE.

关键词： Deep neural networks

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Enhancing participant selection through caching in mobile crowd sensing

Enhancing participant selection through caching in mobile cr...

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International Workshop on Quality of Service

作者： Hanshang Li Ting Li Fan Li Weichao Wang Yu Wang Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China School of Computer Science Beijing Institute of Technology Beijing China

With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve certain level of coverage. In this paper, we introduce a new MCS architecture which leverages the cached sensing data to fulfill partial sensing tasks in order to reduce the size of selected participant set. We present a newly designed participant selection algorithm with caching and evaluate it via extensive simulations with a real-world mobile dataset.

关键词： Sensors Mobile communication Memory Algorithm design and analysis Smart phones Probabilistic logic Data models

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EchoLoc: Accurate Device-Free Hand Localization Using COTS Devices

EchoLoc: Accurate Device-Free Hand Localization Using COTS D...

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International Conference on Parallel processing (ICPP)

作者： Huijie Chen Fan Li Yu Wang School of Computer Science Beijing Institute of Technology Beijing Engineering Research Center for High Volume Language Information Processing and Cloud Computing Applications Beijing China Department of Computer Science College of Computing and Informatics University of North Carolina at Charlotte Charlotte NC USA

ISBN: (纸本)9781509028245

Hand tracking systems are becoming increasingly popular as a fundamental HCI approach. The trajectory of moving hand can be estimated through smoothing the position coordinates collected from continuous localization. Therefore, hand localization is a key component of any hand tracking systems. This paper presents EchoLoc, which locates the human hand by leveraging the speaker array in Commercial Off-The-Shelf (COTS) devices (i.e., a smart phone plugged with a stereo speaker). EchoLoc measures the distance from the hand to the speaker array via the Time Of Flight (TOF) of the chirp. The speaker array and hand yield a unique triangle, therefore, the hand can be localized with triangular geometry. We prototype EchoLoc on iOS as an application, and find it is capable of localization with the average resolution within five centimeters of 73% and three centimeters of 48%.

关键词： Parallel processing

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Labeled morphological segmentation with semi-markov models 19

Labeled morphological segmentation with semi-markov models

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19th Conference on Computational Natural language Learning, CoNLL 2015

作者： Cotterell, Ryan Müller, Thomas Fraser, Alexander Schütze, Hinrich Department of Computer Science Johns Hopkins University United States Center for Information and Language Processing University of Munich Germany

ISBN: (纸本)9781941643778

We present labeled morphological segmentation—an alternative view of morphological processing that unifies several tasks. We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmentation system that, contrary to previous work, explicitly models morphotactics. We show improved performance on three tasks for all six languages: (i) morphological segmentation, (ii) stemming and (iii) morphological tag classification. For morphological segmentation our method shows absolute improvements of 2-6 points F1 over a strong baseline. © 2015 Association for Computational Linguistics.

关键词： Markov processes

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Morphological word-embeddings

Morphological word-embeddings

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Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL HLT 2015

作者： Cotterell, Ryan Schütze, Hinrich Department of Computer Science Johns Hopkins University United States Center for Information and Language Processing University of Munich Germany

ISBN: (纸本)9781941643495

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semi-supervised learning, encouraging the vectors to encode a word's morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study. © 2015 Association for Computational Linguistics.

关键词： Vector spaces

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Joint lemmatization and morphological tagging with LEMMING

Joint lemmatization and morphological tagging with LEMMING

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Conference on Empirical Methods in Natural language processing, EMNLP 2015

作者： Müller, Thomas Cotterell, Ryan Fraser, Alexander Schütze, Hinrich Center for Information and Language Processing University of Munich Germany Department of Computer Science Johns Hopkins University United States

ISBN: (纸本)9781941643327

We present Lemming, a modular loglinear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or analyzers. Lemming sets the new state of the art in token-based statistical lemmatization on six languages;e.g., for Czech lemmatization, we reduce the error by 60%, from 4.05 to 1.58. We also give empirical evidence that jointly modeling morphological tags and lemmata is mutually beneficial. © 2015 Association for Computational Linguistics.

关键词： Natural language processing systems

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Unsupervised speaker adaptation of deep neural network based on the combination of speaker codes and singular value decomposition for speech recognition 40

Unsupervised speaker adaptation of deep neural network based...

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40th IEEE International Conference on Acoustics, speech, and Signal processing, ICASSP 2015

作者： Xue, Shaofei Jiang, Hui Dai, Lirong Liu, Qingfeng National Engineering Laboratory of Speech and Language Information Processing University of Science and Technology of China Hefei China Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (纸本)9781467369978

Recently, we have proposed a general adaptation scheme for deep neural network based on discriminant condition codes and applied it to supervised speaker adaptation in speech recognition based on either frame-level cross-entropy or sequence-level maximum mutual information training criterion [1, 2, 3, 4]. In this case, each condition code is associated with one speaker in data, which is thus called speaker code for convenience. Our previous work has shown that speaker code based methods are quite effective in adapting DNNs even when only a very small amount of adaptation data is available. However, we have to use a large speaker code size and complex processes to obtain the best ASR performance since good initializations of speaker codes and connection weights are very important. In this paper, we propose a method using singular value decomposition (SVD) as in [5] to initialize speaker codes and connection weights to obtain a comparable ASR performance as before but with a smaller speaker code size and much less computation complexity. Meanwhile, we have evaluated unsupervised speaker adaptation with the proposed method in large vocabulary speech recognition in the Switchboard task. Experimental results have shown that it is effective for providing well initializations and suitable in adapting large DNN models. © 2015 IEEE.

关键词： Deep Neural Network (DNN) singular value decomposition (SVD) Speaker Adaptation Speaker Code

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