Bug fixing is one of the most important activities in software development and maintenance. A software project often employs an issue tracking system such as Bugzilla to store and manage their bugs. In the issue track...
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Bug fixing is one of the most important activities in software development and maintenance. A software project often employs an issue tracking system such as Bugzilla to store and manage their bugs. In the issue tracking system, many bugs are invalid but take unnecessary efforts to identify them. In this paper, we mainly focus on bug fixing rate, i.e., The proportion of the fixed bugs in the reported closed bugs. In particular, we study the characteristics of bug fixing rate and investigate the impact of a reporter's different contribution behaviors to the bug fixing rate. We perform an empirical study on all reported bugs of two large open source software communities Eclipse and Mozilla. We find (1) the bug fixing rates of both projects are not high, (2) there exhibits a negative correlation between a reporter's bug fixing rate and the average time cost to close the bugs he/she reports, (3) the amount of bugs a reporter ever fixed has a strong positive impact on his/her bug fixing rate, (4) reporters' bug fixing rates have no big difference, whether their contribution behaviors concentrate on a few products or across many products, (5) reporters' bug fixing rates tend to increase as time goes on, i.e., Developers become more experienced at reporting bugs.
Facial expression and emotion recognition from thermal infrared images has attracted more and more attentions in recent years. However, the features adopted in current work are either temperature statistical parameter...
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Facial expression and emotion recognition from thermal infrared images has attracted more and more attentions in recent years. However, the features adopted in current work are either temperature statistical parameters extracted from the facial regions of interest or several hand-crafted features that are commonly used in visible spectrum. Till now there are no image features specially designed for thermal infrared images. In this paper, we propose using the deep Boltzmann machine to learn thermal features for emotion recognition from thermal infrared facial images. First, the face is located and normalized from the thermal infrared im- ages. Then, a deep Boltzmann machine model composed of two layers is trained. The parameters of the deep Boltzmann machine model are further fine-tuned for emotion recognition after pre-tralning of feature learning. Comparative experimental results on the NVIE database demonstrate that our approach outperforms other approaches using temperature statistic features or hand-crafted features borrowed from visible domain. The learned features from the forehead, eye, and mouth are more effective for discriminating valence dimension of emotion than other facial areas. In addition, our study shows that adding unlabeled data from other database during training can also improve feature learning performance.
No software is an island. It is executed by hardware and interacts with its environment. So-called softwaresystems are complicated hierarchical systems. Competent engineers carefully engineer them. In contrast, compl...
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systems enabling smart city operations are highly adaptive complex systems that pose great challenges in their development and operation. Current user-driven techniques for system domain modeling and requirements engi...
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ISBN:
(纸本)9781467384810
systems enabling smart city operations are highly adaptive complex systems that pose great challenges in their development and operation. Current user-driven techniques for system domain modeling and requirements engineering are not adequate for supporting the development of such systems. In this paper, we propose a development process and an environment-driven modeling approach for the Requirement engineering Context Awareness methodology to be used for smart city applications. To this end, we propose the use of ontologies to build the environment context model. We show that the environment dimension is the most important dimension of context with the highest impact of changes in a dynamic context. We illustrate our approach by presenting an ontology-based context model of I-Parking. We present dynamic models of typical scenarios of interactions. We propose our approach as an important step in developing highly adaptive context-aware systems for smart city operations where uncertainty and changing conditions in the environment need to be carefully modeled and addressed.
Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectr...
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Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the ac- tive appearance model AAM parameters and three defined head motion features are extracted from visible spectrum im- ages, and several thermal statistical features are extracted from infrared （IR） images. Second, feature selection is per- formed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression （NVIE） spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal 1R images＇ supplementary role for visible facial expression recognition.
In this study, we select Middle East countries involving Jordan, Lebanon, Oman, and Saudi Arabia for modeling oil consumption based on computational intelligence methods. The limitations associated with Levenberg- Mar...
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Most existing routing algorithms assume wireless nodes use maximal transmission power or set up the power at the beginning of the network configuration. These static approaches potentially introduce signal interferenc...
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ISBN:
(纸本)9781479984077
Most existing routing algorithms assume wireless nodes use maximal transmission power or set up the power at the beginning of the network configuration. These static approaches potentially introduce signal interference that can be mitigated through power control. In this paper, we propose a Joint Channel assignment, stable Routing and adaptive Power control (JCRP) approach that dynamically controls the transmission power to avoid the channel interference for improving the channel utility. Our JCRP allows a node to control its transmission power to a certain value at which it has a longest channel conflict-free time. Besides, we propose a novel routing metric integrated selecting stability (ISS) to measure the quality of links, which considers node mobility and channel interference, together with the dynamical power control. The simulation results demonstrate that our JCRP significantly outperforms the related routing algorithms in terms of network throughput.
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