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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...
Platforms for publishing research papers are increasing largely that contribute to big data as their volume is humongous and are unstandardized. Classification of this huge chunk of data is one of the biggest challenges in Information Retrieval. In this paper we discuss a scoring based unsupervised learning approach to extract relevant features and classify the research papers according to their content...
Clustering product features is the essential task to mine opinions from unstructured online reviews because different customers usually express the same feature with different words or phrases. Several supervised and unsupervised methods have been applied to accomplish this task. In this paper, we propose an orthogonal nonnegative matrix tri-factorizations model to solve the problem. We first construct...
Recently, the automatic highlighting of anomalous changes in a sequence of social graph snapshots is receiving growing interest due to its numerous applications. For instance, it may be helpful for the identification of attackers or risky users in Online Social Networks (OSNs). Indeed, dynamically monitoring and learning the friendship patterns of users in a large social graph over time for any anomalous...
This paper introduces a novel approach for document re-ranking in information retrieval based on topic-comment structure of texts. While most information retrieval models make the assumption that relevant documents are about the query and that aboutness can be captured considering bags of words only, we rather consider a more sophisticated analysis of discourse to capture document relevance by distinguishing...
The amount of music in digital form increases due to the improvement of internet and recording technologies. With this increase, the automatic organization of musics has emerged as a problem needs to be solved. For this reason, Music Information Retrieval (MIR) is commonly studied research area in recent years. In this context, with the developed Music Information Systems solution is sought for some...
The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for long time in political science and communications research. Media framing offers "interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation...
Multi-document summarization has gained popularity in many real world applications because significant information can be obtained within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences, whose performance relies heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features...
Online health forums provide a large repository for patients, caregivers, and researchers to seek valuable information. The extraction of patient-reported personal health experience from the forums has many important applications. For example, medical researchers can discover trustable knowledge from the extracted experience. Patients can search for peers with similar experience and connect with them...
The study of German literature is mostly based on literary canons, i.e., small sets of specifically chosen documents. In particular, the history of novels has been characterized using a set of only 100 to 250 works. In this paper we address the issue of genre classification in the context of a large set of novels using machine learning methods in order to achieve a better understanding of the genre...
The growing availability of sensors integrated in smartphones provides much more opportunities for context-aware services, such as location-based and profile-based applications. Power consumption of the sensors contributes a significant part of the overall power consumption on current smartphones. Furthermore, smartphone sensors have to be activated in a stable period to match to the request frequency...
Many attempts have been conducted to add emotions to synthesized speech [1]. Few are done for the Arabic language. In the present paper, we introduce a work done to incorporate emotions: anger, joy, sadness, fear and surprise, in an educational Arabic text-to-speech system. After an introduction about emotions, we give a short paragraph of our text-to-speech system, then we discuss our methodology...
In this paper, we propose a method for conversation summarization. For the method, we combine two approaches, a scoring method and a machine learning technique (SVMs). First we compare important utterance extraction by the scoring method and SVMs. In the machine learning technique, we introduce verbal features, such as relations between utterances and anaphora features, and nonverbal features. Next...
Shape descriptor plays very important role in shape retrieval system especially in the case of input shapes are drawn by hand. A good descriptor should be not only deformation tolerant but also compact and less memory consuming. With this in mind, we propose a new shape descriptor by which features are extracted at salient locations of the shape and then encoded using a vocabulary tree. To intuitively...
This paper presents a speech recognition technique based on inhibition/enhancement (In/En) of articulatory features (AFs) by determining the dominant factor between inhibition and enhancement. The proposed method comprises three stages-a) Multilayer Neural Networks (MLNs), b) In/En Network and c)Gram-Schmidt (GS) Orthogonalization. At first stage, the MLNs detects AFs and then In/En network is used...
We analyze email communications within a large company to reveal how email activity patterns depend on content. We characterize email contents using keywords and examine statistics of email transmissions. As a result, we are able to identify differences in network structures and propagation behaviors depending on the type of keyword.
Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the user's daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our...
This paper presents a method that describes the effect of articulatory velocity coefficient (Δ) on neural network based speech recognition. The method consists of three stages: a) two multilayer neural networks (MLNs), where second MLN takes Δ articulatory parameters as input b) Inhibition/Enhancement (In/En) network and c) Gram-Schmidt orthogonalization before connecting with a hidden Markov model...
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