In Infrastructure as a Service clouds, customers lease virtual resources (e.g., CPU, memory, network) offered by cloud providers, paying for the allocated capacity of resources, regardless of their effective use. In t...
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This article describes the instructional design and evaluation of a course about project management in a software engineering post-graduation program using different teaching approaches and with a focus on active lear...
ISBN:
(纸本)9781538611753;9781538611746
This article describes the instructional design and evaluation of a course about project management in a software engineering post-graduation program using different teaching approaches and with a focus on active learning. We use four different approaches: digital educational game, non-digital educational game, hands-on activity, and experiential activity. Each one of the activities is evaluated for the aspects of motivation, user experience, and learning, following the MEEGA evaluation model and Cidral's experiential activity model. To verify the perceptions of students over time, we also assess graduated students that have concluded this course after two to four years, considering the aspects of motivation and learning. Results indicate a high level of approval for dynamic activities, regarding both motivation and learning. Activities with greater impact on motivation and learning are dynamics and educational games, group practical activities, and group theoretical activities. Among the factors that most influence students' motivation, we highlight: active learning, teacher knowledge, the taste of the area, and teaching methods. We realized that there was no significant variation in the perception of the activities by students over time.
There has been much research which proposed for cross-project software defect prediction models but no models that perform very well with various datasets in general. Software defect dataset usually imbalanced because...
There has been much research which proposed for cross-project software defect prediction models but no models that perform very well with various datasets in general. Software defect dataset usually imbalanced because it contains far more the not defected modules than the defected modules. Class imbalances in the dataset can reduce the performance of classifiers in the software defect prediction model. In this study proposed a Random Undersampling algorithm to balance classes and ensemble techniques to reduce misclassification. The ensemble technique used is the AdaBoost and Bagging algorithm. The results showed that the software defect prediction model that integrates the Random Undersampling algorithm and AdaBoost provides better performance and can find more defects than other models.
The purpose of this research is to know how influence of mixture of pertamax with Tuak Nias (Tuonifaro) to fuel consumption at 125 cc engine To prove that Tuak Nias or Tuonifaro can be used as fuel mixture on motorcyc...
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In this paper, we propose a web-based application where the user can instantiate multiple, coordinated panels for exploring data concerning the votes of representatives in Brazil's lower legislative house (the Cha...
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In this paper, we propose a web-based application where the user can instantiate multiple, coordinated panels for exploring data concerning the votes of representatives in Brazil's lower legislative house (the Chamber of Deputies). Open data about roll calls made available by the Chamber allowed us to build a set of interactive visualizations to let users explore deputies' votes and build an understanding of their political profiles. Based on the set of roll call voting results from 1991 to 2016, our application displays the political behaviour of parties in a timeline from which users can select periods and instantiate panels showing the political spectrum of deputies using different methods of dimensionality reduction. Deputies can be separated in clusters based on their position in the political spectrum, and other panels can be instantiated showing details about each cluster. Users can select parts of the timeline and simultaneously analyze the behavior of parties and one or more deputies. Roll calls are represented as a combination of heatmaps and histograms. We illustrate the use of the different visualization techniques in a case study on party cohesiveness over time.
Research in HRI indicates that people follow a robot's instructions even when they are incorrect. However, when a robot's instructions or requests contradict those of a human (e.g. an authoritative experimente...
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Research in HRI indicates that people follow a robot's instructions even when they are incorrect. However, when a robot's instructions or requests contradict those of a human (e.g. an authoritative experimenter), people obey the human instead. This might be due to the experimenter's perceived ingroup status, or to their higher presumed authority compared to the robot. This study manipulated experimenter authority (high, low) and robot group membership (ingroup, neutral) to test how they affected responses to conflicting orders from the two agents depending on the request's importance (big, small). While there was no main effect of group membership and authority on most participant behavior, when experimenter authority was low and the robot an ingroup member, participants defied the experimenter's instructions to turn off an ingroup robot at the end of the experiment, following the robot's instructions instead. Further, request importance affected participant behavior. Participants typically followed the robot's low-importance requests (e.g., moving from one chair to another), but not high-importance requests (e.g., how to perform a simulated task of diagnosing and talking to patients).
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the d...
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At present, the smartphone is equipped with several sensors such as Accelerometer, Gravity sensor, and Gyroscope which can be used to recognize human physical activities such as walking upstair and walking downstairs ...
At present, the smartphone is equipped with several sensors such as Accelerometer, Gravity sensor, and Gyroscope which can be used to recognize human physical activities such as walking upstair and walking downstairs etc. Machine learning is needed to group data and get information. Statistical methods have poor performance in classifying because procedures must be met. To overcome this, an ensemble technique was used. This study proposes the application of the gradientboost ensemble method to classify walking upstair and walking downstairs. The *** system is designed for data retrieval using a smartphone. Then, the dataset will be partitioned into 70% training data and 30% test data. The results show that the performance of the ensemble boosting method produces 81.82% accuracy, 86.11% sensitivity and 77.50% specificity.
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better u...
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Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology ...
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