CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations

Teach For America: A Review of the Evidence

Teach For America has generated glowing press reports, but the evidence regarding whether this alternative teacher-training program works is very unclear, according to a policy brief released today by the Great Lakes Center for Education Research and Practice. The brief, Teach For America: A Review of the Evidence, is written by professor Julian Vasquez Heilig of the University of Texas at Austin, and professor Su Jin Jez of California State University, Sacramento. It offers a comprehensive overview of research on the Teach For America (TFA) program, which recruits graduates of elite colleges to teach for two years in hard-to-staff low-income rural and urban schools. Overall, Jez and Heilig argue, the impact of TFA teachers on student achievement is decidedly mixed and dependent upon the experience level of the TFA teachers and the group of teachers with whom they are compared. Studies show that TFA teachers perform fairly well when compared with one segment of the teaching population: other teachers in the same hard-to-staff schools, who are less likely to be certified or traditionally prepared. Compared with that specific group of teachers, TFA teachers "perform comparably in raising reading scores and a bit better in raising math scores," the brief's authors write. Conversely, studies which compare TFA teachers with credentialed non-TFA teachers find that "the students of novice TFA teachers perform significantly less well in reading and mathematics than those of credentialed beginning teachers," Heilig and Jez write. And in a large-scale Houston study, in which the researchers controlled for experience and teachers' certification status, standard certified teachers consistently outperformed uncertified TFA teachers of comparable experience levels in similar settings. The evidence suggests that TFA teachers do get better -- if they stay long enough to become fully credentialed. Those experienced, fully credentialed TFA teachers "appear to do about as well as other, similarly experienced, credentialed teachers in teaching reading ... [and] as well as, and sometimes better than, that comparison group in teaching mathematics," Heilig and Jez write. However, more than half of TFA teachers leave after two years, and more than 80 percent after three. So it's impossible to know whether those who remain have improved because of additional training and experience -- or simply because of "selection bias:" they were more effective than the four out of five TFA teachers who left. The authors note that this high turnover of TFA teachers also results in significant recurring expenses for recruiting and training replacements. Heilig and Jez urge schools and districts to devote resources to a number of proven remedies for improving achievement, including mentoring programs that pair novice and expert teachers, universal pre-school and reduction in early grade class size. The authors conclude, "Policymakers and stakeholders should consider TFA teachers for what they are -- a slightly better alternative when the hiring pool is comprised primarily of uncertified and emergency teachers -- and continue to consider a broad range of solutions to reshape our system of education to ensure that all students are completing schools with the education they need to be successful."This policy brief was produced by the Education and the Public Interest Center (EPIC) at the University of Colorado and the Education Policy Research Unit (EPRU) at Arizona State University with funding from the Great Lakes Center for Education Research and Practice. About The Great Lakes Center The mission of the Great Lakes Center is to improve public education for all students in the Great Lakes region through the support and dissemination of high quality, academically sound research on education policy and practices

Hierarchically Clustered Representation Learning

The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically-clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Compared with a few prior works, HCRL firstly attempts to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape