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The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as...
Semantic parsing of large-scale 3D point clouds is an important research topic in computer vision and remote sensing fields. Most existing approaches utilize hand-crafted features for each modality independently and combine them in a heuristic manner. They often fail to consider the consistency and complementary information among features adequately, which makes them difficult to capture high-level...
Long short-term memory (LSTM) is a significant approach to capture the long-range temporal context in sequences of arbitrary length. This had shown astonishing performance in sentence and document modeling. To leverage this, we use LSTM network to the encrypted text categorization at character and word level of texts. These texts are transformed in to dense word-vectors by using bag-of-words embedding...
In recent years, deep learning has achieved great success in a series of areas, but there is few published work on deep learning for job recommendation. Most researchers focus on the application of traditional algorithms, most of which still use algorithms like collaborative filtering and content-based filtering. In this paper, we study and improve the existing recommender algorithm based on deep...
Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards...
Pedestrian detection and semantic segmentation are highly correlated tasks which can be jointly used for better performance. In this paper, we propose a pedestrian detection method making use of semantic labeling to improve pedestrian detection results. A deep learning based semantic segmentation method is used to pixel-wise label images into 11 common classes. Semantic segmentation results which...
Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent...
Image categorization is the process of categorizing the images into its respective class or bins. It is still challenging problem in computer vision key area. The existing methodologies for image categorization like semantic modelling approaches, neural network approaches does not provides an accurate solution. This is due to inefficient feature extraction and their processing. Deep Learning is a...
Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic...
Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points...
To improve software reliability, software defect prediction is utilized to assist developers in finding potential bugs and allocating their testing efforts. Traditional defect prediction studies mainly focus on designing hand-crafted features, which are input into machine learning classifiers to identify defective code. However, these hand-crafted features often fail to capture the semantic and structural...
The goal of medical concept extraction is to identify phrases that refer to medical concepts of interest such as problems, treatments and tests from medical documents. In this study, three types of medical concept extraction models are developed and then compared them. The first concept extraction task is mainly based upon semantic features obtained from a domain-knowledge based method using MetaMap,...
The “semantic gap” issue which exists the low-level image pixels captured by machines and high-level semantic concepts perceived by human has always been a key challenge for lots of applications, such as computer vision, pattern recognition, Content-based Image Retrieval (CBIR). The recent success of deep learning researches bring a hope for bridging the semantic gap. Many researchers have attempted...
In this paper, a novel multi-label classification model using convolutional neural networks (CNNs) is proposed. As one of the deep learning architectures, CNNs lead breakthrough in many fields of image processing especially the image classification. Since the applications of CNNs are more concentrating in the background of single-label samples, our model introduce the hidden semantic between different...
With the rapid development of driverless cars, pedestrian detection has been a canonical instance of object detection. Although recent deep learning detectors such as RPN+BF and MS-CNN have shown excellent performance for pedestrian detection, they have limited success for detecting pedestrian, and the importance of final feature receptive field has been awared by previous leading deep learning pedestrian...
Entity pair provide essential information for identifying relation type. Aiming at this characteristic, Position Feature is widely used in current relation classification systems to highlight the words close to them. However, semantic knowledge involved in entity pair has not been fully utilized. To overcome this issue, we propose an Entity-pair-based Attention Mechanism, which is specially designed...
The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for spam detection. In this paper, we propose another form of deep learning, a linguistic attribute hierarchy, embedded with linguistic decision...
With the development of the Internet, it is vital for the security of the Internet to detect web-based anomalies. Clustering based on feature extraction by manually has been verified as a significant way to detect new anomalies. But the presentations of these features can't express semantic information of the URLs. In addition, few studies try to cluster the anomalies into specific types like SQL-injection...
Although several traditional models like bag of words (BOW), n-grams, and their variants of TFIDF exhibit high performance in the field of text classification, neural network methods such as LSTM, GRU and convolutional neural network (CNN) are recently attracting increasing attention. Considering that CNN has surprising capabilities of extracting hierarchical features, combination of LSTM/GRU with...
Content comprehension in text is one of the challenges in natural language processing. Understanding text at a low level has become increasingly relevant due to the surge in the amount of content on the web space, where most of it is stream data. In our case, data streams are considered to be an ordered sequence of short and noisy textual messages that are read once or fewer number of times, for example...
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