This paper proposes an effective fusion of Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA) approach for the risk evaluation in Mobile Commerce (MC) development. The hybrid method employs the comple...
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Outlier detection is a crucial part of robust evaluation for crowd-sourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast ...
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This paper summarizes our efforts for the first time participation in the Violent Scene Detection subtask of the MediaEval 2015 Affective Impact of Movies Task. We build violent scene detectors using both audio and vi...
This paper summarizes our efforts for the first time participation in the Violent Scene Detection subtask of the MediaEval 2015 Affective Impact of Movies Task. We build violent scene detectors using both audio and visual cues. In particular, the audio cue is represented by bag-of-audio-words with fisher vector encoding. The visual cue is exploited by extracting CNN features from video frames. The detectors are implemented using two-class linear SVM classifiers. Evaluation shows that the audio detectors and the visual detectors are comparable and complementary to each other. Among our submissions, multi-modal late fusion leads to the best performance.
This abstract paper sketches our research towards Struc-tured Semantic Embedding of multimedia data. Though a tag may have multiple senses with completely different visual imagery, current semantic embedding methods r...
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Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detec...
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Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.
In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the...
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At the early stage of software lifecycle,the complexity measurement of UML class diagrams plays an important role in software development,testing and maintenance,and provides guidance for developing high quality *** o...
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At the early stage of software lifecycle,the complexity measurement of UML class diagrams plays an important role in software development,testing and maintenance,and provides guidance for developing high quality *** order to study which one is better,simple or complex metrics,this paper analyzes and compares four typical metrics of UML class diagrams from experimental software engineering view ***,analyzability and maintainability were classified and predicted for 27 class diagrams related to a banking system by means of algorithm C5.0 within the famous toolkit *** suggest that the performance of simple metrics is not inferior to that of complex metrics,in some cases even better than that of complex metrics.
Due to the rapid growth of social net services (SNSs), research into SNSs continuance usage has recently emerged as an important issue in information systems adaption. This study develops an integrated model based on ...
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In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from ...
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ISBN:
(纸本)9781467372596
In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from noisy images and short texts; and 2) uncertain roles of modalities where visual content is either highly or weakly relevant to textual cues due to less-constrained data. In this paper, following the detection by ranking approach, we address the problem by learning a robust shared representation from multiple, noisy and complementary features, and integrating both textual and visual graphs into a k-Nearest Neighbor Similarity Graph (k-N 2 SG). Then Non-negative Matrix Factorization using Random walk (NMFR) is introduced to generate topic candidates. An efficient fusion of multiple graphs is then done by a Latent Poisson Deconvolution (LPD) which consists of a poisson deconvolution with sparse basis similarities for each edge. Experiments show significantly improved accuracy of the proposed approach in comparison with the state-of-the-art methods on two public data sets.
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