23,150 research outputs found

    Towards a more natural and intelligent interface with embodied conversation agent

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    Conversational agent also known as chatterbots are computer programs which are designed to converse like a human as much as their intelligent allows. In many ways, they are the embodiment of Turing's vision. The ability for computers to converse with human users using natural language would arguably increase their usefulness. Recent advances in Natural Language Processing (NLP) and Artificial Intelligence (AI) in general have advances this field in realizing the vision of a more humanoid interactive system. This paper presents and discusses the use of embodied conversation agent (ECA) for the imitation games. This paper also presents the technical design of our ECA and its performance. In the interactive media industry, it can also been observed that the ECA are getting popular

    Prediction of Intrinsic Disorder in MERS-CoV/HCoV-EMC Supports a High Oral-Fecal Transmission

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    A novel coronavirus, MERS-CoV (NCoV, HCoV-EMC/2012), originating from the Middle-East, has been discovered. Incoming data reveal that the virus is highly virulent to humans. A model that categorizes coronaviuses according to the hardness of their shells was developed before the discovery of MERS-CoV. Using protein intrinsic disorder prediction, coronaviruses were categorized into three groups that can be linked to the levels of oral-fecal and respiratory transmission regardless of genetic proximity. Using this model, MERS-CoV is placed into disorder group C, which consists of coronaviruses that have relatively hard inner and outer shells. The members of this group are likely to persist in the environment for a longer period of time and possess the highest oral-fecal components but relatively low respiratory transmission components. Oral-urine and saliva transmission are also highly possible since both require harder protective shells. Results show that disorder prediction can be used as a tool that suggests clues to look for in further epidemiological investigations

    Understanding Viral Transmission Behavior via Protein Intrinsic Disorder Prediction: Coronaviruses

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    Besides being a common threat to farm animals and poultry, coronavirus (CoV) was responsible for the human severe acute respiratory syndrome (SARS) epidemic in 2002-4. However, many aspects of CoV behavior, including modes of its transmission, are yet to be fully understood. We show that the amount and the peculiarities of distribution of the protein intrinsic disorder in the viral shell can be used for the efficient analysis of the behavior and transmission modes of CoV. The proposed model allows categorization of the various CoVs by the peculiarities of disorder distribution in their membrane (M) and nucleocapsid (N). This categorization enables quick identification of viruses with similar behaviors in transmission, regardless of genetic proximity. Based on this analysis, an empirical model for predicting the viral transmission behavior is developed. This model is able to explain some behavioral aspects of important coronaviruses that previously were not fully understood. The new predictor can be a useful tool for better epidemiological, clinical, and structural understanding of behavior of both newly emerging viruses and viruses that have been known for a long time. A potentially new vaccine strategy could involve searches for viral strains that are characterized by the evolutionary misfit between the peculiarities of the disorder distribution in their shells and their behavior

    Bow-Tie Microstrip Antenna Design

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    In this paper, the bow-tie microstrip antennas have been designed with two different angles of 40° and 80°. An investigaton on the effect of the angle to the return loss and radiation patterns had been carried out. The impedance matching network with the niicrostrip transmission line feeding was used in this study. Simulation and measurement results for the return loss and radiation patterns were presented

    Nipah shell disorder, modes of infection, and virulence

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    The Nipah Virus (NiV) was first isolated during a 1998–9 outbreak in Malaysia. The outbreak initially infected farm pigs and then moved to humans from pigs with a case-fatality rate (CFR) of about 40%. After 2001, regular outbreaks occurred with higher CFRs (~71%, 2001–5, ~93%, 2008–12). The spread arose from drinking virus-laden palm date sap and human-to-human transmission. Intrinsic disorder analysis revealed strong correlation between the percentage of disorder in the N protein and CFR (Regression: r2 = 0.93, p < 0.01, ANOVA: p < 0.01). Distinct disorder and, therefore, genetic differences can be found in all three group of strains. The fact that the transmission modes of the Malaysia strain are different from those of the Bangladesh strains suggests that the correlations may also be linked to the modes of viral transmission. Analysis of the NiV and related viruses suggests links between modes of transmission and disorder of not just the N protein but, also, of M shell protein. The links among shell disorder, transmission modes, and virulence suggest mechanisms by which viruses are attenuated as they passed through different cell hosts from different animal species. These have implications for development of vaccines and epidemiological molecular analytical tools to contain outbreaks

    An embodied conversational agent for intelligent web interaction on pandemic crisis communication

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    In times of crisis, an effective communication mechanism is paramount in providing accurate and timely information to the community. In this paper we study the use of an intelligent embodied conversational agent (EGA) as the front end interface with the public for a Crisis Communication Network Portal (CCNet). The proposed system, CCNet, is an integration of the intelligent conversation agent, AINI, and an Automated Knowledge Extraction Agent (AKEA). AKEA retrieves first hand information from relevant sources such as government departments and news channels. In this paper, we compare the interaction of AINI against two popular search engines, two question answering systems and two conversational systems
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