We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications. In our method, 3D facial models are restored from 2D images, which prov...
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ISBN:
(纸本)9781467369657
We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications. In our method, 3D facial models are restored from 2D images, which provide crucial clues for the enhancement of robustness to overcome large changes including out-of-plane head rotations, fast head motions and partial facial occlusions. To accurately recognize the emotion, a novel random forest-based algorithm which simultaneously integrates two regressions for 3D facial tracking and continuous emotion estimation is constructed. Moreover, via the reconstructed 3D facial model, temporal information and user-independent emotion presentations are also taken into account through our image fusion process. The experimental results show that our algorithm can achieve state-of-the-art result with higher Pearson's correlation coefficient of continuous emotion recognition in real time.
The ubiquity of mobile technology and advances in wearable health and well-being technologies offer exciting opportunities for technology supported home and community care. But are we ready for digitally enabled self-...
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ISBN:
(纸本)9781450331463
The ubiquity of mobile technology and advances in wearable health and well-being technologies offer exciting opportunities for technology supported home and community care. But are we ready for digitally enabled self-care? How can the CHI community share best practices and methods in order to continue to advance research that crosses methodological and cultural boundaries between Health and HCI? This workshop will bring together key researchers working in and across both HCI and Health to share these existing challenges and opportunities for digital health research and practice and to continue to build capacity in the crossings between HCI and health.
In the recent years, the P2P file sharing systems have adopted rating systems in the hope to stop the propagation of bad files. In a rating system, users rate files after downloading and a file with positive feedback ...
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In the recent years, the P2P file sharing systems have adopted rating systems in the hope to stop the propagation of bad files. In a rating system, users rate files after downloading and a file with positive feedback is considered a good file. However, a dishonest rater can undermine the rating system by giving positive rating to bad files and negative rating to good files. In this paper, we design two filters based on probabilistic models such that the good files with negative feedback are not completely kept out of the system. The first filter is based on the binomial distribution of the ratings of a file, and the second filter considers the confidence of the downloading peer and the difference of positive and negative ratings of a file to calculate the probability to take a risk to download the file or reject it. Our filters only need the ratings of a file and this makes them suitable for popular torrent sharing websites that rank the files using a binary rating system without any information about raters. In addition, we can implement them entirely on the client side without any modification to the content sharing sites.
Locomotion is one of the most fundamental processes in the real world, and its consideration in immersive virtual environments (IVEs) is of major importance for many application domains requiring immersive walkthrough...
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This paper introduces a machine learning ap-proach to distinguish machine translation texts from human texts in the sentence level au-Tomatically. In stead of traditional methods, we extract some linguistic features o...
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This paper introduces a machine learning ap-proach to distinguish machine translation texts from human texts in the sentence level au-Tomatically. In stead of traditional methods, we extract some linguistic features only from the target language side to train the predic-Tion model and these features are independent of the source language. Our prediction mod-el presents an indicator to measure how much a sentence generated by a machine translation system looks like a real human translation. Furthermore, the indicator can directly and ef-fectively enhance statistical machine transla-Tion systems, which can be proved as BLEU score improvements.
human engagement is at the heart of every interactive technology. However, a concrete framework for synergizing the capabilities of humans and technologies to allow fully engaging interactions to happen is yet to be d...
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ISBN:
(纸本)9781450331463
human engagement is at the heart of every interactive technology. However, a concrete framework for synergizing the capabilities of humans and technologies to allow fully engaging interactions to happen is yet to be developed. We posit that such a framework should be grounded in a deeper understanding of human nature (e.g., mind-body relations), which in the field of HCI has primarily been built upon the Western philosophies. There are scattered, underexplored Eastern philosophies (e.g., Yijing, Zen) that may provide new lens and tools to analyze how humans interact with resources in their environments, including technological artefacts. Discussions of leveraging and possibly integrating Eastern and Western insights for human engagement studies will be an exciting and a radical forum for the HCI community.
Word segmentation is helpful in Chinese natural language processing in many aspects. However it is showed that different word segmentation strategies do not affect the performance of Statistical Machine Translation (S...
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Word segmentation is helpful in Chinese natural language processing in many aspects. However it is showed that different word segmentation strategies do not affect the performance of Statistical Machine Translation (SMT) from English to Chinese significantly. In addition, it will cause some confusions in the evaluation of English to Chinese SMT. So we make an empirical attempt to translation English to Chinese in the character level, in both the alignment model and language model. A series of empirical comparison experiments have been conducted to show how different factors affect the performance of character-level English to Chinese SMT. We also apply the recent popular continuous s- pace language model into English to Chinese SMT. The best performance is obtained with the BLEU score 41.56, which improve base- line system (40.31) by around 1.2 BLEU s- core.
Neural network language models (NNLMs) have been shown to outperform traditional n-gram language model. However, too high computational cost of NNLMs becomes the main obstacle of directly integrating it into pinyin IM...
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Neural network language models (NNLMs) have been shown to outperform traditional n-gram language model. However, too high computational cost of NNLMs becomes the main obstacle of directly integrating it into pinyin IME that normally requires a real-Time response. In this paper, an efficient solution is proposed by converting NNLMs into back-off n-gram language models, and we integrate the converted NNLM into pinyin IME. Our exper-imental results show that the proposed method gives better decoding predictive performance for pinyin IME with satisfied efficiency.
In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing...
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In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing. Instead of the sparse features used in traditional methods, we utilize distributed dense feature representations for neural network, which give better feature representations. The proposed parsers are evaluated on English and Chinese Penn Treebanks. Compared to existing work, our parsers give competitive performance with much more efficient inference.
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