143,697 research outputs found
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding
performance and demonstrated the effectiveness of 3D representation for action
recognition. The existing depth-based and RGB+D-based action recognition
benchmarks have a number of limitations, including the lack of large-scale
training samples, realistic number of distinct class categories, diversity in
camera views, varied environmental conditions, and variety of human subjects.
In this work, we introduce a large-scale dataset for RGB+D human action
recognition, which is collected from 106 distinct subjects and contains more
than 114 thousand video samples and 8 million frames. This dataset contains 120
different action classes including daily, mutual, and health-related
activities. We evaluate the performance of a series of existing 3D activity
analysis methods on this dataset, and show the advantage of applying deep
learning methods for 3D-based human action recognition. Furthermore, we
investigate a novel one-shot 3D activity recognition problem on our dataset,
and a simple yet effective Action-Part Semantic Relevance-aware (APSR)
framework is proposed for this task, which yields promising results for
recognition of the novel action classes. We believe the introduction of this
large-scale dataset will enable the community to apply, adapt, and develop
various data-hungry learning techniques for depth-based and RGB+D-based human
activity understanding. [The dataset is available at:
http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
Cross-Domain Labeled LDA for Cross-Domain Text Classification
Cross-domain text classification aims at building a classifier for a target
domain which leverages data from both source and target domain. One promising
idea is to minimize the feature distribution differences of the two domains.
Most existing studies explicitly minimize such differences by an exact
alignment mechanism (aligning features by one-to-one feature alignment,
projection matrix etc.). Such exact alignment, however, will restrict models'
learning ability and will further impair models' performance on classification
tasks when the semantic distributions of different domains are very different.
To address this problem, we propose a novel group alignment which aligns the
semantics at group level. In addition, to help the model learn better semantic
groups and semantics within these groups, we also propose a partial supervision
for model's learning in source domain. To this end, we embed the group
alignment and a partial supervision into a cross-domain topic model, and
propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and
Reuters dataset, extensive quantitative (classification, perplexity etc.) and
qualitative (topic detection) experiments are conducted to show the
effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201
Learning models for semantic classification of insufficient plantar pressure images
Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and
effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set
learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose
an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are
introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset
of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by
using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)-
based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally,
the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained
CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition
methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H)
and time (training and evaluation). The proposed method for the plantar pressure classification task shows high
performance in most indices when comparing with other methods. The transfer learning-based method can be
applied to other insufficient data-sets of sensor imaging fields
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