Cloud Computing is still an emerging field experiencing rapid advancement in both industry and academia. Cloud computing has solved many problems initially faced by internet application developers such as acquisition ...
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Cloud Computing is still an emerging field experiencing rapid advancement in both industry and academia. Cloud computing has solved many problems initially faced by internet application developers such as acquisition of a server with a fixed capacity to handle expected peak application demand which led to under-utilization of provisioned resources, by enabling a new consumption and delivery model for IT services. It involves provisioning of dynamically scalable and virtualized resources as a service over the Internet. Evaluation of algorithms and policies in an exhaustive manner directly on a cloud is infeasible as it involves a lot of time and effort. Moreover, utilization of real cloud infrastructure limits the experiment to the scale of the infrastructure. A desirable alternative would be utilization of simulation tools that enables evaluation of a hypothesis in a repeatable, controlled and timely manner prior to software development in a cloud environment. Taking into consideration these issues and the ever-growing popularity of OpenStack, in this paper we propose OpenSim a simulator of OpenStack services. It has been developed by the authors of this paper with the intention of simulating new algorithms and also to enable a user to study the behavior of an application running under various deployment configuration in an OpenStack environment.
This paper introduces the main characteristics of the digital cultural collections that constitute the use cases presently in use in the CULTURA environment. A section on related work follows giving an account on effo...
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Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breakin...
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ISBN:
(纸本)9781450320160
Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breaking their code. This, in turn, leads to an increase in defect density. The performance of prediction model that is built using confirmation bias was as good as the models that were built with static code or churn metrics. Aims: Collection of confirmation bias metrics may result in partially "missing data" due to developers' tight schedules, evaluation apprehension and lack of motivation as well as staff turnover. In this paper, we employ Expectation-Maximization (EM) algorithm to impute missing confirmation bias data. Method: We used four datasets from two large-scale companies. For each dataset, we generated all possible missing data configurations and then employed Roweis' EM algorithm to impute missing data. We built defect prediction models using the imputed data. We compared the performances of our proposed models with the ones that used complete data. Results: In all datasets, when missing data percentage is less than or equal to 50% on average, our proposed model that used imputed data yielded performance results that are comparable with the performance results of the models that used complete data. Conclusions: We may encounter the "missing data" problem in building defect prediction models. Our results in this study showed that instead of discarding missing or noisy data, in our case confirmation bias metrics, we can use effective techniques such as EM based imputation to overcome this problem.
In this paper, we present a new method of cell classification using geometrical features of shape analysis. Firstly, the contour of cell is detected and approximated by one polygon. Secondly, by using the bounded poly...
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Very recently, the study of social networks has received a huge attention since we can learn and understand many hidden properties of our society. This paper investigates the potential of social network analysis to se...
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The diversity of agendas in the media plays a critical role in shaping the scope of public discourse. Due to the increasing impact of social media, it is important and necessary to examine if it is circulating a wide ...
We study Monte Carlo tree search (MCTS) in zero-sum extensive-form games with perfect information and simultaneous moves. We present a general template of MCTS algorithms for these games, which can be instantiated by ...
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We study Monte Carlo tree search (MCTS) in zero-sum extensive-form games with perfect information and simultaneous moves. We present a general template of MCTS algorithms for these games, which can be instantiated by various selection methods. We formally prove that if a selection method is Ε-Hannan consistent in a matrix game and satisfies additional requirements on exploration, then the MCTS algorithm eventually converges to an approximate Nash equilibrium (NE) of the extensive-form game. We empirically evaluate this claim using regret matching and Exp3 as the selection methods on randomly generated games and empirically selected worst case games. We confirm the formal result and show that additional MCTS variants also converge to approximate NE on the evaluated games.
We propose Dungeons & Swimmers, an interactive audio- And motion-based exergame for swimming. As the first of its kind, we explore its design considerations and opportunities stemming from swimming. We gamify the ...
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ISBN:
(纸本)9781450322157
We propose Dungeons & Swimmers, an interactive audio- And motion-based exergame for swimming. As the first of its kind, we explore its design considerations and opportunities stemming from swimming. We gamify the four different stroke types with an auditory feedback. For minimal interference, we develop a single sensor-based wearable prototype detecting the strokes and stroke types in real time. We conduct a pilot deployment to study initial user experiences.
Natural language is composed of various expressions and phraseology, but humans can properly handle these ambiguities by using their knowledge of words. To have such a human mechanism in a machine such as a robot, it ...
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ISBN:
(纸本)1601322461
Natural language is composed of various expressions and phraseology, but humans can properly handle these ambiguities by using their knowledge of words. To have such a human mechanism in a machine such as a robot, it is necessary to model the knowledge of words and process their associations. knowledge of a word that is common sense to humans was modeled on a machine as a Concept Base. A Concept Base is a knowledge base that defines words as concepts. The concept is defined by a set of attributes representing a characteristic of the concept by using other concepts and weights. This paper proposes a method to add new attributes for concepts that are already defined in the Concept Base. Words suitable as attributes are automatically acquired by the Web and by using the attributes ordered by the chain structure of the Concept Base.
In this paper, the authors propose a method that incorporates mechanisms for handling ambiguity in speech and the ability of humans to create associations, and for formulating replies based on rule base knowledge and ...
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ISBN:
(纸本)1601322461
In this paper, the authors propose a method that incorporates mechanisms for handling ambiguity in speech and the ability of humans to create associations, and for formulating replies based on rule base knowledge and common knowledge, that go beyond the level that can be achieved using only conventional natural language processing and vast repositories of sample patterns. In this paper, the authors propose a method for associated replies elicited from information relating to the place as an example of how the common knowledge and associative ability described earlier are applied.
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