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Packet loss is a serious phenomenon in Vehicular Ad-hoc NETworks (VANETs). Although the multi-source and in-network caching characteristics of Named Data Networking (NDN) can help the consumer to retrieve named content, how to provide reliable named data transmission in vehicular environment is still a problem. To achieve the named data retransmission, previous works follow the traditional RTO algorithms...
Network management activities, such as fault analysis and configuration management, are eventually related to changes in network measurements. Some measurement event might be either a trigger or objective of a management activity. We argue that sharing the semantics of performance data across networks provides a basis for more advanced automation. This paper presents an ontology-based system for sharing...
Traditional feature selection techniques are used to identify a subset of the most useful features, and consider the rest as unimportant, redundant or noisy. In the presence of highly correlated features, many variable selection methods consider correlated features as redundant and need to be removed. In this paper, a novel supervised feature selection algorithm SCANMF is proposed by jointly integrating...
As a popular topic model, Probabilistic Latent Semantic Analysis (PLSA) has been widely applied in text clustering due to its reliability and practicability. While independence assumption contributes to its practicability, it loses the rich local information between words, which in some cases will result in incoherent topics. In this paper, we propose an enhanced PLSA model embedded with word correlation...
Sentence similarity calculation plays an important role in text processing-related research. Many unsupervised techniques such as knowledge-based techniques, corpus-based techniques, string similarity based techniques, and graph alignment techniques are available to measure sentence similarity. However, none of these techniques have been experimented with Tamil. In this paper, we present the first-ever...
Text categorization, or text classification, is one of key tasks for representing the semantic information of documents. Multi-label text categorization is finer-grained approach to text categorization which consists of assigning multiple target labels to documents. It is more challenging compared to the task of multi-class text categorization due to the exponential growth of label combinations. Existing...
Today, video data, as a powerful multimedia component, is accompanied by some problems with increasing usage in communication, health, education, and social media in particular. Classification and detection of concepts in video data by automatic methods are some of these challenging problems. In this study, we propose a video classification system, which incorporates deep convolutional neural networks...
To achieve more effective solution for large-scale image classification (i.e., classifying millions of images into thousands or even tens of thousands of object classes or categories), a deep multi-task learning algorithm is developed by seamlessly integrating deep CNNs with multi-task learning over the concept ontology, where the concept ontology is used to organize large numbers of object classes...
Hashing methods have proven to be useful for a variety of tasks and have attracted extensive attention in recent years. Various hashing approaches have been proposed to capture similarities between textual, visual, and cross-media information. However, most of the existing works use a bag-of-words methods to represent textual information. Since words with different forms may have similar meaning,...
User-generated trajectories (UGT), such as GPS footprints from wearable devices or travel records from bus companies, capture rich information of human mobility and urban dynamics in the offline world. In this paper, our objective is to enrich these raw footprints and discover the users' personal interests by utilizing the semantic information contained in the spatial-and temporal-aware user-generated...
In order to explore the semantic clues and online shopping consumer behavior rules contain online shopping commentary, this paper takes 55560 after-sales evaluation as the research object to put forward the netizens related hypothesis evaluation of social network evolution, and evaluation of innovation of Internet users social network evolution diagram visualization description and empirical analyses...
Neural word vector (NWV) such as word2vec is a powerful text representation tool that can encode extensive semantic information into compact vectors. This ability poses an interesting question in relation to image processing research - Can we learn better semantic image features from NWVs? We empirically explore this question in the context of semantic content-based image retrieval (CBIR). In this...
Due to the pervasiveness of digital technologies in all aspects of human lives, it is increasingly unlikely that a digital device is involved as goal, medium or simply ’witness’ of a criminal event. Forensic investigations include recovery, analysis and presentation of information stored in digital devices and related to computer crimes. These activities often involve the adoption of a wide range...
Zero-shot Learning (ZSL) can leverage attributes to recognise unseen instances. However, the training data is limited and cannot adequately discriminate fine-grained classes with similar attributes. In this paper, we propose a complementary procedure that inversely makes use of attributes to infer discriminative visual features for unseen classes. In this way, ZSL is fully converted into conventional...
Long Short-Term Memory (LSTM) based Recurrent Neural Networks (RNNs) are promising for cognitive intelligence applications like speech recognition, image caption and nature language processing, etc. However, the cascade dependent structure in RNN with huge amount of power inefficient operations like multiplication, memory accessing and nonlinear transformation, could not guarantee high computing speed...
Semantic similarity measures using Gene Ontology (GO) assess the semantic similarity between two proteins on the basis of their respective GO annotations, which represent specific features of the proteins. Proteins will interact with greater likelihood if they have greater number of similar GO annotations. In this paper, we design a semantic similarity measure by combining topological features of...
Latent Semantic Analysis is a novel method to extract the principal components of a text corpus which has been initially used for categorization and information search. However, due to the significant results obtained, similar to human processing, LSA has become much more than a simple method to analyze text. In this work, we propose to use LSA in order to infer similarity degree of syslog messages...
Based on the method of latent semantic analysis (LSA), a text matching algorithm was proposed in this paper for constructing an automated scoring system of Chinese test papers. Firstly, by fully considering the correlation between terms, texts of examinee's answers and standard answers were represented in lower-dimensional space and the model was improved using the way of singular value decomposition...
Popularity of social networking sites and introduction of e-commerce has led to an increase in number of multimedia resources generated every year. The growth in the number of images that are being uploaded has brought a need to develop systems that can store, process and organize them as how and when needed. A survey conducted on working ability of famous e-commerce systems has brought into light...
Because of the challenge of collecting labelled training data, zero-shot learning (ZSL) which transfers semantic knowledge represented by category attributes from seen classes to recognize unseen classes has received a lot of attention recently. Existing methods assume that the source attributes are completely correct in zero-shot learning. However, the source attributes in practice may contain noise...
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