7,486 research outputs found
ServeNet: A Deep Neural Network for Web Services Classification
Automated service classification plays a crucial role in service discovery,
selection, and composition. Machine learning has been widely used for service
classification in recent years. However, the performance of conventional
machine learning methods highly depends on the quality of manual feature
engineering. In this paper, we present a novel deep neural network to
automatically abstract low-level representation of both service name and
service description to high-level merged features without feature engineering
and the length limitation, and then predict service classification on 50
service categories. To demonstrate the effectiveness of our approach, we
conduct a comprehensive experimental study by comparing 10 machine learning
methods on 10,000 real-world web services. The result shows that the proposed
deep neural network can achieve higher accuracy in classification and more
robust than other machine learning methods.Comment: Accepted by ICWS'2
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
Learning from class-imbalanced data continues to be a common and challenging
problem in supervised learning as standard classification algorithms are
designed to handle balanced class distributions. While different strategies
exist to tackle this problem, methods which generate artificial data to achieve
a balanced class distribution are more versatile than modifications to the
classification algorithm. Such techniques, called oversamplers, modify the
training data, allowing any classifier to be used with class-imbalanced
datasets. Many algorithms have been proposed for this task, but most are
complex and tend to generate unnecessary noise. This work presents a simple and
effective oversampling method based on k-means clustering and SMOTE
oversampling, which avoids the generation of noise and effectively overcomes
imbalances between and within classes. Empirical results of extensive
experiments with 71 datasets show that training data oversampled with the
proposed method improves classification results. Moreover, k-means SMOTE
consistently outperforms other popular oversampling methods. An implementation
is made available in the python programming language.Comment: 19 pages, 8 figure
Integrating Information Theory and Adversarial Learning for Cross-modal Retrieval
Accurately matching visual and textual data in cross-modal retrieval has been
widely studied in the multimedia community. To address these challenges posited
by the heterogeneity gap and the semantic gap, we propose integrating Shannon
information theory and adversarial learning. In terms of the heterogeneity gap,
we integrate modality classification and information entropy maximization
adversarially. For this purpose, a modality classifier (as a discriminator) is
built to distinguish the text and image modalities according to their different
statistical properties. This discriminator uses its output probabilities to
compute Shannon information entropy, which measures the uncertainty of the
modality classification it performs. Moreover, feature encoders (as a
generator) project uni-modal features into a commonly shared space and attempt
to fool the discriminator by maximizing its output information entropy. Thus,
maximizing information entropy gradually reduces the distribution discrepancy
of cross-modal features, thereby achieving a domain confusion state where the
discriminator cannot classify two modalities confidently. To reduce the
semantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss
are used to associate the intra- and inter-modality similarity between features
in the shared space. Furthermore, a regularization term based on KL-divergence
with temperature scaling is used to calibrate the biased label classifier
caused by the data imbalance issue. Extensive experiments with four deep models
on four benchmarks are conducted to demonstrate the effectiveness of the
proposed approach.Comment: Accepted by Pattern Recognitio
Temporal Sub-sampling of Audio Feature Sequences for Automated Audio Captioning
Audio captioning is the task of automatically creating a textual description
for the contents of a general audio signal. Typical audio captioning methods
rely on deep neural networks (DNNs), where the target of the DNN is to map the
input audio sequence to an output sequence of words, i.e. the caption. Though,
the length of the textual description is considerably less than the length of
the audio signal, for example 10 words versus some thousands of audio feature
vectors. This clearly indicates that an output word corresponds to multiple
input feature vectors. In this work we present an approach that focuses on
explicitly taking advantage of this difference of lengths between sequences, by
applying a temporal sub-sampling to the audio input sequence. We employ a
sequence-to-sequence method, which uses a fixed-length vector as an output from
the encoder, and we apply temporal sub-sampling between the RNNs of the
encoder. We evaluate the benefit of our approach by employing the freely
available dataset Clotho and we evaluate the impact of different factors of
temporal sub-sampling. Our results show an improvement to all considered
metrics
DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels
The impact of soiling on solar panels is an important and well-studied
problem in renewable energy sector. In this paper, we present the first
convolutional neural network (CNN) based approach for solar panel soiling and
defect analysis. Our approach takes an RGB image of solar panel and
environmental factors as inputs to predict power loss, soiling localization,
and soiling type. In computer vision, localization is a complex task which
typically requires manually labeled training data such as bounding boxes or
segmentation masks. Our proposed approach consists of specialized four stages
which completely avoids localization ground truth and only needs panel images
with power loss labels for training. The region of impact area obtained from
the predicted localization masks are classified into soiling types using the
webly supervised learning. For improving localization capabilities of CNNs, we
introduce a novel bi-directional input-aware fusion (BiDIAF) block that
reinforces the input at different levels of CNN to learn input-specific feature
maps. Our empirical study shows that BiDIAF improves the power loss prediction
accuracy by about 3% and localization accuracy by about 4%. Our end-to-end
model yields further improvement of about 24% on localization when learned in a
weakly supervised manner. Our approach is generalizable and showed promising
results on web crawled solar panel images. Our system has a frame rate of 22
fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected
first of it's kind dataset for solar panel image analysis consisting 45,000+
images.Comment: Accepted for publication at WACV 201
Large Scale Subject Category Classification of Scholarly Papers with Deep Attentive Neural Networks
Subject categories of scholarly papers generally refer to the knowledge
domain(s) to which the papers belong, examples being computer science or
physics. Subject category information can be used for building faceted search
for digital library search engines. This can significantly assist users in
narrowing down their search space of relevant documents. Unfortunately, many
academic papers do not have such information as part of their metadata.
Existing methods for solving this task usually focus on unsupervised learning
that often relies on citation networks. However, a complete list of papers
citing the current paper may not be readily available. In particular, new
papers that have few or no citations cannot be classified using such methods.
Here, we propose a deep attentive neural network (DANN) that classifies
scholarly papers using only their abstracts. The network is trained using 9
million abstracts from Web of Science (WoS). We also use the WoS schema that
covers 104 subject categories. The proposed network consists of two
bi-directional recurrent neural networks followed by an attention layer. We
compare our model against baselines by varying the architecture and text
representation. Our best model achieves micro-F1 measure of 0.76 with F1 of
individual subject categories ranging from 0.50-0.95. The results showed the
importance of retraining word embedding models to maximize the vocabulary
overlap and the effectiveness of the attention mechanism. The combination of
word vectors with TFIDF outperforms character and sentence level embedding
models. We discuss imbalanced samples and overlapping categories and suggest
possible strategies for mitigation. We also determine the subject category
distribution in CiteSeerX by classifying a random sample of one million
academic papers.Comment: submitted to "Frontiers Mining Scientific Papers Volume II: Knowledge
Discovery and Data Exploitation
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