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Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis and video surveillance. It also gains long-standing attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain...
This paper presents a method named SoSVMRank, which integrates the social information of a Web document to generate a high-quality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which the order of a sentence or comment was determined by its informative information. The informative information was measured by a set of local and social features in...
This paper discusses the identification of extend relation in scientific papers based on supervised machine learning. Identification of extend relations is conducted by classifying each sentence in scientific papers into extend category. Extend relation is one type of papers' relations that obtained by using the citation context based approach. Citation context is a set of words or phrases in a sentence...
This paper presents a data-driven approach towards the modeling of agent behaviors in a full-fledged, commercial off-the-shelf simulation milieu for tactical military training. The modeling approach employs machine learning to identify behavioral rules and patterns in data. Potential advantages of this approach are that it may improve modeling efficiency and, perhaps more importantly, increase the...
Dynamic Music Emotion Prediction is crucial to the emerging applications of music retrieval and recommendation. Considering the influence of temporal context and hierarchical structure on emotion in music, we propose a Deep Bidirectional Long Short-Term Memory (DBLSTM) based multi-scale regression method. In this method, a post-processing component is utilised for individual DBSLTM output to further...
Since a real-life environment may encounter various uncertainties due to its dynamic nature, a smart-home system needs to improve its adaptability in response to the inevitable uncertainties. In this regard, a multi-transfer framework was proposed to keep context models adaptable in order to reduce the efforts in retraining context models in the event of an uncertainty. The framework is used to transfer...
Recently, various invasions of malwares and their incurred damages threaten the usability and privacy of computer systems. Due to the dramatic growth of these attacks, malware detection has been brought up as an important topic in computer security. Since traditional signature based techniques embedded in commercial anti-viruses have failed to detect new and obfuscated malwares, machine learning algorithms...
In this paper, we describe a method for leader detection in multi-party spoken discourse that relies on unsupervised topic modeling to segment the discourse automatically. Latent Dirichlet allocation is applied to sliding temporal windows of utterances, resulting in a topic model which captures the fluid transitions from topic to topic which occur in multi-party discourse. Further processing discretizes...
With the development of natural language processing (NLP) technology, the need for automatic named entity recognition (NER) is highlighted in order to enhance the performance of information extraction systems. In this paper, a hybrid model for Chinese person based on conditional random fields model is proposed, which fuses multiple features. It differentiates from most of the previous approaches,...
High accuracy sequence classification often requires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of over fitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of numeric...
Performance advertisers want to maximize the return on their advertising spend. In the online advertising world, this means showing the ad only to those users most likely to convert i.e. buy a product or service. Existing ad targeting solutions such as context targeting and rule-based segment targeting primarily leverage marketing intuition to identify audience segments that would be likely to convert...
A video surveillance system capable of detecting suspicious activities or behaviours is of paramount importance to law enforcement agencies. Such a system will not only reduce the work load of security personnel involved with monitoring the CCTV video feeds but also improve the time required to respond to any incident. There are two well known models to detect suspicious behaviour: misuse detection...
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