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The Internet of Things (IoT) has penetrated various domains, from smart grids to precision agriculture, facilitating remote sensing and control. However, IoT devices are target to a spectrum of reliability and security issues. Therefore, capturing the normal behavior of these devices and detecting abnormalities in program execution is key for reliable deployment. However, existing program anomaly...
This paper reports a mobile application pre-launch scheme thatis based on user’s emotion. Smartphone application’s usage andsmartwatch’s internal sensors are exploited to predict user’s intension.User’s emotion can be extracted from the PPG sensor inthe smartwatch. In this paper, we extend previous App pre-launchservice with user’s emotion data. Applying machine learning algorithmto the training data,...
a recent trend in intrusion detection is toward utilizing knowledge-based IDSs. Knowledge-based IDSs store knowledge about cyber-attacks and possible vulnerabilities and use this knowledge to guide the process of attack prediction. One significant limitation of knowledge-based IDSs is the lack of contextual information and domain knowledge used to detect attacks. Contextual information is not only...
Failing to identify multi-word expression (MWE) may cause serious problems for many Natural Language Processing (NLP) tasks. Previous approaches heavily depend on language specific knowledge and pre-existing natural language processing (NLP) tools. However, many languages (including Chinese language) have less such resources and tools compared to English. An automatically learn effective features...
In these days, music genre classification (MGC) is a quite popular research field. The main goal of the MGC studies is automatically detecting music genre (eg., rap, rock). In literature, features are generally extracted from the music's melodic content or lyrics for this task. In this study, we have performed lyrics based MGC on a Turkish dataset. We have just used lyrics as feature source and considered...
Detection of abnormal state (anomaly) is one of the topics that is frequently studied in the field of computer vision. In this study, we aim to investigate the combination of an unsupervised and discriminative anomaly detection method with a background subtraction technique. In this context, when the discriminative anomaly detection technique is used in conjunction with the recommended background...
A novel extension to Hızlı B-ESA object detection algorithm is proposed in order to learn convolutional context features for determining boundaries of objects better. For input images, the hypothesis windows and their context around those windows are learned through convolutional layers as two parallel networks. The resulting object and context feature maps are combined in such a way that they preserve...
Detection and segmentation of small renal mass (SRM) in renal CT images are important pre-processing for computer-aided diagnosis of renal cancer. However, the task is known to be challenging due to its variety of size, shape, and location. In this paper, we propose an automated method for detecting and segmenting SRM in contrast-enhanced CT images using texture and context feature classification...
In this paper, we propose and describe a novel recommender system for big data applications that provides recommendations on the base of the interactions among users and generated multimedia contents in one or more social media networks, leveraging a collaborative and user-centered approach. Preliminary experiments using data of several online social networks show how our approach obtains very promising...
In this paper, we investigate the interactions between topic persons to help readers construct the background knowledge of a topic. We proposed a rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a tree-based convolution kernel to identify segments that convey person interactions and further construct person interaction...
Computerized prenatal ultrasound (US) image segmentation methods can greatly improve the efficiency and objectiveness of the biometry interpretation. However, the boundary incompleteness and ambiguity in US images hinder the automatic solutions severely. In this paper, we propose a cascaded framework for fully automatic US image segmentation. A customized Fully Convolutional Network (FCN) was utilized...
Online social media has changed the way of interacting among users, nowadays, is used as a tool for expressing polarized opinions related to a global or specific context. Valuable information can be gathered in real-time basis and can help to determine if such data has a social impact on users represented as comfort or discomfort on a political domain. Analyzing data related to political domains like...
In the context of Online Social Networks, Spam profiles are not just a source of unwanted ads, but a serious security threat used by online criminals and terrorists for various malicious purposes. Recently, such criminals were able to steal a number of accounts that belong to NatWest bank's customers. Their attack vector was based on spam tweets posted by a Twitter account which looked very close...
User-generated reviews on the e-commerce site reflect consumers' sentiment about products, which can further direct consumers' purchasing behaviors and sellers' marketing strategies. In this paper, we propose a semi-supervised approach to mine the aspects of product discussed in Chinese online reviews and also the sentiments expressed in different aspects. We first apply the Latent Dirichlet Allocation...
While the majority of exploratory approaches search for correlations among features of different modalities, indirect/nonlinear relations between structure and function have not yet been fully investigated. In this work, we employ a neural machine translation model [1] to relate two modalities: structural MRI (sMRI) spatial components and functional MRI (fMRI) brain states estimated using a dynamic...
The motivation behind the research on overlapping speech has always been dominated by the need to model human-machine interaction for dialog systems and conversation analysis. To have more complex insights of the interlocutors' intentions behind the interaction, we need to understand the type of overlaps. Overlapping speech signals the interlocutor's intention to grab the floor. This act could be...
We propose a new concept for adapting CNN-based acoustic models using spatial diffuseness features as auxiliary information about the acoustic environment: the spatial diffuseness features are simultaneously employed as acoustic-model input features and to estimate environmental cues for context adaptation, where one convolutional layer is factorized into several sub-layers to represent different...
This paper proposes to use low-level spatial features extracted from multichannel audio for sound event detection. We extend the convolutional recurrent neural network to handle more than one type of these multichannel features by learning from each of them separately in the initial stages. We show that instead of concatenating the features of each channel into a single feature vector the network...
While recent advances in deep neural networks have lead to significant improvements in speech recognition, they have been applied mainly to acoustic and language modeling. We instead apply the models to bottleneck feature extraction. Several DNN, CNN, and BLSTM-based bottleneck feature networks are compared using both DNN and BLSTM acoustic models. Multiple variations in network architecture and feature...
Behavioral annotation using signal processing and machine learning is highly dependent on training data and manual annotations of behavioral labels. Previous studies have shown that speech information encodes significant behavioral information and be used in a variety of automated behavior recognition tasks. However, extracting behavior information from speech is still a difficult task due to the...
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