Solving the Traffic Problem by Using A Simulation Model

This paper presents a traffic light simulation model, which is composed of 6 submodels coded in Arena to help analyze the traffic problem. The model adopts average arrival time and average departure time to simulate the arrival and leaving number of cars on roads. In the experiment, each submodel represents a road that has 3 intersections. The simulation results show that different traffic light duration policies will cause different effects on traffic congestion. Therefore, we can use this model to obtain a good traffic light duration policy for solving the traffic problem

Uncovering a Connection between the Teachers’ Professional Development Program and Students’ Learning

Most research suggests professional development improves teachers’ knowledge and pedagogy and enhances teachers’ confidence to facilitate a positive attitude about student learning. This study attempted to investigate the connection between teacher professional development program and students’ Learning. This study took Readers’ Theater Teaching Program (RTTP) for professional development as an example to inquiry how participants applied their new knowledge and skills learned from RTTP to their teaching practice and how the impact influenced students’ reading fluency. This study was a two-year project. In the first year, this study focused on designing and implementing RTTP and evaluating participants’ satisfaction of RTTP, what they learned and how they applied it to design their English reading curriculum. In the second year, the study adopted quasi-experimental design approach and evaluated how participants RT instruction influenced their students’ reading fluency. The participants in this study composed two junior high school English teachers and their students. Data was collected from a number of different sources including teaching observation, semi-structured interviews, teaching diary, teachers’ professional development portfolio, pre/post RT content knowledge tests, teacher survey, and students’ reading fluency tests. The results indicated that teachers learned more RT script writing than other specific contents and hold a positive attitude toward RT instruction and considered it as a very wonderful strategy to meet a variety of needs. All of the experimental group students had a big progress in reading fluency after RT instruction.  The evidences from this study indicated that RT English instruction significantly influenced students’ reading fluency and classroom climate. Keywords: Teacher’s Professional Development, Program Evaluation, Readers’ Theater, English Reading Instruction, Reading fluenc

Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1