Automatic recommendation to appropriate log levels

A log statement is one of the key tactics for a developer to record and monitor important run-time behaviors of our system in a development phase and a maintenance phase. It composes of a message for stating log contents, and a log level (eg, debug or warn) to denote the severity of a message and controlling its visibility at run time. In spite of its usefulness, a developer does not tend to deeply consider which log level is appropriate in writing source code, which causes the system to be unmaintainable. To address this issue, this paper proposes an automatic approach to validating the appropriateness of the log level in consideration of the semantic and syntactic features and recommending a proper alternative log level. We first build the semantic feature vector to quantify the semantic similarity among application log messages using the word vector space, and the syntactic feature vector to capture the application context that surrounds the log statement. Based on the feature vectors and machine learning techniques, the log level is automatically validated, and an alternative log level is recommended if the log level is invalid. For the evaluation, we collected 22 open-source projects from three application domains, and obtained the 77% of precision and 75% of recall in validating the log levels. Also, our approach showed 6% higher accuracy than that of the developer group who has 7 to 8 years of work experience, and 72% of the developers accepted our recommendation.     

Sparse Bayesian Learning for non-Gaussian sources

In this paper, we show that in the multiple measurement vector model we can take advantage of having multiple samples to learn the properties of the distributions of the sources as part of the recovery process and demonstrate that this improves the recovery performance. We propose a method to solve the simultaneous sparse approximation problem using a mixture of Gaussians prior, inspired by existing Sparse Bayesian Learning approaches. We justify our proposed prior by showing that there are a number of signals modelled better by a mixture of Gaussians prior than the standard zero-mean Gaussian prior, such as communications signals which often have a multimodal distribution. We further show that this method can be applied to data distributed according to an alpha-stable distribution. We also show that our proposed method can be applied to compressed sensing of ultrasound images and demonstrate an improvement over existing methods.     

Modeling how incoming knowledge,persistence, affective states,and in-game progress influence student learning from an educational game

This study investigated the relationships among incoming knowledge, persistence, affective states, in-game progress, and consequently learning outcomes for students using the game Physics Playground. We used structural equation modeling to examine these relations. We tested three models, obtaining a model with good fit to the data. We found evidence that both the pretest and the in-game measure of student performance significantly predicted learning outcome, while the in-game measure of performance was predicted by pretest data, frustration, and engaged concentration. Moreover, we found evidence for two indirect paths from engaged concentration and frustration to learning, via the in-game progress measure. We discuss the importance of these findings, and consider viable next steps concerning the design of effective learning supports within game environments.     

Table-based representations can be used to offer easy-to-use,flexible, and adaptable learning scenario editors

This article presents an innovative approach to the design of learning scenario editors for teachers as not-specifically-trained users. The approach features simplicity, flexibility, and easy adaptation to local contexts and needs. It is based on the governing design decision to provide a basic representation device known to be easy to use and adopted by teachers in standard practice: a table, similar to that found in office suites. The table is put in structural correspondence with a pivotal model (a tree), which is used to implement services requiring complex mechanisms that can be found in state-of-the-art systems, for example instantiation support or operationalization. We show how this approach makes it possible to design simple, flexible editors that offer complex services as add-ons and are easy to adapt to users' local needs, practices, and/or technical ecosystems.     

Design and evaluation of a gamified system for ERP training

Enterprise Resource Planning Systems (ERP) facilitate the flow of information within a company by storing data in common databases. These systems offer a holistic view of the organization because they reduce information redundancy, offer information in real time, help with process standardization, and improve information flow and communication among employees. Nevertheless, the benefits attributed to an ERP implementation can be lost without an effective user training. Previous studies have observed that common training mechanisms don't provide meaningful learning to users, and that user satisfaction rates range from neutral to low. Therefore, in this paper we study the effects that gamification has on ERP training concerning user learning and user satisfaction. Gamification is the use of game elements and game design techniques in non-gaming environments. Several applications prove that gamified systems increase user engagement and performance. Our hypotheses were that a gamified system for ERP training improve user learning and user satisfaction levels during the training period. To test our hypotheses, we designed and evaluated a gamified system. The results showed that users trained using a gamified system performed better than those trained using a conventional, non-gamified, training mechanism.     

A SVM controller for the stable walking of biped robots based on small sample sizes

Conventional machine learning methods such as neural network (NN) uses empirical risk minimization (ERM) based on infinite samples, which is disadvantageous to the gait learning control based on small sample sizes for biped robots walking in unstructured, uncertain and dynamic environments. Aiming at the stable walking control problem in the dynamic environments for biped robots, this paper puts forward a method of gait control based on support vector machines (SVM), which provides a solution for the learning control issue based on small sample sizes. The SVM is equipped with a mixed kernel function for the gait learning. Using ankle trajectory and hip trajectory as inputs, and the corresponding trunk trajectory as outputs, the SVM is trained based on small sample sizes to learn the dynamic kinematics relationships between the legs and the trunk of the biped robots. Robustness of the gait control is enhanced, which is propitious to realize the stable biped walking, and the proposed method shows superior performance when compared to SVM with radial basis function (RBF) kernels and polynomial kernels, respectively. Simulation results demonstrate the superiority of the proposed methods.     

Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method

The Critical Clearing Time (CCT) is a key issue for Transient Stability Assessment (TSA) in electrical power system operation, security, and maintenance. However, there are some difficulties in obtaining the CCT, which include the accuracy, fast computation, and robustness for TSA online. Therefore, obtaining the CCT is still an interesting topic for investigation. This paper proposes a new technique for obtaining CCT based on numerical calculations and artificial intelligence techniques. First, the CCT is calculated by the critical trajectory method based on critical generation. Second, the CCT is learned by Extreme Learning Machine (ELM). This proposed method has the ability to obtain the CCT with load changes, different fault occurrences, accuracy, and fast computation, and considering the controller. This proposed method is tested by the IEEE 3-machine 9-bus system and Java-Bali 500 kV 54-machine 25-bus system. The proposed method can provide accurate CCTs with an average error of 0.33% for the Neural Network (NN) method and an average error of 0.06% for the ELM method. The simulation result also shows that this method is a robust algorithm that can address several load changes and different locations of faults occurring. There are 29 load changes used to obtain the CCT, with 20 load changes included for the training process and 9 load changes not included.     

Closing the Knowledge-Application Gap in Organisations through Incentives: Experience from the National Water and Sewerage Corporation in Uganda

Recent decades have seen a steady rise of knowledge and capacity development (KCD) interventions targeting organisations in developing countries. This paper argues that very often the main challenge facing organisations is not the development of knowledge and capacity per se, but their actual use. Drawing on the case of Uganda's National Water and Sewerage Corporation (NWSC), we identify and discuss a number of interrelated factors (internal and external) that have allowed the utility to turn its knowledge base into improved performance. We conclude that utilities can close their knowledge-application gap by introducing a corporate culture that motivates staff to apply their knowledge.     

Creating awareness on bullying and cyberbullying among young people: Validating the effectiveness and design of the serious game Conectado

Serious games have proven to be educational tools with numerous positive effects, capable of promoting learning, changing behavior, or improving training and skills development, among other effects. Serious games can also be applied to address and prevent social problems. This study describes the experiments and analyses we have carried out to test the effectiveness of Conectado, a serious game created to prevent bullying and cyberbullying in schools by increasing awareness of the problem in players. We have used the results from these experiments to answer different research questions regarding the game’s acceptance and effectiveness, validating several characteristics of the game design. We have also studied the influence of players’ characteristics on the effect of the game, as well as the relationship between the in-game behaviors of players and their degree of awareness. We have verified a positive effect of the game in terms of an increase of awareness regarding bullying and cyberbullying for all target users, and have provided further insight into how this increase is related to different players’ characteristics and behaviors.     

Correlation between Facebook use for educational purposes and academic performance of students

In this paper a research on the application of Facebook in education is presented. A total of 139 students of the Faculty of Education in Sombor (Serbia) participated in the research. The results establish a positive correlation between the academic performance of students and the frequency of use of Facebook for educational purposes. The factor of perceived usefulness greatly affected the decision of students to use Facebook as a learning aid. No significant difference in the frequency of use of Facebook for general purposes has been reported between the groups of students divided by the academic performance.