Institution
Coventry University
Education•Coventry, United Kingdom•
About: Coventry University is a education organization based out in Coventry, United Kingdom. It is known for research contribution in the topics: Context (language use) & Population. The organization has 4964 authors who have published 12700 publications receiving 255898 citations. The organization is also known as: Lanchester Polytechnic & Coventry Polytechnic.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors synthesize the current literature on police officers' attributions of rape victim blame, assessments of rapist victim credibility, and rape myth acceptance, and examine the evidence that holding these attitudes impacts on police investigative decision making in rape cases.
87 citations
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TL;DR: In this paper, a longitudinal qualitative study of behavior at one of the major UK clubs was conducted and the concept of ritual was proposed as an explanatory framework for understanding this neo-tribal co-created experience.
87 citations
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TL;DR: In this article, the authors present the method and findings of a pilot study into the accuracy of natural hazard perceptions held by members of the tourism industry in Tanna, an island in the South West Pacific SIDS of Vanuatu.
87 citations
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TL;DR: The method is shown to be able to automatically detect anomalies in the Seismic Electrical Signal that could be used to predict earthquake activity and can be used in combination with crowdsourcing of smartphone data to locate road defects such as potholes and bumps for intervention and repair.
Abstract: Design of a transferable time series anomaly detection method.Novel deep neural network structure facilitates learning short and long-term pattern interdependencies.Detection of anomalies in the Seismic Electrical Signal for predicting earthquake activity.Detection of road anomalies using smartphone data, facilitating crowdsourcing applications. The quest for more efficient real-time detection of anomalies in time series data is critically important in numerous applications and systems ranging from intelligent transportation, structural health monitoring, heart disease, and earthquake prediction. Although the range of application is wide, anomaly detection algorithms are usually domain specific and build on experts knowledge. Here a new signal processing algorithm inspired by the deep learning paradigm is presented that combines wavelets, neural networks, and Hilbert transform. The algorithm performs robustly and is transferable. The proposed neural network structure facilitates learning short and long-term pattern interdependencies; a task usually hard to accomplish using standard neural network training algorithms. The paper provides guidelines for selecting the neural network's buffer size, training algorithm, and anomaly detection features. The algorithm learns the system's normal behavior and does not require the existence of anomalous data for assessing its statistical significance. This is an essential attribute in applications that require customization. Anomalies are detected by analysing hierarchically the instantaneous frequency and amplitude of the residual signal using probabilistic Receiver Operating Characteristics. The method is shown to be able to automatically detect anomalies in the Seismic Electrical Signal that could be used to predict earthquake activity. Furthermore, the method can be used in combination with crowdsourcing of smartphone data to locate road defects such as potholes and bumps for intervention and repair.
87 citations
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27 Aug 2018TL;DR: In this paper, the authors investigated the benefits of integrating CNNs and LSTMs and reported improved accuracy for Arabic sentiment analysis on different datasets, considering the morphological diversity of particular Arabic words by using different sentiment classification levels.
Abstract: Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas. Convolutional Neural Networks (CNNs) offer advantages in selecting good features and Long Short-Term Memory (LSTM) networks have proven good abilities of learning sequential data. Both approaches have been reported to provide improved results in areas such image processing, voice recognition, language translation and other Natural Language Processing (NLP) tasks. Sentiment classification for short text messages from Twitter is a challenging task, and the complexity increases for Arabic language sentiment classification tasks because Arabic is a rich language in morphology. In addition, the availability of accurate pre-processing tools for Arabic is another current limitation, along with limited research available in this area. In this paper, we investigate the benefits of integrating CNNs and LSTMs and report obtained improved accuracy for Arabic sentiment analysis on different datasets. Additionally, we seek to consider the morphological diversity of particular Arabic words by using different sentiment classification levels.
86 citations
Authors
Showing all 5097 results
Name | H-index | Papers | Citations |
---|---|---|---|
Xiang Zhang | 154 | 1733 | 117576 |
Zidong Wang | 122 | 914 | 50717 |
Stephen Joseph | 95 | 485 | 45357 |
Andrew Smith | 87 | 1025 | 34127 |
John F. Allen | 79 | 401 | 23214 |
Craig E. Banks | 77 | 569 | 27520 |
Philip L. Smith | 75 | 291 | 24842 |
Tim H. Sparks | 69 | 315 | 19997 |
Nadine E. Foster | 68 | 320 | 18475 |
Michael G. Burton | 66 | 519 | 16736 |
Sarah E Lamb | 65 | 395 | 28825 |
Michael Gleeson | 65 | 234 | 17603 |
David Alexander | 65 | 520 | 16504 |
Timothy J. Mason | 65 | 225 | 15810 |
David S.G. Thomas | 63 | 228 | 14796 |